techhelp/visualize_timechops_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mDEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\u001b[33m\n",
      "\u001b[0mCollecting plotly\n",
      "  Downloading plotly-5.17.0-py2.py3-none-any.whl (15.6 MB)\n",
      "\u001b[2K     \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mm eta \u001b[36m0:00:01\u001b[0m[36m0:00:01\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.9/site-packages (from plotly) (20.9)\n",
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      "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.9/site-packages (from packaging->plotly) (2.4.7)\n",
      "Installing collected packages: tenacity, plotly\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mDEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\u001b[33m\n",
      "\u001b[0mSuccessfully installed plotly-5.17.0 tenacity-8.2.3\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3.9 -m pip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install plotly\n",
    "\n",
    "from triage.component.timechop.plotting import visualize_chops\n",
    "from triage.component.timechop import Timechop\n",
    "import matplotlib\n",
    "matplotlib.use('Agg')\n",
    "import matplotlib.dates as md\n",
    "import numpy as np\n",
    "from triage.util.conf import convert_str_to_relativedelta\n",
    "import matplotlib.pyplot as plt\n",
    "import yaml\n",
    "import plotly.express as px\n",
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objects as go\n",
    "from datetime import datetime\n",
    "%matplotlib inline\n",
    "import yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_chops_plotly(chopper, selected_splits=None, show_label_timespans=True, show_boxes=True, show_annotations=True):\n",
    "    \"\"\"Visualize time chops of a given Timechop object using plotly\n",
    "\n",
    "    Args:\n",
    "        chopper (triage.component.timechop.Timechop): A fully-configured Timechop object\n",
    "        selected_splits (list): Indices of train-val sets to plot. E.g. [0, 1, 2] plots the 3 most recent splits, [0,-1] plots the first and last splits.\n",
    "            Defaults to None, which plots all splits.\n",
    "        show_label_timespans (bool): Whether or not to draw horizontal lines to show label timespan\n",
    "            for as-of-times\n",
    "        show_boxes (bool): Whether or not to show a rectangle highlighting train-test matrices\n",
    "        show_annotations (bool): Whether or not to add annotations on the latest split, showing what each of the timechop parameters mean\n",
    "    \"\"\"\n",
    "    chops = chopper.chop_time()\n",
    "    chops.reverse() # reverse to get the most recent set first\n",
    "\n",
    "    # Subset to relevant splits if arg specified, and generate titles for each split\n",
    "    if selected_splits is not None:\n",
    "      chops = [chops[i] for i in selected_splits]\n",
    "      titles = tuple(f\"Train-Validation Split {i+1}\" for i in selected_splits)\n",
    "    else:\n",
    "      titles = tuple(f\"Train-Validation Split {i+1}\" for i in range(len(chops)))\n",
    "\n",
    "    fig = make_subplots(rows=len(chops),\n",
    "                        cols=1,\n",
    "                        shared_xaxes=True,\n",
    "                        shared_yaxes=True,\n",
    "                        vertical_spacing=0.05,\n",
    "                        subplot_titles=titles) # adds titles for each subplot\n",
    "\n",
    "    # For each train-val split\n",
    "    for idx, chop in enumerate(chops):\n",
    "        train_as_of_times = chop[\"train_matrix\"][\"as_of_times\"]\n",
    "        test_as_of_times = chop[\"test_matrices\"][0][\"as_of_times\"]\n",
    "\n",
    "        test_label_timespan = chop[\"test_matrices\"][0][\"test_label_timespan\"]\n",
    "        training_label_timespan = chop[\"train_matrix\"][\"training_label_timespan\"]\n",
    "\n",
    "        # Colors for train/test\n",
    "        train_color = \"rgba(3, 37, 126\" # dark blue (left open because we add an opacity argument below)\n",
    "        test_color = \"rgba(139, 0, 0\" # magenta (left open because we add an opacity argument below)\n",
    "        as_of_date_marker_opacity = ', 1)' # the extra ', 1)' defines opacity. 100% solid for markers\n",
    "        label_line_opacity = ', 0.3)' # 30% opacity for the label lines\n",
    "        rectangle_fill_opacity = ', 0.15)' # 15% opacity for rectangle fill\n",
    "\n",
    "        train_as_of_date_color = train_color + as_of_date_marker_opacity\n",
    "        train_label_period_color = train_color + label_line_opacity\n",
    "        train_rectangle_fill = train_color + rectangle_fill_opacity\n",
    "        test_as_of_date_color = test_color + as_of_date_marker_opacity\n",
    "        test_label_period_color = test_color + label_line_opacity\n",
    "        test_rectangle_fill = test_color + rectangle_fill_opacity\n",
    "\n",
    "        # Show legend only if idx = 0 (i.e. first train-val set we are displaying)\n",
    "        if idx == 0:\n",
    "          # Train set as-of-date markers\n",
    "          fig.add_trace(\n",
    "              go.Scatter(x=[x.date() for x in train_as_of_times],\n",
    "                        y=[x for x in range(len(train_as_of_times))],\n",
    "                        mode='markers',\n",
    "                        marker=dict(color=train_as_of_date_color),\n",
    "                        name='Training as-of-date',\n",
    "                        showlegend=True,\n",
    "                        hovertemplate=\"%{x}<extra></extra>\" # the extra extra tag gets rid of a default 'trace' line in the hover output and just shows 'x', the date\n",
    "                        ),\n",
    "              row=idx+1, # row and column of the subplots to add this trace object to\n",
    "              col=1\n",
    "              )\n",
    "          # Validation set as-of-date markers\n",
    "          fig.add_trace(\n",
    "            go.Scatter(x=[x for x in test_as_of_times],\n",
    "                      y=[x for x in range(len(test_as_of_times))],\n",
    "                      mode='markers',\n",
    "                      name='Validation as-of-date',\n",
    "                      showlegend=True,\n",
    "                      marker=dict(color=test_as_of_date_color),\n",
    "                      hovertemplate=\"%{x}<extra></extra>\"),\n",
    "            row=idx+1,\n",
    "            col=1\n",
    "            )\n",
    "        # Suppress legend if not the first subplot; only difference with above is showlegend=False (note, anytime we add a trace, we have to set showlegend=False to suppress useless info in the legend)\n",
    "        else:\n",
    "          # Train set as-of-date markers\n",
    "          fig.add_trace(\n",
    "              go.Scatter(x=[x.date() for x in train_as_of_times],\n",
    "                        y=[x for x in range(len(train_as_of_times))],\n",
    "                        mode='markers',\n",
    "                        marker=dict(color=train_as_of_date_color),\n",
    "                        name='Training as-of-date',\n",
    "                        showlegend=False,\n",
    "                        hovertemplate=\"%{x}<extra></extra>\" # the extra extra tag gets rid of a default 'trace' line in the hover output and just shows 'x', the date\n",
    "                        ),\n",
    "              row=idx+1, # row and column of the subplots to add this trace object to\n",
    "              col=1\n",
    "              )\n",
    "\n",
    "          # Validation set as-of-date markers\n",
    "          fig.add_trace(\n",
    "            go.Scatter(x=[x for x in test_as_of_times],\n",
    "                      y=[x for x in range(len(test_as_of_times))],\n",
    "                      mode='markers',\n",
    "                      name='Validation as-of-date',\n",
    "                      showlegend=False,\n",
    "                      marker=dict(color=test_as_of_date_color),\n",
    "                      hovertemplate=\"%{x}<extra></extra>\"),\n",
    "            row=idx+1,\n",
    "            col=1\n",
    "            )\n",
    "\n",
    "\n",
    "        # Add test_durations annotation if option selected\n",
    "        if idx == 0 and show_annotations==True:\n",
    "\n",
    "          # Add a dashed line to show test_durations span\n",
    "          x0 = test_as_of_times[0]\n",
    "          x1 = test_as_of_times[-1]\n",
    "          x_mid = x0 + (x1-x0)/2\n",
    "          y = -1 # place the test durations labeling below the graph\n",
    "          fig.add_shape(type='line', x0=x0, x1=x1, y0=y, y1=y, line={'color': 'green'}, row=idx+1, col=1)\n",
    "          fig.add_annotation(x=x_mid, y=y-1, text=f\"Test duration: {chop['test_matrices'][0]['test_duration']}\", showarrow=False)\n",
    "\n",
    "        # Add label timespan lines if option selected\n",
    "        if show_label_timespans is True:\n",
    "\n",
    "          # For training as_of_dates\n",
    "          for i in range(len(train_as_of_times)):\n",
    "            fig.add_trace(\n",
    "                go.Scatter(\n",
    "                    x=[train_as_of_times[i].date(), train_as_of_times[i].date() + convert_str_to_relativedelta(training_label_timespan)],\n",
    "                    y=[i,i],\n",
    "                    marker=dict(color=train_label_period_color, line=dict(color=train_label_period_color)),\n",
    "                    hovertemplate=\"%{x}<extra></extra>\",\n",
    "                    showlegend=False\n",
    "                ),\n",
    "              row=idx+1,\n",
    "              col=1\n",
    "            )\n",
    "\n",
    "            # Add annotation showing train label timespan on first bar in first train-val set (if option specified)\n",
    "            if i == len(train_as_of_times)-1 and idx == 0 and show_annotations==True:\n",
    "\n",
    "              # Have the x in between the label timespan\n",
    "              x0 = train_as_of_times[i].date()\n",
    "              x1 = train_as_of_times[i].date() + convert_str_to_relativedelta(training_label_timespan)\n",
    "              x_pos = x0 + (x1 - x0)/2\n",
    "\n",
    "              # Position at a y-value above the bar\n",
    "              y_pos = i\n",
    "              fig.add_annotation(x=x_pos, y=y_pos, text='Label timespan', showarrow=True, arrowhead=1, row=idx+1, col=1)\n",
    "\n",
    "          # For test as_of_dates\n",
    "          for i in range(len(test_as_of_times)):\n",
    "            fig.add_trace(\n",
    "                go.Scatter(\n",
    "                    x=[test_as_of_times[i].date(), test_as_of_times[i].date() + convert_str_to_relativedelta(test_label_timespan)],\n",
    "                    y=[i,i],\n",
    "                    marker=dict(color= test_label_period_color, line=dict(color= test_label_period_color)),\n",
    "                    showlegend=False,\n",
    "                    hovertemplate=\"%{x}<extra></extra>\"),\n",
    "              row=idx+1,\n",
    "              col=1\n",
    "            )\n",
    "\n",
    "            # Add annotation showing test label timespan on first bar in first train-val set (if option specified)\n",
    "            if i == len(test_as_of_times)-1 and idx == 0 and show_annotations==True:\n",
    "\n",
    "                # Have the x in between the label timespan\n",
    "                x0 = test_as_of_times[i].date()\n",
    "                x1 = test_as_of_times[i].date() + convert_str_to_relativedelta(test_label_timespan)\n",
    "                x_pos = x0 + (x1 - x0)/2\n",
    "\n",
    "                # Position at a y-value above the bar\n",
    "                y_pos = i\n",
    "                fig.add_annotation(x=x_pos, y=y_pos, text='Label timespan', showarrow=True, arrowhead=1, row=idx+1, col=1)\n",
    "\n",
    "        # Add rectangles/boxes to mark train-test matrices\n",
    "        if show_boxes is True:\n",
    "\n",
    "          # Training matrix rectangle\n",
    "          # Rectangle params\n",
    "          x0 = min(train_as_of_times).date()\n",
    "          x1 = max(train_as_of_times).date() + convert_str_to_relativedelta(training_label_timespan)\n",
    "          y = max(len(test_as_of_times), len(train_as_of_times))\n",
    "\n",
    "          fig.add_trace(\n",
    "              go.Scatter(x =[x0,x0,x1,x1,x0], y=[0,y,y,0,0],\n",
    "                        fill='toself', fillcolor=train_rectangle_fill,\n",
    "                        showlegend=False,\n",
    "                        marker=dict(color='rgba(0,255,0,0)', line=dict(color='rgba(0,255,0,0)')), # setting 0 opacity so we don't see the lines or markers\n",
    "                        hoverinfo='skip'),\n",
    "              row=idx+1,\n",
    "              col=1,\n",
    "          )\n",
    "\n",
    "          # #Add annotated text to the middle of the training set rectangle -> this code works, but the positioning is a bit weird, so need to tweak\n",
    "          # middle_index = round(len(train_as_of_times)/2)\n",
    "          # x_middle = train_as_of_times[middle_index].date() + convert_str_to_relativedelta(training_label_timespan)\n",
    "          # fig.add_trace(\n",
    "          #     go.Scatter(x =[x_middle], y=[y-1],\n",
    "          #               mode='text',\n",
    "          #               text=\"Training Data\",\n",
    "          #               marker=dict(color='rgba(0,255,0,0)', line=dict(color='rgba(0,255,0,0)')), # setting 0 opacity so we don't see the lines\n",
    "          #               hoverinfo='skip'),\n",
    "          #     row=idx+1,\n",
    "          #     col=1,\n",
    "          # )\n",
    "\n",
    "          # Test set rectangle\n",
    "\n",
    "          # Rectangle params\n",
    "          x0 = min(test_as_of_times).date()\n",
    "          x1 = max(test_as_of_times).date() + convert_str_to_relativedelta(test_label_timespan)\n",
    "          y = max(len(test_as_of_times), len(train_as_of_times))\n",
    "\n",
    "          fig.add_trace(\n",
    "              go.Scatter(x =[x0,x0,x1,x1,x0], y=[0,y,y,0,0],\n",
    "                        fill='toself', fillcolor=test_rectangle_fill,\n",
    "                        showlegend=False,\n",
    "                        marker=dict(color='rgba(0,255,0,0)', line=dict(color='rgba(0,255,0,0)')), # setting 0 opacity so we don't see the lines\n",
    "                        hoverinfo='skip'),\n",
    "              row=idx+1,\n",
    "              col=1,\n",
    "          )\n",
    "\n",
    "          # #Add annotated text to the test set rectangle\n",
    "          # middle_index = round(len(test_as_of_times)/2)\n",
    "          # x_middle = test_as_of_times[middle_index].date() + convert_str_to_relativedelta(test_label_timespan)\n",
    "          # fig.add_trace(\n",
    "          #     go.Scatter(x =[x_middle], y=[y-1],\n",
    "          #               mode='text',\n",
    "          #               text=\"Test Data\",\n",
    "          #               marker=dict(color='rgba(0,255,0,0)', line=dict(color='rgba(0,255,0,0)')), # setting 0 opacity so we don't see the lines\n",
    "          #               hoverinfo='skip'),\n",
    "          #     row=idx+1,\n",
    "          #     col=1,\n",
    "          # )\n",
    "\n",
    "    fig.update_layout(height=500, width=900, showlegend=True)\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "cfg = \"\"\"\n",
    "temporal_config:\n",
    "\n",
    "    # first date our feature data is good\n",
    "    feature_start_time: '2012-01-01'\n",
    "    feature_end_time: '2023-01-01'\n",
    "\n",
    "    # first date our label data is good\n",
    "    # donorschoose: as far back as we have good donation data\n",
    "    label_start_time: '2019-01-01'\n",
    "    label_end_time: '2023-01-01'\n",
    "\n",
    "    model_update_frequency: '3month'\n",
    "\n",
    "    # length of time defining a test set\n",
    "    test_durations: ['1month']\n",
    "    # defines how far back a training set reaches\n",
    "    max_training_histories: ['100year']\n",
    "\n",
    "    # we sample every day, since new projects are posted\n",
    "    # every day\n",
    "    training_as_of_date_frequencies: ['1month']\n",
    "    test_as_of_date_frequencies: ['1month']\n",
    "    \n",
    "    # like our project timeout\n",
    "    training_label_timespans: ['1year']\n",
    "    test_label_timespans: ['1year']\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = yaml.safe_load(cfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "chopper = Timechop(**c['temporal_config'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# artifically set the feature_start_time to the label_start_time to zoom in on the train/validation splits\n",
    "chopper.feature_start_time = chopper.label_start_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3200x1600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_chops(chopper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "%{x}<extra></extra>",
         "marker": {
          "color": "rgba(3, 37, 126, 1)"
         },
         "mode": "markers",
         "name": "Training as-of-date",
         "showlegend": true,
         "type": "scatter",
         "x": [
          "2019-01-01",
          "2019-02-01",
          "2019-03-01",
          "2019-04-01",
          "2019-05-01",
          "2019-06-01",
          "2019-07-01",
          "2019-08-01",
          "2019-09-01",
          "2019-10-01",
          "2019-11-01",
          "2019-12-01",
          "2020-01-01",
          "2020-02-01",
          "2020-03-01",
          "2020-04-01",
          "2020-05-01",
          "2020-06-01",
          "2020-07-01",
          "2020-08-01",
          "2020-09-01",
          "2020-10-01",
          "2020-11-01",
          "2020-12-01"
         ],
         "xaxis": "x",
         "y": [
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         "yaxis": "y"
        },
        {
         "hovertemplate": "%{x}<extra></extra>",
         "marker": {
          "color": "rgba(139, 0, 0, 1)"
         },
         "mode": "markers",
         "name": "Validation as-of-date",
         "showlegend": true,
         "type": "scatter",
         "x": [
          "2021-12-01T00:00:00"
         ],
         "xaxis": "x",
         "y": [
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         "yaxis": "y"
        },
        {
         "hovertemplate": "%{x}<extra></extra>",
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          "line": {
           "color": "rgba(3, 37, 126, 0.3)"
          }
         },
         "showlegend": false,
         "type": "scatter",
         "x": [
          "2019-01-01",
          "2020-01-01"
         ],
         "xaxis": "x",
         "y": [
          0,
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         ],
         "yaxis": "y"
        },
        {
         "hovertemplate": "%{x}<extra></extra>",
         "marker": {
          "color": "rgba(3, 37, 126, 0.3)",
          "line": {
           "color": "rgba(3, 37, 126, 0.3)"
          }
         },
         "showlegend": false,
         "type": "scatter",
         "x": [
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         ],
         "xaxis": "x",
         "y": [
          1,
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         ],
         "yaxis": "y"
        },
        {
         "hovertemplate": "%{x}<extra></extra>",
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          "line": {
           "color": "rgba(3, 37, 126, 0.3)"
          }
         },
         "showlegend": false,
         "type": "scatter",
         "x": [
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         "y": [
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         "yaxis": "y"
        },
        {
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          "line": {
           "color": "rgba(3, 37, 126, 0.3)"
          }
         },
         "showlegend": false,
         "type": "scatter",
         "x": [
          "2019-04-01",
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         ],
         "xaxis": "x",
         "y": [
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         "yaxis": "y"
        },
        {
         "hovertemplate": "%{x}<extra></extra>",
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          "line": {
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         },
         "showlegend": false,
         "type": "scatter",
         "x": [
          "2019-05-01",
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         ],
         "xaxis": "x",
         "y": [
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         ],
         "yaxis": "y"
        },
        {
         "hovertemplate": "%{x}<extra></extra>",
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          "line": {
           "color": "rgba(3, 37, 126, 0.3)"
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         },
         "showlegend": false,
         "type": "scatter",
         "x": [
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         ],
         "xaxis": "x",
         "y": [
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         "yaxis": "y"
        },
        {
         "hovertemplate": "%{x}<extra></extra>",
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          "line": {
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         },
         "showlegend": false,
         "type": "scatter",
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