techhelp/visualize_timechops_example_updated.ipynb

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      "provenance": [],
      "authorship_tag": "ABX9TyOLwFPDuQCnXRhjENvt2uRY",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dssg/mlforpublicpolicylab/blob/master/techhelp/visualize_timechops_example_updated.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fGSEeI-ULNzM"
      },
      "outputs": [],
      "source": [
        "!pip install triage"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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"
      ],
      "metadata": {
        "id": "d6I-0U3rLt74"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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()"
      ],
      "metadata": {
        "id": "Y5Qdzj-2NcC-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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",
        "\"\"\""
      ],
      "metadata": {
        "id": "1VhX8XFMMNru"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "c = yaml.safe_load(cfg)\n",
        "chopper = Timechop(**c['temporal_config'])\n",
        "# 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"
      ],
      "metadata": {
        "id": "g0rWMzyeMVzG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "visualize_chops(chopper)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 720
        },
        "id": "NtMg-D55MmL2",
        "outputId": "713469d9-76bc-4081-fc71-0b20dbd2675f"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3200x1600 with 8 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "visualize_chops_plotly(chopper, selected_splits = [0, 1, 2],show_label_timespans=True, show_boxes=True, show_annotations=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "Zwamif-mMpSb",
        "outputId": "bf09e791-e2d7-4fae-d190-40682fc092c6"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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