Bias_reduction_posthoc.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/msoley/CSCI-535/blob/main/Bias_reduction_posthoc.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W7Jzla7ErAbD"
      },
      "source": [
        "#This is taken from https://github.com/dssg/fairness_tutorial\n",
        "\n",
        "# Addressing Bias and Fairness in Machine Learning: A Practical Guide and Hands-on Tutorial\n",
        "## KDD 2023 Hands-on Tutorial\n",
        "### Rayid Ghani, Kit Rodolfa, Pedro Saleiro, Sérgio Jesus"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KTypj7J8rAbJ"
      },
      "source": [
        "# <font color=red>Exploring Bias Reduction Strategies</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BBNQKdiirAbK"
      },
      "source": [
        "## 1. Install dependencies, import packages and data\n",
        "This is needed every time you open this notebook in **colab** to install dependencies"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandas==1.5.3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FGV4Kypg7TWg",
        "outputId": "3f01dc88-4577-4507-f599-5c73cff55921"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "24sTaU89rAbL",
        "outputId": "45330321-f1a8-497f-d87c-2485af09de6b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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          ]
        }
      ],
      "source": [
        "# packages not installed in colab\n",
        "!pip install pandas==1.5.3\n",
        "!pip install aequitas==0.42.0\n",
        "!pip install fairlearn==0.4.6\n",
        "!pip install hyperparameter-tuning\n",
        "!pip install fairgbm==0.9.14\n",
        "\n",
        "import yaml\n",
        "import os\n",
        "import math\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from aequitas.group import Group\n",
        "from aequitas.bias import Bias\n",
        "from aequitas.fairness import Fairness\n",
        "import aequitas.plot as ap\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "import fairlearn\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "sns.set()\n",
        "DPI = 200\n",
        "DATAPATH = 'https://github.com/dssg/fairness_tutorial/raw/master/data/'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v2U-ALesrAbN"
      },
      "source": [
        "## What has already happened?\n",
        "\n",
        "We've already cleaned data, generated features, created train-test sets, built 1000s of models on each training set and scored each test set with them, and calculated various evaluation metrics.\n",
        "\n",
        "As described earlier, the goal here is to select top 1000 project submissions that are likely to not get funded in order to prioritize resource allocation. That corresponds to the metric **Precision at top 1000**.\n",
        "\n",
        "### <font color=red>We audited the \"best\" model at precision at top 1000 and found that it has disparities for True Positive Rate for all attributes that we care about (poverty_level of the school, sex, and school_location_type)</font>\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "muY_xli8rAbR",
        "scrolled": true,
        "outputId": "64952f80-120c-4eb7-cdaf-6115d26e330d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     model_precision                          model_classpath  \\\n",
              "0              0.552  sklearn.ensemble.RandomForestClassifier   \n",
              "1              0.551  sklearn.ensemble.RandomForestClassifier   \n",
              "2              0.550  sklearn.ensemble.RandomForestClassifier   \n",
              "3              0.549                  lightgbm.LGBMClassifier   \n",
              "4              0.548  sklearn.ensemble.RandomForestClassifier   \n",
              "..               ...                                      ...   \n",
              "395            0.373     sklearn.neural_network.MLPClassifier   \n",
              "396            0.372     sklearn.neural_network.MLPClassifier   \n",
              "397            0.371     sklearn.neural_network.MLPClassifier   \n",
              "398            0.364                  lightgbm.LGBMClassifier   \n",
              "399            0.318                  lightgbm.LGBMClassifier   \n",
              "\n",
              "                                       hyperparameters  \\\n",
              "0    {\"n_jobs\": -1, \"criterion\": \"gini\", \"max_depth...   \n",
              "1    {\"n_jobs\": -1, \"criterion\": \"gini\", \"max_depth...   \n",
              "2    {\"n_jobs\": -1, \"criterion\": \"gini\", \"max_depth...   \n",
              "3    {\"n_jobs\": -1, \"max_depth\": 2, \"num_leaves\": 5...   \n",
              "4    {\"n_jobs\": -1, \"criterion\": \"gini\", \"max_depth...   \n",
              "..                                                 ...   \n",
              "395  {\"alpha\": 0.00009627719435096597, \"beta_1\": 0....   \n",
              "396  {\"alpha\": 0.000022779312051721987, \"beta_1\": 0...   \n",
              "397  {\"alpha\": 0.0000629250653559928, \"beta_1\": 0.9...   \n",
              "398  {\"n_jobs\": -1, \"max_depth\": 14, \"num_leaves\": ...   \n",
              "399  {\"n_jobs\": -1, \"max_depth\": 102, \"num_leaves\":...   \n",
              "\n",
              "                           model_uuid                  predictions_uuid  \\\n",
              "0    a04e2eedd9c5ff18bcf77e84ae9db561  c598fbe93f4c218ac7d325fb478598f1   \n",
              "1    3f8f65db414b74ba1c4773e131bdb51d  2d8e4d82f4e67e1de1de8650a7838fc2   \n",
              "2    8823d053f9fbe6c34efdae3a2f24c39c  5e78e49f70fac0193eaadb8513428f23   \n",
              "3    ffc7abef4fe933b0f0cfed63d5d03910  f04edad1446642411085e84271898506   \n",
              "4    110d892ce37127a79d7c6e0eac9a5d13  09092f371176a13cddca59bac20da4fd   \n",
              "..                                ...                               ...   \n",
              "395  b52e7837725a5a34544c10297256fc81  6020b409648f794f6f55ed235924afe3   \n",
              "396  e79173c1595bc2ba28edee0c01467fa9  b16ad6b2a7eba2e6ba30c14981ebaf3b   \n",
              "397  5423f461c575db9934e52b49c2aabf0f  ca1c9b8e742db3761a660cac81a19102   \n",
              "398  853e04831b0a855f22f4bd46b995223c  3e9271171981a27f976a9ef8c739cad0   \n",
              "399  a2190820ccd19bb46ef4c97d880a36f6  b99e776969120280d81984b926373c70   \n",
              "\n",
              "     target_pp matrix_type matrix_start_date matrix_end_date  \n",
              "0         1000        test        2012-12-01      2013-01-31  \n",
              "1         1000        test        2012-12-01      2013-01-31  \n",
              "2         1000        test        2012-12-01      2013-01-31  \n",
              "3         1000        test        2012-12-01      2013-01-31  \n",
              "4         1000        test        2012-12-01      2013-01-31  \n",
              "..         ...         ...               ...             ...  \n",
              "395       1000        test        2012-12-01      2013-01-31  \n",
              "396       1000        test        2012-12-01      2013-01-31  \n",
              "397       1000        test        2012-12-01      2013-01-31  \n",
              "398       1000        test        2012-12-01      2013-01-31  \n",
              "399       1000        test        2012-12-01      2013-01-31  \n",
              "\n",
              "[400 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-36674f82-429f-4edf-b9b2-f3d53b3d1580\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model_precision</th>\n",
              "      <th>model_classpath</th>\n",
              "      <th>hyperparameters</th>\n",
              "      <th>model_uuid</th>\n",
              "      <th>predictions_uuid</th>\n",
              "      <th>target_pp</th>\n",
              "      <th>matrix_type</th>\n",
              "      <th>matrix_start_date</th>\n",
              "      <th>matrix_end_date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.552</td>\n",
              "      <td>sklearn.ensemble.RandomForestClassifier</td>\n",
              "      <td>{\"n_jobs\": -1, \"criterion\": \"gini\", \"max_depth...</td>\n",
              "      <td>a04e2eedd9c5ff18bcf77e84ae9db561</td>\n",
              "      <td>c598fbe93f4c218ac7d325fb478598f1</td>\n",
              "      <td>1000</td>\n",
              "      <td>test</td>\n",
              "      <td>2012-12-01</td>\n",
              "      <td>2013-01-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.551</td>\n",
              "      <td>sklearn.ensemble.RandomForestClassifier</td>\n",
              "      <td>{\"n_jobs\": -1, \"criterion\": \"gini\", \"max_depth...</td>\n",
              "      <td>3f8f65db414b74ba1c4773e131bdb51d</td>\n",
              "      <td>2d8e4d82f4e67e1de1de8650a7838fc2</td>\n",
              "      <td>1000</td>\n",
              "      <td>test</td>\n",
              "      <td>2012-12-01</td>\n",
              "      <td>2013-01-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.550</td>\n",
              "      <td>sklearn.ensemble.RandomForestClassifier</td>\n",
              "      <td>{\"n_jobs\": -1, \"criterion\": \"gini\", \"max_depth...</td>\n",
              "      <td>8823d053f9fbe6c34efdae3a2f24c39c</td>\n",
              "      <td>5e78e49f70fac0193eaadb8513428f23</td>\n",
              "      <td>1000</td>\n",
              "      <td>test</td>\n",
              "      <td>2012-12-01</td>\n",
              "      <td>2013-01-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.549</td>\n",
              "      <td>lightgbm.LGBMClassifier</td>\n",
              "      <td>{\"n_jobs\": -1, \"max_depth\": 2, \"num_leaves\": 5...</td>\n",
              "      <td>ffc7abef4fe933b0f0cfed63d5d03910</td>\n",
              "      <td>f04edad1446642411085e84271898506</td>\n",
              "      <td>1000</td>\n",
              "      <td>test</td>\n",
              "      <td>2012-12-01</td>\n",
              "      <td>2013-01-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.548</td>\n",
              "      <td>sklearn.ensemble.RandomForestClassifier</td>\n",
              "      <td>{\"n_jobs\": -1, \"criterion\": \"gini\", \"max_depth...</td>\n",
              "      <td>110d892ce37127a79d7c6e0eac9a5d13</td>\n",
              "      <td>09092f371176a13cddca59bac20da4fd</td>\n",
              "      <td>1000</td>\n",
              "      <td>test</td>\n",
              "      <td>2012-12-01</td>\n",
              "      <td>2013-01-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>395</th>\n",
              "      <td>0.373</td>\n",
              "      <td>sklearn.neural_network.MLPClassifier</td>\n",
              "      <td>{\"alpha\": 0.00009627719435096597, \"beta_1\": 0....</td>\n",
              "      <td>b52e7837725a5a34544c10297256fc81</td>\n",
              "      <td>6020b409648f794f6f55ed235924afe3</td>\n",
              "      <td>1000</td>\n",
              "      <td>test</td>\n",
              "      <td>2012-12-01</td>\n",
              "      <td>2013-01-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>0.372</td>\n",
              "      <td>sklearn.neural_network.MLPClassifier</td>\n",
              "      <td>{\"alpha\": 0.000022779312051721987, \"beta_1\": 0...</td>\n",
              "      <td>e79173c1595bc2ba28edee0c01467fa9</td>\n",
              "      <td>b16ad6b2a7eba2e6ba30c14981ebaf3b</td>\n",
              "      <td>1000</td>\n",
              "      <td>test</td>\n",
              "      <td>2012-12-01</td>\n",
              "      <td>2013-01-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>397</th>\n",
              "      <td>0.371</td>\n",
              "      <td>sklearn.neural_network.MLPClassifier</td>\n",
              "      <td>{\"alpha\": 0.0000629250653559928, \"beta_1\": 0.9...</td>\n",
              "      <td>5423f461c575db9934e52b49c2aabf0f</td>\n",
              "      <td>ca1c9b8e742db3761a660cac81a19102</td>\n",
              "      <td>1000</td>\n",
              "      <td>test</td>\n",
              "      <td>2012-12-01</td>\n",
              "      <td>2013-01-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>398</th>\n",
              "      <td>0.364</td>\n",
              "      <td>lightgbm.LGBMClassifier</td>\n",
              "      <td>{\"n_jobs\": -1, \"max_depth\": 14, \"num_leaves\": ...</td>\n",
              "      <td>853e04831b0a855f22f4bd46b995223c</td>\n",
              "      <td>3e9271171981a27f976a9ef8c739cad0</td>\n",
              "      <td>1000</td>\n",
              "      <td>test</td>\n",
              "      <td>2012-12-01</td>\n",
              "      <td>2013-01-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399</th>\n",
              "      <td>0.318</td>\n",
              "      <td>lightgbm.LGBMClassifier</td>\n",
              "      <td>{\"n_jobs\": -1, \"max_depth\": 102, \"num_leaves\":...</td>\n",
              "      <td>a2190820ccd19bb46ef4c97d880a36f6</td>\n",
              "      <td>b99e776969120280d81984b926373c70</td>\n",
              "      <td>1000</td>\n",
              "      <td>test</td>\n",
              "      <td>2012-12-01</td>\n",
              "      <td>2013-01-31</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>400 rows × 9 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"evals_df\",\n  \"rows\": 400,\n  \"fields\": [\n    {\n      \"column\": \"model_precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04870629690703236,\n        \"min\": 0.318,\n        \"max\": 0.552,\n        \"num_unique_values\": 114,\n        \"samples\": [\n          0.465,\n          0.548,\n          0.51\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model_classpath\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"lightgbm.LGBMClassifier\",\n          \"sklearn.linear_model.LogisticRegression\",\n          \"sklearn.ensemble.RandomForestClassifier\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hyperparameters\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 400,\n        \"samples\": [\n          \"{\\\"n_jobs\\\": -1, \\\"max_depth\\\": 64, \\\"num_leaves\\\": 193, \\\"n_estimators\\\": 1494, \\\"random_state\\\": 174648506, \\\"boosting_type\\\": \\\"gbdt\\\", \\\"learning_rate\\\": 0.0956354380621859, \\\"min_child_samples\\\": 23}\",\n          \"{\\\"n_jobs\\\": -1, \\\"max_depth\\\": 51, \\\"num_leaves\\\": 192, \\\"n_estimators\\\": 433, \\\"random_state\\\": 664130526, \\\"boosting_type\\\": \\\"gbdt\\\", \\\"learning_rate\\\": 0.33298425495807965, \\\"min_child_samples\\\": 57}\",\n          \"{\\\"n_jobs\\\": -1, \\\"criterion\\\": \\\"gini\\\", \\\"max_depth\\\": 18, \\\"max_features\\\": \\\"sqrt\\\", \\\"n_estimators\\\": 121, \\\"random_state\\\": 753041955, \\\"min_samples_leaf\\\": 18, \\\"min_samples_split\\\": 40}\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model_uuid\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 400,\n        \"samples\": [\n          \"695dacd853ef3148ae093ff77877d4fa\",\n          \"6582423332d08e8d7bb43ec6def790d3\",\n          \"17e7f8a64ab7d906350270052339e840\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predictions_uuid\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 400,\n        \"samples\": [\n          \"3db70d5f55d5e20544ced27df0bf20e5\",\n          \"f8ffee05d2e6c62ad36c44c4f7ed5da9\",\n          \"175b69a1e3b62411c7f53683b2534873\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target_pp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1000,\n        \"max\": 1000,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"matrix_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"test\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"matrix_start_date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2012-12-01\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"matrix_end_date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2013-01-31\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "evals_df = pd.read_csv(DATAPATH +'split2_evals.csv.gz', compression='gzip')\n",
        "# Let's sort the models by Precision at top 1000 predicted positives (our performance metric of interest for this case study)\n",
        "evals_df.sort_values('model_precision', ascending = False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CFMeQTljrAbT"
      },
      "source": [
        "## Load pre-computed Aequitas audit results\n",
        "\n",
        "The `aequitas_df` contains a table with the bias audit results for the 400 models for all the attributes we care about (we pre-selected the metrics of interest for this tutorial)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "P4WA-GNQrAbU",
        "outputId": "269eb984-67cc-4d92-84cc-bbccc42add98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         model_uuid                  predictions_uuid     k  \\\n",
              "0  a04e2eedd9c5ff18bcf77e84ae9db561  c598fbe93f4c218ac7d325fb478598f1  1000   \n",
              "1  a04e2eedd9c5ff18bcf77e84ae9db561  c598fbe93f4c218ac7d325fb478598f1  1000   \n",
              "2  a04e2eedd9c5ff18bcf77e84ae9db561  c598fbe93f4c218ac7d325fb478598f1  1000   \n",
              "3  a04e2eedd9c5ff18bcf77e84ae9db561  c598fbe93f4c218ac7d325fb478598f1  1000   \n",
              "4  a04e2eedd9c5ff18bcf77e84ae9db561  c598fbe93f4c218ac7d325fb478598f1  1000   \n",
              "5  a04e2eedd9c5ff18bcf77e84ae9db561  c598fbe93f4c218ac7d325fb478598f1  1000   \n",
              "6  3f8f65db414b74ba1c4773e131bdb51d  2d8e4d82f4e67e1de1de8650a7838fc2  1000   \n",
              "7  3f8f65db414b74ba1c4773e131bdb51d  2d8e4d82f4e67e1de1de8650a7838fc2  1000   \n",
              "8  3f8f65db414b74ba1c4773e131bdb51d  2d8e4d82f4e67e1de1de8650a7838fc2  1000   \n",
              "9  3f8f65db414b74ba1c4773e131bdb51d  2d8e4d82f4e67e1de1de8650a7838fc2  1000   \n",
              "\n",
              "  attribute_name attribute_value       tpr  tpr_disparity tpr_ref_group_value  \\\n",
              "0     metro_type  suburban_rural  0.139156       1.000000      suburban_rural   \n",
              "1     metro_type           urban  0.042674       0.306665      suburban_rural   \n",
              "2    teacher_sex          female  0.102390       3.370670                male   \n",
              "3  poverty_level           lower  0.137859       1.000000               lower   \n",
              "4  poverty_level         highest  0.050771       0.368285               lower   \n",
              "5    teacher_sex            male  0.030377       1.000000                male   \n",
              "6  poverty_level           lower  0.149405       1.000000               lower   \n",
              "7  poverty_level         highest  0.039846       0.266696               lower   \n",
              "8    teacher_sex          female  0.101613       2.986684                male   \n",
              "9    teacher_sex            male  0.034022       1.000000                male   \n",
              "\n",
              "    pp   tp  group_label_pos      prev  group_size matrix_type  \\\n",
              "0  626  333             2393  0.367814        6506        test   \n",
              "1  192  120             2812  0.308502        9115        test   \n",
              "2  951  527             5147  0.341019       15093        test   \n",
              "3  753  394             2858  0.370976        7704        test   \n",
              "4  247  158             3112  0.312043        9973        test   \n",
              "5   49   25              823  0.318498        2584        test   \n",
              "6  805  427             2858  0.370976        7704        test   \n",
              "7  195  124             3112  0.312043        9973        test   \n",
              "8  944  523             5147  0.341019       15093        test   \n",
              "9   56   28              823  0.318498        2584        test   \n",
              "\n",
              "  matrix_start_date matrix_end_date  \n",
              "0        2012-12-01      2013-01-31  \n",
              "1        2012-12-01      2013-01-31  \n",
              "2        2012-12-01      2013-01-31  \n",
              "3        2012-12-01      2013-01-31  \n",
              "4        2012-12-01      2013-01-31  \n",
              "5        2012-12-01      2013-01-31  \n",
              "6        2012-12-01      2013-01-31  \n",
              "7        2012-12-01      2013-01-31  \n",
              "8        2012-12-01      2013-01-31  \n",
              "9        2012-12-01      2013-01-31  "
            ],
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              "      <th></th>\n",
              "      <th>model_uuid</th>\n",
              "      <th>predictions_uuid</th>\n",
              "      <th>k</th>\n",
              "      <th>attribute_name</th>\n",
              "      <th>attribute_value</th>\n",
              "      <th>tpr</th>\n",
              "      <th>tpr_disparity</th>\n",
              "      <th>tpr_ref_group_value</th>\n",
              "      <th>pp</th>\n",
              "      <th>tp</th>\n",
              "      <th>group_label_pos</th>\n",
              "      <th>prev</th>\n",
              "      <th>group_size</th>\n",
              "      <th>matrix_type</th>\n",
              "      <th>matrix_start_date</th>\n",
              "      <th>matrix_end_date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>a04e2eedd9c5ff18bcf77e84ae9db561</td>\n",
              "      <td>c598fbe93f4c218ac7d325fb478598f1</td>\n",
              "      <td>1000</td>\n",
              "      <td>metro_type</td>\n",
              "      <td>suburban_rural</td>\n",
              "      <td>0.139156</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>suburban_rural</td>\n",
              "      <td>626</td>\n",
              "      <td>333</td>\n",
              "      <td>2393</td>\n",
              "      <td>0.367814</td>\n",
              "      <td>6506</td>\n",
              "      <td>test</td>\n",
              "      <td>2012-12-01</td>\n",
              "      <td>2013-01-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>a04e2eedd9c5ff18bcf77e84ae9db561</td>\n",
              "      <td>c598fbe93f4c218ac7d325fb478598f1</td>\n",
              "      <td>1000</td>\n",
              "      <td>metro_type</td>\n",
              "      <td>urban</td>\n",
              "      <td>0.042674</td>\n",
              "      <td>0.306665</td>\n",
              "      <td>suburban_rural</td>\n",
              "      <td>192</td>\n",
              "      <td>120</td>\n",
              "      <td>2812</td>\n",
              "      <td>0.308502</td>\n",
              "      <td>9115</td>\n",
              "      <td>test</td>\n",
              "      <td>2012-12-01</td>\n",
              "      <td>2013-01-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>a04e2eedd9c5ff18bcf77e84ae9db561</td>\n",
              "      <td>c598fbe93f4c218ac7d325fb478598f1</td>\n",
              "      <td>1000</td>\n",
              "      <td>teacher_sex</td>\n",
              "      <td>female</td>\n",
              "      <td>0.102390</td>\n",
              "      <td>3.370670</td>\n",
              "      <td>male</td>\n",
              "      <td>951</td>\n",
              "      <td>527</td>\n",
              "      <td>5147</td>\n",
              "      <td>0.341019</td>\n",
              "      <td>15093</td>\n",
              "      <td>test</td>\n",
              "      <td>2012-12-01</td>\n",
              "      <td>2013-01-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>a04e2eedd9c5ff18bcf77e84ae9db561</td>\n",
              "      <td>c598fbe93f4c218ac7d325fb478598f1</td>\n",
              "      <td>1000</td>\n",
              "      <td>poverty_level</td>\n",
              "      <td>lower</td>\n",
              "      <td>0.137859</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>lower</td>\n",
              "      <td>753</td>\n",
              "      <td>394</td>\n",
              "      <td>2858</td>\n",
              "      <td>0.370976</td>\n",
              "      <td>7704</td>\n",
              "      <td>test</td>\n",
              "      <td>2012-12-01</td>\n",
              "      <td>2013-01-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>a04e2eedd9c5ff18bcf77e84ae9db561</td>\n",
              "      <td>c598fbe93f4c218ac7d325fb478598f1</td>\n",
              "      <td>1000</td>\n",
              "      <td>poverty_level</td>\n",
              "      <td>highest</td>\n",
              "      <td>0.050771</td>\n",
              "      <td>0.368285</td>\n",
              "      <td>lower</td>\n",
              "      <td>247</td>\n",
              "      <td>158</td>\n",
              "      <td>3112</td>\n",
              "      <td>0.312043</td>\n",
              "      <td>9973</td>\n",
              "      <td>test</td>\n",
              "      <td>2012-12-01</td>\n",
              "      <td>2013-01-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>a04e2eedd9c5ff18bcf77e84ae9db561</td>\n",
              "      <td>c598fbe93f4c218ac7d325fb478598f1</td>\n",
              "      <td>1000</td>\n",
              "      <td>teacher_sex</td>\n",
              "      <td>male</td>\n",
              "      <td>0.030377</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>male</td>\n",
              "      <td>49</td>\n",
              "      <td>25</td>\n",
              "      <td>823</td>\n",
              "      <td>0.318498</td>\n",
              "      <td>2584</td>\n",
              "      <td>test</td>\n",
              "      <td>2012-12-01</td>\n",
              "      <td>2013-01-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3f8f65db414b74ba1c4773e131bdb51d</td>\n",
              "      <td>2d8e4d82f4e67e1de1de8650a7838fc2</td>\n",
              "      <td>1000</td>\n",
              "      <td>poverty_level</td>\n",
              "      <td>lower</td>\n",
              "      <td>0.149405</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>lower</td>\n",
              "      <td>805</td>\n",
              "      <td>427</td>\n",
              "      <td>2858</td>\n",
              "      <td>0.370976</td>\n",
              "      <td>7704</td>\n",
              "      <td>test</td>\n",
              "      <td>2012-12-01</td>\n",
              "      <td>2013-01-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>3f8f65db414b74ba1c4773e131bdb51d</td>\n",
              "      <td>2d8e4d82f4e67e1de1de8650a7838fc2</td>\n",
              "      <td>1000</td>\n",
              "      <td>poverty_level</td>\n",
              "      <td>highest</td>\n",
              "      <td>0.039846</td>\n",
              "      <td>0.266696</td>\n",
              "      <td>lower</td>\n",
              "      <td>195</td>\n",
              "      <td>124</td>\n",
              "      <td>3112</td>\n",
              "      <td>0.312043</td>\n",
              "      <td>9973</td>\n",
              "      <td>test</td>\n",
              "      <td>2012-12-01</td>\n",
              "      <td>2013-01-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>3f8f65db414b74ba1c4773e131bdb51d</td>\n",
              "      <td>2d8e4d82f4e67e1de1de8650a7838fc2</td>\n",
              "      <td>1000</td>\n",
              "      <td>teacher_sex</td>\n",
              "      <td>female</td>\n",
              "      <td>0.101613</td>\n",
              "      <td>2.986684</td>\n",
              "      <td>male</td>\n",
              "      <td>944</td>\n",
              "      <td>523</td>\n",
              "      <td>5147</td>\n",
              "      <td>0.341019</td>\n",
              "      <td>15093</td>\n",
              "      <td>test</td>\n",
              "      <td>2012-12-01</td>\n",
              "      <td>2013-01-31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>3f8f65db414b74ba1c4773e131bdb51d</td>\n",
              "      <td>2d8e4d82f4e67e1de1de8650a7838fc2</td>\n",
              "      <td>1000</td>\n",
              "      <td>teacher_sex</td>\n",
              "      <td>male</td>\n",
              "      <td>0.034022</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>male</td>\n",
              "      <td>56</td>\n",
              "      <td>28</td>\n",
              "      <td>823</td>\n",
              "      <td>0.318498</td>\n",
              "      <td>2584</td>\n",
              "      <td>test</td>\n",
              "      <td>2012-12-01</td>\n",
              "      <td>2013-01-31</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a49a9597-bd37-41cf-ba6b-aa1d9fe01c4e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a49a9597-bd37-41cf-ba6b-aa1d9fe01c4e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a49a9597-bd37-41cf-ba6b-aa1d9fe01c4e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-337511ed-6d61-4acd-b95e-d33e399f9af4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-337511ed-6d61-4acd-b95e-d33e399f9af4')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-337511ed-6d61-4acd-b95e-d33e399f9af4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "aequitas_df",
              "summary": "{\n  \"name\": \"aequitas_df\",\n  \"rows\": 2400,\n  \"fields\": [\n    {\n      \"column\": \"model_uuid\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 400,\n        \"samples\": [\n          \"8a4d626efeec78fcbe570619da5a2abd\",\n          \"cd39c174167a014f46fef26806cc58f9\",\n          \"56709b3a414124be2bce7c4ee51331ed\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predictions_uuid\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 400,\n        \"samples\": [\n          \"cc8a13917ab5ce47d248638e393dd527\",\n          \"58ca933c1697a0a4bbbcb26fb96f6aee\",\n          \"4e8c20048ce5587e87cb475c01764c48\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"k\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1000,\n        \"max\": 1000,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"attribute_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"metro_type\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"attribute_value\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"suburban_rural\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tpr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03344015594402884,\n        \"min\": 0.0039118065433854,\n        \"max\": 0.166736314249896,\n        \"num_unique_values\": 827,\n        \"samples\": [\n          0.146256123163051\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tpr_disparity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6068661456944745,\n        \"min\": 0.0234610352338884,\n        \"max\": 5.72209886480335,\n        \"num_unique_values\": 943,\n        \"samples\": [\n          0.25944991846789\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tpr_ref_group_value\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"suburban_rural\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 267,\n        \"min\": 28,\n        \"max\": 972,\n        \"num_unique_values\": 774,\n        \"samples\": [\n          506\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 134,\n        \"min\": 11,\n        \"max\": 533,\n        \"num_unique_values\": 496,\n        \"samples\": [\n          294\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"group_label_pos\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1269,\n        \"min\": 823,\n        \"max\": 5147,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2393\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prev\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02548632777799823,\n        \"min\": 0.308502468458585,\n        \"max\": 0.370976116303219,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.367814325238242\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"group_size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3778,\n        \"min\": 2584,\n        \"max\": 15093,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          6506\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"matrix_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"test\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"matrix_start_date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2012-12-01\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"matrix_end_date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2013-01-31\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "aequitas_df = pd.read_csv(DATAPATH + 'split2_aequitas.csv.gz', compression='gzip')\n",
        "aequitas_df.head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "igzvCivmrAbV"
      },
      "source": [
        "Combining the `evals_df` with the `aequitas_df` allow us to get a clearer picture how the different models compare with each other in both performance and bias of our groups of interest.\n",
        "\n",
        "The `create_scatter_disparity_performance` method will help us to easily plot the 400 models bias-performance tradeoffs and it's going to be used throughout the notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Gfzhh8EnrAbV"
      },
      "outputs": [],
      "source": [
        "def create_scatter_disparity_performance(evals_df, aequitas_df,  attr_col, group_name,\n",
        "                                         performance_col='model_precision', bias_metric='tpr', flip_disparity=False,\n",
        "                                         mitigated_tags=[], mitigated_bdfs=[], mitigated_performances=[], ylim=None):\n",
        "    disparity_df = aequitas_df.loc[(aequitas_df['attribute_name']==attr_col) & (aequitas_df['attribute_value']==group_name)].copy()\n",
        "    disparity_metric = bias_metric + '_disparity'\n",
        "    scatter_schema = ['model_uuid', performance_col, 'attribute_name', 'attribute_value', bias_metric, disparity_metric, 'model_tag']\n",
        "    if flip_disparity:\n",
        "        disparity_df[disparity_metric]= disparity_df.apply(lambda x: 1/x[disparity_metric] , axis=1)\n",
        "    scatter = pd.merge(evals_df, disparity_df, how='left', on=['model_uuid'], sort=True, copy=True)\n",
        "    scatter = scatter[['model_uuid', performance_col, 'attribute_name', 'attribute_value', bias_metric, disparity_metric]].copy()\n",
        "    scatter['model_tag'] = 'Other Models'\n",
        "    scatter.sort_values('model_precision', ascending = False, inplace=True, ignore_index=True)\n",
        "    scatter['model_tag'] = scatter.apply(lambda x: 'Highest Precision at 1000' if int(x.name) < 1 else x['model_tag'], axis=1)\n",
        "    mitigated_points = []\n",
        "    scatter_final = pd.DataFrame()\n",
        "    if mitigated_bdfs and mitigated_performances:\n",
        "        for i in range(len(mitigated_bdfs)):\n",
        "            if not mitigated_bdfs[i].empty and mitigated_performances[i] !=None:\n",
        "                mitigated_bdfs[i][performance_col] = mitigated_performances[i]\n",
        "                mitigated_bdfs[i]['model_tag'] = mitigated_tags[i]\n",
        "                new_disparity_df = mitigated_bdfs[i].loc[(mitigated_bdfs[i]['attribute_name']==attr_col) & (mitigated_bdfs[i]['attribute_value']==group_name)].copy()\n",
        "                if flip_disparity:\n",
        "                    new_disparity_df[disparity_metric]= new_disparity_df.apply(lambda x: 1/x[disparity_metric] , axis=1)\n",
        "                scatter_new = new_disparity_df[[c for c in new_disparity_df.columns if c in scatter_schema]].copy()\n",
        "                mitigated_points.append(scatter_new)\n",
        "        scatter_final = pd.concat(mitigated_points , axis=0)\n",
        "\n",
        "    ax = sns.scatterplot(\n",
        "        x='model_precision', y=disparity_metric, hue='model_tag',\n",
        "        data=scatter,\n",
        "        alpha=0.4, s=20, palette = np.array(sns.color_palette()[0:2])\n",
        "\n",
        "\n",
        "    )\n",
        "    if not scatter_final.empty:\n",
        "        ax1 = sns.scatterplot(\n",
        "            x='model_precision', y=disparity_metric, hue='model_tag',\n",
        "            data=scatter_final,\n",
        "            alpha = 0.95, s=20,  palette = np.array(sns.color_palette()[2:3] + sns.color_palette()[4:])\n",
        "        )\n",
        "\n",
        "    if ylim:\n",
        "        plt.ylim(0, 10)\n",
        "    flip_placeholder = 'Flipped' if flip_disparity else ''\n",
        "    ax.set_title('{} {} vs.{} for {}:{}'.format(flip_placeholder, disparity_metric, performance_col, attr_col,group_name ), y=1., fontsize='xx-small')\n",
        "    #plt.gcf().set_size_inches((4, 3))\n",
        "    plt.legend(loc='upper left', fontsize='xx-small', prop={'size': 5})\n",
        "    plt.gcf().set_dpi(DPI)\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Let's see if we could have picked a better model for fairness in Poverty Level\n",
        "Each point in the scatterplot represents a model. We highlight the model we picked before (highest global performance)."
      ],
      "metadata": {
        "id": "TW6rdxyo-fo9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "create_scatter_disparity_performance(evals_df, aequitas_df, 'poverty_level', 'highest', flip_disparity=True )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 928
        },
        "id": "SBrLn9z6-N3Y",
        "outputId": "f63ef562-d794-43b1-dca9-0956a9f70f1f"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1280x960 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "#Let's see if we could have picked a better model for fairness in Metro Type\n"
      ],
      "metadata": {
        "id": "YgYbIAwB-ptz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "create_scatter_disparity_performance(evals_df, aequitas_df, 'metro_type', 'urban', flip_disparity=True, ylim=10 )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 928
        },
        "id": "IkK0IF0p-tUu",
        "outputId": "3fa2fd7c-e3c6-4a52-a3e1-aa69e30cbae1"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1280x960 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "#Let's see if we could have picked a better model for fairness in Teacher sex"
      ],
      "metadata": {
        "id": "cBtpCsbF-7_G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "create_scatter_disparity_performance(evals_df, aequitas_df, 'teacher_sex', 'female', flip_disparity=False )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 928
        },
        "id": "az1QA8vY-_BG",
        "outputId": "65840099-81e1-4ac7-e750-1cb65d2d4aa7"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1280x960 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hz6HP95UrAbZ"
      },
      "source": [
        "# Bias Reduction Strategies\n",
        "We can try a few different bias reduction strategies now and compare the audit results with the original models. Let's try Post-hoc adjustment\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "BF9eheOe3lLN"
      },
      "outputs": [],
      "source": [
        "# We will first load the dataset\n",
        "traindf = pd.read_csv(DATAPATH + 'train_20120501_20120801.csv.gz', compression='gzip')\n",
        "testdf = pd.read_csv(DATAPATH + 'test_20121201_20130201.csv.gz', compression='gzip')\n",
        "train_attrdf = pd.read_csv(DATAPATH + 'train_20120501_20120801_protected.csv.gz', compression='gzip')\n",
        "test_attrdf = pd.read_csv(DATAPATH + 'test_20121201_20130201_protected.csv.gz', compression='gzip')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "gWGiQRoG3lLO"
      },
      "outputs": [],
      "source": [
        "#Let's load the hyperparameters of the highest performance model to a dictionary\n",
        "import ast\n",
        "hyperparameters= ast.literal_eval(evals_df['hyperparameters'][0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "wzCQIr4T3lLO"
      },
      "outputs": [],
      "source": [
        "# Since the model with highest performance is a RandomForestClassifier let's create a classifier with the fetched hyperparameters\n",
        "rf = RandomForestClassifier(**hyperparameters)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1gynI-OB3lLO"
      },
      "source": [
        "### Defining label, group of interest and reference group for the attribute column we care about\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "4YKjlmhr3lLO"
      },
      "outputs": [],
      "source": [
        "label_col = 'quickstart_label'\n",
        "attribute_col = 'poverty_level'\n",
        "\n",
        "group_of_interest = 'highest'\n",
        "reference_group = 'lower'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "epI9kK9U3lLO"
      },
      "source": [
        "### Look at training data distributions\n",
        "\n",
        "#### Poverty_level=Highest\n",
        "label_pos_poverty_highest =  P(poverty_level=highest | not_funded)\n",
        "\n",
        "label_neg_poverty_highest =  P(poverty_level=highest | funded)\n",
        "\n",
        "\n",
        "#### Poverty_level=Lower\n",
        "label_pos_poverty_lower =  P(poverty_level=lower | not_funded)\n",
        "\n",
        "label_neg_poverty_lower =  P(poverty_level=lower | funded)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "C5waVSoR3lLO",
        "outputId": "3813b716-af20-45e4-b162-57787dd72b51",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "highest    9448\n",
              "lower      7342\n",
              "Name: poverty_level, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "train_attrdf[attribute_col].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "traindf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "E64vw25diBjQ",
        "outputId": "e59f013b-b20b-47ae-fb05-576e33717c67"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       entity_id     as_of_date  \\\n",
              "0         251733  1335913200000   \n",
              "1         251734  1335913200000   \n",
              "2         251735  1335913200000   \n",
              "3         251736  1335913200000   \n",
              "4         251737  1335913200000   \n",
              "...          ...            ...   \n",
              "16785     235210  1343775600000   \n",
              "16786     235211  1343775600000   \n",
              "16787     235212  1343775600000   \n",
              "16788     235204  1343775600000   \n",
              "16789     235213  1343775600000   \n",
              "\n",
              "       project_features_entity_id_all_grade_level_Grades35_sum  \\\n",
              "0                                                    0.0         \n",
              "1                                                    0.0         \n",
              "2                                                    0.0         \n",
              "3                                                    0.0         \n",
              "4                                                    0.0         \n",
              "...                                                  ...         \n",
              "16785                                                0.0         \n",
              "16786                                                0.0         \n",
              "16787                                                0.0         \n",
              "16788                                                0.0         \n",
              "16789                                                0.0         \n",
              "\n",
              "       project_features_entity_id_all_grade_level_Grades68_sum  \\\n",
              "0                                                    1.0         \n",
              "1                                                    1.0         \n",
              "2                                                    1.0         \n",
              "3                                                    0.0         \n",
              "4                                                    1.0         \n",
              "...                                                  ...         \n",
              "16785                                                0.0         \n",
              "16786                                                0.0         \n",
              "16787                                                0.0         \n",
              "16788                                                0.0         \n",
              "16789                                                0.0         \n",
              "\n",
              "       project_features_entity_id_all_grade_level_Grades912_sum  \\\n",
              "0                                                    0.0          \n",
              "1                                                    0.0          \n",
              "2                                                    0.0          \n",
              "3                                                    0.0          \n",
              "4                                                    0.0          \n",
              "...                                                  ...          \n",
              "16785                                                0.0          \n",
              "16786                                                0.0          \n",
              "16787                                                0.0          \n",
              "16788                                                0.0          \n",
              "16789                                                0.0          \n",
              "\n",
              "       project_features_entity_id_all_grade_level_GradesPreK2_sum  \\\n",
              "0                                                    0.0            \n",
              "1                                                    0.0            \n",
              "2                                                    0.0            \n",
              "3                                                    1.0            \n",
              "4                                                    0.0            \n",
              "...                                                  ...            \n",
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              "type": "dataframe",
              "variable_name": "traindf"
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "6xKuTsSO3lLO"
      },
      "outputs": [],
      "source": [
        "def get_group_stats(df, attrdf, label_col, attribute_col, group_name):\n",
        "    \"\"\"Calculates group-wise stats: prevalence, group_size, num label pos, num label neg\n",
        "       Returns: prevalence, group_size, df of group label pos, df of group label neg\n",
        "    \"\"\"\n",
        "    group_size = len(df.loc[attrdf[attribute_col]==group_name])\n",
        "    label_pos = df.loc[(attrdf[attribute_col]==group_name) & (df[label_col] > 0)].copy()\n",
        "    label_neg = df.loc[(attrdf[attribute_col]==group_name) & (df[label_col] < 1.0)].copy()\n",
        "    assert (group_size == len(label_pos) + len(label_neg)), \"label pos + label neg is different from group size!\"\n",
        "    prev = len(label_pos) / group_size\n",
        "    print(\"{} == '{}'\\n\\t group size: {} \\n\\t prevalence: {:.3f}\".format(attribute_col,group_name, group_size, prev))\n",
        "    return prev, group_size, label_pos, label_neg"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "Zkzlmvt53lLP",
        "outputId": "1af797cf-b62d-4581-c05f-57f55eda2114",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "poverty_level == 'highest'\n",
            "\t group size: 9448 \n",
            "\t prevalence: 0.338\n"
          ]
        }
      ],
      "source": [
        "_ = get_group_stats(traindf, train_attrdf, label_col, attribute_col, group_of_interest)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "AUCWCefR3lLP",
        "outputId": "6c530d82-a5f4-49a8-aef2-58f142e1ffeb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "poverty_level == 'lower'\n",
            "\t group size: 7342 \n",
            "\t prevalence: 0.426\n"
          ]
        }
      ],
      "source": [
        "_ = get_group_stats(traindf, train_attrdf, label_col, attribute_col, reference_group)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "QANLG92t3lLP",
        "outputId": "10e1ad73-81b4-4e76-f785-60008971735e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recapping the group of interest and reference we defined above: \n",
            "\n",
            " Attribute: poverty_level\n",
            " Group of interest: highest\n",
            " Reference Group: lower\n"
          ]
        }
      ],
      "source": [
        "print('Recapping the group of interest and reference we defined above: \\n\\n Attribute: {}\\n Group of interest: {}\\n Reference Group: {}'.format(attribute_col, group_of_interest, reference_group))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j2a_ozRlrAbk"
      },
      "source": [
        "## Before and After: Run Aequitas\n",
        "\n",
        "\n",
        "Let's define an audit util method to use throghout the notebook when audit different models."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "-YxbaVcJ3lLT"
      },
      "outputs": [],
      "source": [
        "# This method will help auditing the predictions we made with Aequitas\n",
        "def aequitas_audit(input_df, ref_groups_dict):\n",
        "    g = Group()\n",
        "    b = Bias()\n",
        "    xtab, _ = g.get_crosstabs(input_df[['score','label_value'] + list(ref_groups_dict.keys())].copy())\n",
        "    bdf = b.get_disparity_predefined_groups(xtab, input_df, ref_groups_dict)\n",
        "    return bdf"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LSNc5sdDrAbk"
      },
      "source": [
        "#### Define the configurations for running the bias audits:\n",
        "\n",
        "    - a dictionary for reference groups in the format attribute:group\n",
        "    - the metrics of interest\n",
        "    - the fairness threshold (the ratio of each group value compared to reference group on a given metric)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "lq226TNW3lLT"
      },
      "outputs": [],
      "source": [
        "# Definitions for the audit\n",
        "ref_groups_dict={'poverty_level':'lower', 'metro_type':'suburban_rural', 'teacher_sex':'male'}\n",
        "attributes = ref_groups_dict.keys()\n",
        "metrics = ['tpr']\n",
        "fairness_threshold = 1.3"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y87lRSXDrAbl"
      },
      "source": [
        "For reference, let's start with looking at the \"best\" model we chose in terms of overall precision at 1,000:\n",
        "\n",
        "#### Now let's start with loading the predictions from the \"best\" model chosen earlier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J3BHUfzxrAb6"
      },
      "source": [
        "## <font color=red>Bias Reduction Strategy: Post-Hoc Disparity Mitigation</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HrhZcuD9rAb6"
      },
      "source": [
        "### What has already happened?\n",
        "\n",
        "We've already cleaned data, generated features, created train-test sets, built 1000s of models on each training set and scored each test set with them, and calculated various evaluation metrics. We then used these results to pick a \"best\" model in terms of performance on the \"accuracy\" metric we care about: **Precision at the top 1000** (corresponding to our goal of selecting 1000 project submissions that are most likely to not get funded in order to prioritize resource allocation).\n",
        "\n",
        "When we audited this selected model with Aequitas, however, we found biases across many attributes, including the poverty level of the schools. Here, we explore a method of using post-hoc disparity mitigation to reduce this bias in the selected model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jbMduPAgrAb6"
      },
      "source": [
        "### <font color=green>Intro to Post-Hoc Bias Mitigation</font>\n",
        "\n",
        "![Diagram of Post-Hoc Adjustments](post_hoc_adj.png \"Post-Hoc Adjustments\")\n",
        "\n",
        "One approach to improving the fairness of our model is to make post-hoc adjustments to the thresholds used for each subgroup to choose the 1,000 projects on which to intervene. Because our fairness metric here (**recall** aka **tpr** aka **equality of opportunity**) is monatonically increasing with the depth of the score, we should be able to find score thresholds for each subgroup that will equalize this metric across the groups, subject to the constraint that we want to choose a total of 1,000 projects for our intervation.\n",
        "\n",
        "In short, here's how this will work (see the references below for a more detailed discussion):\n",
        "1. Train the model as usual on a training set, predict scores on a test set\n",
        "2. Split this test set by subgroups on our protected attribute (here, poverty level)\n",
        "3. Sort each subgroup by score and calculate the cumulative tpr/recall up to and including each row in the set, storing this \"rolling within-subgroup recall\" value\n",
        "4. Recombine the subgroups, and sort the entire set by this new value\n",
        "5. Take the top 1,000 projects from this re-ordered list and use it to calculate \"top k\" sizes for each subgroup that equalize recall\n",
        "6. Then, on a future test set, use these calculated subgroup list sizes to assess the impact of disparities and overall precision\n",
        "\n",
        "References:\n",
        "- Hardt, et al, [Equality of Opportunity in Supervised Learning](http://papers.nips.cc/paper/6373-equality-of-opportunity-in-supervised-learning)\n",
        "- Rodolfa, et al, [Case Study: Predictive Fairness to Reduce Misdemeanor Recidivism Through Social Service Interventions](https://dl.acm.org/doi/abs/10.1145/3351095.3372863?casa_token=zc196JJrqkkAAAAA:bPmqmKrA91esJhIxHPT4K1crWWb5JGcflVFDkTgODctMzLpUX50_56Kyyh4NJ2GTd_QSydqhNpjT)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6xdNWoWRrAb7"
      },
      "source": [
        "### Load the train, test, and protected attributes from the first split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "qp0sSsSerAb7"
      },
      "outputs": [],
      "source": [
        "split1_traindf = pd.read_csv(DATAPATH + 'train_20111101_20120201.csv.gz', compression='gzip')\n",
        "split1_testdf = pd.read_csv(DATAPATH + 'test_20120601_20120801.csv.gz', compression='gzip')\n",
        "split1_attrdf = pd.read_csv(DATAPATH + 'test_20120601_20120801_protected.csv.gz', compression='gzip')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EzJSFvOgrAb7"
      },
      "source": [
        "Let's just take a quick look at the data to make sure it makes sense:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "MLYr-KKJrAb7",
        "outputId": "948de96e-39cc-410f-f04b-815ffca6ea4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   entity_id     as_of_date  \\\n",
              "0     313448  1320105600000   \n",
              "1     313441  1320105600000   \n",
              "2     313449  1320105600000   \n",
              "3     313447  1320105600000   \n",
              "4     313446  1320105600000   \n",
              "\n",
              "   project_features_entity_id_all_grade_level_Grades35_sum  \\\n",
              "0                                                0.0         \n",
              "1                                                1.0         \n",
              "2                                                0.0         \n",
              "3                                                1.0         \n",
              "4                                                0.0         \n",
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              "   project_features_entity_id_all_grade_level_Grades68_sum  \\\n",
              "0                                                0.0         \n",
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              "2                                                0.0         \n",
              "3                                                0.0         \n",
              "4                                                0.0         \n",
              "\n",
              "   project_features_entity_id_all_grade_level_Grades912_sum  \\\n",
              "0                                                1.0          \n",
              "1                                                0.0          \n",
              "2                                                1.0          \n",
              "3                                                0.0          \n",
              "4                                                0.0          \n",
              "\n",
              "   project_features_entity_id_all_grade_level_GradesPreK2_sum  \\\n",
              "0                                                0.0            \n",
              "1                                                0.0            \n",
              "2                                                0.0            \n",
              "3                                                0.0            \n",
              "4                                                1.0            \n",
              "\n",
              "   project_features_entity_id_all_grade_level__NULL_sum  \\\n",
              "0                                                0.0      \n",
              "1                                                0.0      \n",
              "2                                                0.0      \n",
              "3                                                0.0      \n",
              "4                                                0.0      \n",
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              "0                                                0.0        \n",
              "1                                                0.0        \n",
              "2                                                0.0        \n",
              "3                                                0.0        \n",
              "4                                                0.0        \n",
              "\n",
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              "0                                                0.0        \n",
              "1                                                0.0        \n",
              "2                                                0.0        \n",
              "3                                                0.0        \n",
              "4                                                0.0        \n",
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              "0                                                1.0          ...   \n",
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              "2                                                0.0          ...   \n",
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              "4                                                1.0          ...   \n",
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              "   donation_features_entity_id_all_zip_avg_donations_2yr_imp  \\\n",
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              "2                                                0.0           \n",
              "3                                                1.0           \n",
              "4                                                0.0           \n",
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              "   donation_features_entity_id_all_zip_funding_rate_1yr_sum  \\\n",
              "0                                           0.636364          \n",
              "1                                           0.000000          \n",
              "2                                           0.300000          \n",
              "3                                           0.000000          \n",
              "4                                           0.111111          \n",
              "\n",
              "   donation_features_entity_id_all_zip_funding_rate_1yr_imp  \\\n",
              "0                                                0.0          \n",
              "1                                                1.0          \n",
              "2                                                0.0          \n",
              "3                                                1.0          \n",
              "4                                                0.0          \n",
              "\n",
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              "4                                           0.652174          \n",
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              "0                                                0.0                      1.0   \n",
              "1                                                1.0                      0.0   \n",
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              "4                                                0.0                      0.0   \n",
              "\n",
              "   project_features_entity_id_all_poverty_level_highpoverty  \\\n",
              "0                                                1.0          \n",
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              "2                                                1.0          \n",
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              "   project_features_entity_id_all_poverty_level_highestpoverty  \\\n",
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              "2                                                0.0             \n",
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              "4                                                0.0             \n",
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              "1                                                0.0             \n",
              "2                                                0.0             \n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
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          "metadata": {},
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T2_aZeR_rAb7"
      },
      "source": [
        "### Set up some parameters we'll need below\n",
        "\n",
        "Note that the classifier type and hyperparameters here are from the best-performing model we chose above based on precision on the top 1000."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "8H8fzeVFrAb7"
      },
      "outputs": [],
      "source": [
        "hyperparameters = {\n",
        "    'criterion': 'gini',\n",
        "    'max_depth': 30,\n",
        "    'max_features': 'sqrt',\n",
        "    'min_samples_leaf': 44,\n",
        "    'min_samples_split': 3,\n",
        "    'n_estimators': 87,\n",
        "    'n_jobs': -1,\n",
        "    'random_state': 213500298\n",
        "}\n",
        "clf = RandomForestClassifier(**hyperparameters)\n",
        "\n",
        "top_k = 1000\n",
        "\n",
        "label_col = 'quickstart_label'\n",
        "entity_col = 'entity_id'\n",
        "date_col = 'as_of_date'\n",
        "exclude_cols = [label_col, entity_col, date_col] # columns to exclude from the X matrices for the classifier\n",
        "\n",
        "protected_attribute_col = 'poverty_level'\n",
        "\n",
        "# Parameters for Aequitas\n",
        "\n",
        "metrics = ['tpr']\n",
        "disparity_threshold = 1.3\n",
        "protected_attribute_ref_group = {protected_attribute_col:'lower'}\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zIbdJJ9erAb8"
      },
      "source": [
        "### Train the model and predict on the test set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "v-1FMnEDrAb8"
      },
      "outputs": [],
      "source": [
        "# train\n",
        "y_train = split1_traindf[label_col].values\n",
        "clf.fit(split1_traindf.drop(exclude_cols, axis = 1), y_train)\n",
        "\n",
        "# test set predictions\n",
        "split1_preds = split1_testdf[[entity_col, date_col, label_col]].copy()\n",
        "split1_preds['predict_proba'] = clf.predict_proba(split1_testdf.drop(exclude_cols, axis = 1))[:,1]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Z2cNQEzrAb8"
      },
      "source": [
        "Let's take a quick look at the predictions to make sure they look good:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "ZQzMEARlrAb8",
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          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   entity_id     as_of_date  quickstart_label  predict_proba\n",
              "0     246363  1338591600000               1.0       0.382485\n",
              "1     246362  1338591600000               1.0       0.188013\n",
              "2     246361  1338591600000               0.0       0.398983\n",
              "3     246360  1338591600000               0.0       0.422042\n",
              "4     246359  1338591600000               1.0       0.403111"
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "split1_preds",
              "summary": "{\n  \"name\": \"split1_preds\",\n  \"rows\": 11307,\n  \"fields\": [\n    {\n      \"column\": \"entity_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8205,\n        \"min\": 171,\n        \"max\": 246440,\n        \"num_unique_values\": 11307,\n        \"samples\": [\n          237359,\n          236378,\n          242183\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"as_of_date\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1563038055,\n        \"min\": 1338591600000,\n        \"max\": 1343775600000,\n        \"num_unique_values\": 61,\n        \"samples\": [\n          1338591600000,\n          1339023600000,\n          1342566000000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quickstart_label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4870685847182687,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predict_proba\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10358019729662814,\n        \"min\": 0.08702890815897547,\n        \"max\": 0.6204893692741115,\n        \"num_unique_values\": 11271,\n        \"samples\": [\n          0.4038948633211899,\n          0.46723040292166734\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "split1_preds.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PghyKbGBrAb8"
      },
      "source": [
        "### Combine predictions with protected attributes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "E26kYW0VrAb8"
      },
      "outputs": [],
      "source": [
        "df = pd.merge(split1_preds, split1_attrdf, how='left', on=[entity_col,date_col], sort=True, copy=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nb0GFlGlrAb9"
      },
      "source": [
        "### Sort by score, then split by protected attribute (poverty level)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "SjDW283hrAb9"
      },
      "outputs": [],
      "source": [
        "protected_attribute_groups = df[protected_attribute_col].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "xRmXcAjtrAb9"
      },
      "outputs": [],
      "source": [
        "df = df.sort_values('predict_proba', ascending=False)\n",
        "subgroup_dfs = []\n",
        "for grp in protected_attribute_groups:\n",
        "    subgroup_dfs.append(df[df[protected_attribute_col]==grp].copy())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9GhbKz0LrAb9"
      },
      "source": [
        "### Calculate within-subgroup cumulative recall\n",
        "\n",
        "Here, we calculate the recall up to and including each row within the highest-poverty and lower-poverty subsets of the test set. Doing this allows us to recombine and sort the sets in a way that will let find recall-equalizing \"top k\" list sizes for each subgroup."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "IHFVwb8hrAb9"
      },
      "outputs": [],
      "source": [
        "for subgrp_df in subgroup_dfs:\n",
        "    subgrp_df['cumsum_recall'] = subgrp_df[label_col].cumsum() / subgrp_df[label_col].sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "McJ2GzYVrAb-"
      },
      "source": [
        "### Recombine the subgroup sets and sort by this cumulative recall"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "b64e0VEirAb-"
      },
      "outputs": [],
      "source": [
        "recall_df = pd.concat(subgroup_dfs, axis=0).sort_values('cumsum_recall', ascending=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ltEQv_LrAb-"
      },
      "source": [
        "### Find subgroup sizes, holding the overall list size (1000) constant\n",
        "\n",
        "Now we can simply threshold this re-sorted list by `top_k` (here, 1000) to identify how many individuals from each group we should apply in the future.\n",
        "\n",
        "Notice here that each subgroup will still be ordered by their predicted score, but the scores will no longer be perfectly ordered across subgroups."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "p0S6xAbJrAb_",
        "outputId": "39e16d81-0246-49f1-e1dd-f0bdf2817d61",
        "colab": {
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          "height": 206
        }
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      entity_id     as_of_date  quickstart_label  predict_proba poverty_level  \\\n",
              "1830     236962  1343343600000               0.0       0.423424       highest   \n",
              "1695     236827  1343430000000               1.0       0.470249         lower   \n",
              "6585     241717  1341183600000               1.0       0.423386       highest   \n",
              "9908     245042  1339455600000               0.0       0.470204         lower   \n",
              "1226     236358  1343516400000               1.0       0.470228         lower   \n",
              "\n",
              "     teacher_sex      metro_type  cumsum_recall  \n",
              "1830      female           urban       0.137341  \n",
              "1695      female  suburban_rural       0.137405  \n",
              "6585      female  suburban_rural       0.137780  \n",
              "9908      female  suburban_rural       0.137882  \n",
              "1226      female  suburban_rural       0.137882  "
            ],
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              "      <th>predict_proba</th>\n",
              "      <th>poverty_level</th>\n",
              "      <th>teacher_sex</th>\n",
              "      <th>metro_type</th>\n",
              "      <th>cumsum_recall</th>\n",
              "    </tr>\n",
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              "      <th>1830</th>\n",
              "      <td>236962</td>\n",
              "      <td>1343343600000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.423424</td>\n",
              "      <td>highest</td>\n",
              "      <td>female</td>\n",
              "      <td>urban</td>\n",
              "      <td>0.137341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1695</th>\n",
              "      <td>236827</td>\n",
              "      <td>1343430000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.470249</td>\n",
              "      <td>lower</td>\n",
              "      <td>female</td>\n",
              "      <td>suburban_rural</td>\n",
              "      <td>0.137405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6585</th>\n",
              "      <td>241717</td>\n",
              "      <td>1341183600000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.423386</td>\n",
              "      <td>highest</td>\n",
              "      <td>female</td>\n",
              "      <td>suburban_rural</td>\n",
              "      <td>0.137780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9908</th>\n",
              "      <td>245042</td>\n",
              "      <td>1339455600000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.470204</td>\n",
              "      <td>lower</td>\n",
              "      <td>female</td>\n",
              "      <td>suburban_rural</td>\n",
              "      <td>0.137882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1226</th>\n",
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              "      <td>0.470228</td>\n",
              "      <td>lower</td>\n",
              "      <td>female</td>\n",
              "      <td>suburban_rural</td>\n",
              "      <td>0.137882</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"new_pp\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"entity_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3841,\n        \"min\": 236358,\n        \"max\": 245042,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          236827,\n          236358,\n          241717\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"as_of_date\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1810899480,\n        \"min\": 1339455600000,\n        \"max\": 1343516400000,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1343430000000,\n          1343516400000,\n          1341183600000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quickstart_label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5477225575051662,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predict_proba\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02564555429190042,\n        \"min\": 0.42338565637392517,\n        \"max\": 0.47024859187992335,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.47024859187992335,\n          0.47022818506211544\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"poverty_level\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"lower\",\n          \"highest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"teacher_sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"metro_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"suburban_rural\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cumsum_recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0002644009905296767,\n        \"min\": 0.137340939008337,\n        \"max\": 0.13788167938931298,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.13740458015267176\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "new_pp = recall_df.head(top_k).copy()\n",
        "new_pp.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "vi0iKqfTrAcB",
        "outputId": "f4da1995-5c20-42a6-f175-037c77a3079b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "highest    554\n",
              "lower      446\n",
              "Name: poverty_level, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "new_pp[protected_attribute_col].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0JQqE2u6rAcB"
      },
      "source": [
        "Let's just store this to re-use below:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "eH5QmW0wrAcC"
      },
      "outputs": [],
      "source": [
        "subgroup_k = {}\n",
        "for grp in protected_attribute_groups:\n",
        "  subgroup_k[grp] = new_pp[protected_attribute_col].value_counts()[grp]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FssBodUHrAcC"
      },
      "source": [
        "### Apply these subgroup-specific sizes to future test set data\n",
        "\n",
        "Now we have calculated the number of projects we need to select from each poverty level, we can apply these to the most recent split to assess how well this method reduces the recall disparities we saw initially and whether this has any impact on the overall precision of the model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e6wcWvFhrAcC"
      },
      "source": [
        "### Load the predictions and protected attributes from the future test set\n",
        "\n",
        "Note that the predictions here correspond to the same model + hyperparameters we specified above"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "cIYTYLyErAcD"
      },
      "outputs": [],
      "source": [
        "split2_preds = pd.read_csv(DATAPATH + 'predictions_c598fbe93f4c218ac7d325fb478598f1.csv.gz', compression='gzip')\n",
        "split2_attrdf = pd.read_csv(DATAPATH + 'test_20121201_20130201_protected.csv.gz', compression='gzip')\n",
        "\n",
        "df2 = pd.merge(split2_preds, split2_attrdf, how='left', on=[entity_col, date_col], sort=True, copy=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aBpVLOZqrAcD"
      },
      "source": [
        "Take a quick look to make sure the data loaded without any issue:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "lUtxFwM8rAcD",
        "outputId": "0401c669-21ef-4aed-914a-b009bb6eb7b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   entity_id     as_of_date  quickstart_label  predict_proba poverty_level  \\\n",
              "0        119  1359504000000               0.0       0.364484         lower   \n",
              "1        120  1359158400000               0.0       0.347160       highest   \n",
              "2        121  1358812800000               1.0       0.503259         lower   \n",
              "3        122  1358726400000               0.0       0.442060         lower   \n",
              "4        123  1358726400000               0.0       0.325577         lower   \n",
              "\n",
              "  teacher_sex      metro_type  \n",
              "0      female  suburban_rural  \n",
              "1      female         unknown  \n",
              "2      female  suburban_rural  \n",
              "3      female         unknown  \n",
              "4      female           urban  "
            ],
            "text/html": [
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df2",
              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 17677,\n  \"fields\": [\n    {\n      \"column\": \"entity_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6387,\n        \"min\": 119,\n        \"max\": 186063,\n        \"num_unique_values\": 17677,\n        \"samples\": [\n          182238,\n          173504,\n          169832\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"as_of_date\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1672437969,\n        \"min\": 1354320000000,\n        \"max\": 1359590400000,\n        \"num_unique_values\": 62,\n        \"samples\": [\n          1355443200000,\n          1354924800000,\n          1359504000000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quickstart_label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4729483311340095,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predict_proba\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09456565377788237,\n        \"min\": 0.1305710961565569,\n        \"max\": 0.6588955103884144,\n        \"num_unique_values\": 17547,\n        \"samples\": [\n          0.4462610483820812,\n          0.4119312420536172\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"poverty_level\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"highest\",\n          \"lower\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"teacher_sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"male\",\n          \"female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"metro_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"suburban_rural\",\n          \"unknown\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "df2.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dwcOovWYrAcE"
      },
      "source": [
        "### Split the test set by poverty level to apply the thresholds"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "cuT-p3fPrAcE"
      },
      "outputs": [],
      "source": [
        "df2 = df2.sort_values('predict_proba', ascending=False)\n",
        "new_subgroup_dfs = {}\n",
        "for grp in protected_attribute_groups:\n",
        "    new_subgroup_dfs[grp] = df2[df2[protected_attribute_col]==grp].copy()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pZHFdav5rAcE"
      },
      "source": [
        "### Choose the number of projects from each subgroup found above\n",
        "\n",
        "Notice here that we're choosing the \"top k\" individuals within each subgroup based on their predicted score -- in a deployment, we wouldn't know the true labels to calculate recall values, which is why we had to go one step back in time to find these group sizes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "7dPkwdcVrAcF"
      },
      "outputs": [],
      "source": [
        "pp_dfs = []\n",
        "for grp in protected_attribute_groups:\n",
        "    pp_dfs.append(new_subgroup_dfs[grp].head(subgroup_k[grp]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "am65wwNwrAcF"
      },
      "source": [
        "### Recombine and create a predicted class label for this resulting set\n",
        "That is, 1,000 projects with a label 1 chosen by this process and 0 otherwise"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "eZ-s1JQ9rAcF"
      },
      "outputs": [],
      "source": [
        "new_pp2 = pd.concat(pp_dfs, axis=0).sort_values('predict_proba', ascending=True)\n",
        "\n",
        "mitigated_df = df2.copy()\n",
        "mitigated_df = mitigated_df.rename(columns = {label_col:'label_value'}) # naming for Aequitas\n",
        "\n",
        "# create a \"score\" column with the predicted class (named \"score\" for use with Aequitas below)\n",
        "mitigated_df['score'] = mitigated_df.apply(lambda x: 1.0 if x.name in new_pp2.index.tolist() else 0, axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6CgOyZhnrAcF"
      },
      "source": [
        "for comparison, let's also look at the unmitigated result again..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "ZHleARoErAcF"
      },
      "outputs": [],
      "source": [
        "unadjusted_df = df2.sort_values('predict_proba', ascending=False).copy()\n",
        "unadjusted_df = unadjusted_df.rename(columns = {label_col:'label_value'}) # naming for Aequitas\n",
        "\n",
        "# create a \"score\" column with the predicted class (named \"score\" for use with Aequitas below)\n",
        "unadjusted_df['score'] = unadjusted_df.apply(lambda x: 1.0 if x.name in unadjusted_df.head(top_k).index.tolist() else 0, axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HO4fVQpdrAcF"
      },
      "source": [
        "### Running Aequitas - Before and After"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mSWlF1i6rAcG"
      },
      "source": [
        "#### For the original score, without post-hoc adjustment for equity"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "ybg9apkhrAcG",
        "outputId": "dad2c754-ad06-4d16-f6d3-fe61b4b84e99",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "get_disparity_predefined_group()\n"
          ]
        },
        {
          "output_type": "execute_result",
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              "alt.LayerChart(...)"
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          },
          "metadata": {},
          "execution_count": 37
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      ],
      "source": [
        "bdf_unadjusted = aequitas_audit(unadjusted_df, protected_attribute_ref_group)\n",
        "ap.disparity(bdf_unadjusted, metrics, protected_attribute_col, fairness_threshold=disparity_threshold)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-N-WtYo7rAcG"
      },
      "source": [
        "#### For the score, with post-hoc disparity mitigation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "get_disparity_predefined_group()\n"
          ]
        },
        {
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[\"attribute_value\"]}}, \"transform\": [{\"calculate\": \"'TPR'\", \"as\": \"metric_variable\"}]}]}]}, {\"layer\": [{\"mark\": {\"type\": \"point\", \"filled\": true, \"opacity\": 1, \"size\": 39.47841760435743}, \"encoding\": {\"color\": {\"condition\": {\"type\": \"nominal\", \"field\": \"attribute_value\", \"legend\": null, \"scale\": {\"domain\": [\"lower\", \"highest\"], \"range\": [\"rgb(117,117,117)\", \"rgb(100, 143, 255)\", \"rgb(254, 97, 0)\", \"rgb(220, 38, 127)\", \"rgb(255, 176, 0)\", \"rgb(120, 94, 240)\"]}, \"selection\": \"selector005\"}, \"value\": \"rgb(224,224,224)\"}, \"shape\": {\"type\": \"nominal\", \"field\": \"attribute_value\", \"legend\": null, \"scale\": {\"domain\": [\"lower\", \"highest\"], \"range\": [\"cross\", \"circle\"]}}, \"x\": {\"value\": 804}, \"y\": {\"type\": \"nominal\", \"axis\": {\"domain\": false, \"grid\": false, \"labels\": false, \"ticks\": false, \"title\": \" \"}, \"field\": \"attribute_value\", \"scale\": {\"domain\": [\"lower\", \"highest\"], \"range\": [71, 125]}}}, \"selection\": {\"selector005\": {\"type\": \"multi\", \"empty\": \"all\", \"fields\": [\"attribute_value\"]}}}, {\"mark\": {\"type\": \"text\", \"align\": \"left\", \"baseline\": \"middle\", \"font\": \"Arial\", \"fontSize\": 12, \"fontWeight\": \"normal\"}, \"encoding\": {\"color\": {\"condition\": {\"type\": \"nominal\", \"field\": \"attribute_value\", \"legend\": null, \"scale\": {\"domain\": [\"lower\", \"highest\"], \"range\": [\"rgb(117,117,117)\", \"rgb(100, 143, 255)\", \"rgb(254, 97, 0)\", \"rgb(220, 38, 127)\", \"rgb(255, 176, 0)\", \"rgb(120, 94, 240)\"]}, \"selection\": \"selector005\"}, \"value\": \"rgb(224,224,224)\"}, \"text\": {\"type\": \"nominal\", \"field\": \"label\"}, \"x\": {\"value\": 812}, \"y\": {\"type\": \"nominal\", \"axis\": {\"domain\": false, \"grid\": false, \"labels\": false, \"ticks\": false, \"title\": \" \"}, \"field\": \"attribute_value\", \"scale\": {\"domain\": [\"lower\", \"highest\"], \"range\": [71, 125]}}}, \"selection\": {\"selector008\": {\"type\": \"multi\", \"empty\": \"all\", \"fields\": [\"attribute_value\"]}}}, {\"data\": {\"name\": \"data-79dd248bdaa0ea02c535240e8ad022c8\"}, \"mark\": {\"type\": \"text\", \"align\": \"left\", \"baseline\": \"middle\", \"color\": \"rgb(117,117,117)\", \"font\": \"Arial\", \"fontSize\": 12, \"fontWeight\": \"normal\"}, \"encoding\": {\"text\": {\"value\": \"Click to highlight a group.\"}, \"x\": {\"value\": 800}, \"y\": {\"value\": 49}}}, {\"data\": {\"name\": \"data-79dd248bdaa0ea02c535240e8ad022c8\"}, \"mark\": {\"type\": \"text\", \"align\": \"left\", \"baseline\": \"middle\", \"color\": \"rgb(117,117,117)\", \"font\": \"Arial\", \"fontSize\": 14, \"fontWeight\": \"normal\"}, \"encoding\": {\"text\": {\"value\": \"Groups\"}, \"x\": {\"value\": 800}, \"y\": {\"value\": 30}}}], \"data\": {\"name\": \"data-a5ebce136adcce28635ff1807ca7d132\"}}], \"height\": 250, \"padding\": {\"top\": 15, \"bottom\": 10}, \"resolve\": {\"scale\": {\"size\": \"independent\", \"y\": \"independent\"}}, \"title\": \"Disparities on Poverty_Level\", \"width\": 800, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-1f2af75cfead9a3854ee453922b4eaab\": [{\"metric\": \"TPR\", \"x\": -2, \"x2\": 2}], \"data-79dd248bdaa0ea02c535240e8ad022c8\": [{\"a\": 1, \"b\": 0}, {\"a\": 1, \"b\": 0}], \"data-c233327f4cd9b4c2df31bf8015c83e4a\": [{\"value\": 1, \"label\": 2}, {\"value\": 2, \"label\": 3}, {\"value\": -2, \"label\": 3}, {\"value\": -1, \"label\": 2}, {\"value\": 0, \"label\": \"=\"}], \"data-68125cfc4fc9557defd07e8d14c561c8\": [{\"min\": -0.30000000000000004, \"max\": 0.30000000000000004, \"lower_end\": -2, \"upper_end\": 2}], \"data-0723708aeaa24a9875addd912b017670\": [{\"attribute_name\": \"poverty_level\", \"attribute_value\": \"highest\", \"group_size\": 9973, \"total_entities\": 17677, \"tpr\": 0.10732647814910026, \"tpr_disparity\": 1.2418586014175246, \"tpr_disparity_scaled\": 0.24185860141752458, \"tooltip_group_size\": \"9,973 (56.42%)\", \"tooltip_disparity_explanation_tpr\": \"1.24 times larger TPR than the reference group\"}, {\"attribute_name\": \"poverty_level\", \"attribute_value\": \"lower\", \"group_size\": 7704, \"total_entities\": 17677, \"tpr\": 0.08642407277816655, \"tpr_disparity\": 1.0, \"tpr_disparity_scaled\": 0.0, \"tooltip_group_size\": \"7,704 (43.58%)\", \"tooltip_disparity_explanation_tpr\": \"Reference group\"}], \"data-a5ebce136adcce28635ff1807ca7d132\": [{\"attribute_value\": \"lower\", \"label\": \"lower [REF]\"}, {\"attribute_value\": \"highest\", \"label\": \"highest\"}]}}, {\"mode\": \"vega-lite\"});\n",
              "</script>"
            ],
            "text/plain": [
              "alt.LayerChart(...)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "bdf_mitigated = aequitas_audit(mitigated_df, ref_groups_dict)\n",
        "\n",
        "ap.disparity(bdf_mitigated, metrics, protected_attribute_col, fairness_threshold=disparity_threshold)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-tVKDd-ErAcH"
      },
      "source": [
        "So, it looks the post-hoc adjustments have actually manage to mitigate the existing disparity pretty well (perhaps even over-shooting somewhat, though still within our fairness threshold of 1.3).\n",
        "\n",
        "However, the natural question here is where there is a fairness-accuracy trade-off here: What cost did we incur in terms of model performance, that is overall precision?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MzvV4AqBrAcH"
      },
      "source": [
        "#### Before: Precision of the original, unadjusted score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "4AhwXfukrAcH",
        "outputId": "6623e087-f605-4746-9c3a-baca208b83b1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.552"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "unadjusted_df.loc[unadjusted_df['score']==1]['label_value'].mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "reI9ombCrAcH"
      },
      "source": [
        "#### After: Precision of the new, disparity-mitigated score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "y4ASkfUZrAcH",
        "outputId": "012effc0-d137-4e23-e092-e024d59e321f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.581"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "mitigated_df.loc[mitigated_df['score']==1]['label_value'].mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tFawIxgVrAcI"
      },
      "source": [
        "Somewhat surprisingly, we actually don't seem to see any trade-off with the disparity mitigation here!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t2fr1FSxrAcI"
      },
      "source": [
        "### Adding to the model selection (tradeoff) graph\n",
        "\n",
        "Finally, let's look at how this new option stacks up against the other models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "q04Ql5LgrAcI",
        "outputId": "9050c6d4-92bc-47ea-b2e7-13d9afd09e8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 928
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1280x960 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "mitigated_precision_6 = mitigated_df.loc[mitigated_df['score']==1]['label_value'].mean()\n",
        "plot_configs = {\n",
        "    'evals_df':evals_df,\n",
        "    'aequitas_df':aequitas_df,\n",
        "    'attr_col':'poverty_level',\n",
        "    'group_name':'highest',\n",
        "    'performance_col':'model_precision',\n",
        "    'bias_metric':'tpr',\n",
        "    'flip_disparity':True,\n",
        "    'mitigated_tags':['Equalizing Recall'],\n",
        "    'mitigated_bdfs':[bdf_mitigated],\n",
        "    'mitigated_performances':[mitigated_precision_6],\n",
        "    'ylim':None\n",
        "}\n",
        "create_scatter_disparity_performance(**plot_configs)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.4"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}