From 1a053ea0cd1191ab0b7bc63dc17141924a07f51c Mon Sep 17 00:00:00 2001
From: Fiona Victoria <37321147+fionavictoria@users.noreply.github.com>
Date: Fri, 16 Sep 2022 11:08:56 -0700
Subject: [PATCH] Fix - Get notebooks in examples/sklearn/ to work in Google
 colab (Part 1/3) #378

Signed-off-by: fionavictoria <fiona.victoria@gmail.com>
---
 ...rch_reduction_classification_sklearn.ipynb | 2060 ++++++++++-------
 1 file changed, 1201 insertions(+), 859 deletions(-)

diff --git a/examples/sklearn/demo_grid_search_reduction_classification_sklearn.ipynb b/examples/sklearn/demo_grid_search_reduction_classification_sklearn.ipynb
index 1c7417f2..6e1e204e 100644
--- a/examples/sklearn/demo_grid_search_reduction_classification_sklearn.ipynb
+++ b/examples/sklearn/demo_grid_search_reduction_classification_sklearn.ipynb
@@ -1,865 +1,1207 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Sklearn compatible Grid Search for classification\n",
-    "\n",
-    "Grid search is an in-processing technique that can be used for fair classification or fair regression. For classification it reduces fair classification to a sequence of cost-sensitive classification problems, returning the deterministic classifier with the lowest empirical error subject to fair classification constraints among\n",
-    "the candidates searched. The code for grid search wraps the source class `fairlearn.reductions.GridSearch` available in the https://github.com/fairlearn/fairlearn library, licensed under the MIT Licencse, Copyright Microsoft Corporation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "from aif360.sklearn.inprocessing import GridSearchReduction\n",
-    "\n",
-    "from aif360.sklearn.datasets import fetch_adult\n",
-    "from aif360.sklearn.metrics import average_odds_error"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Loading data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Datasets are formatted as separate `X` (# samples x # features) and `y` (# samples x # labels) DataFrames. The index of each DataFrame contains protected attribute values per sample. Datasets may also load a `sample_weight` object to be used with certain algorithms/metrics. All of this makes it so that aif360 is compatible with scikit-learn objects.\n",
-    "\n",
-    "For example, we can easily load the Adult dataset from UCI with the following line:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<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></th>\n",
-       "      <th>age</th>\n",
-       "      <th>workclass</th>\n",
-       "      <th>education</th>\n",
-       "      <th>education-num</th>\n",
-       "      <th>marital-status</th>\n",
-       "      <th>occupation</th>\n",
-       "      <th>relationship</th>\n",
-       "      <th>race</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>capital-gain</th>\n",
-       "      <th>capital-loss</th>\n",
-       "      <th>hours-per-week</th>\n",
-       "      <th>native-country</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>race</th>\n",
-       "      <th>sex</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Non-white</th>\n",
-       "      <th>Male</th>\n",
-       "      <td>25.0</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>11th</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>Never-married</td>\n",
-       "      <td>Machine-op-inspct</td>\n",
-       "      <td>Own-child</td>\n",
-       "      <td>Black</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>United-States</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">White</th>\n",
-       "      <th>Male</th>\n",
-       "      <td>38.0</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>HS-grad</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>Married-civ-spouse</td>\n",
-       "      <td>Farming-fishing</td>\n",
-       "      <td>Husband</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>United-States</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Male</th>\n",
-       "      <td>28.0</td>\n",
-       "      <td>Local-gov</td>\n",
-       "      <td>Assoc-acdm</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>Married-civ-spouse</td>\n",
-       "      <td>Protective-serv</td>\n",
-       "      <td>Husband</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>United-States</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Non-white</th>\n",
-       "      <th>Male</th>\n",
-       "      <td>44.0</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>Some-college</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>Married-civ-spouse</td>\n",
-       "      <td>Machine-op-inspct</td>\n",
-       "      <td>Husband</td>\n",
-       "      <td>Black</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>7688.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>United-States</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>White</th>\n",
-       "      <th>Male</th>\n",
-       "      <td>34.0</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>10th</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>Never-married</td>\n",
-       "      <td>Other-service</td>\n",
-       "      <td>Not-in-family</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>United-States</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JaLHu46TbUmy"
+      },
+      "source": [
+        "# Sklearn compatible Grid Search for classification\n",
+        "\n",
+        "Grid search is an in-processing technique that can be used for fair classification or fair regression. For classification it reduces fair classification to a sequence of cost-sensitive classification problems, returning the deterministic classifier with the lowest empirical error subject to fair classification constraints among\n",
+        "the candidates searched. The code for grid search wraps the source class `fairlearn.reductions.GridSearch` available in the https://github.com/fairlearn/fairlearn library, licensed under the MIT Licencse, Copyright Microsoft Corporation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install tk\n",
+        "!pip install 'aif360[LawSchoolGPA]'"
       ],
-      "text/plain": [
-       "                 age  workclass     education  education-num  \\\n",
-       "race      sex                                                  \n",
-       "Non-white Male  25.0    Private          11th            7.0   \n",
-       "White     Male  38.0    Private       HS-grad            9.0   \n",
-       "          Male  28.0  Local-gov    Assoc-acdm           12.0   \n",
-       "Non-white Male  44.0    Private  Some-college           10.0   \n",
-       "White     Male  34.0    Private          10th            6.0   \n",
-       "\n",
-       "                    marital-status         occupation   relationship   race  \\\n",
-       "race      sex                                                                 \n",
-       "Non-white Male       Never-married  Machine-op-inspct      Own-child  Black   \n",
-       "White     Male  Married-civ-spouse    Farming-fishing        Husband  White   \n",
-       "          Male  Married-civ-spouse    Protective-serv        Husband  White   \n",
-       "Non-white Male  Married-civ-spouse  Machine-op-inspct        Husband  Black   \n",
-       "White     Male       Never-married      Other-service  Not-in-family  White   \n",
-       "\n",
-       "                 sex  capital-gain  capital-loss  hours-per-week  \\\n",
-       "race      sex                                                      \n",
-       "Non-white Male  Male           0.0           0.0            40.0   \n",
-       "White     Male  Male           0.0           0.0            50.0   \n",
-       "          Male  Male           0.0           0.0            40.0   \n",
-       "Non-white Male  Male        7688.0           0.0            40.0   \n",
-       "White     Male  Male           0.0           0.0            30.0   \n",
-       "\n",
-       "               native-country  \n",
-       "race      sex                  \n",
-       "Non-white Male  United-States  \n",
-       "White     Male  United-States  \n",
-       "          Male  United-States  \n",
-       "Non-white Male  United-States  \n",
-       "White     Male  United-States  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X, y, sample_weight = fetch_adult()\n",
-    "X.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can then map the protected attributes to integers,"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X.index = pd.MultiIndex.from_arrays(X.index.codes, names=X.index.names)\n",
-    "y.index = pd.MultiIndex.from_arrays(y.index.codes, names=y.index.names)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "and the target classes to 0/1,"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y = pd.Series(y.factorize(sort=True)[0], index=y.index)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "split the dataset,"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "(X_train, X_test,\n",
-    " y_train, y_test) = train_test_split(X, y, train_size=0.7, random_state=1234567)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We use Pandas for one-hot encoding for easy reference to columns associated with protected attributes, information necessary for grid search reduction."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<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></th>\n",
-       "      <th>age</th>\n",
-       "      <th>education-num</th>\n",
-       "      <th>capital-gain</th>\n",
-       "      <th>capital-loss</th>\n",
-       "      <th>hours-per-week</th>\n",
-       "      <th>workclass_Private</th>\n",
-       "      <th>workclass_Self-emp-not-inc</th>\n",
-       "      <th>workclass_Self-emp-inc</th>\n",
-       "      <th>workclass_Federal-gov</th>\n",
-       "      <th>workclass_Local-gov</th>\n",
-       "      <th>...</th>\n",
-       "      <th>native-country_Guatemala</th>\n",
-       "      <th>native-country_Nicaragua</th>\n",
-       "      <th>native-country_Scotland</th>\n",
-       "      <th>native-country_Thailand</th>\n",
-       "      <th>native-country_Yugoslavia</th>\n",
-       "      <th>native-country_El-Salvador</th>\n",
-       "      <th>native-country_Trinadad&amp;Tobago</th>\n",
-       "      <th>native-country_Peru</th>\n",
-       "      <th>native-country_Hong</th>\n",
-       "      <th>native-country_Holand-Netherlands</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>race</th>\n",
-       "      <th>sex</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
-       "      <th>1</th>\n",
-       "      <td>58.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>51.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>26.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1887.0</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>44.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>33.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 102 columns</p>\n",
-       "</div>"
+      "metadata": {
+        "id": "oqUKUXH3dFVT",
+        "outputId": "1f00c58a-f2bc-4eda-a29c-c97e3f33fb09",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+            "Collecting tk\n",
+            "  Downloading tk-0.1.0-py3-none-any.whl (3.9 kB)\n",
+            "Installing collected packages: tk\n",
+            "Successfully installed tk-0.1.0\n",
+            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+            "Requirement already satisfied: aif360[LawSchoolGPA] in /usr/local/lib/python3.7/dist-packages (0.5.0)\n",
+            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from aif360[LawSchoolGPA]) (1.3.5)\n",
+            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from aif360[LawSchoolGPA]) (3.2.2)\n",
+            "Requirement already satisfied: scikit-learn>=1.0 in /usr/local/lib/python3.7/dist-packages (from aif360[LawSchoolGPA]) (1.0.2)\n",
+            "Requirement already satisfied: numpy>=1.16 in /usr/local/lib/python3.7/dist-packages (from aif360[LawSchoolGPA]) (1.21.6)\n",
+            "Requirement already satisfied: scipy>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from aif360[LawSchoolGPA]) (1.7.3)\n",
+            "Collecting tempeh\n",
+            "  Downloading tempeh-0.1.12-py3-none-any.whl (39 kB)\n",
+            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->aif360[LawSchoolGPA]) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->aif360[LawSchoolGPA]) (2022.2.1)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24.0->aif360[LawSchoolGPA]) (1.15.0)\n",
+            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=1.0->aif360[LawSchoolGPA]) (3.1.0)\n",
+            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=1.0->aif360[LawSchoolGPA]) (1.1.0)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->aif360[LawSchoolGPA]) (0.11.0)\n",
+            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->aif360[LawSchoolGPA]) (1.4.4)\n",
+            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->aif360[LawSchoolGPA]) (3.0.9)\n",
+            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->aif360[LawSchoolGPA]) (4.1.1)\n",
+            "Requirement already satisfied: pytest in /usr/local/lib/python3.7/dist-packages (from tempeh->aif360[LawSchoolGPA]) (3.6.4)\n",
+            "Collecting memory-profiler\n",
+            "  Downloading memory_profiler-0.60.0.tar.gz (38 kB)\n",
+            "Collecting shap\n",
+            "  Downloading shap-0.41.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (569 kB)\n",
+            "\u001b[K     |████████████████████████████████| 569 kB 7.5 MB/s \n",
+            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from tempeh->aif360[LawSchoolGPA]) (2.23.0)\n",
+            "Requirement already satisfied: psutil in /usr/local/lib/python3.7/dist-packages (from memory-profiler->tempeh->aif360[LawSchoolGPA]) (5.4.8)\n",
+            "Requirement already satisfied: py>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from pytest->tempeh->aif360[LawSchoolGPA]) (1.11.0)\n",
+            "Requirement already satisfied: more-itertools>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from pytest->tempeh->aif360[LawSchoolGPA]) (8.14.0)\n",
+            "Requirement already satisfied: pluggy<0.8,>=0.5 in /usr/local/lib/python3.7/dist-packages (from pytest->tempeh->aif360[LawSchoolGPA]) (0.7.1)\n",
+            "Requirement already satisfied: atomicwrites>=1.0 in /usr/local/lib/python3.7/dist-packages (from pytest->tempeh->aif360[LawSchoolGPA]) (1.4.1)\n",
+            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from pytest->tempeh->aif360[LawSchoolGPA]) (57.4.0)\n",
+            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.7/dist-packages (from pytest->tempeh->aif360[LawSchoolGPA]) (22.1.0)\n",
+            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->tempeh->aif360[LawSchoolGPA]) (3.0.4)\n",
+            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->tempeh->aif360[LawSchoolGPA]) (1.24.3)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->tempeh->aif360[LawSchoolGPA]) (2022.6.15)\n",
+            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->tempeh->aif360[LawSchoolGPA]) (2.10)\n",
+            "Requirement already satisfied: tqdm>4.25.0 in /usr/local/lib/python3.7/dist-packages (from shap->tempeh->aif360[LawSchoolGPA]) (4.64.1)\n",
+            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.7/dist-packages (from shap->tempeh->aif360[LawSchoolGPA]) (1.5.0)\n",
+            "Requirement already satisfied: packaging>20.9 in /usr/local/lib/python3.7/dist-packages (from shap->tempeh->aif360[LawSchoolGPA]) (21.3)\n",
+            "Collecting slicer==0.0.7\n",
+            "  Downloading slicer-0.0.7-py3-none-any.whl (14 kB)\n",
+            "Requirement already satisfied: numba in /usr/local/lib/python3.7/dist-packages (from shap->tempeh->aif360[LawSchoolGPA]) (0.56.2)\n",
+            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from numba->shap->tempeh->aif360[LawSchoolGPA]) (4.12.0)\n",
+            "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.7/dist-packages (from numba->shap->tempeh->aif360[LawSchoolGPA]) (0.39.1)\n",
+            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->numba->shap->tempeh->aif360[LawSchoolGPA]) (3.8.1)\n",
+            "Building wheels for collected packages: memory-profiler\n",
+            "  Building wheel for memory-profiler (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for memory-profiler: filename=memory_profiler-0.60.0-py3-none-any.whl size=31284 sha256=433aefeb0eaa28520ae538e18cd0a293757e0ab19c4e6c61ea3e02ed0659e9e9\n",
+            "  Stored in directory: /root/.cache/pip/wheels/67/2b/fb/326e30d638c538e69a5eb0aa47f4223d979f502bbdb403950f\n",
+            "Successfully built memory-profiler\n",
+            "Installing collected packages: slicer, shap, memory-profiler, tempeh\n",
+            "Successfully installed memory-profiler-0.60.0 shap-0.41.0 slicer-0.0.7 tempeh-0.1.12\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "BmjX6xLmbUm0"
+      },
+      "outputs": [],
+      "source": [
+        "import warnings\n",
+        "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "6N6TJuzXbUm0"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.metrics import accuracy_score\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "from aif360.sklearn.inprocessing import GridSearchReduction\n",
+        "\n",
+        "from aif360.sklearn.datasets import fetch_adult\n",
+        "from aif360.sklearn.metrics import average_odds_error"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "39M3bDclbUm0"
+      },
+      "source": [
+        "### Loading data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cuRMM729bUm1"
+      },
+      "source": [
+        "Datasets are formatted as separate `X` (# samples x # features) and `y` (# samples x # labels) DataFrames. The index of each DataFrame contains protected attribute values per sample. Datasets may also load a `sample_weight` object to be used with certain algorithms/metrics. All of this makes it so that aif360 is compatible with scikit-learn objects.\n",
+        "\n",
+        "For example, we can easily load the Adult dataset from UCI with the following line:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "um_jEG9xbUm1",
+        "outputId": "e35cd307-a489-4d3b-ac86-81edfe4586e3",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 368
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                 age  workclass     education  education-num  \\\n",
+              "race      sex                                                  \n",
+              "Non-white Male  25.0    Private          11th            7.0   \n",
+              "White     Male  38.0    Private       HS-grad            9.0   \n",
+              "          Male  28.0  Local-gov    Assoc-acdm           12.0   \n",
+              "Non-white Male  44.0    Private  Some-college           10.0   \n",
+              "White     Male  34.0    Private          10th            6.0   \n",
+              "\n",
+              "                    marital-status         occupation   relationship   race  \\\n",
+              "race      sex                                                                 \n",
+              "Non-white Male       Never-married  Machine-op-inspct      Own-child  Black   \n",
+              "White     Male  Married-civ-spouse    Farming-fishing        Husband  White   \n",
+              "          Male  Married-civ-spouse    Protective-serv        Husband  White   \n",
+              "Non-white Male  Married-civ-spouse  Machine-op-inspct        Husband  Black   \n",
+              "White     Male       Never-married      Other-service  Not-in-family  White   \n",
+              "\n",
+              "                 sex  capital-gain  capital-loss  hours-per-week  \\\n",
+              "race      sex                                                      \n",
+              "Non-white Male  Male           0.0           0.0            40.0   \n",
+              "White     Male  Male           0.0           0.0            50.0   \n",
+              "          Male  Male           0.0           0.0            40.0   \n",
+              "Non-white Male  Male        7688.0           0.0            40.0   \n",
+              "White     Male  Male           0.0           0.0            30.0   \n",
+              "\n",
+              "               native-country  \n",
+              "race      sex                  \n",
+              "Non-white Male  United-States  \n",
+              "White     Male  United-States  \n",
+              "          Male  United-States  \n",
+              "Non-white Male  United-States  \n",
+              "White     Male  United-States  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-f1364eb8-1652-4c5e-9cf5-03f794fd8e54\">\n",
+              "    <div 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></th>\n",
+              "      <th>age</th>\n",
+              "      <th>workclass</th>\n",
+              "      <th>education</th>\n",
+              "      <th>education-num</th>\n",
+              "      <th>marital-status</th>\n",
+              "      <th>occupation</th>\n",
+              "      <th>relationship</th>\n",
+              "      <th>race</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>capital-gain</th>\n",
+              "      <th>capital-loss</th>\n",
+              "      <th>hours-per-week</th>\n",
+              "      <th>native-country</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>race</th>\n",
+              "      <th>sex</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
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+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Non-white</th>\n",
+              "      <th>Male</th>\n",
+              "      <td>25.0</td>\n",
+              "      <td>Private</td>\n",
+              "      <td>11th</td>\n",
+              "      <td>7.0</td>\n",
+              "      <td>Never-married</td>\n",
+              "      <td>Machine-op-inspct</td>\n",
+              "      <td>Own-child</td>\n",
+              "      <td>Black</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>40.0</td>\n",
+              "      <td>United-States</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"2\" valign=\"top\">White</th>\n",
+              "      <th>Male</th>\n",
+              "      <td>38.0</td>\n",
+              "      <td>Private</td>\n",
+              "      <td>HS-grad</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>Married-civ-spouse</td>\n",
+              "      <td>Farming-fishing</td>\n",
+              "      <td>Husband</td>\n",
+              "      <td>White</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>50.0</td>\n",
+              "      <td>United-States</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Male</th>\n",
+              "      <td>28.0</td>\n",
+              "      <td>Local-gov</td>\n",
+              "      <td>Assoc-acdm</td>\n",
+              "      <td>12.0</td>\n",
+              "      <td>Married-civ-spouse</td>\n",
+              "      <td>Protective-serv</td>\n",
+              "      <td>Husband</td>\n",
+              "      <td>White</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>40.0</td>\n",
+              "      <td>United-States</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Non-white</th>\n",
+              "      <th>Male</th>\n",
+              "      <td>44.0</td>\n",
+              "      <td>Private</td>\n",
+              "      <td>Some-college</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>Married-civ-spouse</td>\n",
+              "      <td>Machine-op-inspct</td>\n",
+              "      <td>Husband</td>\n",
+              "      <td>Black</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>7688.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>40.0</td>\n",
+              "      <td>United-States</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>White</th>\n",
+              "      <th>Male</th>\n",
+              "      <td>34.0</td>\n",
+              "      <td>Private</td>\n",
+              "      <td>10th</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>Never-married</td>\n",
+              "      <td>Other-service</td>\n",
+              "      <td>Not-in-family</td>\n",
+              "      <td>White</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>30.0</td>\n",
+              "      <td>United-States</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f1364eb8-1652-4c5e-9cf5-03f794fd8e54')\"\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 0 24 24\"\n",
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+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\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",
+              "    [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-f1364eb8-1652-4c5e-9cf5-03f794fd8e54 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-f1364eb8-1652-4c5e-9cf5-03f794fd8e54');\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",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
       ],
-      "text/plain": [
-       "           age  education-num  capital-gain  capital-loss  hours-per-week  \\\n",
-       "race sex                                                                    \n",
-       "1    1    58.0           11.0           0.0           0.0            42.0   \n",
-       "     0    51.0           12.0           0.0           0.0            30.0   \n",
-       "     1    26.0           14.0           0.0        1887.0            40.0   \n",
-       "     1    44.0            3.0           0.0           0.0            40.0   \n",
-       "     1    33.0            6.0           0.0           0.0            40.0   \n",
-       "\n",
-       "          workclass_Private  workclass_Self-emp-not-inc  \\\n",
-       "race sex                                                  \n",
-       "1    1                    0                           1   \n",
-       "     0                    0                           1   \n",
-       "     1                    1                           0   \n",
-       "     1                    1                           0   \n",
-       "     1                    1                           0   \n",
-       "\n",
-       "          workclass_Self-emp-inc  workclass_Federal-gov  workclass_Local-gov  \\\n",
-       "race sex                                                                       \n",
-       "1    1                         0                      0                    0   \n",
-       "     0                         0                      0                    0   \n",
-       "     1                         0                      0                    0   \n",
-       "     1                         0                      0                    0   \n",
-       "     1                         0                      0                    0   \n",
-       "\n",
-       "          ...  native-country_Guatemala  native-country_Nicaragua  \\\n",
-       "race sex  ...                                                       \n",
-       "1    1    ...                         0                         0   \n",
-       "     0    ...                         0                         0   \n",
-       "     1    ...                         0                         0   \n",
-       "     1    ...                         0                         0   \n",
-       "     1    ...                         0                         0   \n",
-       "\n",
-       "          native-country_Scotland  native-country_Thailand  \\\n",
-       "race sex                                                     \n",
-       "1    1                          0                        0   \n",
-       "     0                          0                        0   \n",
-       "     1                          0                        0   \n",
-       "     1                          0                        0   \n",
-       "     1                          0                        0   \n",
-       "\n",
-       "          native-country_Yugoslavia  native-country_El-Salvador  \\\n",
-       "race sex                                                          \n",
-       "1    1                            0                           0   \n",
-       "     0                            0                           0   \n",
-       "     1                            0                           0   \n",
-       "     1                            0                           0   \n",
-       "     1                            0                           0   \n",
-       "\n",
-       "          native-country_Trinadad&Tobago  native-country_Peru  \\\n",
-       "race sex                                                        \n",
-       "1    1                                 0                    0   \n",
-       "     0                                 0                    0   \n",
-       "     1                                 0                    0   \n",
-       "     1                                 0                    0   \n",
-       "     1                                 0                    0   \n",
-       "\n",
-       "          native-country_Hong  native-country_Holand-Netherlands  \n",
-       "race sex                                                          \n",
-       "1    1                      0                                  0  \n",
-       "     0                      0                                  0  \n",
-       "     1                      0                                  0  \n",
-       "     1                      0                                  0  \n",
-       "     1                      0                                  0  \n",
-       "\n",
-       "[5 rows x 102 columns]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_train, X_test = pd.get_dummies(X_train), pd.get_dummies(X_test)\n",
-    "X_train = X_train.drop(columns=['sex_Female'])\n",
-    "X_test = X_test.drop(columns=['sex_Female'])\n",
-    "X_train.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The protected attribute information is also replicated in the labels:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "race  sex\n",
-       "1     1      0\n",
-       "      0      1\n",
-       "      1      1\n",
-       "      1      0\n",
-       "      1      0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y_train.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Running metrics"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the data in this format, we can easily train a scikit-learn model and get predictions for the test data:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.8453600648632712\n"
-     ]
-    }
-   ],
-   "source": [
-    "y_pred = LogisticRegression(solver='liblinear', random_state=1234).fit(X_train, y_train).predict(X_test)\n",
-    "lr_acc = accuracy_score(y_test, y_pred)\n",
-    "print(lr_acc)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can assess how close the predictions are to equality of odds.\n",
-    "\n",
-    "`average_odds_error()` computes the (unweighted) average of the absolute values of the true positive rate (TPR) difference and false positive rate (FPR) difference, i.e.:\n",
-    "\n",
-    "$$ \\tfrac{1}{2}\\left(|FPR_{D = \\text{unprivileged}} - FPR_{D = \\text{privileged}}| + |TPR_{D = \\text{unprivileged}} - TPR_{D = \\text{privileged}}|\\right) $$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.09356509680536546\n"
-     ]
-    }
-   ],
-   "source": [
-    "lr_aoe = average_odds_error(y_test, y_pred, prot_attr='sex')\n",
-    "print(lr_aoe)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Grid Search"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Choose a base model for the candidate classifiers. Base models should implement a fit method that can take a sample weight as input. For details refer to the docs. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "estimator = LogisticRegression(solver='liblinear', random_state=1234)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Determine the columns associated with the protected attribute(s). Grid search can handle more than one attribute but it is computationally expensive. A similar method with less computational overhead is exponentiated gradient reduction, detailed at [examples/sklearn/demo_exponentiated_gradient_reduction_sklearn.ipynb](sklearn/demo_exponentiated_gradient_reduction_sklearn.ipynb)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prot_attr = 'sex_Male'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Search for the best classifier and observe test accuracy. Other options for `constraints` include \"DemographicParity\", \"TruePositiveRateParity\", \"FalsePositiveRateParity\", and \"ErrorRateParity\"."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.8455074813886637\n"
-     ]
-    }
-   ],
-   "source": [
-    "np.random.seed(0) #need for reproducibility\n",
-    "grid_search_red = GridSearchReduction(prot_attr=prot_attr, \n",
-    "                                      estimator=estimator, \n",
-    "                                      constraints=\"EqualizedOdds\",\n",
-    "                                      grid_size=20,\n",
-    "                                      drop_prot_attr=False)\n",
-    "grid_search_red.fit(X_train, y_train)\n",
-    "gs_acc = grid_search_red.score(X_test, y_test)\n",
-    "print(gs_acc)\n",
-    "\n",
-    "#Check if accuracy is comparable\n",
-    "assert abs(lr_acc-gs_acc)<0.03"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.06715455716850638\n"
-     ]
-    }
-   ],
-   "source": [
-    "gs_aoe = average_odds_error(y_test, grid_search_red.predict(X_test), prot_attr='sex')\n",
-    "print(gs_aoe)\n",
-    "\n",
-    "#Check if average odds error improved\n",
-    "assert gs_aoe<lr_aoe"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Instead of passing in a string value for `constraints`, we can also pass a `fairlearn.reductions.moment` object. You could use a predefined moment as we do below or create a custom moment using the fairlearn library."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.8455074813886637"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
+      "source": [
+        "X, y, sample_weight = fetch_adult()\n",
+        "X.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Cag_LIXjbUm1"
+      },
+      "source": [
+        "We can then map the protected attributes to integers,"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "8vAisxoobUm2"
+      },
+      "outputs": [],
+      "source": [
+        "X.index = pd.MultiIndex.from_arrays(X.index.codes, names=X.index.names)\n",
+        "y.index = pd.MultiIndex.from_arrays(y.index.codes, names=y.index.names)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mcB6jgPQbUm2"
+      },
+      "source": [
+        "and the target classes to 0/1,"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "03nKpFSubUm2"
+      },
+      "outputs": [],
+      "source": [
+        "y = pd.Series(y.factorize(sort=True)[0], index=y.index)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3aPscca1bUm2"
+      },
+      "source": [
+        "split the dataset,"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "_twkdFtTbUm2"
+      },
+      "outputs": [],
+      "source": [
+        "(X_train, X_test,\n",
+        " y_train, y_test) = train_test_split(X, y, train_size=0.7, random_state=1234567)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "K5aTddBDbUm3"
+      },
+      "source": [
+        "We use Pandas for one-hot encoding for easy reference to columns associated with protected attributes, information necessary for grid search reduction."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "gZhUUFMYbUm3",
+        "outputId": "dcb04ca8-1aee-44dd-87b9-6920be4a4275",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 366
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "           age  education-num  capital-gain  capital-loss  hours-per-week  \\\n",
+              "race sex                                                                    \n",
+              "1    1    58.0           11.0           0.0           0.0            42.0   \n",
+              "     0    51.0           12.0           0.0           0.0            30.0   \n",
+              "     1    26.0           14.0           0.0        1887.0            40.0   \n",
+              "     1    44.0            3.0           0.0           0.0            40.0   \n",
+              "     1    33.0            6.0           0.0           0.0            40.0   \n",
+              "\n",
+              "          workclass_Private  workclass_Self-emp-not-inc  \\\n",
+              "race sex                                                  \n",
+              "1    1                    0                           1   \n",
+              "     0                    0                           1   \n",
+              "     1                    1                           0   \n",
+              "     1                    1                           0   \n",
+              "     1                    1                           0   \n",
+              "\n",
+              "          workclass_Self-emp-inc  workclass_Federal-gov  workclass_Local-gov  \\\n",
+              "race sex                                                                       \n",
+              "1    1                         0                      0                    0   \n",
+              "     0                         0                      0                    0   \n",
+              "     1                         0                      0                    0   \n",
+              "     1                         0                      0                    0   \n",
+              "     1                         0                      0                    0   \n",
+              "\n",
+              "          ...  native-country_Guatemala  native-country_Nicaragua  \\\n",
+              "race sex  ...                                                       \n",
+              "1    1    ...                         0                         0   \n",
+              "     0    ...                         0                         0   \n",
+              "     1    ...                         0                         0   \n",
+              "     1    ...                         0                         0   \n",
+              "     1    ...                         0                         0   \n",
+              "\n",
+              "          native-country_Scotland  native-country_Thailand  \\\n",
+              "race sex                                                     \n",
+              "1    1                          0                        0   \n",
+              "     0                          0                        0   \n",
+              "     1                          0                        0   \n",
+              "     1                          0                        0   \n",
+              "     1                          0                        0   \n",
+              "\n",
+              "          native-country_Yugoslavia  native-country_El-Salvador  \\\n",
+              "race sex                                                          \n",
+              "1    1                            0                           0   \n",
+              "     0                            0                           0   \n",
+              "     1                            0                           0   \n",
+              "     1                            0                           0   \n",
+              "     1                            0                           0   \n",
+              "\n",
+              "          native-country_Trinadad&Tobago  native-country_Peru  \\\n",
+              "race sex                                                        \n",
+              "1    1                                 0                    0   \n",
+              "     0                                 0                    0   \n",
+              "     1                                 0                    0   \n",
+              "     1                                 0                    0   \n",
+              "     1                                 0                    0   \n",
+              "\n",
+              "          native-country_Hong  native-country_Holand-Netherlands  \n",
+              "race sex                                                          \n",
+              "1    1                      0                                  0  \n",
+              "     0                      0                                  0  \n",
+              "     1                      0                                  0  \n",
+              "     1                      0                                  0  \n",
+              "     1                      0                                  0  \n",
+              "\n",
+              "[5 rows x 102 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-5830dfbe-cf4a-4c5b-a55d-e2ac0e40d3f8\">\n",
+              "    <div 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></th>\n",
+              "      <th>age</th>\n",
+              "      <th>education-num</th>\n",
+              "      <th>capital-gain</th>\n",
+              "      <th>capital-loss</th>\n",
+              "      <th>hours-per-week</th>\n",
+              "      <th>workclass_Private</th>\n",
+              "      <th>workclass_Self-emp-not-inc</th>\n",
+              "      <th>workclass_Self-emp-inc</th>\n",
+              "      <th>workclass_Federal-gov</th>\n",
+              "      <th>workclass_Local-gov</th>\n",
+              "      <th>...</th>\n",
+              "      <th>native-country_Guatemala</th>\n",
+              "      <th>native-country_Nicaragua</th>\n",
+              "      <th>native-country_Scotland</th>\n",
+              "      <th>native-country_Thailand</th>\n",
+              "      <th>native-country_Yugoslavia</th>\n",
+              "      <th>native-country_El-Salvador</th>\n",
+              "      <th>native-country_Trinadad&amp;Tobago</th>\n",
+              "      <th>native-country_Peru</th>\n",
+              "      <th>native-country_Hong</th>\n",
+              "      <th>native-country_Holand-Netherlands</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>race</th>\n",
+              "      <th>sex</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
+              "      <th>1</th>\n",
+              "      <td>58.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>42.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>51.0</td>\n",
+              "      <td>12.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>30.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>26.0</td>\n",
+              "      <td>14.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1887.0</td>\n",
+              "      <td>40.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>44.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>40.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>33.0</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>40.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 102 columns</p>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5830dfbe-cf4a-4c5b-a55d-e2ac0e40d3f8')\"\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 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\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",
+              "    [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-5830dfbe-cf4a-4c5b-a55d-e2ac0e40d3f8 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-5830dfbe-cf4a-4c5b-a55d-e2ac0e40d3f8');\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",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ],
+      "source": [
+        "X_train, X_test = pd.get_dummies(X_train), pd.get_dummies(X_test)\n",
+        "X_train = X_train.drop(columns=['sex_Female'])\n",
+        "X_test = X_test.drop(columns=['sex_Female'])\n",
+        "X_train.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "H_YLKxL2bUm3"
+      },
+      "source": [
+        "The protected attribute information is also replicated in the labels:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "0lZcS0bmbUm3",
+        "outputId": "5ffb9347-e863-4725-dc51-be4f20ade173",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "race  sex\n",
+              "1     1      0\n",
+              "      0      1\n",
+              "      1      1\n",
+              "      1      0\n",
+              "      1      0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ],
+      "source": [
+        "y_train.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oTUVhotNbUm3"
+      },
+      "source": [
+        "### Running metrics"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lQN64AqmbUm3"
+      },
+      "source": [
+        "With the data in this format, we can easily train a scikit-learn model and get predictions for the test data:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "GBhbEdWrbUm3",
+        "outputId": "f22a975f-0f2f-4a78-b4ca-d4b3fae1c870",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.8453600648632712\n"
+          ]
+        }
+      ],
+      "source": [
+        "y_pred = LogisticRegression(solver='liblinear', random_state=1234).fit(X_train, y_train).predict(X_test)\n",
+        "lr_acc = accuracy_score(y_test, y_pred)\n",
+        "print(lr_acc)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zGU6upwubUm4"
+      },
+      "source": [
+        "We can assess how close the predictions are to equality of odds.\n",
+        "\n",
+        "`average_odds_error()` computes the (unweighted) average of the absolute values of the true positive rate (TPR) difference and false positive rate (FPR) difference, i.e.:\n",
+        "\n",
+        "$$ \\tfrac{1}{2}\\left(|FPR_{D = \\text{unprivileged}} - FPR_{D = \\text{privileged}}| + |TPR_{D = \\text{unprivileged}} - TPR_{D = \\text{privileged}}|\\right) $$"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "1wRHNc1GbUm4",
+        "outputId": "9165efe3-0e50-4f16-e984-2ffb0ae79f7b",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.09356509680536546\n"
+          ]
+        }
+      ],
+      "source": [
+        "lr_aoe = average_odds_error(y_test, y_pred, prot_attr='sex')\n",
+        "print(lr_aoe)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vzJDTOXebUm4"
+      },
+      "source": [
+        "### Grid Search"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IuG-VsAnbUm4"
+      },
+      "source": [
+        "Choose a base model for the candidate classifiers. Base models should implement a fit method that can take a sample weight as input. For details refer to the docs. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "id": "w8zUuKZcbUm4"
+      },
+      "outputs": [],
+      "source": [
+        "estimator = LogisticRegression(solver='liblinear', random_state=1234)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vScj2bAhbUm4"
+      },
+      "source": [
+        "Determine the columns associated with the protected attribute(s). Grid search can handle more than one attribute but it is computationally expensive. A similar method with less computational overhead is exponentiated gradient reduction, detailed at [examples/sklearn/demo_exponentiated_gradient_reduction_sklearn.ipynb](sklearn/demo_exponentiated_gradient_reduction_sklearn.ipynb)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "zEthhIr4bUm4"
+      },
+      "outputs": [],
+      "source": [
+        "prot_attr = 'sex_Male'"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "F3cM_dipbUm4"
+      },
+      "source": [
+        "Search for the best classifier and observe test accuracy. Other options for `constraints` include \"DemographicParity\", \"TruePositiveRateParity\", \"FalsePositiveRateParity\", and \"ErrorRateParity\"."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "Jxy3XOwubUm4",
+        "outputId": "a482a204-9b74-4415-83bb-53c118fea0cf",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.8458760227021449\n"
+          ]
+        }
+      ],
+      "source": [
+        "np.random.seed(0) #need for reproducibility\n",
+        "grid_search_red = GridSearchReduction(prot_attr=prot_attr, \n",
+        "                                      estimator=estimator, \n",
+        "                                      constraints=\"EqualizedOdds\",\n",
+        "                                      grid_size=20,\n",
+        "                                      drop_prot_attr=False)\n",
+        "grid_search_red.fit(X_train, y_train)\n",
+        "gs_acc = grid_search_red.score(X_test, y_test)\n",
+        "print(gs_acc)\n",
+        "\n",
+        "#Check if accuracy is comparable\n",
+        "assert abs(lr_acc-gs_acc)<0.03"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "id": "73lRvF3CbUm4",
+        "outputId": "363d6088-d895-49c2-ed23-55685247f70b",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.05787745779072595\n"
+          ]
+        }
+      ],
+      "source": [
+        "gs_aoe = average_odds_error(y_test, grid_search_red.predict(X_test), prot_attr='sex')\n",
+        "print(gs_aoe)\n",
+        "\n",
+        "#Check if average odds error improved\n",
+        "assert gs_aoe<lr_aoe"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3TJYnxRQbUm4"
+      },
+      "source": [
+        "Instead of passing in a string value for `constraints`, we can also pass a `fairlearn.reductions.moment` object. You could use a predefined moment as we do below or create a custom moment using the fairlearn library."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "scrolled": true,
+        "id": "npccSTUDbUm4",
+        "outputId": "9318dd3c-fd34-4812-c27c-298e425a0fe7",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.8458760227021449"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 16
+        }
+      ],
+      "source": [
+        "import fairlearn.reductions as red\n",
+        "\n",
+        "\n",
+        "np.random.seed(0) #need for reproducibility\n",
+        "grid_search_red = GridSearchReduction(prot_attr=prot_attr, \n",
+        "                                      estimator=estimator, \n",
+        "                                      constraints=red.EqualizedOdds(),\n",
+        "                                      grid_size=20,\n",
+        "                                      drop_prot_attr=False)\n",
+        "grid_search_red.fit(X_train, y_train)\n",
+        "grid_search_red.score(X_test, y_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "id": "wFOn1K7NbUm5",
+        "outputId": "615fa345-403e-4580-8acf-b3c75055e831",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.05787745779072595"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 17
+        }
+      ],
+      "source": [
+        "average_odds_error(y_test, grid_search_red.predict(X_test), prot_attr='sex')"
+      ]
     }
-   ],
-   "source": [
-    "import fairlearn.reductions as red\n",
-    "\n",
-    "\n",
-    "np.random.seed(0) #need for reproducibility\n",
-    "grid_search_red = GridSearchReduction(prot_attr=prot_attr, \n",
-    "                                      estimator=estimator, \n",
-    "                                      constraints=red.EqualizedOdds(),\n",
-    "                                      grid_size=20,\n",
-    "                                      drop_prot_attr=False)\n",
-    "grid_search_red.fit(X_train, y_train)\n",
-    "grid_search_red.score(X_test, y_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.06715455716850638"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3.9.7 ('aif360')",
+      "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": "3.9.7"
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "d0c5ced7753e77a483fec8ff7063075635521cce6e0bd54998c8f174742209dd"
+      }
+    },
+    "colab": {
+      "provenance": []
     }
-   ],
-   "source": [
-    "average_odds_error(y_test, grid_search_red.predict(X_test), prot_attr='sex')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.9.7 ('aif360')",
-   "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": "3.9.7"
   },
-  "vscode": {
-   "interpreter": {
-    "hash": "d0c5ced7753e77a483fec8ff7063075635521cce6e0bd54998c8f174742209dd"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file