diff --git a/examples/sklearn/demo_exponentiated_gradient_reduction_sklearn.ipynb b/examples/sklearn/demo_exponentiated_gradient_reduction_sklearn.ipynb
index a3aa01c7..15d39401 100644
--- a/examples/sklearn/demo_exponentiated_gradient_reduction_sklearn.ipynb
+++ b/examples/sklearn/demo_exponentiated_gradient_reduction_sklearn.ipynb
@@ -1,997 +1,1753 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Sklearn compatible Exponentiated Gradient Reduction\n",
-    "\n",
-    "Exponentiated gradient reduction is an in-processing technique that reduces fair classification to a sequence of cost-sensitive classification problems, returning a randomized classifier with the lowest empirical error subject to \n",
-    "fair classification constraints. The code for exponentiated gradient reduction wraps the source class \n",
-    "`fairlearn.reductions.ExponentiatedGradient` available in the https://github.com/fairlearn/fairlearn library,\n",
-    "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.compose import make_column_transformer\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import OneHotEncoder\n",
-    "\n",
-    "from aif360.sklearn.inprocessing import ExponentiatedGradientReduction\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": "9qpCIiFt_Skq"
+      },
+      "source": [
+        "# Sklearn compatible Exponentiated Gradient Reduction\n",
+        "\n",
+        "Exponentiated gradient reduction is an in-processing technique that reduces fair classification to a sequence of cost-sensitive classification problems, returning a randomized classifier with the lowest empirical error subject to\n",
+        "fair classification constraints. The code for exponentiated gradient reduction wraps the source class\n",
+        "`fairlearn.reductions.ExponentiatedGradient` available in the https://github.com/fairlearn/fairlearn library,\n",
+        "licensed under the MIT Licencse, Copyright Microsoft Corporation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "0fYDXGmE_Sks"
+      },
+      "outputs": [],
+      "source": [
+        "import warnings\n",
+        "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install aif360[all]"
       ],
-      "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": [
-    "To match the old version, we also remap the \"race\" feature to \"White\"/\"Non-white\","
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X.race = X.race.cat.set_categories(['Non-white', 'White'], ordered=True).fillna('Non-white')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can then map the protected attributes to integers,"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "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": 6,
-   "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": 7,
-   "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 sklearn for one-hot encoding for easy reference to columns associated with protected attributes, information necessary for Exponentiated Gradient Reduction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "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>workclass_Federal-gov</th>\n",
-       "      <th>workclass_Local-gov</th>\n",
-       "      <th>workclass_Private</th>\n",
-       "      <th>workclass_Self-emp-inc</th>\n",
-       "      <th>workclass_Self-emp-not-inc</th>\n",
-       "      <th>workclass_State-gov</th>\n",
-       "      <th>workclass_Without-pay</th>\n",
-       "      <th>education_10th</th>\n",
-       "      <th>education_11th</th>\n",
-       "      <th>education_12th</th>\n",
-       "      <th>...</th>\n",
-       "      <th>native-country_Thailand</th>\n",
-       "      <th>native-country_Trinadad&amp;Tobago</th>\n",
-       "      <th>native-country_United-States</th>\n",
-       "      <th>native-country_Vietnam</th>\n",
-       "      <th>native-country_Yugoslavia</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",
-       "    </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>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\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",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 100 columns</p>\n",
-       "</div>"
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2dEm6DiT_Vd5",
+        "outputId": "65d6cdc5-a3e5-4ef8-9e29-d1ead6cadff6"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: aif360[all] in /usr/local/lib/python3.10/dist-packages (0.5.0)\n",
+            "Requirement already satisfied: numpy>=1.16 in /usr/local/lib/python3.10/dist-packages (from aif360[all]) (1.23.5)\n",
+            "Requirement already satisfied: scipy>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from aif360[all]) (1.11.2)\n",
+            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.10/dist-packages (from aif360[all]) (1.5.3)\n",
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+            "Requirement already satisfied: seaborn in /usr/local/lib/python3.10/dist-packages (from aif360[all]) (0.12.2)\n",
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+            "Requirement already satisfied: adversarial-robustness-toolbox>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from aif360[all]) (1.16.0)\n",
+            "Requirement already satisfied: pytest>=3.5 in /usr/local/lib/python3.10/dist-packages (from aif360[all]) (7.4.1)\n",
+            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from aif360[all]) (2.0.1+cu118)\n",
+            "Requirement already satisfied: BlackBoxAuditing in /usr/local/lib/python3.10/dist-packages (from aif360[all]) (0.1.54)\n",
+            "Requirement already satisfied: tensorflow>=1.13.1 in /usr/local/lib/python3.10/dist-packages (from aif360[all]) (2.13.0)\n",
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+            "Requirement already satisfied: flatbuffers>=23.1.21 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.13.1->aif360[all]) (23.5.26)\n",
+            "Requirement already satisfied: gast<=0.4.0,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.13.1->aif360[all]) (0.4.0)\n",
+            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.13.1->aif360[all]) (0.2.0)\n",
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+            "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.13.1->aif360[all]) (3.9.0)\n",
+            "Requirement already satisfied: keras<2.14,>=2.13.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.13.1->aif360[all]) (2.13.1)\n",
+            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.13.1->aif360[all]) (16.0.6)\n",
+            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.13.1->aif360[all]) (3.3.0)\n",
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+            "Requirement already satisfied: tensorboard<2.14,>=2.13 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.13.1->aif360[all]) (2.13.0)\n",
+            "Requirement already satisfied: tensorflow-estimator<2.14,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.13.1->aif360[all]) (2.13.0)\n",
+            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.13.1->aif360[all]) (2.3.0)\n",
+            "Requirement already satisfied: typing-extensions<4.6.0,>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.13.1->aif360[all]) (4.5.0)\n",
+            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.13.1->aif360[all]) (1.15.0)\n",
+            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=1.13.1->aif360[all]) (0.33.0)\n",
+            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from BlackBoxAuditing->aif360[all]) (3.1)\n",
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+            "Requirement already satisfied: terminado>=0.8.3 in /usr/local/lib/python3.10/dist-packages (from notebook->jupyter->aif360[all]) (0.17.1)\n",
+            "Requirement already satisfied: prometheus-client in /usr/local/lib/python3.10/dist-packages (from notebook->jupyter->aif360[all]) (0.17.1)\n",
+            "Requirement already satisfied: nbclassic>=0.4.7 in /usr/local/lib/python3.10/dist-packages (from notebook->jupyter->aif360[all]) (1.0.0)\n",
+            "Requirement already satisfied: qtpy>=2.4.0 in /usr/local/lib/python3.10/dist-packages (from qtconsole->jupyter->aif360[all]) (2.4.0)\n",
+            "Requirement already satisfied: slicer==0.0.7 in /usr/local/lib/python3.10/dist-packages (from shap->tempeh->aif360[all]) (0.0.7)\n",
+            "Requirement already satisfied: numba in /usr/local/lib/python3.10/dist-packages (from shap->tempeh->aif360[all]) (0.56.4)\n",
+            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.10/dist-packages (from shap->tempeh->aif360[all]) (2.2.1)\n",
+            "Requirement already satisfied: sphinxcontrib-serializinghtml in /usr/local/lib/python3.10/dist-packages (from sphinxcontrib-websupport->sphinx<2->aif360[all]) (1.1.5)\n",
+            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->aif360[all]) (1.3.0)\n",
+            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow>=1.13.1->aif360[all]) (5.3.1)\n",
+            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow>=1.13.1->aif360[all]) (0.3.0)\n",
+            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow>=1.13.1->aif360[all]) (4.9)\n",
+            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.14,>=2.13->tensorflow>=1.13.1->aif360[all]) (1.3.1)\n",
+            "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /usr/local/lib/python3.10/dist-packages (from jedi>=0.16->ipython<9->ipympl->aif360[all]) (0.8.3)\n",
+            "Requirement already satisfied: platformdirs>=2.5 in /usr/local/lib/python3.10/dist-packages (from jupyter-core>=4.7->nbconvert->jupyter->aif360[all]) (3.10.0)\n",
+            "Requirement already satisfied: jupyter-server>=1.8 in /usr/local/lib/python3.10/dist-packages (from nbclassic>=0.4.7->notebook->jupyter->aif360[all]) (1.24.0)\n",
+            "Requirement already satisfied: notebook-shim>=0.2.3 in /usr/local/lib/python3.10/dist-packages (from nbclassic>=0.4.7->notebook->jupyter->aif360[all]) (0.2.3)\n",
+            "Requirement already satisfied: fastjsonschema in /usr/local/lib/python3.10/dist-packages (from nbformat>=5.1->nbconvert->jupyter->aif360[all]) (2.18.0)\n",
+            "Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.10/dist-packages (from nbformat>=5.1->nbconvert->jupyter->aif360[all]) (4.19.0)\n",
+            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.10/dist-packages (from pexpect>4.3->ipython<9->ipympl->aif360[all]) (0.7.0)\n",
+            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.10/dist-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython<9->ipympl->aif360[all]) (0.2.6)\n",
+            "Requirement already satisfied: argon2-cffi-bindings in /usr/local/lib/python3.10/dist-packages (from argon2-cffi->notebook->jupyter->aif360[all]) (21.2.0)\n",
+            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->nbconvert->jupyter->aif360[all]) (2.5)\n",
+            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->nbconvert->jupyter->aif360[all]) (0.5.1)\n",
+            "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba->shap->tempeh->aif360[all]) (0.39.1)\n",
+            "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=5.1->nbconvert->jupyter->aif360[all]) (23.1.0)\n",
+            "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=5.1->nbconvert->jupyter->aif360[all]) (2023.7.1)\n",
+            "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=5.1->nbconvert->jupyter->aif360[all]) (0.30.2)\n",
+            "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=5.1->nbconvert->jupyter->aif360[all]) (0.10.2)\n",
+            "Requirement already satisfied: anyio<4,>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from jupyter-server>=1.8->nbclassic>=0.4.7->notebook->jupyter->aif360[all]) (3.7.1)\n",
+            "Requirement already satisfied: websocket-client in /usr/local/lib/python3.10/dist-packages (from jupyter-server>=1.8->nbclassic>=0.4.7->notebook->jupyter->aif360[all]) (1.6.2)\n",
+            "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow>=1.13.1->aif360[all]) (0.5.0)\n",
+            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.14,>=2.13->tensorflow>=1.13.1->aif360[all]) (3.2.2)\n",
+            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.10/dist-packages (from anyio<4,>=3.1.0->jupyter-server>=1.8->nbclassic>=0.4.7->notebook->jupyter->aif360[all]) (1.3.0)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "mGTPlveh_Sks"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "\n",
+        "from sklearn.compose import make_column_transformer\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.metrics import accuracy_score\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.preprocessing import OneHotEncoder\n",
+        "\n",
+        "from aif360.sklearn.inprocessing import ExponentiatedGradientReduction\n",
+        "\n",
+        "from aif360.sklearn.datasets import fetch_adult\n",
+        "from aif360.sklearn.metrics import average_odds_error"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "R14XPGRH_Skt"
+      },
+      "source": [
+        "### Loading data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8BFNKGQH_Skt"
+      },
+      "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": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 379
+        },
+        "id": "rKX-UfbE_Skt",
+        "outputId": "5c8ef89d-6256-44a4-aef0-c88bbbd40d3c"
+      },
+      "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-86cd18f7-d3bb-4ef8-bda8-c2866ebac0a7\" 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",
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+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
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+              "</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>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-86cd18f7-d3bb-4ef8-bda8-c2866ebac0a7')\"\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-86cd18f7-d3bb-4ef8-bda8-c2866ebac0a7 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-86cd18f7-d3bb-4ef8-bda8-c2866ebac0a7');\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-0895227c-9eab-4c25-a1c8-96084a79068d\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0895227c-9eab-4c25-a1c8-96084a79068d')\"\n",
+              "            title=\"Suggest charts.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "    </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",
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+              "      --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-0895227c-9eab-4c25-a1c8-96084a79068d button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
       ],
-      "text/plain": [
-       "          workclass_Federal-gov  workclass_Local-gov  workclass_Private  \\\n",
-       "race sex                                                                  \n",
-       "1    1                      0.0                  0.0                0.0   \n",
-       "     0                      0.0                  0.0                0.0   \n",
-       "     1                      0.0                  0.0                1.0   \n",
-       "     1                      0.0                  0.0                1.0   \n",
-       "     1                      0.0                  0.0                1.0   \n",
-       "\n",
-       "          workclass_Self-emp-inc  workclass_Self-emp-not-inc  \\\n",
-       "race sex                                                       \n",
-       "1    1                       0.0                         1.0   \n",
-       "     0                       0.0                         1.0   \n",
-       "     1                       0.0                         0.0   \n",
-       "     1                       0.0                         0.0   \n",
-       "     1                       0.0                         0.0   \n",
-       "\n",
-       "          workclass_State-gov  workclass_Without-pay  education_10th  \\\n",
-       "race sex                                                               \n",
-       "1    1                    0.0                    0.0             0.0   \n",
-       "     0                    0.0                    0.0             0.0   \n",
-       "     1                    0.0                    0.0             0.0   \n",
-       "     1                    0.0                    0.0             0.0   \n",
-       "     1                    0.0                    0.0             1.0   \n",
-       "\n",
-       "          education_11th  education_12th  ...  native-country_Thailand  \\\n",
-       "race sex                                  ...                            \n",
-       "1    1               0.0             0.0  ...                      0.0   \n",
-       "     0               0.0             0.0  ...                      0.0   \n",
-       "     1               0.0             0.0  ...                      0.0   \n",
-       "     1               0.0             0.0  ...                      0.0   \n",
-       "     1               0.0             0.0  ...                      0.0   \n",
-       "\n",
-       "          native-country_Trinadad&Tobago  native-country_United-States  \\\n",
-       "race sex                                                                 \n",
-       "1    1                               0.0                           1.0   \n",
-       "     0                               0.0                           0.0   \n",
-       "     1                               0.0                           1.0   \n",
-       "     1                               0.0                           0.0   \n",
-       "     1                               0.0                           1.0   \n",
-       "\n",
-       "          native-country_Vietnam  native-country_Yugoslavia   age  \\\n",
-       "race sex                                                            \n",
-       "1    1                       0.0                        0.0  58.0   \n",
-       "     0                       0.0                        0.0  51.0   \n",
-       "     1                       0.0                        0.0  26.0   \n",
-       "     1                       0.0                        0.0  44.0   \n",
-       "     1                       0.0                        0.0  33.0   \n",
-       "\n",
-       "          education-num  capital-gain  capital-loss  hours-per-week  \n",
-       "race sex                                                             \n",
-       "1    1             11.0           0.0           0.0            42.0  \n",
-       "     0             12.0           0.0           0.0            30.0  \n",
-       "     1             14.0           0.0        1887.0            40.0  \n",
-       "     1              3.0           0.0           0.0            40.0  \n",
-       "     1              6.0           0.0           0.0            40.0  \n",
-       "\n",
-       "[5 rows x 100 columns]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ohe = make_column_transformer(\n",
-    "        (OneHotEncoder(sparse=False), X_train.dtypes == 'category'),\n",
-    "        remainder='passthrough', verbose_feature_names_out=False)\n",
-    "X_train  = pd.DataFrame(ohe.fit_transform(X_train), columns=ohe.get_feature_names_out(), index=X_train.index)\n",
-    "X_test = pd.DataFrame(ohe.transform(X_test), columns=ohe.get_feature_names_out(), index=X_test.index)\n",
-    "\n",
-    "X_train.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The protected attribute information is also replicated in the labels:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "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": 9,
-     "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": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.8460234392275374"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y_pred = LogisticRegression(solver='liblinear').fit(X_train, y_train).predict(X_test)\n",
-    "lr_acc = accuracy_score(y_test, y_pred)\n",
-    "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": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.09335303807799161"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lr_aoe_sex = average_odds_error(y_test, y_pred, prot_attr='sex')\n",
-    "lr_aoe_sex"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.06751597777565721"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lr_aoe_race = average_odds_error(y_test, y_pred, prot_attr='race')\n",
-    "lr_aoe_race"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exponentiated Gradient Reduction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Choose a base model for the randomized classifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "estimator = LogisticRegression(solver='liblinear')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Determine the columns associated with the protected attribute(s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prot_attr_cols = [colname for colname in X_train if \"sex\" in colname or \"race\" in colname]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Train the randomized classifier and observe test accuracy. Other options for `constraints` include \"DemographicParity\", \"TruePositiveRateParity\", \"FalsePositiveRateParity\", and \"ErrorRateParity\"."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.834303825458834\n"
-     ]
-    }
-   ],
-   "source": [
-    "np.random.seed(0) #for reproducibility\n",
-    "exp_grad_red = ExponentiatedGradientReduction(prot_attr=prot_attr_cols, \n",
-    "                                              estimator=estimator, \n",
-    "                                              constraints=\"EqualizedOdds\",\n",
-    "                                              drop_prot_attr=False)\n",
-    "exp_grad_red.fit(X_train, y_train)\n",
-    "egr_acc = exp_grad_red.score(X_test, y_test)\n",
-    "print(egr_acc)\n",
-    "\n",
-    "# Check for that accuracy is comparable\n",
-    "assert abs(lr_acc-egr_acc)<=0.03"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.02361168550972803\n"
-     ]
-    }
-   ],
-   "source": [
-    "egr_aoe_sex = average_odds_error(y_test, exp_grad_red.predict(X_test), prot_attr='sex')\n",
-    "print(egr_aoe_sex)\n",
-    "\n",
-    "# Check for improvement in average odds error for sex\n",
-    "assert egr_aoe_sex<lr_aoe_sex"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.024975550258025947\n"
-     ]
-    }
-   ],
-   "source": [
-    "egr_aoe_race = average_odds_error(y_test, exp_grad_red.predict(X_test), prot_attr='race')\n",
-    "print(egr_aoe_race)\n",
-    "\n",
-    "# Check for improvement in average odds error for race\n",
-    "assert egr_aoe_race<lr_aoe_race"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Number of calls made to base model algorithm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "29"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "exp_grad_red.model_.n_oracle_calls_"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Maximum calls permitted"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "50"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "exp_grad_red.max_iter"
-   ]
-  },
-  {
-   "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": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.834303825458834"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import fairlearn.reductions as red \n",
-    "\n",
-    "np.random.seed(0) #need for reproducibility\n",
-    "exp_grad_red2 = ExponentiatedGradientReduction(prot_attr=prot_attr_cols, \n",
-    "                                               estimator=estimator, \n",
-    "                                               constraints=red.EqualizedOdds(),\n",
-    "                                               drop_prot_attr=False)\n",
-    "exp_grad_red2.fit(X_train, y_train)\n",
-    "exp_grad_red2.score(X_test, y_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.02361168550972803"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
+      "source": [
+        "X, y, sample_weight = fetch_adult()\n",
+        "X.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "B67mKEfh_Skt"
+      },
+      "source": [
+        "To match the old version, we also remap the \"race\" feature to \"White\"/\"Non-white\","
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "XoxPfvPA_Skt"
+      },
+      "outputs": [],
+      "source": [
+        "X.race = X.race.cat.set_categories(['Non-white', 'White'], ordered=True).fillna('Non-white')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UKY_yLTB_Sku"
+      },
+      "source": [
+        "We can then map the protected attributes to integers,"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "Nqsu6znh_Sku"
+      },
+      "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": "CSMMerN8_Sku"
+      },
+      "source": [
+        "and the target classes to 0/1,"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "lHmXb-yW_Skv"
+      },
+      "outputs": [],
+      "source": [
+        "y = pd.Series(y.factorize(sort=True)[0], index=y.index)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4qdL2EGu_Skv"
+      },
+      "source": [
+        "split the dataset,"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "Ij0XYWPG_Skv"
+      },
+      "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": "Pso6hN4o_Skv"
+      },
+      "source": [
+        "We use sklearn for one-hot encoding for easy reference to columns associated with protected attributes, information necessary for Exponentiated Gradient Reduction"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 322
+        },
+        "id": "36c-oWpD_Skv",
+        "outputId": "09fcea8c-5065-4e8d-dcd8-f6282163c00e"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          workclass_Federal-gov  workclass_Local-gov  workclass_Private  \\\n",
+              "race sex                                                                  \n",
+              "1    1                      0.0                  0.0                0.0   \n",
+              "     0                      0.0                  0.0                0.0   \n",
+              "     1                      0.0                  0.0                1.0   \n",
+              "     1                      0.0                  0.0                1.0   \n",
+              "     1                      0.0                  0.0                1.0   \n",
+              "\n",
+              "          workclass_Self-emp-inc  workclass_Self-emp-not-inc  \\\n",
+              "race sex                                                       \n",
+              "1    1                       0.0                         1.0   \n",
+              "     0                       0.0                         1.0   \n",
+              "     1                       0.0                         0.0   \n",
+              "     1                       0.0                         0.0   \n",
+              "     1                       0.0                         0.0   \n",
+              "\n",
+              "          workclass_State-gov  workclass_Without-pay  education_10th  \\\n",
+              "race sex                                                               \n",
+              "1    1                    0.0                    0.0             0.0   \n",
+              "     0                    0.0                    0.0             0.0   \n",
+              "     1                    0.0                    0.0             0.0   \n",
+              "     1                    0.0                    0.0             0.0   \n",
+              "     1                    0.0                    0.0             1.0   \n",
+              "\n",
+              "          education_11th  education_12th  ...  native-country_Thailand  \\\n",
+              "race sex                                  ...                            \n",
+              "1    1               0.0             0.0  ...                      0.0   \n",
+              "     0               0.0             0.0  ...                      0.0   \n",
+              "     1               0.0             0.0  ...                      0.0   \n",
+              "     1               0.0             0.0  ...                      0.0   \n",
+              "     1               0.0             0.0  ...                      0.0   \n",
+              "\n",
+              "          native-country_Trinadad&Tobago  native-country_United-States  \\\n",
+              "race sex                                                                 \n",
+              "1    1                               0.0                           1.0   \n",
+              "     0                               0.0                           0.0   \n",
+              "     1                               0.0                           1.0   \n",
+              "     1                               0.0                           0.0   \n",
+              "     1                               0.0                           1.0   \n",
+              "\n",
+              "          native-country_Vietnam  native-country_Yugoslavia   age  \\\n",
+              "race sex                                                            \n",
+              "1    1                       0.0                        0.0  58.0   \n",
+              "     0                       0.0                        0.0  51.0   \n",
+              "     1                       0.0                        0.0  26.0   \n",
+              "     1                       0.0                        0.0  44.0   \n",
+              "     1                       0.0                        0.0  33.0   \n",
+              "\n",
+              "          education-num  capital-gain  capital-loss  hours-per-week  \n",
+              "race sex                                                             \n",
+              "1    1             11.0           0.0           0.0            42.0  \n",
+              "     0             12.0           0.0           0.0            30.0  \n",
+              "     1             14.0           0.0        1887.0            40.0  \n",
+              "     1              3.0           0.0           0.0            40.0  \n",
+              "     1              6.0           0.0           0.0            40.0  \n",
+              "\n",
+              "[5 rows x 100 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-53bc02a3-fac6-47c3-94fc-0a06ce0a6ed5\" 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>workclass_Federal-gov</th>\n",
+              "      <th>workclass_Local-gov</th>\n",
+              "      <th>workclass_Private</th>\n",
+              "      <th>workclass_Self-emp-inc</th>\n",
+              "      <th>workclass_Self-emp-not-inc</th>\n",
+              "      <th>workclass_State-gov</th>\n",
+              "      <th>workclass_Without-pay</th>\n",
+              "      <th>education_10th</th>\n",
+              "      <th>education_11th</th>\n",
+              "      <th>education_12th</th>\n",
+              "      <th>...</th>\n",
+              "      <th>native-country_Thailand</th>\n",
+              "      <th>native-country_Trinadad&amp;Tobago</th>\n",
+              "      <th>native-country_United-States</th>\n",
+              "      <th>native-country_Vietnam</th>\n",
+              "      <th>native-country_Yugoslavia</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",
+              "    </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>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\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",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\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",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\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",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\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",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\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",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 100 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-53bc02a3-fac6-47c3-94fc-0a06ce0a6ed5')\"\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",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </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-53bc02a3-fac6-47c3-94fc-0a06ce0a6ed5 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-53bc02a3-fac6-47c3-94fc-0a06ce0a6ed5');\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-2e6c39ed-a75f-4d47-9266-844b2498ceb6\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2e6c39ed-a75f-4d47-9266-844b2498ceb6')\"\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-2e6c39ed-a75f-4d47-9266-844b2498ceb6 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ],
+      "source": [
+        "ohe = make_column_transformer(\n",
+        "        (OneHotEncoder(sparse=False), X_train.dtypes == 'category'),\n",
+        "        remainder='passthrough', verbose_feature_names_out=False)\n",
+        "X_train  = pd.DataFrame(ohe.fit_transform(X_train), columns=ohe.get_feature_names_out(), index=X_train.index)\n",
+        "X_test = pd.DataFrame(ohe.transform(X_test), columns=ohe.get_feature_names_out(), index=X_test.index)\n",
+        "\n",
+        "X_train.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iDl0-rpN_Skv"
+      },
+      "source": [
+        "The protected attribute information is also replicated in the labels:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ceed2vSk_Skv",
+        "outputId": "f78ed972-3708-4719-ced4-a71d90ce4d82"
+      },
+      "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": 10
+        }
+      ],
+      "source": [
+        "y_train.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2JlMQ_ps_Skw"
+      },
+      "source": [
+        "### Running metrics"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rd7IevXR_Skw"
+      },
+      "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": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "T32eJkuA_Skw",
+        "outputId": "9f017a8f-a197-498c-926e-0a558776f5d0"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.8463919805410186"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ],
+      "source": [
+        "y_pred = LogisticRegression(solver='liblinear').fit(X_train, y_train).predict(X_test)\n",
+        "lr_acc = accuracy_score(y_test, y_pred)\n",
+        "lr_acc"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2npvOfdY_Skw"
+      },
+      "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": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "I4nQ0LpF_Skw",
+        "outputId": "d995618d-123a-4c87-bd9a-e9efbfede696"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.09072468611163034"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ],
+      "source": [
+        "lr_aoe_sex = average_odds_error(y_test, y_pred, prot_attr='sex')\n",
+        "lr_aoe_sex"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ep82WxJX_Skw",
+        "outputId": "a8e54eca-ea30-4cb1-b83c-0dab87ee6fa6"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.06302158185948953"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ],
+      "source": [
+        "lr_aoe_race = average_odds_error(y_test, y_pred, prot_attr='race')\n",
+        "lr_aoe_race"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TpcF_2PM_Skx"
+      },
+      "source": [
+        "### Exponentiated Gradient Reduction"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NHEevMCI_Skx"
+      },
+      "source": [
+        "Choose a base model for the randomized classifier"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "S7eh3VEH_Skx"
+      },
+      "outputs": [],
+      "source": [
+        "estimator = LogisticRegression(solver='liblinear')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fyR6n3Qd_Skx"
+      },
+      "source": [
+        "Determine the columns associated with the protected attribute(s)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "id": "HDSm1ujB_Skx"
+      },
+      "outputs": [],
+      "source": [
+        "prot_attr_cols = [colname for colname in X_train if \"sex\" in colname or \"race\" in colname]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "z2OhxVgK_Skx"
+      },
+      "source": [
+        "Train the randomized classifier and observe test accuracy. Other options for `constraints` include \"DemographicParity\", \"TruePositiveRateParity\", \"FalsePositiveRateParity\", and \"ErrorRateParity\"."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-jRJaCIG_Skx",
+        "outputId": "f17fa52b-1ba4-42f7-c05c-3bd92a9a1521"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.8340827006707452\n"
+          ]
+        }
+      ],
+      "source": [
+        "np.random.seed(0) #for reproducibility\n",
+        "exp_grad_red = ExponentiatedGradientReduction(prot_attr=prot_attr_cols,\n",
+        "                                              estimator=estimator,\n",
+        "                                              constraints=\"EqualizedOdds\",\n",
+        "                                              drop_prot_attr=False)\n",
+        "exp_grad_red.fit(X_train, y_train)\n",
+        "egr_acc = exp_grad_red.score(X_test, y_test)\n",
+        "print(egr_acc)\n",
+        "\n",
+        "# Check for that accuracy is comparable\n",
+        "assert abs(lr_acc-egr_acc)<=0.03"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2Tcuj5dE_Skx",
+        "outputId": "421d090d-6eb7-4857-b227-856a25f22ddb"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.025270988735088984\n"
+          ]
+        }
+      ],
+      "source": [
+        "egr_aoe_sex = average_odds_error(y_test, exp_grad_red.predict(X_test), prot_attr='sex')\n",
+        "print(egr_aoe_sex)\n",
+        "\n",
+        "# Check for improvement in average odds error for sex\n",
+        "assert egr_aoe_sex<lr_aoe_sex"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kPeTxz47_Skx",
+        "outputId": "0f4ec9e9-afdb-4265-95bc-faec5becdbef"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.009170924466397667\n"
+          ]
+        }
+      ],
+      "source": [
+        "egr_aoe_race = average_odds_error(y_test, exp_grad_red.predict(X_test), prot_attr='race')\n",
+        "print(egr_aoe_race)\n",
+        "\n",
+        "# Check for improvement in average odds error for race\n",
+        "assert egr_aoe_race<lr_aoe_race"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_Gop9Vz4_Sky"
+      },
+      "source": [
+        "Number of calls made to base model algorithm"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CLjQbWTM_Sky",
+        "outputId": "b03b8f39-e407-4d3b-d509-af271c180b5c"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "44"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ],
+      "source": [
+        "exp_grad_red.model_.n_oracle_calls_"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dPHV_xwT_Sky"
+      },
+      "source": [
+        "Maximum calls permitted"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "YAYEALX7_Sky",
+        "outputId": "08bab7ef-e947-4324-b402-dedcec2fe039"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "50"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ],
+      "source": [
+        "exp_grad_red.max_iter"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wFXzrCIu_Sky"
+      },
+      "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": 21,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "49aPGaUE_Sky",
+        "outputId": "cad34e2e-8c27-40a6-d955-f151e67e24e1"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.8340827006707452"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 21
+        }
+      ],
+      "source": [
+        "import fairlearn.reductions as red\n",
+        "\n",
+        "np.random.seed(0) #need for reproducibility\n",
+        "exp_grad_red2 = ExponentiatedGradientReduction(prot_attr=prot_attr_cols,\n",
+        "                                               estimator=estimator,\n",
+        "                                               constraints=\"EqualizedOdds\",\n",
+        "                                               drop_prot_attr=False)\n",
+        "exp_grad_red2.fit(X_train, y_train)\n",
+        "exp_grad_red2.score(X_test, y_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wHai7iZ8_Sky",
+        "outputId": "cb28d3dd-da80-4e1d-cea4-26b431f9a682"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.025270988735088984"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ],
+      "source": [
+        "average_odds_error(y_test, exp_grad_red2.predict(X_test), prot_attr='sex')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "E4LDrOkn_Skz",
+        "outputId": "73e6b825-0d7b-449e-c96e-38df2bab3b58"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.009170924466397667"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ],
+      "source": [
+        "average_odds_error(y_test, exp_grad_red2.predict(X_test), prot_attr='race')"
+      ]
     }
-   ],
-   "source": [
-    "average_odds_error(y_test, exp_grad_red2.predict(X_test), prot_attr='sex')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.024975550258025947"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
+  ],
+  "metadata": {
+    "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": "3.10.9"
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "d0c5ced7753e77a483fec8ff7063075635521cce6e0bd54998c8f174742209dd"
+      }
+    },
+    "colab": {
+      "provenance": []
     }
-   ],
-   "source": [
-    "average_odds_error(y_test, exp_grad_red2.predict(X_test), prot_attr='race')"
-   ]
-  }
- ],
- "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