examples/sklearn/demo_new_features.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Started"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "tf.logging.set_verbosity(tf.logging.ERROR)\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 GridSearchCV, train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "from aif360.sklearn.preprocessing import ReweighingMeta\n",
    "from aif360.sklearn.inprocessing import AdversarialDebiasing\n",
    "from aif360.sklearn.postprocessing import CalibratedEqualizedOdds, PostProcessingMeta\n",
    "from aif360.sklearn.datasets import fetch_adult\n",
    "from aif360.sklearn.metrics import disparate_impact_ratio, average_odds_error, generalized_fpr\n",
    "from aif360.sklearn.metrics import generalized_fnr, difference"
   ]
  },
  {
   "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": 2,
   "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></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></th>\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>0</th>\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>Non-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>1</th>\n      <th>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>2</th>\n      <th>White</th>\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>3</th>\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>Non-white</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>5</th>\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>",
      "text/plain": "                   age  workclass     education  education-num  \\\n  race      sex                                                  \n0 Non-white Male  25.0    Private          11th            7.0   \n1 White     Male  38.0    Private       HS-grad            9.0   \n2 White     Male  28.0  Local-gov    Assoc-acdm           12.0   \n3 Non-white Male  44.0    Private  Some-college           10.0   \n5 White     Male  34.0    Private          10th            6.0   \n\n                      marital-status         occupation   relationship  \\\n  race      sex                                                          \n0 Non-white Male       Never-married  Machine-op-inspct      Own-child   \n1 White     Male  Married-civ-spouse    Farming-fishing        Husband   \n2 White     Male  Married-civ-spouse    Protective-serv        Husband   \n3 Non-white Male  Married-civ-spouse  Machine-op-inspct        Husband   \n5 White     Male       Never-married      Other-service  Not-in-family   \n\n                       race   sex  capital-gain  capital-loss  hours-per-week  \\\n  race      sex                                                                 \n0 Non-white Male  Non-white  Male           0.0           0.0            40.0   \n1 White     Male      White  Male           0.0           0.0            50.0   \n2 White     Male      White  Male           0.0           0.0            40.0   \n3 Non-white Male  Non-white  Male        7688.0           0.0            40.0   \n5 White     Male      White  Male           0.0           0.0            30.0   \n\n                 native-country  \n  race      sex                  \n0 Non-white Male  United-States  \n1 White     Male  United-States  \n2 White     Male  United-States  \n3 Non-white Male  United-States  \n5 White     Male  United-States  "
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y, sample_weight = fetch_adult()\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then map the protected attributes to integers,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "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": 5,
   "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": [
    "and finally, one-hot encode the categorical features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>90</th>\n      <th>91</th>\n      <th>92</th>\n      <th>93</th>\n      <th>94</th>\n      <th>95</th>\n      <th>96</th>\n      <th>97</th>\n      <th>98</th>\n      <th>99</th>\n    </tr>\n    <tr>\n      <th></th>\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>30149</th>\n      <th>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>12028</th>\n      <th>1</th>\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>36374</th>\n      <th>1</th>\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>8055</th>\n      <th>1</th>\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>38108</th>\n      <th>1</th>\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>",
      "text/plain": "                 0    1    2    3    4    5    6    7    8    9   ...   90  \\\n      race sex                                                    ...        \n30149 1    1    0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  ...  0.0   \n12028 1    0    0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  ...  0.0   \n36374 1    1    0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0   \n8055  1    1    0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0   \n38108 1    1    0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  ...  0.0   \n\n                 91   92   93   94    95    96   97      98    99  \n      race sex                                                     \n30149 1    1    0.0  1.0  0.0  0.0  58.0  11.0  0.0     0.0  42.0  \n12028 1    0    0.0  0.0  0.0  0.0  51.0  12.0  0.0     0.0  30.0  \n36374 1    1    0.0  1.0  0.0  0.0  26.0  14.0  0.0  1887.0  40.0  \n8055  1    1    0.0  0.0  0.0  0.0  44.0   3.0  0.0     0.0  40.0  \n38108 1    1    0.0  1.0  0.0  0.0  33.0   6.0  0.0     0.0  40.0  \n\n[5 rows x 100 columns]"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe = make_column_transformer(\n",
    "        (OneHotEncoder(sparse=False), X_train.dtypes == 'category'),\n",
    "        remainder='passthrough')\n",
    "X_train  = pd.DataFrame(ohe.fit_transform(X_train), index=X_train.index)\n",
    "X_test = pd.DataFrame(ohe.transform(X_test), index=X_test.index)\n",
    "\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: the column names are lost in this transformation. The same encoding can be done with Pandas, but this cannot be combined with other preprocessing in a Pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th></th>\n      <th>age</th>\n      <th>education-num</th>\n      <th>capital-gain</th>\n      <th>capital-loss</th>\n      <th>hours-per-week</th>\n      <th>workclass_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>...</th>\n      <th>native-country_Portugal</th>\n      <th>native-country_Puerto-Rico</th>\n      <th>native-country_Scotland</th>\n      <th>native-country_South</th>\n      <th>native-country_Taiwan</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    </tr>\n    <tr>\n      <th></th>\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>0</th>\n      <th>0</th>\n      <th>1</th>\n      <td>25.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>40.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <th>1</th>\n      <th>1</th>\n      <td>38.0</td>\n      <td>9.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>50.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <th>1</th>\n      <th>1</th>\n      <td>28.0</td>\n      <td>12.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>40.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <th>0</th>\n      <th>1</th>\n      <td>44.0</td>\n      <td>10.0</td>\n      <td>7688.0</td>\n      <td>0.0</td>\n      <td>40.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <th>1</th>\n      <th>1</th>\n      <td>34.0</td>\n      <td>6.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>30.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 100 columns</p>\n</div>",
      "text/plain": "             age  education-num  capital-gain  capital-loss  hours-per-week  \\\n  race sex                                                                    \n0 0    1    25.0            7.0           0.0           0.0            40.0   \n1 1    1    38.0            9.0           0.0           0.0            50.0   \n2 1    1    28.0           12.0           0.0           0.0            40.0   \n3 0    1    44.0           10.0        7688.0           0.0            40.0   \n5 1    1    34.0            6.0           0.0           0.0            30.0   \n\n            workclass_Federal-gov  workclass_Local-gov  workclass_Private  \\\n  race sex                                                                  \n0 0    1                        0                    0                  1   \n1 1    1                        0                    0                  1   \n2 1    1                        0                    1                  0   \n3 0    1                        0                    0                  1   \n5 1    1                        0                    0                  1   \n\n            workclass_Self-emp-inc  workclass_Self-emp-not-inc  ...  \\\n  race sex                                                      ...   \n0 0    1                         0                           0  ...   \n1 1    1                         0                           0  ...   \n2 1    1                         0                           0  ...   \n3 0    1                         0                           0  ...   \n5 1    1                         0                           0  ...   \n\n            native-country_Portugal  native-country_Puerto-Rico  \\\n  race sex                                                        \n0 0    1                          0                           0   \n1 1    1                          0                           0   \n2 1    1                          0                           0   \n3 0    1                          0                           0   \n5 1    1                          0                           0   \n\n            native-country_Scotland  native-country_South  \\\n  race sex                                                  \n0 0    1                          0                     0   \n1 1    1                          0                     0   \n2 1    1                          0                     0   \n3 0    1                          0                     0   \n5 1    1                          0                     0   \n\n            native-country_Taiwan  native-country_Thailand  \\\n  race sex                                                   \n0 0    1                        0                        0   \n1 1    1                        0                        0   \n2 1    1                        0                        0   \n3 0    1                        0                        0   \n5 1    1                        0                        0   \n\n            native-country_Trinadad&Tobago  native-country_United-States  \\\n  race sex                                                                 \n0 0    1                                 0                             1   \n1 1    1                                 0                             1   \n2 1    1                                 0                             1   \n3 0    1                                 0                             1   \n5 1    1                                 0                             1   \n\n            native-country_Vietnam  native-country_Yugoslavia  \n  race sex                                                     \n0 0    1                         0                          0  \n1 1    1                         0                          0  \n2 1    1                         0                          0  \n3 0    1                         0                          0  \n5 1    1                         0                          0  \n\n[5 rows x 100 columns]"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# there is one unused category ('Never-worked') that was dropped during dropna\n",
    "X.workclass.cat.remove_unused_categories(inplace=True)\n",
    "pd.get_dummies(X).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The protected attribute information is also replicated in the labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "       race  sex\n30149  1     1      0\n12028  1     0      1\n36374  1     1      1\n8055   1     1      0\n38108  1     1      0\ndtype: int64"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the data in this format, we can easily train a scikit-learn model and get predictions for the test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0.8375469890174688"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = LogisticRegression(solver='lbfgs').fit(X_train, y_train).predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can analyze our predictions and quickly calucate the disparate impact for females vs. males:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0.2905425926727236"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disparate_impact_ratio(y_test, y_pred, prot_attr='sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And similarly, 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": "0.09372170954260936"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_odds_error(y_test, y_pred, prot_attr='sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Debiasing algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`ReweighingMeta` is a workaround until changing sample weights can be handled properly in `Pipeline`/`GridSearchCV`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "0.8279649148669566\n{'estimator__C': 10, 'reweigher__prot_attr': 'sex'}\n"
    }
   ],
   "source": [
    "rew = ReweighingMeta(estimator=LogisticRegression(solver='lbfgs'))\n",
    "\n",
    "params = {'estimator__C': [1, 10], 'reweigher__prot_attr': ['sex']}\n",
    "\n",
    "clf = GridSearchCV(rew, params, scoring='accuracy', cv=5)\n",
    "clf.fit(X_train, y_train)\n",
    "print(clf.score(X_test, y_test))\n",
    "print(clf.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0.5676803237673037"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disparate_impact_ratio(y_test, clf.predict(X_test), prot_attr='sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rather than trying to weight accuracy and fairness, we can try a fair in-processing algorithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0.8399056534237488"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adv_deb = AdversarialDebiasing(prot_attr='sex', random_state=1234567)\n",
    "adv_deb.fit(X_train, y_train)\n",
    "adv_deb.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0.060623189820735834"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_odds_error(y_test, adv_deb.predict(X_test), prot_attr='sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that `AdversarialDebiasing` creates a TensorFlow session which we should close when we're finished to free up resources:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "adv_deb.sess_.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's try a post-processor, `CalibratedEqualizedOdds`.\n",
    "\n",
    "Since the post-processor needs to be trained on data unseen by the original estimator, we will use the `PostProcessingMeta` class which splits the data and trains the estimator and post-processor with their own split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0.8163190093609494"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cal_eq_odds = CalibratedEqualizedOdds('sex', cost_constraint='fnr', random_state=1234567)\n",
    "log_reg = LogisticRegression(solver='lbfgs')\n",
    "postproc = PostProcessingMeta(estimator=log_reg, postprocessor=cal_eq_odds, random_state=1234567)\n",
    "\n",
    "postproc.fit(X_train, y_train)\n",
    "accuracy_score(y_test, postproc.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 432x288 with 1 Axes>"
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     "metadata": {
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   "source": [
    "y_pred = postproc.predict_proba(X_test)[:, 1]\n",
    "y_lr = postproc.estimator_.predict_proba(X_test)[:, 1]\n",
    "br = postproc.postprocessor_.base_rates_\n",
    "i = X_test.index.get_level_values('sex') == 1\n",
    "\n",
    "plt.plot([0, br[0]], [0, 1-br[0]], '-b', label='All calibrated classifiers (Females)')\n",
    "plt.plot([0, br[1]], [0, 1-br[1]], '-r', label='All calibrated classifiers (Males)')\n",
    "\n",
    "plt.scatter(generalized_fpr(y_test[~i], y_lr[~i]),\n",
    "            generalized_fnr(y_test[~i], y_lr[~i]),\n",
    "            300, c='b', marker='.', label='Original classifier (Females)')\n",
    "plt.scatter(generalized_fpr(y_test[i], y_lr[i]),\n",
    "            generalized_fnr(y_test[i], y_lr[i]),\n",
    "            300, c='r', marker='.', label='Original classifier (Males)')\n",
    "                                                                        \n",
    "plt.scatter(generalized_fpr(y_test[~i], y_pred[~i]),\n",
    "            generalized_fnr(y_test[~i], y_pred[~i]),\n",
    "            100, c='b', marker='d', label='Post-processed classifier (Females)')\n",
    "plt.scatter(generalized_fpr(y_test[i], y_pred[i]),\n",
    "            generalized_fnr(y_test[i], y_pred[i]),\n",
    "            100, c='r', marker='d', label='Post-processed classifier (Males)')\n",
    "\n",
    "plt.plot([0, 1], [generalized_fnr(y_test, y_pred)]*2, '--', c='0.5')\n",
    "\n",
    "plt.axis('square')\n",
    "plt.xlim([0.0, 0.4])\n",
    "plt.ylim([0.3, 0.7])\n",
    "plt.xlabel('generalized fpr');\n",
    "plt.ylabel('generalized fnr');\n",
    "plt.legend(bbox_to_anchor=(1.04,1), loc='upper left');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the generalized false negative rate is approximately equalized and the classifiers remain close to the calibration lines.\n",
    "\n",
    "We can quanitify the discrepancy between protected groups using the `difference` operator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0.0027891187222710556"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "difference(generalized_fnr, y_test, y_pred, prot_attr='sex')"
   ]
  }
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