From 591fb1c7044674f487502d7206e9770ecd938b91 Mon Sep 17 00:00:00 2001
From: Samuel Hoffman <hoffman.sc@gmail.com>
Date: Thu, 2 Dec 2021 22:00:20 -0500
Subject: [PATCH] support get_feature_names_out

Signed-off-by: Samuel Hoffman <hoffman.sc@gmail.com>
---
 examples/sklearn/demo_new_features.ipynb | 845 +++++++++++++++++++++--
 setup.py                                 |   2 +-
 2 files changed, 800 insertions(+), 47 deletions(-)

diff --git a/examples/sklearn/demo_new_features.ipynb b/examples/sklearn/demo_new_features.ipynb
index a9b8433c..d0a85f2f 100644
--- a/examples/sklearn/demo_new_features.ipynb
+++ b/examples/sklearn/demo_new_features.ipynb
@@ -58,8 +58,180 @@
    "outputs": [
     {
      "data": {
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-      "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  "
+      "text/html": [
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+       "  <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",
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+       "      <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",
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+       "      <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",
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+       "      <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>"
+      ],
+      "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": 2,
      "metadata": {},
@@ -113,7 +285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -130,15 +302,267 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 24,
    "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]"
+      "text/html": [
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+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\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 × 103 columns</p>\n",
+       "</div>"
+      ],
+      "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 103 columns]"
+      ]
      },
-     "execution_count": 6,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -146,9 +570,9 @@
    "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",
+    "        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()"
    ]
@@ -167,8 +591,271 @@
    "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]"
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>education-num</th>\n",
+       "      <th>capital-gain</th>\n",
+       "      <th>capital-loss</th>\n",
+       "      <th>hours-per-week</th>\n",
+       "      <th>workclass_Private</th>\n",
+       "      <th>workclass_Self-emp-not-inc</th>\n",
+       "      <th>workclass_Self-emp-inc</th>\n",
+       "      <th>workclass_Federal-gov</th>\n",
+       "      <th>workclass_Local-gov</th>\n",
+       "      <th>...</th>\n",
+       "      <th>native-country_Guatemala</th>\n",
+       "      <th>native-country_Nicaragua</th>\n",
+       "      <th>native-country_Scotland</th>\n",
+       "      <th>native-country_Thailand</th>\n",
+       "      <th>native-country_Yugoslavia</th>\n",
+       "      <th>native-country_El-Salvador</th>\n",
+       "      <th>native-country_Trinadad&amp;Tobago</th>\n",
+       "      <th>native-country_Peru</th>\n",
+       "      <th>native-country_Hong</th>\n",
+       "      <th>native-country_Holand-Netherlands</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>race</th>\n",
+       "      <th>sex</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>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>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 103 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           age  education-num  capital-gain  capital-loss  hours-per-week  \\\n",
+       "race sex                                                                    \n",
+       "0    1    25.0            7.0           0.0           0.0            40.0   \n",
+       "1    1    38.0            9.0           0.0           0.0            50.0   \n",
+       "     1    28.0           12.0           0.0           0.0            40.0   \n",
+       "0    1    44.0           10.0        7688.0           0.0            40.0   \n",
+       "1    1    34.0            6.0           0.0           0.0            30.0   \n",
+       "\n",
+       "          workclass_Private  workclass_Self-emp-not-inc  \\\n",
+       "race sex                                                  \n",
+       "0    1                    1                           0   \n",
+       "1    1                    1                           0   \n",
+       "     1                    0                           0   \n",
+       "0    1                    1                           0   \n",
+       "1    1                    1                           0   \n",
+       "\n",
+       "          workclass_Self-emp-inc  workclass_Federal-gov  workclass_Local-gov  \\\n",
+       "race sex                                                                       \n",
+       "0    1                         0                      0                    0   \n",
+       "1    1                         0                      0                    0   \n",
+       "     1                         0                      0                    1   \n",
+       "0    1                         0                      0                    0   \n",
+       "1    1                         0                      0                    0   \n",
+       "\n",
+       "          ...  native-country_Guatemala  native-country_Nicaragua  \\\n",
+       "race sex  ...                                                       \n",
+       "0    1    ...                         0                         0   \n",
+       "1    1    ...                         0                         0   \n",
+       "     1    ...                         0                         0   \n",
+       "0    1    ...                         0                         0   \n",
+       "1    1    ...                         0                         0   \n",
+       "\n",
+       "          native-country_Scotland  native-country_Thailand  \\\n",
+       "race sex                                                     \n",
+       "0    1                          0                        0   \n",
+       "1    1                          0                        0   \n",
+       "     1                          0                        0   \n",
+       "0    1                          0                        0   \n",
+       "1    1                          0                        0   \n",
+       "\n",
+       "          native-country_Yugoslavia  native-country_El-Salvador  \\\n",
+       "race sex                                                          \n",
+       "0    1                            0                           0   \n",
+       "1    1                            0                           0   \n",
+       "     1                            0                           0   \n",
+       "0    1                            0                           0   \n",
+       "1    1                            0                           0   \n",
+       "\n",
+       "          native-country_Trinadad&Tobago  native-country_Peru  \\\n",
+       "race sex                                                        \n",
+       "0    1                                 0                    0   \n",
+       "1    1                                 0                    0   \n",
+       "     1                                 0                    0   \n",
+       "0    1                                 0                    0   \n",
+       "1    1                                 0                    0   \n",
+       "\n",
+       "          native-country_Hong  native-country_Holand-Netherlands  \n",
+       "race sex                                                          \n",
+       "0    1                      0                                  0  \n",
+       "1    1                      0                                  0  \n",
+       "     1                      0                                  0  \n",
+       "0    1                      0                                  0  \n",
+       "1    1                      0                                  0  \n",
+       "\n",
+       "[5 rows x 103 columns]"
+      ]
      },
      "execution_count": 7,
      "metadata": {},
@@ -176,8 +863,6 @@
     }
    ],
    "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()"
    ]
   },
@@ -195,7 +880,15 @@
    "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"
+      "text/plain": [
+       "race  sex\n",
+       "1     1      0\n",
+       "      0      1\n",
+       "      1      1\n",
+       "      1      0\n",
+       "      1      0\n",
+       "dtype: int64"
+      ]
      },
      "execution_count": 8,
      "metadata": {},
@@ -227,7 +920,9 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "0.8375469890174688"
+      "text/plain": [
+       "0.8455074813886637"
+      ]
      },
      "execution_count": 9,
      "metadata": {},
@@ -235,7 +930,7 @@
     }
    ],
    "source": [
-    "y_pred = LogisticRegression(solver='lbfgs').fit(X_train, y_train).predict(X_test)\n",
+    "y_pred = LogisticRegression(solver='liblinear').fit(X_train, y_train).predict(X_test)\n",
     "accuracy_score(y_test, y_pred)"
    ]
   },
@@ -253,7 +948,9 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "0.2905425926727236"
+      "text/plain": [
+       "0.26889803976599136"
+      ]
      },
      "execution_count": 10,
      "metadata": {},
@@ -282,7 +979,9 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "0.09372170954260936"
+      "text/plain": [
+       "0.09875694175767563"
+      ]
      },
      "execution_count": 11,
      "metadata": {},
@@ -290,7 +989,39 @@
     }
    ],
    "source": [
-    "average_odds_error(y_test, y_pred, prot_attr='sex')"
+    "average_odds_error(y_test, y_pred, priv_group=(1, 1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In that case, we chose to look at the intersection of all protected attributes (race and sex) and designate a single combination (white males) as privileged.\n",
+    "\n",
+    "If we wish to do something more complex, we can pass a custom array of protected attributes, like so (note: this choice of protected groups is just for demonstration):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.3844295196608744"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "race = y_test.index.get_level_values('race').to_numpy()\n",
+    "sex = y_test.index.get_level_values('sex').to_numpy()\n",
+    "prot_attr = np.where(race ^ sex, 0, 1)\n",
+    "disparate_impact_ratio(y_test, y_pred, prot_attr=prot_attr)"
    ]
   },
   {
@@ -309,17 +1040,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
-     "text": "0.8279649148669566\n{'estimator__C': 10, 'reweigher__prot_attr': 'sex'}\n"
+     "text": [
+      "0.839979361686445\n",
+      "{'estimator__C': 1, 'reweigher__prot_attr': 'sex'}\n"
+     ]
     }
    ],
    "source": [
-    "rew = ReweighingMeta(estimator=LogisticRegression(solver='lbfgs'))\n",
+    "rew = ReweighingMeta(estimator=LogisticRegression(solver='liblinear'))\n",
     "\n",
     "params = {'estimator__C': [1, 10], 'reweigher__prot_attr': ['sex']}\n",
     "\n",
@@ -331,14 +1065,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": "0.5676803237673037"
+      "text/plain": [
+       "0.5843724951518126"
+      ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -356,14 +1092,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2021-11-24 16:59:47.326474: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+     ]
+    },
     {
      "data": {
-      "text/plain": "0.8399056534237488"
+      "text/plain": [
+       "0.8380629468563426"
+      ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -376,14 +1122,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": "0.060623189820735834"
+      "text/plain": [
+       "0.08330040163726551"
+      ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -401,7 +1149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -419,21 +1167,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": "0.8163190093609494"
+      "text/plain": [
+       "0.8199307142330655"
+      ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "cal_eq_odds = CalibratedEqualizedOdds('sex', cost_constraint='fnr', random_state=1234567)\n",
-    "log_reg = LogisticRegression(solver='lbfgs')\n",
+    "log_reg = LogisticRegression(solver='liblinear')\n",
     "postproc = PostProcessingMeta(estimator=log_reg, postprocessor=cal_eq_odds, random_state=1234567)\n",
     "\n",
     "postproc.fit(X_train, y_train)\n",
@@ -442,14 +1192,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
      },
      "metadata": {
       "needs_background": "light"
@@ -501,14 +1252,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": "0.0027891187222710556"
+      "text/plain": [
+       "0.0008138491285430982"
+      ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -534,9 +1287,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9-final"
+   "version": "3.7.11"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/setup.py b/setup.py
index 83f9a511..e03cdc3d 100644
--- a/setup.py
+++ b/setup.py
@@ -41,7 +41,7 @@
           'numpy>=1.16',
           'scipy>=1.2.0,<1.6.0',
           'pandas>=0.24.0',
-          'scikit-learn>=0.22.1',
+          'scikit-learn>=1.0',
           'matplotlib',
           'tempeh',
       ],