examples/pzmm_regression_assess_model_bias.ipynb

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    "Copyright © 2023, SAS Institute Inc., Cary, NC, USA.  All Rights Reserved.\n",
    "SPDX-License-Identifier: Apache-2.0"
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    "# Exams Dataset : Assess Bias of Regression Models\n",
    "\n",
    "This notebook provides an example of how to assess bias in python models using the exam dataset [^1].  The `assess_model_bias()` function featured in this notebook produces JSON files that can be uploaded into Model Manager on SAS Viya 4. Lines of code that must be modified by the user, such as directory paths or the host server are noted with the comment \"_Changes required by user._\"\n",
    "\n",
    "_**Note:** If you download only this notebook and not the rest of the repository, you must also download the exams.csv file from the data folder in the examples directory. These files are used when executing this notebook example._\n",
    "\n",
    "Here are the steps shown in this notebook:\n",
    "\n",
    "1. [Import Modules](#section_0)\n",
    "2. [Import, review, and preprocess data for model training.](#section_1)\n",
    "3. [Build, train, and assess a scikit-learn models.](#section_2)\n",
    "4. [Create score tables for models.](#section_3)\n",
    "5. [Call `assess_model_bias()` to create JSON files and return relevant data frames.](#section_4)\n",
    "6. [Review data frames from assess bias function and generate graphics similar to Model Studio.](#section_5)\n",
    "\n",
    "[^1]: source: http://roycekimmons.com/tools/generated_data/exams"
   ]
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   "source": [
    "<a id='section_0'></a>"
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   "source": [
    "## Import Modules"
   ]
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   "source": [
    "from sasctl import Session\n",
    "import sasctl.pzmm as pzmm\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import r2_score\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "import getpass\n",
    "import matplotlib.pyplot as plt"
   ]
  },
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   "source": [
    "<a id='section_1'></a>"
   ]
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   "source": [
    "## Load, Assess, and Pre-process Data"
   ]
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   "execution_count": 8,
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     "end_time": "2023-07-18T19:13:15.682403900Z",
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       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>race/ethnicity</th>\n",
       "      <th>parental level of education</th>\n",
       "      <th>lunch</th>\n",
       "      <th>test preparation course</th>\n",
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       "      <td>62</td>\n",
       "      <td>59</td>\n",
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       "      <th>1</th>\n",
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       "      <td>completed</td>\n",
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       "      <td>97</td>\n",
       "      <td>96</td>\n",
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       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>group D</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
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       "      <th>4</th>\n",
       "      <td>male</td>\n",
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       "      <td>associate's degree</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>73</td>\n",
       "      <td>62</td>\n",
       "      <td>57</td>\n",
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       "  </tbody>\n",
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       "   gender race/ethnicity parental level of education         lunch  \\\n",
       "0    male        group A                 high school      standard   \n",
       "1  female        group E            some high school  free/reduced   \n",
       "2  female        group E                some college      standard   \n",
       "3  female        group D                some college      standard   \n",
       "4    male        group E          associate's degree  free/reduced   \n",
       "\n",
       "  test preparation course  math score  reading score  writing score  \n",
       "0                    none          62             59             53  \n",
       "1               completed          56             62             64  \n",
       "2               completed          91             97             96  \n",
       "3               completed          81             93             99  \n",
       "4                    none          73             62             57  "
      ]
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   "source": [
    "df_raw = pd.read_csv(\"data/exams.csv\")\n",
    "df_raw.head()"
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       "      <th colspan=\"8\" halign=\"left\">math score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race/ethnicity</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>group A</th>\n",
       "      <td>62.0</td>\n",
       "      <td>67.387097</td>\n",
       "      <td>14.602217</td>\n",
       "      <td>33.0</td>\n",
       "      <td>57.25</td>\n",
       "      <td>69.0</td>\n",
       "      <td>78.75</td>\n",
       "      <td>100.0</td>\n",
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       "    <tr>\n",
       "      <th>group B</th>\n",
       "      <td>199.0</td>\n",
       "      <td>63.738693</td>\n",
       "      <td>16.392042</td>\n",
       "      <td>24.0</td>\n",
       "      <td>53.00</td>\n",
       "      <td>64.0</td>\n",
       "      <td>76.00</td>\n",
       "      <td>96.0</td>\n",
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       "      <th>group C</th>\n",
       "      <td>342.0</td>\n",
       "      <td>64.909357</td>\n",
       "      <td>14.419377</td>\n",
       "      <td>19.0</td>\n",
       "      <td>56.00</td>\n",
       "      <td>66.0</td>\n",
       "      <td>74.00</td>\n",
       "      <td>100.0</td>\n",
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       "      <th>group D</th>\n",
       "      <td>245.0</td>\n",
       "      <td>67.987755</td>\n",
       "      <td>14.207329</td>\n",
       "      <td>24.0</td>\n",
       "      <td>58.00</td>\n",
       "      <td>67.0</td>\n",
       "      <td>78.00</td>\n",
       "      <td>100.0</td>\n",
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       "      <th>group E</th>\n",
       "      <td>152.0</td>\n",
       "      <td>76.578947</td>\n",
       "      <td>14.315983</td>\n",
       "      <td>40.0</td>\n",
       "      <td>67.00</td>\n",
       "      <td>77.0</td>\n",
       "      <td>87.25</td>\n",
       "      <td>100.0</td>\n",
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       "               math score                                                  \\\n",
       "                    count       mean        std   min    25%   50%    75%   \n",
       "race/ethnicity                                                              \n",
       "group A              62.0  67.387097  14.602217  33.0  57.25  69.0  78.75   \n",
       "group B             199.0  63.738693  16.392042  24.0  53.00  64.0  76.00   \n",
       "group C             342.0  64.909357  14.419377  19.0  56.00  66.0  74.00   \n",
       "group D             245.0  67.987755  14.207329  24.0  58.00  67.0  78.00   \n",
       "group E             152.0  76.578947  14.315983  40.0  67.00  77.0  87.25   \n",
       "\n",
       "                       \n",
       "                  max  \n",
       "race/ethnicity         \n",
       "group A         100.0  \n",
       "group B          96.0  \n",
       "group C         100.0  \n",
       "group D         100.0  \n",
       "group E         100.0  "
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(df_raw, x=\"race/ethnicity\", y=\"math score\")\n",
    "df_raw[[\"race/ethnicity\", \"math score\"]].groupby(\"race/ethnicity\").describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-18T19:13:17.169325900Z",
     "start_time": "2023-07-18T19:13:16.523559400Z"
    },
    "collapsed": false
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">math score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</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>female</th>\n",
       "      <td>495.0</td>\n",
       "      <td>64.880808</td>\n",
       "      <td>14.911885</td>\n",
       "      <td>24.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>505.0</td>\n",
       "      <td>69.786139</td>\n",
       "      <td>15.373840</td>\n",
       "      <td>19.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       math score                                                     \n",
       "            count       mean        std   min   25%   50%   75%    max\n",
       "gender                                                                \n",
       "female      495.0  64.880808  14.911885  24.0  56.0  65.0  75.0  100.0\n",
       "male        505.0  69.786139  15.373840  19.0  60.0  70.0  81.0  100.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(df_raw, x=\"gender\", y=\"math score\")\n",
    "df_raw[[\"gender\", \"math score\"]].groupby(\"gender\").describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-18T19:13:17.342339500Z",
     "start_time": "2023-07-18T19:13:17.074870300Z"
    },
    "collapsed": false
   },
   "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>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "      <th>gender_male</th>\n",
       "      <th>race/ethnicity_group A</th>\n",
       "      <th>race/ethnicity_group B</th>\n",
       "      <th>race/ethnicity_group C</th>\n",
       "      <th>race/ethnicity_group D</th>\n",
       "      <th>parental level of education_associate's degree</th>\n",
       "      <th>parental level of education_bachelor's degree</th>\n",
       "      <th>parental level of education_high school</th>\n",
       "      <th>parental level of education_some college</th>\n",
       "      <th>parental level of education_some high school</th>\n",
       "      <th>lunch_free/reduced</th>\n",
       "      <th>test preparation course_none</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>62</td>\n",
       "      <td>59</td>\n",
       "      <td>53</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>62</td>\n",
       "      <td>64</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>91</td>\n",
       "      <td>97</td>\n",
       "      <td>96</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>81</td>\n",
       "      <td>93</td>\n",
       "      <td>99</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>73</td>\n",
       "      <td>62</td>\n",
       "      <td>57</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   math score  reading score  writing score  gender_male  \\\n",
       "0          62             59             53         True   \n",
       "1          56             62             64        False   \n",
       "2          91             97             96        False   \n",
       "3          81             93             99        False   \n",
       "4          73             62             57         True   \n",
       "\n",
       "   race/ethnicity_group A  race/ethnicity_group B  race/ethnicity_group C  \\\n",
       "0                    True                   False                   False   \n",
       "1                   False                   False                   False   \n",
       "2                   False                   False                   False   \n",
       "3                   False                   False                   False   \n",
       "4                   False                   False                   False   \n",
       "\n",
       "   race/ethnicity_group D  parental level of education_associate's degree  \\\n",
       "0                   False                                           False   \n",
       "1                   False                                           False   \n",
       "2                   False                                           False   \n",
       "3                    True                                           False   \n",
       "4                   False                                            True   \n",
       "\n",
       "   parental level of education_bachelor's degree  \\\n",
       "0                                          False   \n",
       "1                                          False   \n",
       "2                                          False   \n",
       "3                                          False   \n",
       "4                                          False   \n",
       "\n",
       "   parental level of education_high school  \\\n",
       "0                                     True   \n",
       "1                                    False   \n",
       "2                                    False   \n",
       "3                                    False   \n",
       "4                                    False   \n",
       "\n",
       "   parental level of education_some college  \\\n",
       "0                                     False   \n",
       "1                                     False   \n",
       "2                                      True   \n",
       "3                                      True   \n",
       "4                                     False   \n",
       "\n",
       "   parental level of education_some high school  lunch_free/reduced  \\\n",
       "0                                         False               False   \n",
       "1                                          True                True   \n",
       "2                                         False               False   \n",
       "3                                         False               False   \n",
       "4                                         False                True   \n",
       "\n",
       "   test preparation course_none  \n",
       "0                          True  \n",
       "1                         False  \n",
       "2                         False  \n",
       "3                         False  \n",
       "4                          True  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_raw.dropna()\n",
    "df = pd.get_dummies(\n",
    "    df,\n",
    "    columns=df.drop([\"math score\", \"reading score\", \"writing score\"], axis=1).columns,\n",
    ")\n",
    "df = df.drop(\n",
    "    [\n",
    "        \"gender_female\",\n",
    "        \"race/ethnicity_group E\",\n",
    "        \"parental level of education_master's degree\",\n",
    "        \"lunch_standard\",\n",
    "        \"test preparation course_completed\",\n",
    "    ],\n",
    "    axis=1,\n",
    ")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-18T19:13:17.342339500Z",
     "start_time": "2023-07-18T19:13:17.121651300Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "new_cols = []\n",
    "for col in df.columns:\n",
    "    col = col.replace(\"/\", \"\")\n",
    "    col = col.replace(\" \", \"_\")\n",
    "    col = col.replace(\"'\", \"\")\n",
    "    new_cols.append(col)\n",
    "\n",
    "df.columns = new_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-18T19:13:17.342339500Z",
     "start_time": "2023-07-18T19:13:17.137762900Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['math_score', 'reading_score', 'writing_score', 'gender_male',\n",
       "       'raceethnicity_group_A', 'raceethnicity_group_B',\n",
       "       'raceethnicity_group_C', 'raceethnicity_group_D',\n",
       "       'parental_level_of_education_associates_degree',\n",
       "       'parental_level_of_education_bachelors_degree',\n",
       "       'parental_level_of_education_high_school',\n",
       "       'parental_level_of_education_some_college',\n",
       "       'parental_level_of_education_some_high_school', 'lunch_freereduced',\n",
       "       'test_preparation_course_none'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "<a id='section_2'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Train and Assess Python Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-18T19:13:22.305796800Z",
     "start_time": "2023-07-18T19:13:22.273569600Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "target = \"math_score\"\n",
    "features = df.drop(target, axis=1).columns\n",
    "\n",
    "# train python models #\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(\n",
    "    df[features], df[target], train_size=0.7, test_size=0.3, random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-18T19:13:23.683505Z",
     "start_time": "2023-07-18T19:13:22.513478Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingRegressor(random_state=42)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "rfr = RandomForestRegressor(random_state=42)\n",
    "gbr = GradientBoostingRegressor(random_state=42)\n",
    "\n",
    "lr.fit(X_train, Y_train)\n",
    "rfr.fit(X_train, Y_train)\n",
    "gbr.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-18T19:13:23.746289Z",
     "start_time": "2023-07-18T19:13:23.452540400Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def sort_feature_importance(model, data):\n",
    "    features = {}\n",
    "    for importance, name in sorted(\n",
    "        zip(model.feature_importances_, data.columns), reverse=True\n",
    "    ):\n",
    "        features[name] = str(np.round(importance * 100, 2)) + \"%\"\n",
    "    return features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-18T19:13:23.916954300Z",
     "start_time": "2023-07-18T19:13:23.468499200Z"
    },
    "collapsed": false
   },
   "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>RandomForest</th>\n",
       "      <th>GradientBoosting</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>reading_score</th>\n",
       "      <td>63.15%</td>\n",
       "      <td>44.4%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>writing_score</th>\n",
       "      <td>19.72%</td>\n",
       "      <td>37.32%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender_male</th>\n",
       "      <td>9.92%</td>\n",
       "      <td>13.88%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_preparation_course_none</th>\n",
       "      <td>1.24%</td>\n",
       "      <td>0.75%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lunch_freereduced</th>\n",
       "      <td>1.19%</td>\n",
       "      <td>1.56%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>raceethnicity_group_C</th>\n",
       "      <td>0.75%</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parental_level_of_education_high_school</th>\n",
       "      <td>0.67%</td>\n",
       "      <td>0.11%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parental_level_of_education_associates_degree</th>\n",
       "      <td>0.59%</td>\n",
       "      <td>0.18%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>raceethnicity_group_D</th>\n",
       "      <td>0.59%</td>\n",
       "      <td>0.36%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>raceethnicity_group_B</th>\n",
       "      <td>0.52%</td>\n",
       "      <td>0.4%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parental_level_of_education_some_high_school</th>\n",
       "      <td>0.51%</td>\n",
       "      <td>0.19%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parental_level_of_education_some_college</th>\n",
       "      <td>0.48%</td>\n",
       "      <td>0.02%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parental_level_of_education_bachelors_degree</th>\n",
       "      <td>0.33%</td>\n",
       "      <td>0.08%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>raceethnicity_group_A</th>\n",
       "      <td>0.32%</td>\n",
       "      <td>0.25%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              RandomForest GradientBoosting\n",
       "reading_score                                       63.15%            44.4%\n",
       "writing_score                                       19.72%           37.32%\n",
       "gender_male                                          9.92%           13.88%\n",
       "test_preparation_course_none                         1.24%            0.75%\n",
       "lunch_freereduced                                    1.19%            1.56%\n",
       "raceethnicity_group_C                                0.75%             0.5%\n",
       "parental_level_of_education_high_school              0.67%            0.11%\n",
       "parental_level_of_education_associates_degree        0.59%            0.18%\n",
       "raceethnicity_group_D                                0.59%            0.36%\n",
       "raceethnicity_group_B                                0.52%             0.4%\n",
       "parental_level_of_education_some_high_school         0.51%            0.19%\n",
       "parental_level_of_education_some_college             0.48%            0.02%\n",
       "parental_level_of_education_bachelors_degree         0.33%            0.08%\n",
       "raceethnicity_group_A                                0.32%            0.25%"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importances = pd.DataFrame.from_dict(\n",
    "    sort_feature_importance(rfr, X_train), orient=\"index\"\n",
    ").rename(columns={0: \"RandomForest\"})\n",
    "importances[\"GradientBoosting\"] = pd.DataFrame.from_dict(\n",
    "    sort_feature_importance(gbr, X_train), orient=\"index\"\n",
    ")\n",
    "importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-18T19:13:24.331777Z",
     "start_time": "2023-07-18T19:13:23.515812600Z"
    },
    "collapsed": false
   },
   "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>MSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>RSQ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LinearRegression</th>\n",
       "      <td>31.545118</td>\n",
       "      <td>4.548155</td>\n",
       "      <td>0.865483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForest</th>\n",
       "      <td>40.655216</td>\n",
       "      <td>5.211450</td>\n",
       "      <td>0.826635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GradientBoost</th>\n",
       "      <td>32.999321</td>\n",
       "      <td>4.665781</td>\n",
       "      <td>0.859282</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        MSE       MAE       RSQ\n",
       "LinearRegression  31.545118  4.548155  0.865483\n",
       "RandomForest      40.655216  5.211450  0.826635\n",
       "GradientBoost     32.999321  4.665781  0.859282"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics = {\n",
    "    \"LinearRegression\": {\n",
    "        \"MSE\": mean_squared_error(Y_test, lr.predict(X_test)),\n",
    "        \"MAE\": mean_absolute_error(Y_test, lr.predict(X_test)),\n",
    "        \"RSQ\": r2_score(Y_test, lr.predict(X_test)),\n",
    "    },\n",
    "    \"RandomForest\": {\n",
    "        \"MSE\": mean_squared_error(Y_test, rfr.predict(X_test)),\n",
    "        \"MAE\": mean_absolute_error(Y_test, rfr.predict(X_test)),\n",
    "        \"RSQ\": r2_score(Y_test, rfr.predict(X_test)),\n",
    "    },\n",
    "    \"GradientBoost\": {\n",
    "        \"MSE\": mean_squared_error(Y_test, gbr.predict(X_test)),\n",
    "        \"MAE\": mean_absolute_error(Y_test, gbr.predict(X_test)),\n",
    "        \"RSQ\": r2_score(Y_test, gbr.predict(X_test)),\n",
    "    },\n",
    "}\n",
    "\n",
    "pd.DataFrame(metrics).transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "<a id='section_3'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Create Score Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-18T19:13:49.183613300Z",
     "start_time": "2023-07-18T19:13:49.060343800Z"
    },
    "collapsed": false
   },
   "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>raceethnicity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>group_D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>group_D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>group_A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>group_C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>group_C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  raceethnicity\n",
       "0       group_D\n",
       "1       group_D\n",
       "2       group_A\n",
       "3       group_C\n",
       "4       group_C"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reconstructing race/ethnicity variable\n",
    "race = pd.from_dummies(\n",
    "    X_test[\n",
    "        [\n",
    "            \"raceethnicity_group_A\",\n",
    "            \"raceethnicity_group_B\",\n",
    "            \"raceethnicity_group_C\",\n",
    "            \"raceethnicity_group_D\",\n",
    "        ]\n",
    "    ],\n",
    "    sep=\"_\",\n",
    "    default_category={\"raceethnicity\": \"group_E\"},\n",
    ")\n",
    "race.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-07-18T19:13:28.056653600Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# recreating gender variable\n",
    "gender = X_test[\"gender_male\"].apply(lambda x: \"male\" if x else \"female\")\n",
    "gender.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-07-18T19:13:28.070551700Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def build_score_table(model):\n",
    "    score_data = {\n",
    "        \"Predicted_Math_Score\": model.predict(X_test),\n",
    "        \"Math_Score\": Y_test.to_numpy(),\n",
    "        \"Race\": race.to_numpy()[:, 0],\n",
    "        \"Gender\": gender,\n",
    "    }\n",
    "    data = pd.DataFrame(score_data)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-07-18T19:13:28.070551700Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "score_tables = {\n",
    "    \"LinearRegression\": build_score_table(lr),\n",
    "    \"RandomForest\": build_score_table(rfr),\n",
    "    \"GradientBoost\": build_score_table(gbr),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-07-18T19:13:28.070551700Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for k, v in score_tables.items():\n",
    "    print(f\"{k} \\n {v.head()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "<a id='section_4'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Using `assess_model_bias()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-07-18T19:13:28.070551700Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# setting up environment\n",
    "hostname = \"\"\n",
    "username = input(\"Username: \")\n",
    "password = getpass.getpass(\"Password: \")  # changes required by user\n",
    "\n",
    "sess = Session(hostname, username, password, protocol=\"http\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-07-18T19:13:28.101073800Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Model names within SAS Model Manager\n",
    "model_prefix = [\"LinearRegression\", \"RandomForest\", \"GradientBoost\"]\n",
    "# Directory location for the model files\n",
    "zip_folder = [\n",
    "    Path.cwd() / \"data/BiasMetrics/examModels/LinearRegression/\",\n",
    "    Path.cwd() / \"data/BiasMetrics/examModels/RandomForest/\",\n",
    "    Path.cwd() / \"data/BiasMetrics/examModels/GradientBoost\",\n",
    "]  # Changes required by user\n",
    "model = [lr, rfr, gbr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-07T13:48:15.988744800Z",
     "start_time": "2023-07-07T13:47:57.803618300Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# creating assess bias json files and getting dataframes\n",
    "assess_bias_dfs = {}\n",
    "\n",
    "for model, path in zip(model_prefix, zip_folder):\n",
    "    dfs = pzmm.JSONFiles.assess_model_bias(\n",
    "        score_table=score_tables[model],\n",
    "        actual_values=\"Math_Score\",  # variable for actual value in score table\n",
    "        sensitive_values=[\n",
    "            \"Race\",\n",
    "            \"Gender\",\n",
    "        ],  # variables for sensitive values in score table\n",
    "        pred_values=\"Predicted_Math_Score\",  # variable for predicted values in score table (regression)\n",
    "        json_path=path,  # directory for json files, optional\n",
    "        return_dataframes=True,  # returns group metrics, bias metrics, and max differences data frames, default is False\n",
    "    )\n",
    "\n",
    "    assess_bias_dfs[model] = dfs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "<a id = 'section_5' > </a >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Assess Model Bias Output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Examples of the Data Frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-07T13:48:16.004944100Z",
     "start_time": "2023-07-07T13:48:16.000802300Z"
    },
    "collapsed": false
   },
   "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>LEVEL</th>\n",
       "      <th>Predicted_Math_Score</th>\n",
       "      <th>VLABEL</th>\n",
       "      <th>_DATAROLE_</th>\n",
       "      <th>_VARIABLE_</th>\n",
       "      <th>_ase_</th>\n",
       "      <th>_avgyhat_</th>\n",
       "      <th>_mae_</th>\n",
       "      <th>_msle_</th>\n",
       "      <th>_nobs_</th>\n",
       "      <th>_rase_</th>\n",
       "      <th>_rmae_</th>\n",
       "      <th>_rmsle_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>group_A</td>\n",
       "      <td>65.939598</td>\n",
       "      <td></td>\n",
       "      <td>TEST</td>\n",
       "      <td>Race</td>\n",
       "      <td>43.580554</td>\n",
       "      <td>65.939598</td>\n",
       "      <td>5.440171</td>\n",
       "      <td>0.009283</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6.601557</td>\n",
       "      <td>2.332417</td>\n",
       "      <td>0.096346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>group_B</td>\n",
       "      <td>65.635744</td>\n",
       "      <td></td>\n",
       "      <td>TEST</td>\n",
       "      <td>Race</td>\n",
       "      <td>38.598601</td>\n",
       "      <td>65.635744</td>\n",
       "      <td>5.097320</td>\n",
       "      <td>0.010109</td>\n",
       "      <td>55.0</td>\n",
       "      <td>6.212777</td>\n",
       "      <td>2.257725</td>\n",
       "      <td>0.100543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>group_C</td>\n",
       "      <td>63.858842</td>\n",
       "      <td></td>\n",
       "      <td>TEST</td>\n",
       "      <td>Race</td>\n",
       "      <td>27.848870</td>\n",
       "      <td>63.858842</td>\n",
       "      <td>4.270016</td>\n",
       "      <td>0.008939</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5.277203</td>\n",
       "      <td>2.066402</td>\n",
       "      <td>0.094544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>group_D</td>\n",
       "      <td>68.684169</td>\n",
       "      <td></td>\n",
       "      <td>TEST</td>\n",
       "      <td>Race</td>\n",
       "      <td>28.578134</td>\n",
       "      <td>68.684169</td>\n",
       "      <td>4.249801</td>\n",
       "      <td>0.007391</td>\n",
       "      <td>77.0</td>\n",
       "      <td>5.345852</td>\n",
       "      <td>2.061505</td>\n",
       "      <td>0.085971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>group_E</td>\n",
       "      <td>75.143961</td>\n",
       "      <td></td>\n",
       "      <td>TEST</td>\n",
       "      <td>Race</td>\n",
       "      <td>30.779962</td>\n",
       "      <td>75.143961</td>\n",
       "      <td>4.640106</td>\n",
       "      <td>0.005884</td>\n",
       "      <td>34.0</td>\n",
       "      <td>5.547969</td>\n",
       "      <td>2.154090</td>\n",
       "      <td>0.076708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>65.049752</td>\n",
       "      <td></td>\n",
       "      <td>TEST</td>\n",
       "      <td>Gender</td>\n",
       "      <td>29.756506</td>\n",
       "      <td>65.049752</td>\n",
       "      <td>4.464487</td>\n",
       "      <td>0.007656</td>\n",
       "      <td>155.0</td>\n",
       "      <td>5.454952</td>\n",
       "      <td>2.112933</td>\n",
       "      <td>0.087499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>68.798429</td>\n",
       "      <td></td>\n",
       "      <td>TEST</td>\n",
       "      <td>Gender</td>\n",
       "      <td>33.457083</td>\n",
       "      <td>68.798429</td>\n",
       "      <td>4.637594</td>\n",
       "      <td>0.009270</td>\n",
       "      <td>145.0</td>\n",
       "      <td>5.784210</td>\n",
       "      <td>2.153507</td>\n",
       "      <td>0.096281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     LEVEL  Predicted_Math_Score VLABEL _DATAROLE_ _VARIABLE_      _ase_  \\\n",
       "0  group_A             65.939598              TEST       Race  43.580554   \n",
       "1  group_B             65.635744              TEST       Race  38.598601   \n",
       "2  group_C             63.858842              TEST       Race  27.848870   \n",
       "3  group_D             68.684169              TEST       Race  28.578134   \n",
       "4  group_E             75.143961              TEST       Race  30.779962   \n",
       "0   female             65.049752              TEST     Gender  29.756506   \n",
       "1     male             68.798429              TEST     Gender  33.457083   \n",
       "\n",
       "   _avgyhat_     _mae_    _msle_  _nobs_    _rase_    _rmae_   _rmsle_  \n",
       "0  65.939598  5.440171  0.009283    23.0  6.601557  2.332417  0.096346  \n",
       "1  65.635744  5.097320  0.010109    55.0  6.212777  2.257725  0.100543  \n",
       "2  63.858842  4.270016  0.008939   111.0  5.277203  2.066402  0.094544  \n",
       "3  68.684169  4.249801  0.007391    77.0  5.345852  2.061505  0.085971  \n",
       "4  75.143961  4.640106  0.005884    34.0  5.547969  2.154090  0.076708  \n",
       "0  65.049752  4.464487  0.007656   155.0  5.454952  2.112933  0.087499  \n",
       "1  68.798429  4.637594  0.009270   145.0  5.784210  2.153507  0.096281  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assess_bias_dfs[\"LinearRegression\"][\"groupMetricsData\"].head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-07T13:48:16.075438200Z",
     "start_time": "2023-07-07T13:48:16.006624800Z"
    },
    "collapsed": false
   },
   "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>BASE</th>\n",
       "      <th>COMPARE</th>\n",
       "      <th>Metric</th>\n",
       "      <th>MetricLabel</th>\n",
       "      <th>VLABEL</th>\n",
       "      <th>_DATAROLE_</th>\n",
       "      <th>_VARIABLE_</th>\n",
       "      <th>maxdiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>group_E</td>\n",
       "      <td>group_C</td>\n",
       "      <td>Predicted_Math_Score</td>\n",
       "      <td>Average Predicted_Math_Score</td>\n",
       "      <td></td>\n",
       "      <td>TEST</td>\n",
       "      <td>Race</td>\n",
       "      <td>11.285119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>group_A</td>\n",
       "      <td>group_C</td>\n",
       "      <td>ASE</td>\n",
       "      <td>Average Square Error</td>\n",
       "      <td></td>\n",
       "      <td>TEST</td>\n",
       "      <td>Race</td>\n",
       "      <td>15.731684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>group_A</td>\n",
       "      <td>group_C</td>\n",
       "      <td>RASE</td>\n",
       "      <td>Root Average Square Error</td>\n",
       "      <td></td>\n",
       "      <td>TEST</td>\n",
       "      <td>Race</td>\n",
       "      <td>1.324354</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      BASE  COMPARE                Metric                   MetricLabel  \\\n",
       "0  group_E  group_C  Predicted_Math_Score  Average Predicted_Math_Score   \n",
       "1  group_A  group_C                   ASE          Average Square Error   \n",
       "2  group_A  group_C                  RASE     Root Average Square Error   \n",
       "\n",
       "  VLABEL _DATAROLE_ _VARIABLE_    maxdiff  \n",
       "0              TEST       Race  11.285119  \n",
       "1              TEST       Race  15.731684  \n",
       "2              TEST       Race   1.324354  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# race/ethnicity\n",
    "assess_bias_dfs[\"LinearRegression\"][\"maxDifferencesData\"].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-07T13:48:16.075438200Z",
     "start_time": "2023-07-07T13:48:16.024329200Z"
    },
    "collapsed": false
   },
   "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>BASE</th>\n",
       "      <th>COMPARE</th>\n",
       "      <th>Metric</th>\n",
       "      <th>MetricLabel</th>\n",
       "      <th>VLABEL</th>\n",
       "      <th>_DATAROLE_</th>\n",
       "      <th>_VARIABLE_</th>\n",
       "      <th>maxdiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>male</td>\n",
       "      <td>female</td>\n",
       "      <td>MSLE</td>\n",
       "      <td>Mean Square Logarithmic Error</td>\n",
       "      <td></td>\n",
       "      <td>TEST</td>\n",
       "      <td>Gender</td>\n",
       "      <td>0.001614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>male</td>\n",
       "      <td>female</td>\n",
       "      <td>RMSLE</td>\n",
       "      <td>Root Mean Square Logarithmic Error</td>\n",
       "      <td></td>\n",
       "      <td>TEST</td>\n",
       "      <td>Gender</td>\n",
       "      <td>0.008782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>male</td>\n",
       "      <td>female</td>\n",
       "      <td>PREDICTED</td>\n",
       "      <td>Average Prediction</td>\n",
       "      <td></td>\n",
       "      <td>TEST</td>\n",
       "      <td>Gender</td>\n",
       "      <td>3.748677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   BASE COMPARE     Metric                         MetricLabel VLABEL  \\\n",
       "5  male  female       MSLE       Mean Square Logarithmic Error          \n",
       "6  male  female      RMSLE  Root Mean Square Logarithmic Error          \n",
       "7  male  female  PREDICTED                  Average Prediction          \n",
       "\n",
       "  _DATAROLE_ _VARIABLE_   maxdiff  \n",
       "5       TEST     Gender  0.001614  \n",
       "6       TEST     Gender  0.008782  \n",
       "7       TEST     Gender  3.748677  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gender\n",
    "assess_bias_dfs[\"LinearRegression\"][\"maxDifferencesData\"].tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-07T13:48:16.075438200Z",
     "start_time": "2023-07-07T13:48:16.035682500Z"
    },
    "collapsed": false
   },
   "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>Metric</th>\n",
       "      <th>MetricLabel</th>\n",
       "      <th>Value</th>\n",
       "      <th>Base</th>\n",
       "      <th>Compare</th>\n",
       "      <th>Note</th>\n",
       "      <th>_VARIABLE_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PredictiveParity</td>\n",
       "      <td>Predictive Parity</td>\n",
       "      <td>11.285119</td>\n",
       "      <td>group_E</td>\n",
       "      <td>group_C</td>\n",
       "      <td></td>\n",
       "      <td>Race</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PredictiveParity</td>\n",
       "      <td>Predictive Parity</td>\n",
       "      <td>3.748677</td>\n",
       "      <td>male</td>\n",
       "      <td>female</td>\n",
       "      <td></td>\n",
       "      <td>Gender</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Metric        MetricLabel      Value     Base  Compare Note  \\\n",
       "0  PredictiveParity  Predictive Parity  11.285119  group_E  group_C        \n",
       "0  PredictiveParity  Predictive Parity   3.748677     male   female        \n",
       "\n",
       "  _VARIABLE_  \n",
       "0       Race  \n",
       "0     Gender  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assess_bias_dfs[\"LinearRegression\"][\"biasMetricsData\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-07T13:48:16.075438200Z",
     "start_time": "2023-07-07T13:48:16.048340600Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gm_lr = assess_bias_dfs[\"LinearRegression\"][\"groupMetricsData\"]\n",
    "gm_rfr = assess_bias_dfs[\"RandomForest\"][\"groupMetricsData\"]\n",
    "gm_gbr = assess_bias_dfs[\"GradientBoost\"][\"groupMetricsData\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-07T13:48:16.123619Z",
     "start_time": "2023-07-07T13:48:16.054688300Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "md_lr = assess_bias_dfs[\"LinearRegression\"][\"maxDifferencesData\"]\n",
    "md_rfr = assess_bias_dfs[\"RandomForest\"][\"maxDifferencesData\"]\n",
    "md_gbr = assess_bias_dfs[\"GradientBoost\"][\"maxDifferencesData\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Performance Bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-07T13:48:16.155298Z",
     "start_time": "2023-07-07T13:48:16.064467100Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [12, 4]\n",
    "plt.rcParams[\"figure.autolayout\"] = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Average Squared Error: $$ASE = \\frac{1}{n-p} \\sum_{i=1}^{n} (y_i - \\hat{y_i})^2$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-07T13:48:17.373831400Z",
     "start_time": "2023-07-07T13:48:16.075438200Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3)\n",
    "fig.suptitle(\"Average Squared Error by Race\")\n",
    "\n",
    "sns.barplot(\n",
    "    data=gm_lr[gm_lr[\"_VARIABLE_\"] == \"Race\"], x=\"LEVEL\", y=\"_ase_\", ax=axes[0]\n",
    ").set(title=\"Linear Regression\")\n",
    "sns.barplot(\n",
    "    data=gm_rfr[gm_rfr[\"_VARIABLE_\"] == \"Race\"], x=\"LEVEL\", y=\"_ase_\", ax=axes[1]\n",
    ").set(title=\"Random Forest\")\n",
    "sns.barplot(\n",
    "    data=gm_gbr[gm_gbr[\"_VARIABLE_\"] == \"Race\"], x=\"LEVEL\", y=\"_ase_\", ax=axes[2]\n",
    ").set(title=\"Gradient Boosting\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-07T13:48:19.116986100Z",
     "start_time": "2023-07-07T13:48:17.373831400Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3)\n",
    "fig.suptitle(\"Average Squared Error by Sex\")\n",
    "\n",
    "sns.barplot(\n",
    "    data=gm_lr[gm_lr[\"_VARIABLE_\"] == \"Gender\"], x=\"LEVEL\", y=\"_ase_\", ax=axes[0]\n",
    ").set(title=\"Linear Regression\")\n",
    "sns.barplot(\n",
    "    data=gm_rfr[gm_rfr[\"_VARIABLE_\"] == \"Gender\"], x=\"LEVEL\", y=\"_ase_\", ax=axes[1]\n",
    ").set(title=\"Random Forest\")\n",
    "sns.barplot(\n",
    "    data=gm_gbr[gm_gbr[\"_VARIABLE_\"] == \"Gender\"], x=\"LEVEL\", y=\"_ase_\", ax=axes[2]\n",
    ").set(title=\"Gradient Boosting\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-07T13:48:19.136357700Z",
     "start_time": "2023-07-07T13:48:19.121928300Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "md_ase_lr_race = md_lr[(md_lr[\"Metric\"] == \"ASE\") & (md_lr[\"_VARIABLE_\"] == \"Race\")][\n",
    "    \"maxdiff\"\n",
    "]\n",
    "md_ase_lr_sex = md_lr[(md_lr[\"Metric\"] == \"ASE\") & (md_lr[\"_VARIABLE_\"] == \"Gender\")][\n",
    "    \"maxdiff\"\n",
    "]\n",
    "md_ase_lr = [x.values[0] for x in [md_ase_lr_race, md_ase_lr_sex]]\n",
    "\n",
    "md_ase_rfr_race = md_rfr[\n",
    "    (md_rfr[\"Metric\"] == \"ASE\") & (md_rfr[\"_VARIABLE_\"] == \"Race\")\n",
    "][\"maxdiff\"]\n",
    "md_ase_rfr_sex = md_rfr[\n",
    "    (md_rfr[\"Metric\"] == \"ASE\") & (md_rfr[\"_VARIABLE_\"] == \"Gender\")\n",
    "][\"maxdiff\"]\n",
    "md_ase_rfr = [x.values[0] for x in [md_ase_rfr_race, md_ase_rfr_sex]]\n",
    "\n",
    "md_ase_gbr_race = md_gbr[\n",
    "    (md_gbr[\"Metric\"] == \"ASE\") & (md_gbr[\"_VARIABLE_\"] == \"Race\")\n",
    "][\"maxdiff\"]\n",
    "md_ase_gbr_sex = md_gbr[\n",
    "    (md_gbr[\"Metric\"] == \"ASE\") & (md_gbr[\"_VARIABLE_\"] == \"Gender\")\n",
    "][\"maxdiff\"]\n",
    "md_ase_gbr = [x.values[0] for x in [md_ase_gbr_race, md_ase_gbr_sex]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-07T13:48:19.744824600Z",
     "start_time": "2023-07-07T13:48:19.141984900Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3)\n",
    "fig.suptitle(\"Max Average Squared Error Difference by Sensitive Variable\")\n",
    "\n",
    "sns.barplot(y=md_ase_lr, x=[\"Race\", \"Sex\"], ax=axes[0]).set(title=\"Linear Regression\")\n",
    "sns.barplot(y=md_ase_rfr, x=[\"Race\", \"Sex\"], ax=axes[1]).set(title=\"Random Forest\")\n",
    "sns.barplot(y=md_ase_gbr, x=[\"Race\", \"Sex\"], ax=axes[2]).set(title=\"Gradient Boost\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Root Average Squared Error: $$RASE = \\sqrt{\\frac{1}{n-p} \\sum_{i=1}^{n} (y_i - \\hat{y_i})^2}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-07T13:48:20.452981400Z",
     "start_time": "2023-07-07T13:48:19.744824600Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3)\n",
    "fig.suptitle(\"Root Average Squared Error by Race\")\n",
    "\n",
    "sns.barplot(\n",
    "    data=gm_lr[gm_lr[\"_VARIABLE_\"] == \"Race\"], x=\"LEVEL\", y=\"_rase_\", ax=axes[0]\n",
    ").set(title=\"Linear Regression\")\n",
    "sns.barplot(\n",
    "    data=gm_rfr[gm_rfr[\"_VARIABLE_\"] == \"Race\"], x=\"LEVEL\", y=\"_rase_\", ax=axes[1]\n",
    ").set(title=\"Random Forest\")\n",
    "sns.barplot(\n",
    "    data=gm_gbr[gm_gbr[\"_VARIABLE_\"] == \"Race\"], x=\"LEVEL\", y=\"_rase_\", ax=axes[2]\n",
    ").set(title=\"Gradient Boosting\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-07T13:48:20.966726Z",
     "start_time": "2023-07-07T13:48:20.436480100Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3)\n",
    "fig.suptitle(\"Root Average Squared Error by Sex\")\n",
    "\n",
    "sns.barplot(\n",
    "    data=gm_lr[gm_lr[\"_VARIABLE_\"] == \"Gender\"], x=\"LEVEL\", y=\"_rase_\", ax=axes[0]\n",
    ").set(title=\"Linear Regression\")\n",
    "sns.barplot(\n",
    "    data=gm_rfr[gm_rfr[\"_VARIABLE_\"] == \"Gender\"], x=\"LEVEL\", y=\"_rase_\", ax=axes[1]\n",
    ").set(title=\"Random Forest\")\n",
    "sns.barplot(\n",
    "    data=gm_gbr[gm_gbr[\"_VARIABLE_\"] == \"Gender\"], x=\"LEVEL\", y=\"_rase_\", ax=axes[2]\n",
    ").set(title=\"Gradient Boosting\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-07T13:48:20.980629100Z",
     "start_time": "2023-07-07T13:48:20.962995200Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "md_rase_lr_race = md_lr[(md_lr[\"Metric\"] == \"RASE\") & (md_lr[\"_VARIABLE_\"] == \"Race\")][\n",
    "    \"maxdiff\"\n",
    "]\n",
    "md_rase_lr_sex = md_lr[(md_lr[\"Metric\"] == \"RASE\") & (md_lr[\"_VARIABLE_\"] == \"Gender\")][\n",
    "    \"maxdiff\"\n",
    "]\n",
    "md_rase_lr = [x.values[0] for x in [md_rase_lr_race, md_rase_lr_sex]]\n",
    "\n",
    "md_rase_rfr_race = md_rfr[\n",
    "    (md_rfr[\"Metric\"] == \"RASE\") & (md_rfr[\"_VARIABLE_\"] == \"Race\")\n",
    "][\"maxdiff\"]\n",
    "md_rase_rfr_sex = md_rfr[\n",
    "    (md_rfr[\"Metric\"] == \"RASE\") & (md_rfr[\"_VARIABLE_\"] == \"Gender\")\n",
    "][\"maxdiff\"]\n",
    "md_rase_rfr = [x.values[0] for x in [md_rase_rfr_race, md_rase_rfr_sex]]\n",
    "\n",
    "md_rase_gbr_race = md_gbr[\n",
    "    (md_gbr[\"Metric\"] == \"RASE\") & (md_gbr[\"_VARIABLE_\"] == \"Race\")\n",
    "][\"maxdiff\"]\n",
    "md_rase_gbr_sex = md_gbr[\n",
    "    (md_gbr[\"Metric\"] == \"RASE\") & (md_gbr[\"_VARIABLE_\"] == \"Gender\")\n",
    "][\"maxdiff\"]\n",
    "md_rase_gbr = [x.values[0] for x in [md_rase_gbr_race, md_rase_gbr_sex]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-07T13:48:21.468355500Z",
     "start_time": "2023-07-07T13:48:20.972487100Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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njlGvXj2bdVOmTDE8PT1t/n0MwzC6dOli1KxZ07BYLIZhGIa7u7vRpUuXZONLcP/5vN+SJUsMd3d34+DBg4Zh/N+1IcH9ywnf++zZs4ZhGMapU6cMDw+PRL/HGTNmGF5eXsa1a9es+/Hx8TFiYmKsZX755RfD3d09Ub3+4IMPjJo1axp37twxDOPueapTp45NHQsMDDTc3d2t+2/ZsqXRokUL63XCMAxj48aNRoMGDYzw8PBHrvcJ/3b3XsOio6ONmjVrWrc7deqU4e7ubqxYscJa5qOPPjIaNGiQ7H4NwzD+/fdfo1OnTtY64+HhYTRt2tSYNWuWcf369cdynsqXL2989tln1s8tFosxadIkIzQ01DCMxP++Cd//Xu7u7sbs2bMNw/i/a+i9dfvDDz80GjVqZBhGyn+HSYmNjTWqVatmDBs2zGZ9y5YtjXfeeccwDMP4448/jDfeeMP4559/bMr07NnTaNiwoXU5qd/a/df/+fPnW69PCSIiImzam4RtxowZY1OuV69eRvXq1Q2LxZLo2paaay4A4PGixxQAZACurq6qW7euzXC+jRs3qnHjxol6NxUuXFiLFi1SpUqVdO7cOe3atUtfffWV/ve//9n0HMqSJYsmT56sc+fOafjw4WrZsqUaNWr00FgKFiyoFStWaMWKFQoJCVHlypVVqFAhTZo0Se3bt7fGk/D0/P43Nr366qtycnLS3r17dfr0aYWHhyfq9fXCCy/I19c30RP4B7l27Zp+/PFHNWzYUDdv3tTNmzdVunRpFSlSxGY43/PPP6+KFSvaDK/ZuHGjGjVqJBcXF506dUoXL15U3bp1bXrWVKlSRTly5NCuXbtsjnvvvCouLi76/PPP1aRJE126dEl79uzRN998Y52gPjY2VmfOnNGFCxcSnev7z9Pu3btVtmxZFS5c2BqDo6OjXn75Zf36668PPBeVK1fWd999p8WLF+udd96Rl5eXDh48qBEjRqhTp07WHlT79+9XhQoVlD9/fuu2lSpVUpEiRVJyym1MmzZNXbp00bVr17R//36tXLnS2nPo3non2Z6zAwcOKDo6OtH5ThgmuGvXLkVHR+v3339XnTp1bPZjz7w+n332mbXe3vuX1ETwSc2Vc/+6ffv2ydfXN9G5at68ucLDw3Xq1KkH7s9exv/v6fKw3ozJ2bNnjwzDSPI8x8TEKDQ01Fq2RIkScnFxsS7v3r1bDg4OqlWrVqJtw8PDdfz4cWtZLy8vOTk5WZcTJt+OiopSdHS0/vjjD9WrV8/mezRp0kRbt25VgQIFUlXvs2TJogYNGliXs2bNqpdfflm//fabJOnFF19UpUqVtHbtWklSdHS0Nm/enGxvqXu/w6JFi7Rx40YNHjxYtWrV0vnz5zVnzhy9+uqr1iF4aXWeJMnPz0+BgYHq16+fli9fritXrmjw4MGqWLHiA2NNTtWqVfXcc89p48aNku4OOdy2bZu1t1RKfofJyZIli1577TV999131t/68ePH9fvvv1t7PJUtW1Zff/21ihQpor///ls7d+7U559/rlOnTj3w+pCU7t27a9KkSfrvv/909OhRbdq0SXPnzpWU+FrTpEkTm+X69evr6tWrNr/PBKmpewCAx4vJzwEgg2jcuLH69OmjixcvKmvWrNq9e7fef//9JMuuW7dO06dP17///qs8efKobNmycnV1TVSubNmy8vDw0NGjRxPd9CfHxcVFXl5e1uWKFSuqVatWevvtt7V8+XK9+OKLkmSdO+j+SbadnZ2VN29e3bp1S9evX5ck69CyexUoUMCuuT3WrVunO3fuKDAwMNEr0s+fP6+TJ0+qZMmSku7OJzV27FhFRETo3LlzOnPmjCZMmCBJ1pjGjBmjMWPGJDrO5cuXbZazZ89us/zzzz9rwoQJOnXqlLJnz64yZcooW7Zsku4mFxKGAd2bDEr4vve6fv26zpw5I09PzyS/b1RUlNzc3JI9H46OjqpSpYp10uEbN25oxowZWrp0qVasWKGOHTvqxo0bSU6MXbhw4WT3m5wjR45ozJgxOnLkiNzc3FSqVCk999xzkpRozrJ7z1nC+e7Ro0eS+718+bJu3LghwzCUN29em88S3kiWEu7u7ime/Pz+f1NJ1n/DBMmdu4R/x3vnTLp/20dx6dIlSY/2byP933m+PwF6//6lxN//+vXrMgwj2aTI5cuXrcmE++uko+PdZ5zx8fHWf8f76/79x3rUep83b17r8RLkz5/f5t+idevWGjZsmP7991+Fhobqv//+sxl+/CClSpVSqVKl1K1bN925c0erVq3Sxx9/rOnTp2v27Nlpdp4kacaMGQoODtbmzZu1detWOTo6qkaNGvr4448fKXHs4OCgZs2aafny5froo4/0448/KjIyUs2aNZOUst/hg7Rq1UpffPGFduzYoQYNGmj16tUqWLCgateubS3zxRdfKDg4WNevX1eBAgVUvnx5ubm56datWzb7etjv5dq1axo1apS2bdsmBwcHFStWTJUrV5aU+Fpz/3U1oe7duHEjUZuY2msuAODxITEFABnEyy+/rOzZs2vLli3Kli2bihYtap3b5F779+/X4MGD1alTJ7311lvWG9kpU6bY9IqQ7s5HcvToUZUpU0bjx49X9erV7X7Dn5ubmyZNmqTXX39dQ4cO1dKlS+Xg4GCdmyc8PNzmRurOnTuKiIhQ3rx5lSdPHknSlStXEu03PDw8USLiQVauXGmdz+VekZGR6tWrl5YuXWqd4Lhx48YaN26ctm3bplOnTqlIkSKqVKmSJFm//6BBg1S1atVEx0n4Xkn5559/1Lt3b9WrV09z587V888/LwcHBy1ZskQ///yzpP/rGXH16lWbbe9fzpkzp6pWrapBgwYleax7e7Tc6/3339f169f15ZdfJop75MiR2rRpk06cOCHp7o18Uuf++vXr1jcZJvRsuX8C5Hsngr99+7a6d+8uDw8Pbdy4USVKlJCjo6N27typrVu3JhlngoTzPXXqVBUvXjzR5wUKFFCePHnk6OiYKNaEm2kz5M6d2zrR/L0S1tlTd1Pi119/VbFixR45MZVwnhcuXJhk4i0hiZiUnDlzKlu2bFq0aFGSn6f0rZc5cuSQg4ODNTmbICYmRnv27JGPj88j13tJunXrlgzDsOmNdeXKFZu31zVq1Ejjxo3Tli1btH//ftWsWfOB53ThwoX67LPPrG9ATZAlSxa9/vrr2rlzp/X3lFbnKWFfH374oT788EOdOnVKP/zwgz799FONGTNG8+bNS/F+7vXaa69p7ty52rt3rzZt2qQqVapYr80p+R0+SOnSpeXj46MNGzaoXr16Wr9+vVq2bGntFbZ+/XpNmjRJH374oQICAqz/Ju+9956OHDli1/f44IMPdOrUKX355Zfy9fWVi4uLoqKi9O233yYqm/CAJEHCNSSp5Ghq6h4A4PFiKB8AZBAuLi6qV6+etm7dqs2bNyfb8+HAgQOKj49X3759rTdcFovFOhQh4Yn8+fPnNXnyZLVu3VrBwcG6deuWxo8f/0ixeXt7q23btjpw4IB1kt+EpE7C0JEEGzdulMViUaVKlfTiiy+qYMGC2rBhg02Zs2fP6uDBg9aeB/cOeUnKkSNH9NdffykgIEB+fn42f3Xq1FG1atW0du1a6xC2XLlyqU6dOvrhhx+0detWNW/e3HozW6JECeXPn1/nzp2Tl5eX9a9w4cKaNm3aA3txHT16VDExMerRo4deeOEF6z4TklKGYeiZZ57RCy+8oO+//95m2++++85muWrVqjp9+rRefPFFmzjWrl2rFStWJHtOihUrpj179ujgwYOJPrt8+bIiIyPl7u4uSapevboOHDhg01vm1KlTOnPmjHU5R44ckmx71Ny5c0eHDx+22eb69evq3LmzSpUqZe398dNPP0lK+g2BCXx8fJQlSxZdunTJ5ns6Oztr+vTpOnfunLJmzSpfX1999913Nj0itm/fnux+H7cqVarowIEDiSYjX7dunQoWLGhXEuJhduzYoSNHjuiNN9545H0k9CiJiIiwOc/Xrl3TrFmzHpjkq1q1qiIjI2UYhs22f/31l+bMmaO4uLgUxZA9e3aVLVvWOrQ1wU8//aQePXro8uXLj1zvpbs9Wu6dHPy///7Tjh075OfnZ12XLVs2NWnSRBs2bNCuXbseOoyvVKlSioiISPJtbhaLRWfPnrX+ntLqPJ0/f97mTawlSpTQ22+/rRo1aujChQtJbnN/T7GklCxZUp6entq4caN27typ5s2bWz9Lye/wYVq1aqWffvpJv/zyiy5fvmx9G590d5L5XLlyqXv37tak1H///afQ0NAHXh+SEhoaqgYNGsjPz8+aLEruWrNjxw6b5Y0bN+rZZ59N8veZmroHAHi86DEFABlIkyZN1LNnTzk6Oib7enNvb29J0scff6xWrVrpxo0bWrJkiY4dOybpbk+X7Nmza/jw4XJzc9OgQYOUO3duvf/++5owYYIaNmxonVfEHu+//742b96sadOmqX79+ipVqpRatmyp2bNnKyoqSlWqVNGff/6poKAg+fn56aWXXpKjo6MGDBigoUOHauDAgWrevLkiIiIUFBSk3LlzW19VnzNnTkl3bzJy586tMmXK2Bx75cqVieaXuddrr72mX3/9VZs2bbLeiDZv3lz9+vWTxWKxzrMi3U2C9e/fXyNHjpSTk5Pq1Kmjmzdv6tNPP9WlS5eSHeYhSZ6ennJ2dtYnn3yibt26KTY2VqtWrbLeHEVGRsrBwUH9+vXTBx98oFGjRql+/fo6duyY9c1oCTeYb775ptauXas333xT3bp1U968ebVp0yZ9++23Gjp0aLIxdOvWTdu2bVPXrl3Vvn17+fn5yc3NTX/99ZdCQkJUunRp6zno0qWL9a2Iffv2lWEYmjlzps3NXe7cueXr66uvvvpKxYoVU+7cubVo0SJFR0dbh9y8+OKLypEjh4KDg+Xs7CxnZ2dt3bpVK1askCSbt/LdL2/evOrevbtmzZql27dvy8/PT5cuXdKsWbPk4OBg/bceMGCAunTpoj59+uj111/X6dOnFRwcnOx+7/fnn38m2TtMkooUKZJoyOnDdO3aVevWrdObb76pPn36KE+ePFqzZo327NmjCRMmpChRcL/Y2FhrQtEwDN28eVP79+/XokWL5Ofnp44dO9q9zwQeHh5q3ry5RowYofPnz6t8+fI6ffq0ZsyYoaJFiybZSyZBrVq1VKVKFfXq1Uu9evVSyZIldfjwYc2ePVsvvfSSTY+kh+nXr5/effddDRgwQC1atNCVK1c0ffp01atXT+7u7o9c76W7vZiGDRumAQMGKEeOHJo3b56io6PVq1cvm3KtW7fW66+/rty5c6tevXoP3GfNmjXVtGlTTZ8+XWFhYWrYsKHy5cunixcv6ptvvtHFixc1c+bMND1PRYoU0TPPPKNx48bp9u3beuGFF3T06FHt3LlTPXv2THKbhB5PGzZskI+PT5LDTKW718LJkyfL2dnZZp67lP4OH+TVV1/VxIkTNX78eFWtWtWmTnl7e2vp0qWaNGmS6tSpo8uXL+vzzz/XlStXHtgLNSne3t5av369PD099cwzz+h///uf5s2bJwcHh0TXmq+++krZs2dXuXLltHHjRv3888+aMmVKknO1pabuAQAeLxJTAJCB1KhRQ7ly5dKzzz5rnS/pfn5+fho5cqS++OILbdmyRQUKFJCfn5+CgoLUu3dvhYaG6ty5c9q9e7dmzpxpvSno1KmT1q9fr5EjR6pixYrWYXYplTdvXr333nv6+OOPNWfOHA0ePFjjx49XsWLFtHLlSs2fP1+FChVS586d1atXL+uNe0BAgLJnz665c+eqd+/eypEjh1566SUNGDDAmiwoXbq0mjZtah0Sd28Pq5iYGG3cuFE1a9ZMNuYGDRpozJgx+uabb6xJmVq1ailnzpx6/vnnrfNiJWjTpo2yZ8+uBQsWaNmyZcqWLZsqVqyoqVOnJnvDJ93trTRt2jQFBQXp3XffVe7cuVWhQgV99dVX6tSpk/bv3y8PDw81a9ZMkZGR+vzzz7Vy5UqVLl1aw4cP1/Dhw63JnsKFC+ubb77RtGnTNHr0aMXExKh48eIaP358kq9QT5A7d24tW7ZM8+fP1/bt27V06VLduXNHRYoUUdOmTdWjRw/r3Cp58+bVkiVLNGHCBA0ZMkQ5cuRQ9+7dEw1FmjRpksaOHauPPvpIOXLkUOvWrVWpUiUtX75c0t3E4aeffqopU6bovffes/aMWbx4sd5++23t37//gcnO999/XwULFtTXX3+tBQsWKHfu3KpevboGDBhgTUpWrlxZ8+fP1/Tp09WnTx8VLVpUEyZM0DvvvJPsfu/Vp0+fZD8bOnSo3nzzzRTtJ0HBggW1dOlSTZs2TePGjdOdO3dUpkwZffrpp3rllVfs2leC8PBwvf7669blbNmy6cUXX1S/fv3UqVMnZcmS5ZH2m2DixImaO3euNaGSP39+NWnSRO+///4De4M4Ojpq3rx5mjVrlubOnaurV6+qcOHC6tq1q3r37m1XDHXq1FFwcLD1epQvXz41a9ZMffv2lfTo9V6S8uXLp4EDB2r69OkKDw+Xj4+PFi9erBIlStiUq1ChgvLkyaMmTZqkaHjWJ598oqpVq2rdunX66KOPFBkZqXz58qlmzZqaOHGi9ZqQlucpKChI06dP16xZsxQREaFnn31Wffr0SXYOqAYNGmjt2rUaMmSIWrdurdGjRydZrmnTppoyZYrq1Klj/W0lSMnv8EFy5Mihhg0bas2aNXr33XdtPmvZsqXOnTunlStX6uuvv1bhwoVVq1YttW/fXiNGjLCZA/BhEq5HY8eOlSQVL15cY8aM0bp167R//36bsuPGjdOCBQs0c+ZMPf/885o+fXqyvY1TU/cAAI+Xg3H/LIIAACBVNmzYoHLlytncMO/YsUM9e/bU2rVrU9Q74XGqW7euqlatqkmTJpkaB/A4HDp0SG3bts0QvzUAAPBw9JgCACCNrVu3TjNmzND777+vZ599VmfOnNHs2bNVtWpVbpSBx2Tv3r3au3ev1qxZI39/f35rAABkEiSmAABIY5MnT9a0adP0ySef6Nq1aypQoIAaNWqkfv36mR0a8MSKiIjQF198odKlS2vcuHFmhwMAAFKIoXwAAAAAAAAwhf2vlAEAAAAAAADSAIkpAAAAAAAAmILEFAAAAAAAAExBYgoAAAAAAACmIDEFAAAAAAAAU5CYAgAAAAAAgClITAEAAAAAAMAUJKYAAAAAAABgChJTAAAAAAAAMAWJKQAAAAAAAJiCxBQAAAAAAABMQWIKAAAAAAAApiAxBQAAAAAAAFOQmAIAAAAAAIApSEwBAAAAAADAFCSmAAAAAAAAYAoSUwAAAAAAADAFiSkAAAAAAACYgsQUAAAAAAAATEFiCgAAAAAAAKYgMQUAAIA0ZRiG2SEAwBMvM19rM3PsSHskppCmOnXqpE6dOj2wzJAhQ1S3bt10iujRderUSR4eHjZ/ZcqUUcWKFRUQEKC1a9eaHWKaO3funDw8PLRq1SqzQwGADHcd9vDwUGBgYLoe8/7jJ/fn6+trWlz3Cw0NVY8ePcwOA8BT6uTJkxo7dqwaNmwoHx8fVapUSe3atdPXX3+tuLi4x3rs++9zUnJv9CguXryoHj166Pz588mWSfj/+nv/PD099fLLL2vkyJG6du1amseVEjdv3tSgQYO0f/9+U46PjMnZ7ADw9OnVq5c6d+5sdhgpUq5cOY0aNcq6bLFYdPHiRX355ZcaNGiQ8uTJo1q1apkYYdoqVKiQli1bphdeeMHsUABA0tN3HX6Y1q1bq02bNonWOzpmnGeNy5cv18mTJ80OA8BTaNOmTRo6dKhKliyprl276sUXX1R0dLR27typCRMm6Oeff9ann34qBweHdInn3vYrLf3666/auXNnisq+++67ql27tiQpJiZGp0+fVmBgoE6cOKGvv/76scT3IH/++afWrl2rVq1apfuxkXGRmEK6y0xJjxw5cqhChQqJ1r/88suqXr26Vq1a9UTdELm4uCT5fQHALE/bdfhhnnnmGa7TAJCEkydPaujQoXrppZc0c+ZMOTv/361urVq15Ofnp379+mnz5s1q0qRJusRUqlSpdDnOg7zwwgs27Yafn5+yZMmiYcOG6fjx4ypdurR5wQH/X8Z5vIanxv1dXOvWravZs2dr8uTJqlGjhry9vfXWW2/p77//ttlu//796tixo3x8fFS1alUNHjw4URfU3377TW+99ZaqVKmi8uXLq27dugoMDFR8fLyk/+vS+sUXX6hRo0by8fHRypUr7f4OWbNmlYuLi83Tlvj4eM2bN0/169dX+fLl1bBhQ3311VeJtv3888/1yiuvyNvbW+3atdP27dvl4eGhvXv3SpICAwNVv359BQUFqWrVqvL399eNGzck3X0K/eqrr6p8+fKqXbu2AgMDZbFYrPu+du2aBg4cqJo1a8rLy0uvvfaa1qxZYxPjjBkzVLduXev5mTZtmu7cuWNzfu4dyvf333+rX79+qlmzpipUqKBOnTopNDTU+nnCNps3b1a/fv3k6+urqlWr6qOPPlJkZKTd5xYAUiKp6/C1a9c0ZswY1alTR+XLl1fVqlXVu3dvnTt3zlqmU6dOGj58uObNm6fatWvLy8tL7dq10+HDh232v2/fPr3++uvy8fFRw4YN9euvvyaK4datW5o4caLq1asnLy8vNW3aVCtWrLApU7duXQUFBWnChAny8/OTr6+vBg4cqP/++0/z5s3Tyy+/rEqVKqlv376KiIhIk3OT0rgmTJigLl26yNvbW8OHD5ckXb9+XSNHjlSNGjXk5eWltm3bavfu3Tbb7tq1S23btpWvr6+qVKmid99919pDasiQIVq9erXOnz/P0HAA6WrBggVydHTUmDFjbJJSCRo2bKgWLVrYrPPw8FBQUJACAgLk7e2toKAgSQ+/p5CkGzduaOjQoapataqqVKmiTz75xOZzKfFQvpTcLzysnVq1apWGDh0qSXrllVc0ZMgQu89V7ty5JcmmDU1J22GxWLRkyRI1a9ZM3t7eql27tqZOnaqYmBhrmQfdj+zdu9c6cqZz586PZZgjMid6TCFDWLRokSpVqqSJEyfqxo0bGj9+vAYPHqxly5ZJuts4dO3aVdWqVdPMmTN148YNzZo1S507d9aKFSvk6uqqY8eO6c0331SjRo00Y8YMGYah9evXKygoSCVKlNCrr75qPV5gYKCGDx+uHDlyyMfHJ9m4DMOwGYtusVh0/vx5zZkzR//9959ee+0162ejR4/WqlWr1LNnT/n6+uq3337ThAkTdPPmTfXu3VuSFBQUpDlz5uitt95StWrV9PPPP+v9999PdNwLFy5o586dmjFjhq5fv67cuXNr7ty5mjFjhjp27KihQ4fqzz//VGBgoP79919NmDBBkvThhx/q6tWrGjNmjHLkyKG1a9dq8ODBeuaZZ1StWjXNnz9fS5cu1eDBg/X888/r0KFDmjFjhrJkyaJ+/foliuPEiRNq27atihcvro8++khZsmTRokWL1KVLF4WEhKhq1arWsqNGjVKrVq306aef6vDhw5oxY4by5s2rgQMHprAWAEBiKb0OG4ahnj176saNG/rggw9UoEABhYWFaebMmRo1apQ+//xz6z62bt2qkiVL6qOPPpJhGJo8ebL69u2r7du3y8nJSb///ru6deumatWqafbs2Tp37pwGDBhgE1d0dLTat2+vq1evql+/fipSpIi2bdum4cOH68qVK3rnnXesZUNCQlSzZk3NmDFDR48e1bRp0/T777+rUKFCGjt2rM6dO6fx48erQIECDx32ER8fn+QcKQk3YfbEtWTJEnXt2lVvv/22smfPrpiYGHXp0kVXrlxR//79VahQIa1cuVLdu3fXggULVL16dZ09e1a9evVSq1atNGDAAN28eVPTp09Xjx499P3336tXr166du2a/vjjDwUFBWWqXtIAMrcffvhB1apVU/78+ZMtM3ny5ETrgoODNXDgQL344osqUqRIiu4p4uPj1b17d50/f16DBw9Wnjx5tGDBAh05ckSFChVK9vgpuV+QHtxO1a5dW++++64+++wzBQUFycPD44Hn5d52Iy4uTn///bc+/fRTVatWzdqjK6Vtx8iRI7V27Vq9/fbbqly5sv744w/NmTNHf/75pxYsWCAHB4cH3o+UL19eI0eO1Mcff6yRI0fKz8/vgbHjKWIAaahjx45Gx44dH1hm8ODBRp06dazLderUMerUqWPExcVZ1wUGBhru7u7GtWvXDMMwjNdff91o2rSpTZlTp04ZZcuWNRYvXmwYhmGsXr3a6N69u2GxWKxlLBaLUalSJWPEiBGGYRjG2bNnDXd3d2PYsGEp+i7u7u6J/jw8PIxmzZoZmzdvtonFw8PDmDt3rs0+ZsyYYXh5eRnXrl0z/vvvP8Pb29sYO3asTZkRI0YY7u7uxp49ewzDMIzZs2cb7u7uxm+//WYtc/PmTcPb29sYOXKkzbbffvut4e7ubvz111+GYRhG+fLljc8++8zm+0+aNMkIDQ01DMMwunXrZnTt2tVmH1999ZWxZs0am/OzcuVKwzAM47333jP8/PyMW7duWcvfuXPHaNiwodGqVSubbT744AOb/Xbq1Mlo2rTpA88xADyIPdfhixcvGp06dbK5dhqGYYwdO9YoX768zT59fHxsrmurV6823N3djSNHjhiGYRh9+/Y1Xn75ZSM2NtZaZuPGjYa7u7sxe/ZswzAMY8mSJYa7u7vxv//9z+Z4w4YNM7y8vIyIiAjDMO62cS+99JJx584da5lGjRoZvr6+xs2bN63revbsaTRv3vyB5yOpc5Hwd/nyZbvjqlevnk2ZZcuWGe7u7sbBgwet6+Lj440OHToYAQEBhmEYxoYNGwx3d3fj4sWL1jKHDh0ypk+fbj2n97fzAPC4Xb9+3XB3dzcmTZqU6LM7d+7Y/N17P+Hu7m506dLFpnxK7il+/PFHw93d3di5c6e1zH///Wf4+fnZXP/uvTdKyf1CwjYPa6dWrlxpuLu7G2fPnk32nCT8P3pSf1WrVjXCwsKsZVPSdhw/ftxwd3dPFP+aNWsMd3d3Y8eOHYZhPPx+ZM+ePTb3PoBhGAY9ppAheHl5ycnJybr8zDPPSJKioqLk6uqqQ4cO6a233rJ5cv7888+rZMmS2rVrlzp06KAWLVqoRYsW1kn9zpw5oz///FMWi8U6VC1B2bJlUxSXp6enxowZI0m6fPmyZs6cqTt37mjmzJkqUaKEtdyePXtkGIbq1q1r8yS7bt26+uyzzxQaGqps2bIpOjpajRo1sjlG06ZNrT3DkovxwIEDio6OTnL/0t1hFaVLl5afn58CAwP1xx9/6KWXXlKtWrU0ePBga3k/Pz9NmzZN7du3V926dVW7dm117Ngx2e+/b98+1alTRzly5LCuc3Z21quvvmrtrZDg/jlPnnnmmQe+KQQAUiKl1+HChQtr0aJFMgxD586d05kzZ3Tq1Cn973//U2xsrM0+S5UqZXNdK1y4sKS7bY50961yderUUZYsWaxlGjRoYNNO7du3T0WKFEn0NrzmzZtrxYoVOnTokHXuK29vb5thJQUKFFC2bNmUM2dO67o8efLor7/+euj5aNu2rdq2bZtofd68ee2O6/62cPfu3SpYsKA8PT1t2po6depoypQpunHjhnx8fJQ1a1a1bt1ajRo10ssvvyw/Pz95e3s/NHYAeFzuH0KX4MyZM2rQoIHNuiJFimj79u3W5fuvhSm5p9i/f7+yZMmil156ybpdtmzZVKtWLf32229JxpKS+4V69epJeng7ZY8+ffpYJz+Pi4vTv//+q0WLFqldu3b66quv5OnpmaK2I+H/6+8dhZKwPHToUO3du9c6l9eD7keApJCYQobg5uZms5zwdqH4+HjdvHlT8fHxmj9/vubPn59o26xZs0q62wV17NixWrt2reLi4lS0aFH5+vrK2dlZhmHYbJMtW7YUxZU9e3Z5eXlZl318fNS8eXN169ZNq1atUr58+STdnZNDSnyhTnDp0iXrWO6EbRIk1904e/bs1v9O2H9yr9++fPmyJGnGjBkKDg7W5s2btXXrVjk6OqpGjRr6+OOPVaRIEXXv3l3Zs2fXypUrNXXqVH3yyScqXbq0PvroI1WrVi3Rfm/cuKECBQokWl+gQAEZhqHbt29b1yX1b3j/eQcAe6X0OixJ69at0/Tp0/Xvv/8qT548Klu2rFxdXRPt80FtjnT32peQ6Eng7Oxss+7GjRsqWLBgon0nXDNv3rxpXXfvzUWClLZD9ytUqJDN+bifPXHdH8P169cVHh4uT0/PJPcdHh6uUqVKafHixZo3b55WrFihRYsWKVeuXGrfvr3ef//9dHvTFQDcK2/evMqWLVuih6LPPvuszTxJc+bMSfQQ4P5rYUruKW7cuKE8efIkuuYldf1NkJL7hQQPa6fsUaRIEZt2w9fXV7Vq1bLOVxscHJyitiNhztv7yyW0j7du3ZL08PsRICkkppDhZc+eXQ4ODnrzzTeTvJAnXLjHjx+vrVu3aubMmapRo4a1kalevXqaxVKgQAGNHDlS7733nsaPH69p06ZJknLlyiVJWrhwoU1CKcFzzz2n06dPS5KuXr1q85T//gnck5Kw/6lTp6p48eJJxiVJOXPm1IcffqgPP/xQp06d0g8//KBPP/1UY8aM0bx58+To6KgOHTqoQ4cOunr1qnbu3Kng4GD17dtXu3btSrTf3Llz68qVK4nWh4eHS7r7PwEJSTEASA/JXYf379+vwYMHq1OnTnrrrbesT5enTJli88KGlMiTJ0+ia59hGNb/KZfuXh/PnDmTaNt7r49mSE1cOXPmVPHixTV16tQkPy9atKgkWScIjo2NVWhoqJYtW6bg4GCVKVNGjRs3ToNvAQD2q1u3rn788Ufdvn3b+kDAxcXFJimTJ0+eh+4nJfcUefPmVUREhCwWi01v2oTkU1JScr+QXrJnz64SJUpY24uUtB0Jiafw8HCbBNOdO3cUERFhbV8edj8CJIW38iHDy5Ejh8qVK6dTp07Jy8vL+le6dGkFBgZa32YXGhoqPz8/1atXz9qAHD16VNeuXXukpwvJadSokV566SVt2LBB+/btkyRVrlxZkhQREWET47Vr1zRr1ixdv35dZcqUUc6cOfX999/b7O+777576DF9fHyUJUsWXbp0yWb/zs7Omj59us6dO6fz58+rVq1a2rJliySpRIkSevvtt1WjRg1duHBBktSuXTuNGzdO0t2eWgEBAerQoYNu3rxp0/spQZUqVawNfAKLxaKNGzfKy8tLLi4uj3AGASB1kroOHzhwQPHx8erbt681KWWxWKxv07OnHahevbp++uknmyETP//8s82w8CpVquj8+fM6cOCAzbbr1q1TlixZTBvalpq4qlatqn///Vf58+e3aWt27dqlBQsWyMnJSV9++aXq1Kmj2NhYubi4qHr16ho7dqwkWduahCf7AJCeevToobi4OH300UeJhnBLd3tCnT179qH7Sck9RfXq1RUXF6dt27ZZt4uNjU3yQW+ClNwvpFRqr7O3bt3S6dOnVaxYMUkpazsSXnq0ceNGmzIbN26UxWJRpUqVUnQ/cm8iD0hAjymkuYsXL+rLL79MtN7d3V01atR4pH0OGDBAPXr00MCBA9W8eXNZLBaFhITo0KFD6tWrl6S7T3A3b96spUuXqmTJkjp27Jg+++wzOTg4PNJ47AcZNmyYmjdvrnHjxmn16tXy8PBQ8+bNNWLECJ0/f17ly5fX6dOnNWPGDBUtWlTFixeXk5OTunfvrtmzZ8vNzU1Vq1bVvn37tHTpUkkPbmDy5s2r7t27a9asWbp9+7b8/Px06dIlzZo1Sw4ODtak1zPPPKNx48bp9u3beuGFF3T06FHt3LlTPXv2lHS30QkJCVGBAgXk6+urS5cu6YsvvlDVqlWVL18+RUZG2hy3T58++umnn9S5c2f16NFDWbJk0eLFi3X27FktWLAgTc8pANjj/utwQsLl448/VqtWrXTjxg0tWbJEx44dkyRFRkYmOaQuKb1799a2bdv01ltvqXv37rp27ZpmzpxpM+dUQECAvv76a/Xu3Vv9+vVT0aJFtX37dq1cuVJ9+vSxPhlPb6mJKyAgQIsXL1bXrl31zjvv6Nlnn9Wvv/6q+fPnq2PHjsqSJYuqVaumqVOnqnfv3urYsaOcnJz0zTffyMXFRXXq1JF0t1fAlStXtHPnTpUtW/aBb6gCgLTi4eGhTz75REOHDlVAQIBat24tDw8PxcXF6cCBA1qxYoWuXLmi7t27P3A/KbmnqF69uvz9/fXRRx/p6tWrKlKkiBYtWqRr164lO01HSu4XUirhWv7999/r5ZdfVsmSJZMt+88//+jgwYPW5StXrmjBggW6ffu29VykpO3IlSuXWrZsqdmzZysqKkpVqlTRn3/+qaCgIPn5+emll16So6PjQ+9HEuZX3LFjh3Lnzq0yZcqk+HvjyUViCmnun3/+0cSJExOtb9269SMnpvz9/fX5558rKChI/fr1U5YsWeTp6akvvvjCOun2kCFDrBPixsbGqmjRonr33Xd14sQJbd++XRaLJTVfy0aJEiXUqVMnhYSEaOnSperYsaMmTpyouXPn6ptvvtHFixeVP39+NWnSRO+//771yUDPnj1lGIaWLVumzz//XD4+Pvrggw80ceLEh8438v7776tgwYL6+uuvtWDBAuXOnVvVq1fXgAEDrBf4oKAgTZ8+XbNmzVJERISeffZZ9enTxzo31XvvvScXFxetXLlSc+bMUc6cOVW3bl0NHDgwyWOWLl1aX3/9taZPn66hQ4fKwcFB3t7eWrRokfWpDwCYIanr8MiRI/XFF19oy5YtKlCggPz8/BQUFKTevXsrNDTUOun3wxQvXlyLFy/WpEmT1L9/f+XPn1+DBw/WpEmTrGXc3Nz01Vdfadq0ada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      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3)\n",
    "fig.suptitle(\"Max Root Average Squared Error Difference by Sensitive Variable\")\n",
    "\n",
    "sns.barplot(y=md_rase_lr, x=[\"Race\", \"Sex\"], ax=axes[0]).set(title=\"Linear Regression\")\n",
    "sns.barplot(y=md_rase_rfr, x=[\"Race\", \"Sex\"], ax=axes[1]).set(title=\"Random Forest\")\n",
    "sns.barplot(y=md_rase_gbr, x=[\"Race\", \"Sex\"], ax=axes[2]).set(title=\"Gradient Boost\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Prediction Bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-07T13:48:22.235991800Z",
     "start_time": "2023-07-07T13:48:21.456589800Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vLy/NmTNHe/fuVcuWLXXgwAHGggOAA7JFrQAAZG/UCgDI2dI1x9SmTZsUFhamV155RT/++KNiY2Pl4uKi999/Xzdv3lTbtm2t2g5jwQEg+7JVrQAAZF/UCgBAuq6Ymj59uoYPH66BAweaJs/t3LmzxowZo08++cSmAQIAHBO1AgCQGmoFACBdjalz586pQoUKSZYHBAToypUrGY0JAJANUCsAAKmhVgAA0tWYKlWqlHbs2JFk+dq1a1WqVKkMBwUAcHzUCgBAaqgVAIB0zTEVFhamHj166JdfftG9e/c0e/ZsnTt3TocPH9asWbNsHSMAwAFRKwAAqaFWAADSdcVUpUqVtHHjRpUsWVKhoaG6ceOGKlSooG+//VbVqlWzdYwAAAdErQAApIZaAQBI1xVTPXv21IABA/T222/bOh4AQDZBrQAApIZaAQBI1xVTv//+u1xc0tXTAgDkENQKAEBqqBUAgHRVgbZt26pfv35q06aNihQpIjc3N4vXK1eubJPgAACOi1oBAEgNtQIAkK7G1MyZMyVJQ4cOTfKawWDQ0aNHMxYVAMDhUSsAAKmhVgAA0tWYOnbsmK3jAABkM9QKAEBqqBUAgHQP6L5z547Wr1+v06dPKy4uTiVKlFDTpk2VP39+G4YHAHBk1AoAQGqoFQCQs6Vr8vMTJ07oueee06xZs3Tp0iVdunRJc+bMUZMmTXTq1ClbxwgAcEDUCgBAaqgVAIB0XTEVHh6uGjVqaNSoUaa7aMTGxmrw4MEaM2aMFi5caNMgAQCOh1oBAEgNtQIAkK4rpvbv368333zT4tauLi4uevPNN7Vv3z6bBQcAcFzUCgBAaqgVAIB0NaYKFSqkv/76K8nyv/76S56enhkOCgDg+KgVAIDUUCsAAOkaytemTRsNHjxYb7/9tgICAiRJBw4c0LRp0/Tyyy/bNEAAgGOyVa3YvHmzevfubbGsUaNGmjZtmo4cOaJhw4bpxIkTKlWqlEaMGKHy5cvbdD8AAJmH4woAQLoaU126dFF0dLQmTpyomzdvSpJ8fHzUsWNHde7c2aYBAgAck61qxalTp1SvXj2NGjXKtMzNzU1RUVHq1q2bmjdvrnHjxmnZsmXq3r27Nm/eLA8PD5vvDwDA9jiuAACkqzFlMBjUp08fdevWTbdv35abm5tu3bqlIkWK2Do+AICDslWtOH36tEqXLq1ChQpZLF+1apXc3Nz03nvvyWAwaNCgQdq+fbs2btyoli1b2nJXAACZhOMKAEC65pi6cOGCWrdurWnTpsnb21teXl5q1aqVXn31VV2+fNnWMQIAHJCtasXp06dVvHjxJMsPHDig4OBgGQwGSQkHNxUrVtT+/fvTHGtcXFyyj+wqpf192CO7Ihdm5MKMXJhl9v5yXAEASNcVU8OHD1fRokUtLq/99ttvNWzYMI0YMUKzZs2yWYAAAMdki1phNBp19uxZ/fTTT5ozZ47i4uLUuHFj9e3bVxERESpVqpTF+t7e3jp58mSaYz106FCSZe7u7ipbtmyat+UIjh8/rujoaKvXJxdm5MKMXJiRi/Sz1XEF8xECgONKV2Nq7969+vLLL+Xt7W1aVqBAAfXr10+tWrWyWXAAAMdli1px6dIlRUdHy9XVVVOnTtWFCxc0evRo3blzx7T8fq6uroqJiUlzrP7+/nJ2dk7z+xyVn5+fvUPIMsiFGbkwIxdmyeUiLi4u2YZ+etjquIL5CAHAcaWrMVWgQAEdOXJETz31lMXyM2fOyMvLyyaBAQAcmy1qRdGiRbV7927ly5dPBoNBZcqUUXx8vN59912FhIQkaULFxMQod+7caY7V2dk5RzWmctK+poZcmJELM3Jhltm5sNVxBfMRAoDjSldjqn379hoyZIhOnz6tcuXKSZKOHTumTz/9lLtnAAAk2a5W5M+f3+J5yZIldffuXRUqVEiRkZEWr0VGRsrX1zfDsQMAHg1b1YrTp0+revXqSZY/bD5CGlMAkDWkqzHVqVMnubu7a+XKlZo/f75cXFxUrFgxhYWF6YUXXrB6O4wFB4Dsyxa1YseOHXrnnXf0448/yt3dXZJ09OhR5c+fX8HBwZo3b56MRqMMBoOMRqN+//139ejRIzN3CwBgQ7aoFY9qPsKUJn3PrlfYpWeSe3JhRi7MyIVZTspFWvKTrsaUJLVp00Zt2rRJ79slMRYcALK7jNaKoKAgubm5afDgwerVq5fOnz+v8ePHq2vXrmrcuLEmTZqk8PBwtWnTRsuXL1d0dLSaNGliwz0AAGS2jNaKRzUfITfKeDhyYUYuzMiFGblIWZoaU1FRUfrxxx9Vp04deXp6SpI+++wz7dq1SwUKFNAbb7yhMmXKWL09xoIDQPZjy1rh5eWlBQsWaMyYMWrVqpU8PT3Vpk0bde3aVQaDQXPmzNGwYcO0cuVK+fn5ae7cuZzAAAAHYMta8ajmI+RGGTkXuTAjF2bkwiyjN8qwujH1119/qV27dvrvv/+0bt06eXp6atSoUfr888/VpEkTubq6ql27dpo3b54qVqxo1TYZCw4A2Utm1IpnnnlGn3zySbKvBQQEaO3atbbcBQBAJsuMWvEo5iPkRhk5F7kwIxdm5MIso7mwujE1efJkBQYGatKkSXJ1ddXVq1e1fPlyPf/885o4caIk6emnn9ZHH32kzz77LNXtMRY8czDO1YxcmJELM3JhltGx4Mmxda0AAGQ/tq4VzEcIAI7N6sbUrl279Omnn5rGaG/btk3x8fF66aWXTOvUqFFD06ZNs2p7jAXPHIxzNSMXZuTCjFyYZXQseHJsXSsAANmPrWsF8xECgGOzujEVHR2tPHnymJ7v2rVLuXPnVuXKlc0bc7F+yirGgmcOxrmakQszcmFGLswyOhY8ObauFQCA7MfWtYL5CAHAsVn9i1+qVCkdPHhQTzzxhKKiorR9+3bVrFnT4sqmLVu2qGTJklZ/OGPBbS8n7WtqyIUZuTAjF2aZkYvMqBUAgOwlM2oF8xECgOOyujHVuXNnDR06VAcOHNCBAwcUHR2trl27SpKuXLmi7777TjNmzNDQoUOt2h5jwQEg+7F1rQAAZD/UCgDA/axuTDVr1ky5c+fWl19+KV9fXy1cuFCBgYGSpLlz5+qrr75S3759rb5rHmPBASD7sXWtAABkP9QKAMD90jTRR4MGDdSgQYMkywcMGKBBgwbJycnJYvnXX3+t0NDQZMdwMxYcALInW9YKAED2RK0AACSyyQy0KRWIoUOHKjAwMMXXGQsOADlHemsFACDnoFYAQM7jlPoq6Wc0GjNz8wCAbIBaAQBIDbUCALKvTG1MAQAAAAAAACmhMQUAAAAAAAC7oDEFAAAAAAAAu6AxBQAAAAAAALugMQUAAAAAAAC7yNTGVI0aNeTu7p6ZHwEAcHDUCgBAaqgVAJB9uaT3jT/99JOOHTumu3fvJrl9a+/evSVJ06dPz1h0AACHRq0AAKSGWgEAOVu6GlPjx4/XJ598omeffVZeXl4WrxkMBpsEBgBwbNQKAEBqqBUAgHQ1pr744gtNmjRJTZs2tXU8AIBsgloBAEgNtQIAkK45ppydnVWuXDlbxwIAyEaoFQCA1FArAADpaky1a9dO06ZN0927d20dDwAgm6BWAABSQ60AAFg9lC80NNQ0zttoNOrSpUvatGmTfHx85ORk2d/6/vvvbRslAMAhUCsAAKmhVgAA7md1Y6pPnz6ZGQcAIBugVgAAUkOtAADcz+rG1EsvvWT68/Tp09WlSxe5u7tbrHP79m1u5QoAORi1AgCQGmoFAOB+Vjemzpw5o2vXrkmSZsyYoWeffVb58uWzWOfEiRNavny53n//fdtGCQBwCNQKAEBqqBUAgPtZ3Zi6evWqOnbsaHreu3fvJOu4u7urQ4cONgkMAOB4MrtWdOvWTQULFtS4ceMkSUeOHNGwYcN04sQJlSpVSiNGjFD58uXTtW0AwKPBcQUA4H5WN6aqVq2qY8eOSUqYsHDVqlUqWLBgpgUGAHA8mVkrvvnmG23bts00BCQqKkrdunVT8+bNNW7cOC1btkzdu3fX5s2b5eHhYZPPBADYHscVAID7OaW+SlJbt25NsXhcvXo1QwEBALIHW9aKGzduaPz48fL39zct+/bbb+Xm5qb33ntPJUuW1KBBg+Tp6amNGzdmKG4AwKPDcQUAwOorpu535swZTZw4UadOnVJcXJykhFu9xsTE6J9//tGRI0dsGiQAwPHYslZ8+OGHeuGFFywOUg4cOKDg4GDTLccNBoMqVqyo/fv3q2XLlrbdGQBApuC4AgCQriumhgwZon/++UddunRRZGSkOnfurMaNG+v27dsKDw+3dYwAAAdkq1qxa9cu7dmzRz179rRYHhERIV9fX4tl3t7eunz5cppjjYuLS/aRXaW0vw97ZFfkwoxcmJELs8ze38w4rujWrZvFpOlHjhzRyy+/rMDAQLVq1UqHDx+2VfgAABtI1xVThw4d0ooVK1SmTBmtW7dOJUqU0Ouvv66nn35aq1atsrgFLAAgZ7JFrbh7966GDRumoUOHKnfu3BavRUdHy9XV1WKZq6urYmJi0hXrg9zd3VW2bNk0b8sRHD9+XNHR0VavTy7MyIUZuTAjF+ln6+MK5iMEAMeTrsaUi4uL8uTJI0kqUaKEjh49qmrVqql69er68MMPbRogAMAx2aJWTJ8+XeXLl1etWrWSvObm5pakCRUTE5OkgWUNf39/OTs7p/l9jsrPz8/eIWQZ5MKMXJiRC7PkchEXF5dsQz89bHlckdp8hAaDQYMGDdL27du1ceNGhn0DQBaRrqF8QUFBWrBgge7cuaPy5ctr69atMhqNOnz4sNzc3NIVCJfcAkD2Yota8c0332jLli0KCgpSUFCQvvrqK3311VcKCgpS4cKFFRkZabF+ZGRkkuF91nB2dk72kV2ltL8Pe2RX5MKMXJiRC7PM3l9bHlckzkdYqlQp07KHzUcIAMga0tWYCgsL008//aTPP/9cL7zwgq5du6aQkBD1799fbdu2TfP2Ei+5TZR4yW2lSpW0Zs0aBQUFqXv37oqKikpPuAAAO7BFrVi8eLG++uorrVu3TuvWrVNoaKhCQ0O1bt06BQYGat++fTIajZISJsv9/fffFRgYmJm7BQCwIVsdVzAfoe0x15oZuTAjF2bkwiyj+5uuoXylSpXSpk2bdOfOHbm7u2v16tX69ddflT9/flWoUCFN2+KSWwDInmxRK4oWLWrx3NPTU5JUrFgxeXt7a9KkSQoPD1ebNm20fPlyRUdHq0mTJrbeFQBAJrFFrWA+wszBXGtm5MKMXJiRC7OMzkdodWPq0qVLyS6/fv26JKl06dKm9YoUKWJ1ANwCHACyj8yqFcnx8vLSnDlzNGzYMK1cuVJ+fn6aO3cuk9kCQBZn61rBfISZg7nWzMiFGbkwIxdmGZ2P0OrGVGhoqKlRJMk0dCKRwWCQ0WiUwWDQ0aNHrdpm4iW3X331lYYPH25aHhERYTE2XEq45PbkyZPWhmuS0uVj2bWopOfyQHJhRi7MyIVZTspFRi8xzoxacb9x48ZZPA8ICNDatWvTFywAwC5sXSu++eYbRUZGKigoSJJMjajvvvtOzZo1s/l8hDlFTtrX1JALM3JhRi7MMpoLqxtTAQEB+uOPP+Tv76/nnntOtWvXlru7e7o/mEtuMweXE5qRCzNyYUYuzDLjFuC2rhUAgOzH1rVi8eLFio2NNT2fOHGiJOmdd97Rb7/9pnnz5pkaXYnzEfbo0SPD+wEAsA2rG1MrV67U1atX9f3332vLli2aOXOmKlWqpIYNGyo0NFQFChRI0wdzyW3m4HJCM3JhRi7MyIVZZtwC3Na1AgCQ/di6VjAfIQA4tjRNfu7r66vXXntNr732mm7fvq0ff/xRW7Zs0bhx4/Tss8+qYcOGatCggVVjwbnkNnPkpH1NDbkwIxdm5MIss3Jhy1oBAMieHlWtYD5CAMj60nVXPinhR75Zs2Zq1qyZYmJitHjxYn300UcaO3asVWPBueQWALK/jNYKAED2Z+tawXyEAOBY0t2YiouL06+//qqtW7dq69atioyMVLVq1VS/fn2r3s8ltwCQ/WW0VgAAsj9qBQDkbGlqTN2+fVvbtm3T999/rx07dsjZ2Vn16tVTWFiYatSoYbMJbrnkFgAc16OqFQAAx0WtAAAksrox1bFjR+3Zs0dFixZVaGioZs2apeDgYItbvWYEl9wCgOPL7FoBAHB81AoAwP2sbkz98ssvcnFx0b179/Tdd99p06ZNKa77/fff2yQ4AIBjoVYAAFJDrQAA3M/qxtTYsWMzMw4AQDZArQAApIZaAQC4n9WNqZdeeinNG+/WrZtGjx4tX1/fNL8XAOB4qBUAgNRQKwAA93PKzI3/9ttvunv3bmZ+BADAwVErAACpoVYAQPaVqY0pAAAAAAAAICU0pgAAAAAAAGAXNKYAAAAAAABgFzSmAAAAAAAAYBc0pgAAAAAAAGAXNKYAAAAAAABgF5namPrwww/l4+OTmR8BAHBw1AoAQGqoFQCQfblYu2JoaKgMBoNV637//feSpOeeey59UQEAHBK1AgCQGmoFAOB+Vjem+vTpY/rzX3/9pc8++0yvvfaa/P39lStXLh05ckRLlixRhw4dMiVQAEDWR60AAKSGWgEAuJ/VjamXXnrJ9OeWLVsqPDxcTZo0MS2rX7++ypQpo6lTp6pnz562jRIA4BCoFQCA1FArAAD3S9ccU2fPnlXp0qWTLH/yySd18eLFDAcFAHB81AoAQGqoFQCAdDWmgoODNWbMGF25csW07Pz58xo9erRq1apls+AAAI6LWgEASA21AgCQrsbUmDFj9N9//6lu3bqqWrWqqlSpoueee065cuXSqFGjbB0jAMABUSsAAKmhVgAArJ5j6n6+vr5avny5Tp06pVOnTkmSnnnmGZUsWdKmwQEAHBe1AgCQGmoFACBdV0xJUlxcnC5cuKDLly+revXqun37tv79919bxgYAcHC2qBXnzp1Tly5dFBQUpLp162r+/Pmm186fP6+OHTuqQoUKatq0qX766Sdb7wIAIJNxXAEAOVu6rpj6+++/1blzZ928eVM3b95U/fr1NX/+fO3bt08LFiyQn5+freMEADgYW9SK+Ph4devWTf7+/lq7dq3OnTun/v37q3DhwmrWrJl69eql0qVLa/Xq1dqyZYt69+6tb7/9VkWKFHkEewgAyCiOKwAA6bpiauTIkapUqZJ27NghV1dXSdLkyZNVvXp1jR492qYBAgAcky1qRWRkpMqUKaPhw4erePHiqlOnjqpVq6a9e/fql19+0fnz5zVy5EiVLFlS3bt3V4UKFbR69erM3C0AgA1xXAEASFdjas+ePercubOcnZ1Ny3LlyqWePXvq8OHDNgsOAOC4bFErfH19NXXqVHl5ecloNGrv3r367bffFBISogMHDqhs2bLy8PAwrR8cHKz9+/fbelcAAJnEVscVDPsGAMeVrsZU7ty5de3atSTLz549Ky8vrwwHBQBwfLauFaGhoWrbtq2CgoLUqFEjRUREyNfX12Idb29vXb58Oc3bjouLS/aRXaW0vw97ZFfkwoxcmJELs8zeX1vUisRh3wUKFNDatWs1YsQIzZo1S1999ZWMRqN69eolHx8frV69Wi+88IJ69+6tS5cu2WwfAAAZk645ptq0aaOhQ4fqvffek5RQOH799VdNmTJFL7/8stXbOXfunEaOHKnff/9d+fLlU7t27dS1a1dJCWc2hgwZov3796tIkSL64IMPVLNmzfSECwCwA1vVikTTpk1TZGSkhg8frrFjxyo6Oto07CORq6urYmJi0rztQ4cOJVnm7u6usmXLpnlbjuD48eOKjo62en1yYUYuzMiFGblIP1vUivuHfXt5eal48eKmYd8+Pj46f/68li9fLg8PD5UsWVK7du3S6tWr1adPn0zbLwCA9dLVmOrVq5fy5s2r4cOHKzo6Wt26dZO3t7c6duyoLl26WLUNJrQFgOzNFrXifv7+/pKku3fv6p133lGrVq2SHCzFxMQod+7c6dr2/cNIsjsmEzYjF2bkwoxcmCWXi7i4uGQb+ulhi1qROOxbkoxGo37//Xf99ttvGjZsmE2Hfad0pVh2rR/puTKOXJiRCzNyYZaTcpGW/KSrMXXp0iW9/vrrat++vaKiohQXF6c8efIoLi5OR48eVbly5VLdBmc2ACB7s1Wt2L9/vxo0aGBaVqpUKd27d0+FChXSmTNnkqz/4PA+azg7O2fb/ygkJyfta2rIhRm5MCMXZpmdC1vUivuFhobq0qVLqlevnho1aqQxY8bYbNg3V9c+HLkwIxdm5MKMXKQsXY2p+vXra+fOnSpYsKDF2YcLFy6obdu2OnDgQKrbeFRnNgAA9mGLWnHhwgX17t1b27ZtU+HChSVJhw8fVsGCBRUcHKyFCxfqzp07pquk9u7dq+Dg4MzZIQCAzdmiVtwvM4d9c3VtzkUuzMiFGbkwy+jVtVY3pr744gvNnj1bUkIjqVWrVnJyspw7/datWypZsqS1mzTJzDMbXHKbOnJhRi7MyIVZTspFRie0tXWt8Pf3V7ly5fTBBx8oLCxMFy9e1IQJE9SjRw+FhITo8ccfV1hYmHr27KkffvhBBw8e1NixYzO0DwCAzJWZxxWZOeybq2tzLnJhRi7MyIVZRnNhdWPqxRdfVK5cuRQfH68PPvhAnTp1Up48eUyvGwwGubu7q2rVqmkOggltbYfLCc3IhRm5MCMXZpkxoa2ta4Wzs7NmzpypUaNG6dVXX5W7u7vat2+vN954QwaDQTNnztSgQYPUsmVLFStWTDNmzGAuQgDI4mxdKx7VsG8AQOawujGVK1cuvfjii5KkJ554QhUrVtTNmzfl7e0tSdq3b5/KlSuXpKFkDSa0tR0uJzQjF2bkwoxcmGXGhLaZUSsKFy6s6dOnJ/tasWLFtGTJknTHCwB49GxdKxj2DQCOzSn1VZLKkyeP6tevrwULFpiWvfPOO2rcuLFOnjxp1TYiIyO1ZcsWi2X3n9mIjIxMsn5GJrR98JFdpbS/D3tkV+TCjFyYkQuzzN5fW9QKAED2Zotacf+w71OnTmnbtm3JDvs+efKk5s6dq4MHD6p169aZtUsAgDRKV2Nq5MiRatiwofr162datnnzZoWGhmrkyJFWbSPxzMaVK1dMy+4/s/HHH3/ozp07ptf27t2rwMDA9IQLALADW9QKAED2ZotakTjs293dXa+++qoGDRpkGvad+FpERIRatmyp9evXM+wbALKYdN2V7+jRoxo/frxy5cplWubk5KQ33nhDL7zwglXbYEJbAMjebFErAADZm61qBcO+AcBxpeuKqccff1y7du1Ksvz333+Xj4+PVdvgzAYAZG+2qBUAgOyNWgEASNcVUz169NCgQYO0b98+lS9fXpJ07NgxrV+/XsOGDbN6O5zZAIDsy1a1AgCQfVErAADpaky98MILKliwoFauXKlly5bJxcVFxYoV04IFC1SpUiVbxwgAcEDUCgBAaqgVAIB0NaYkqVatWqpVq5YtYwEAZDPUCgBAaqgVAJCzWd2YCgsL06BBg+Tl5aWwsLCHrssk5QCQM1ErAACpoVYAAO6XrsnPAQAAAAAAgIyy+oqp+89WcOYCAJAcagUAIDXUCgDA/axuTKV097zk9O7dO13BAAAcG7UCAJAaagUA4H5WN6Z2795t+nN8fLz27t0rX19flSlTRrly5dKxY8f0999/q3bt2pkSKAAg66NWAABSQ60AANzP6sbU4sWLTX8eNWqUSpYsqaFDh8rFJWETRqNR48aNU2RkpO2jBAA4BGoFACA11AoAwP2sbkzdb82aNVqzZo2peEiSwWBQmzZt9NJLL9ksOACA46JWAABSQ60AAKTrrny+vr7asWNHkuWbNm3Sk08+meGgAACOj1oBAEgNtQIAkK4rpt555x3169dPP/zwg5599llJ0qFDh3T48GHNmjXLpgECABwTtQIAkBpqBQAgXVdMNWz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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3)\n",
    "fig.suptitle(\"Predicted Math Score by Race\")\n",
    "\n",
    "sns.barplot(\n",
    "    data=gm_lr[gm_lr[\"_VARIABLE_\"] == \"Race\"],\n",
    "    x=\"LEVEL\",\n",
    "    y=\"Predicted_Math_Score\",\n",
    "    ax=axes[0],\n",
    ").set(title=\"Linear Regression\")\n",
    "sns.barplot(\n",
    "    data=gm_rfr[gm_rfr[\"_VARIABLE_\"] == \"Race\"],\n",
    "    x=\"LEVEL\",\n",
    "    y=\"Predicted_Math_Score\",\n",
    "    ax=axes[1],\n",
    ").set(title=\"Random Forest\")\n",
    "sns.barplot(\n",
    "    data=gm_gbr[gm_gbr[\"_VARIABLE_\"] == \"Race\"],\n",
    "    x=\"LEVEL\",\n",
    "    y=\"Predicted_Math_Score\",\n",
    "    ax=axes[2],\n",
    ").set(title=\"Gradient Boosting\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-07T13:48:22.723860200Z",
     "start_time": "2023-07-07T13:48:22.223046200Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3)\n",
    "fig.suptitle(\"Predicted Math Score by Sex\")\n",
    "\n",
    "sns.barplot(\n",
    "    data=gm_lr[gm_lr[\"_VARIABLE_\"] == \"Gender\"],\n",
    "    x=\"LEVEL\",\n",
    "    y=\"Predicted_Math_Score\",\n",
    "    ax=axes[0],\n",
    ").set(title=\"Decision Tree\")\n",
    "sns.barplot(\n",
    "    data=gm_rfr[gm_rfr[\"_VARIABLE_\"] == \"Gender\"],\n",
    "    x=\"LEVEL\",\n",
    "    y=\"Predicted_Math_Score\",\n",
    "    ax=axes[1],\n",
    ").set(title=\"Random Forest\")\n",
    "sns.barplot(\n",
    "    data=gm_gbr[gm_gbr[\"_VARIABLE_\"] == \"Gender\"],\n",
    "    x=\"LEVEL\",\n",
    "    y=\"Predicted_Math_Score\",\n",
    "    ax=axes[2],\n",
    ").set(title=\"Gradient Boosting\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-07T13:48:22.738388900Z",
     "start_time": "2023-07-07T13:48:22.716900400Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "md_pe_lr_race = md_lr[\n",
    "    (md_lr[\"Metric\"] == \"Predicted_Math_Score\") & (md_lr[\"_VARIABLE_\"] == \"Race\")\n",
    "][\"maxdiff\"]\n",
    "md_pe_lr_sex = md_lr[\n",
    "    (md_lr[\"Metric\"] == \"Predicted_Math_Score\") & (md_lr[\"_VARIABLE_\"] == \"Gender\")\n",
    "][\"maxdiff\"]\n",
    "md_pe_lr = [x.values[0] for x in [md_pe_lr_race, md_pe_lr_sex]]\n",
    "\n",
    "md_pe_rfr_race = md_rfr[\n",
    "    (md_rfr[\"Metric\"] == \"Predicted_Math_Score\") & (md_rfr[\"_VARIABLE_\"] == \"Race\")\n",
    "][\"maxdiff\"]\n",
    "md_pe_rfr_sex = md_rfr[\n",
    "    (md_rfr[\"Metric\"] == \"Predicted_Math_Score\") & (md_rfr[\"_VARIABLE_\"] == \"Gender\")\n",
    "][\"maxdiff\"]\n",
    "md_pe_rfr = [x.values[0] for x in [md_pe_rfr_race, md_pe_rfr_sex]]\n",
    "\n",
    "md_pe_gbr_race = md_gbr[\n",
    "    (md_gbr[\"Metric\"] == \"Predicted_Math_Score\") & (md_gbr[\"_VARIABLE_\"] == \"Race\")\n",
    "][\"maxdiff\"]\n",
    "md_pe_gbr_sex = md_gbr[\n",
    "    (md_gbr[\"Metric\"] == \"Predicted_Math_Score\") & (md_gbr[\"_VARIABLE_\"] == \"Gender\")\n",
    "][\"maxdiff\"]\n",
    "md_pe_gbr = [x.values[0] for x in [md_pe_gbr_race, md_pe_gbr_sex]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-07T13:48:23.220390900Z",
     "start_time": "2023-07-07T13:48:22.739648400Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3)\n",
    "fig.suptitle(\"Max Predicted Math Score Difference by Sensitive Variable\")\n",
    "\n",
    "sns.barplot(y=md_pe_lr, x=[\"Race\", \"Sex\"], ax=axes[0]).set(title=\"Linear Regression\")\n",
    "sns.barplot(y=md_pe_rfr, x=[\"Race\", \"Sex\"], ax=axes[1]).set(title=\"Random Forest\")\n",
    "sns.barplot(y=md_pe_gbr, x=[\"Race\", \"Sex\"], ax=axes[2]).set(title=\"Gradient Boost\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "***"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}