Space Titanic Fairness.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b7ab39ba",
   "metadata": {},
   "source": [
    "# Space Titanic Dataset from Kaggle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b13b85c1",
   "metadata": {},
   "source": [
    "Downloading and setting up the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5c163bae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "86747e30",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.utils import Bunch\n",
    "from sklearn.metrics import (\n",
    "    balanced_accuracy_score,\n",
    "    roc_auc_score,\n",
    "    accuracy_score,\n",
    "    recall_score,\n",
    "    confusion_matrix,\n",
    "    roc_auc_score,\n",
    "    roc_curve)\n",
    "from sklearn import set_config\n",
    "\n",
    "set_config(display=\"diagram\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0d09f346",
   "metadata": {},
   "outputs": [],
   "source": [
    "from fairlearn.metrics import (\n",
    "    MetricFrame,\n",
    "    true_positive_rate,\n",
    "    false_positive_rate,\n",
    "    false_negative_rate,\n",
    "    selection_rate,\n",
    "    count,\n",
    "    false_negative_rate_difference\n",
    ")\n",
    "\n",
    "from fairlearn.postprocessing import ThresholdOptimizer, plot_threshold_optimizer\n",
    "from fairlearn.postprocessing._interpolated_thresholder import InterpolatedThresholder\n",
    "from fairlearn.postprocessing._threshold_operation import ThresholdOperation\n",
    "from fairlearn.reductions import ExponentiatedGradient, EqualizedOdds, TruePositiveRateParity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37d16a39",
   "metadata": {},
   "source": [
    "### Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "592ccae7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "63e386b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>PassengerId</th>\n",
       "      <th>HomePlanet</th>\n",
       "      <th>CryoSleep</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Age</th>\n",
       "      <th>VIP</th>\n",
       "      <th>RoomService</th>\n",
       "      <th>FoodCourt</th>\n",
       "      <th>ShoppingMall</th>\n",
       "      <th>Spa</th>\n",
       "      <th>VRDeck</th>\n",
       "      <th>Name</th>\n",
       "      <th>Transported</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0001_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>B/0/P</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>39.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Maham Ofracculy</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0002_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>F/0/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>24.0</td>\n",
       "      <td>False</td>\n",
       "      <td>109.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Juanna Vines</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0003_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>A/0/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>58.0</td>\n",
       "      <td>True</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3576.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6715.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Altark Susent</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0003_02</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>A/0/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>33.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1283.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>3329.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>Solam Susent</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0004_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>F/1/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>16.0</td>\n",
       "      <td>False</td>\n",
       "      <td>303.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Willy Santantines</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PassengerId HomePlanet CryoSleep  Cabin  Destination   Age    VIP  \\\n",
       "0     0001_01     Europa     False  B/0/P  TRAPPIST-1e  39.0  False   \n",
       "1     0002_01      Earth     False  F/0/S  TRAPPIST-1e  24.0  False   \n",
       "2     0003_01     Europa     False  A/0/S  TRAPPIST-1e  58.0   True   \n",
       "3     0003_02     Europa     False  A/0/S  TRAPPIST-1e  33.0  False   \n",
       "4     0004_01      Earth     False  F/1/S  TRAPPIST-1e  16.0  False   \n",
       "\n",
       "   RoomService  FoodCourt  ShoppingMall     Spa  VRDeck               Name  \\\n",
       "0          0.0        0.0           0.0     0.0     0.0    Maham Ofracculy   \n",
       "1        109.0        9.0          25.0   549.0    44.0       Juanna Vines   \n",
       "2         43.0     3576.0           0.0  6715.0    49.0      Altark Susent   \n",
       "3          0.0     1283.0         371.0  3329.0   193.0       Solam Susent   \n",
       "4        303.0       70.0         151.0   565.0     2.0  Willy Santantines   \n",
       "\n",
       "   Transported  \n",
       "0        False  \n",
       "1         True  \n",
       "2        False  \n",
       "3        False  \n",
       "4         True  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b5c3a836",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HomePlanet</th>\n",
       "      <td>Earth</td>\n",
       "      <td>Europa</td>\n",
       "      <td>Mars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CryoSleep</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Destination</th>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>55 Cancri e</td>\n",
       "      <td>PSO J318.5-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIP</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transported</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       0            1              2\n",
       "HomePlanet         Earth       Europa           Mars\n",
       "CryoSleep          False         True               \n",
       "Destination  TRAPPIST-1e  55 Cancri e  PSO J318.5-22\n",
       "VIP                False         True               \n",
       "Transported         True        False               "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the values of all binary and categorical features\n",
    "categorical_values = {}\n",
    "for col in df:\n",
    "  if col not in {'PassengerId', 'Age',\n",
    "                 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Name', 'Cabin'}:\n",
    "    categorical_values[col] = pd.Series(df[col].value_counts().index.values)\n",
    "categorical_values_df = pd.DataFrame(categorical_values).fillna('')\n",
    "categorical_values_df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fe2debb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>RoomService</th>\n",
       "      <th>FoodCourt</th>\n",
       "      <th>ShoppingMall</th>\n",
       "      <th>Spa</th>\n",
       "      <th>VRDeck</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8514.000000</td>\n",
       "      <td>8512.000000</td>\n",
       "      <td>8510.000000</td>\n",
       "      <td>8485.000000</td>\n",
       "      <td>8510.000000</td>\n",
       "      <td>8505.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>28.827930</td>\n",
       "      <td>224.687617</td>\n",
       "      <td>458.077203</td>\n",
       "      <td>173.729169</td>\n",
       "      <td>311.138778</td>\n",
       "      <td>304.854791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.489021</td>\n",
       "      <td>666.717663</td>\n",
       "      <td>1611.489240</td>\n",
       "      <td>604.696458</td>\n",
       "      <td>1136.705535</td>\n",
       "      <td>1145.717189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>79.000000</td>\n",
       "      <td>14327.000000</td>\n",
       "      <td>29813.000000</td>\n",
       "      <td>23492.000000</td>\n",
       "      <td>22408.000000</td>\n",
       "      <td>24133.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age   RoomService     FoodCourt  ShoppingMall           Spa  \\\n",
       "count  8514.000000   8512.000000   8510.000000   8485.000000   8510.000000   \n",
       "mean     28.827930    224.687617    458.077203    173.729169    311.138778   \n",
       "std      14.489021    666.717663   1611.489240    604.696458   1136.705535   \n",
       "min       0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%      19.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%      27.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%      38.000000     47.000000     76.000000     27.000000     59.000000   \n",
       "max      79.000000  14327.000000  29813.000000  23492.000000  22408.000000   \n",
       "\n",
       "             VRDeck  \n",
       "count   8505.000000  \n",
       "mean     304.854791  \n",
       "std     1145.717189  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%       46.000000  \n",
       "max    24133.000000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1a6ec0e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8693 entries, 0 to 8692\n",
      "Data columns (total 12 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   HomePlanet    8492 non-null   object \n",
      " 1   CryoSleep     8476 non-null   object \n",
      " 2   Cabin         8494 non-null   object \n",
      " 3   Destination   8511 non-null   object \n",
      " 4   Age           8514 non-null   float64\n",
      " 5   VIP           8490 non-null   object \n",
      " 6   RoomService   8512 non-null   float64\n",
      " 7   FoodCourt     8510 non-null   float64\n",
      " 8   ShoppingMall  8485 non-null   float64\n",
      " 9   Spa           8510 non-null   float64\n",
      " 10  VRDeck        8505 non-null   float64\n",
      " 11  Transported   8693 non-null   bool   \n",
      "dtypes: bool(1), float64(6), object(5)\n",
      "memory usage: 755.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "4cb21cc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
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     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Transported.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "7884e237",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.HomePlanet.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bdd62e8",
   "metadata": {},
   "source": [
    "### Understanding the distribution of the columns that has numeric values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "0cfacfb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(5,1,  figsize=(10, 10))\n",
    "plt.subplots_adjust(top = 2)\n",
    "\n",
    "sns.histplot(df['Age'], color='b', bins=50, ax=ax[0]);\n",
    "sns.histplot(df['FoodCourt'], color='b', bins=50, ax=ax[1]);\n",
    "sns.histplot(df['ShoppingMall'], color='b', bins=50, ax=ax[2]);\n",
    "sns.histplot(df['Spa'], color='b', bins=50, ax=ax[3]);\n",
    "sns.histplot(df['VRDeck'], color='b', bins=50, ax=ax[4]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "e2e398c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ArRHsAAAAWyPYAQAgQRqa3KnOAkSwAwBAQqxYV6zfzFqrzYUVqc5Kt0ewAwBAAryzvkSS9OrqfanNCAh2AACAvRHsAIBJhmGoqdmT6mwAiBLBDgCY9PIH+3THzM91qMyV6qx0SQ45Up0FdFMEOwBg0todxyVJ724oSW1GuihDRqqzgG4qrmDn+eef1y9+8YugaYWFhZo6dapGjhypiRMn6pVXXgma7/V6NWfOHI0fP14jR47UbbfdptLSUsvTAAAAkOIIdl5//XXNnj07aFp1dbVuvvlmXXDBBVq2bJl+85vfaMaMGVq2bJl/mblz52rx4sV69NFHtWTJEnm9Xt16661qbm62LA0AAACfzGhXqKio0IMPPqi8vDxdeOGFQfPeeOMNZWVl6ZFHHlFmZqYuuugiHT58WC+88IJuvPFGNTc3a+HChbrnnns0YcIESdKsWbM0fvx4rVmzRpMnT7YkDQAAAJ+oa3b27NmjrKwsvfPOO7riiiuC5uXn52vMmDHKzDwbQ40dO1YlJSU6efKk9u7dq7q6Oo0bN84/PycnR8OGDdOWLVssSwMAAMAn6pqdiRMnauLEiSHnlZeXa8iQIUHT+vfvL0kqKytTeXm5JGnQoEEdlvHNsyKNWGVmWtteOyPDGfQX6YXySW/pXD5Op8Py60VXEmvZZGY4u+d+cyT3eEnEueN0nn2TriuWYdTBTmcaGxvVo0ePoGk9e/aUJDU1NamhoUGSQi5z+vRpy9KIhdPpUN++vWNevzM5Ob0Ski6sQfmkt3Qsn6ysjIRdL7qSaMumT5/sbrnfnA6l5Htbee5kZ2f5P3fFMrQ02MnOzu7QSLipqUmSdM455yg7O1uS1Nzc7P/sW6ZXr16WpRELr9eQy1Uf8/qhZGQ4lZPTSy5Xgzwer6VpI36UT3pL5/JpafGourou1dlImVjL5syZxm6537yGkvq9E3HuNDa2+D+nUxnm5PQyVYNlabAzcOBAVVZWBk3z/XvAgAFyu93+aRdccEHQMpdccollacTK7U7MBdXj8SYsbcSP8klv6Vg+Xq+RdnlKhWjLxp2GZZkURmqOFyvPHa/3bB9JXbEMLX3wlpubq61bt8rjOdud+qZNmzR48GD169dPQ4cO1bnnnqu8vDz/fJfLpYKCAuXm5lqWBgAAgI+lwc6NN96o2tpaPfDAAyoqKtLy5cu1aNEi3X777ZJa29lMnTpVM2bM0Mcff6y9e/fqzjvv1MCBAzVp0iTL0gAAAPCx9DFWv379NH/+fD322GOaMmWKzj//fN13332aMmWKf5lp06bJ7XZr+vTpamxsVG5urhYsWKCsrCzL0gAAAPCJK9h54oknOkwbMWKEli5dGnadjIwM3Xvvvbr33nvDLmNFGgCA9MJAoEiVrveyPAAAQBQIdgAAScGo50gVgh0AAGBrBDsAAMDWCHYAAEggh4OG2alGsAMA3YirrlnvrD+kKldjqrMCJA3BDgB0I/P+uVsr1h3Sk0u2pzor3YZh0DA71Qh2AKAb2XukRpJUUWXtwMdAOiPYAQAAtkawAwAAbI1gBwAA2BrBDgAgKRgbC6lCsAMAAGyNYAcAANgawQ4AICkYCBSpQrADAEACMVxE6hHsAAAAWyPYAQAAtkawAwBAAjE2VuoR7AAAAFsj2AEAALZGsAMAAGyNYAcAANgawQ4AICnMjo317oYSrdpYktjMoFvJTHUGAADwqWts0fK1xZKk60Z9Q+dkc5tC/KjZAQCkDbfn7GvaXpu8sk0PyqlHsAMAAGyNYAcAANgawQ4AICkY9RypQrADAABsjWAHAIAEYmys1CPYAQAAtkaw04Ws31WmuSt2q8XtSXVWAAA2ZRiGjp2olcfrTXVWLEOw04UsWFWo/L2V+mzbcdPrFB07rWWfHyRAAgCY8lH+Uf1pwWa9uLIg1VmxDMFOF1TX2GJ62b+8ulWrNh7W+5uOJDBHAAC7eG/TYUnS5sLKFOfEOgQ73cTxU3WpzgIAAClBsAMASAqzA4HaDcNFpB7BDgCkkGEY2nekWmfqm1OdFcC2GE4WAFJo674Tmrtit3pnZ+qZ312T6uwAtkTNDgCk0Paik5KkukZ3inMC2BfBDgAgYaLuPZjehpEABDsAgKTorgOBMlxE6hHsAADSB28uIQEIdgAAgK0R7AAA0MU1NXv07oYSHT9JB7KhEOwAANDFLV9brOVrizV9fl7I+d293RDBDgAACZSMHpSLj58OO+/9TYd193PrdaKmIeH5SFcEO4haaWWtlq89qIYm+gUBgHT35mcHVVPbrDc+LUp1VlKGHpQRtQcXbpYk1Te6NXXSJSnODYCuwtTYWN38cUtCdeNdS81ON5GIatQjFbWWpwkAgNUIdrqI/aU1ca3f3RunAQC6L4KdLuL9TYdTnQUASDw6FUQCEOwAANANdOf6fYIdAAASiGYEqUewg5h110H90D18tu2YNu0pT3U2ujyuEumjOz8g5NVzAGinytWoV1bvkyRdPWxAUjqF6w74gYRUIdhBzEz1mQF0QXSY2bnTtU3qkd0j1dkATOMxFgB0grqIYPWNLfrt7HX6r+nvJWYDNmzfQs1g6hHsAEB73JzCOsao2l2W/cJI8wh2AKAz3fkOkQoEmkgAy4OdiooKXXLJJR3+W758uSSpsLBQU6dO1ciRIzVx4kS98sorQet7vV7NmTNH48eP18iRI3XbbbeptLQ0aJlIaSA5aGwIAF1Hdw4jLW+gvHfvXvXs2VMfffRR0HPKPn36qLq6WjfffLMmTpyohx9+WNu3b9fDDz+s3r1768Ybb5QkzZ07V4sXL9YTTzyhgQMH6sknn9Stt96qlStXqkePHqbSAACrtAb13fk2YR1eakCqWB7s7N+/XxdeeKH69+/fYd7LL7+srKwsPfLII8rMzNRFF12kw4cP64UXXtCNN96o5uZmLVy4UPfcc48mTJggSZo1a5bGjx+vNWvWaPLkyXrjjTc6TQPJw4ULdsWRDdiL5cHOvn37dNFFF4Wcl5+frzFjxigz8+xmx44dq+eff14nT57U8ePHVVdXp3Hjxvnn5+TkaNiwYdqyZYsmT54cMY2vfe1rMec9M9Pap3oZGc6gv/FwOM9efp1OR9R5dTqiXydinhzW77NksrJ8YL1Ulk9GwHGdkeFUZrs8OCw8nwJrwJN9PsWyvcDyMFM23oC3qzIyIu+3zIyz+8PM8l1Fwr+HI3C/hT53HE6T+QiI9n3LO52pO06tkJCanb59++qmm27SoUOH9M1vflN33HGHrrnmGpWXl2vIkCFBy/tqgMrKylRe3tpb6aBBgzos45sXKY1Ygx2n06G+fXvHtG4kOTm94k4jKyvD/zk7u0fUee3RM9Py75eZmZGwfRYvwzDk9niVlZkRcVkrygeJk4jyKa04o5XrivWT7w3R+X07pl/b7PV/7tu3d4dgJyvLumO/Z8+zl+Fkn0+xbK9PTaP/s5my8XrPBjs5Ob0ibzPgnP3qV3srp3fX78/H4Ujc/cUnMADxlUv78snKMncfcAYETr7ls7OzOkzrSiwNdtxut4qLi3XxxRfr/vvv17nnnqtVq1bpV7/6lV566SU1NjaqR4/gA7dnz56SpKamJjU0NEhSyGVOnz4tSRHTiJXXa8jlqo95/VAyMpzKyekll6tBHo838gqdaGnx+D83Njarujq61z+bm9xRrxOJ2+2xPE2rzH17lzbtqdDM//0dfe2roS/IVpYPrOcrn+qaOi18t1DDLuyrsZcNtCTtu2Z/rsZmj/aWVOnhW8Z0mO9yNfg/V1fXdQh2WlqsO/abAjowTPb5FMv2zpw5G+yYOXcCa3ZcrgZVV3cevNTUnr2O19TUydPcEnUe043XMBJetoHl4HI1hLy2NTebuw8Elplv+cbGlg7T0kFOTi9TNYyWBjuZmZnKy8tTRkaGsrOzJUmXX365Dhw4oAULFig7O1vNzc1B6/gClHPOOce/TnNzs/+zb5levVpvWJHSiIfbnZgbnsfjjTttI+DXkddrRJ2e14h+nYh5MsLvs7JTdZq7Yrcmj7tQVw8bYOl2zdi0p0KS9FH+Uf3HhNCPVX2sKB9Yb3NBhb7Wr7dKj5/WZ9uO6bNtx3TVJR3bAsaisbn1x0NJuStk2QfeINxub4fXzw0Lz6fAQSKTfRzGsr3AfWPm3Am8cXo8kfebxx2YfnT72TAMNbd41bNH5BrdpErA9bfDJryB+9nr/xu43c6u2cGJnf3oWz6whq4rXi8tf/DWu3fvoEBFkr797W+roqJCAwcOVGVlZdA8378HDBjgf3wVapkBA1pvmJHSQPJ09ur5wlWFOnaiTs+/syeJOYJdVJ9p0rPLd+mhFzfJVd8ceYUEsmGHvsmVxP336up9umPm5zp4/HTyNmpCuvSgnB65SA1Lg50DBw5o9OjRysvLC5q+e/duXXzxxcrNzdXWrVvl8Zx9JLNp0yYNHjxY/fr109ChQ3XuuecGre9yuVRQUKDc3FxJipgG0kNjwGM3IFpnUhzgILx4gj9TfXPFERh8tv24JGnl+pKY04A9WRrsXHTRRfrWt76lRx55RPn5+Tp48KAef/xxbd++XXfccYduvPFG1dbW6oEHHlBRUZGWL1+uRYsW6fbbb5fU2lZn6tSpmjFjhj7++GPt3btXd955pwYOHKhJkyZJUsQ0kDy8eo7ugaodoKuztM2O0+nUvHnz9NRTT+l3v/udXC6Xhg0bppdeesn/BtX8+fP12GOPacqUKTr//PN13333acqUKf40pk2bJrfbrenTp6uxsVG5ublasGCBsrJaW4L369cvYhqAD708J8b6XWUa1K+3vvUvOanOSkKky2OHdMSu6bq689XQ8lfPv/a1r+nxxx8PO3/EiBFaunRp2PkZGRm69957de+998achp3sKDqp0srauA9SLt6wyv7SGi1YVShJWnj/xBTnJvFoswMENzTviiwPdmCtp9/aKSm4oy2Yl86P2gzD6JJBaNmp9HntFAili9+XY5PAS8n2Ayf1Qd6RxG0gCbpeN4jdlNsT39lrJODs5xFR7D7ML9Vdz61XeZW1fTvZXWllLY2XuzBTPz66ZaRiATNtv2NMes6ynTGumT4IdoAU+MdHB3S6tlmvf7g/1VnpUh5cuFn/Z84XCd9O4E2BW2+wrhCLdMEKUyQYwQ5sLd1rnxJR42Y/3LmQXgI72OtKumaurUGwg5ilc3sY2EmKL9Hd+Q4RQsJrTQI24DWMtAssio+7dMfMz7Vmc9duw9LdEOzA1gjIgK7rkUVbNH1+XtQBTyIrTF96v1Atbq+WfFKUuI3AcgQ73URXfOsHSJmA0yXdH4XGI6+gQgePpdfQCoGqXE0qr6pXVcDgo1ZqaHIrr6BCDQGDsVrl023HLE+zU1ziO0Wwg5jZ+SYAdAfPv7NHj726NdXZsJzZ33YLVhXq+Xf26MWVBZZuv/pMk15dvc/SNK3QneMhgh3YWqoDMldds1ZtLFFNbVNK82Elu4W4XsNQY3PwL/ugt7Hs9oXjZKf98eX+E5Kk7UUnLU23/fGUFDYql0Qg2OkmeOsnNZ59e5eWfV6s2W/sSHVWuqxEH7pPLdmu/5m5VidPNyR2Q91U1D84uFYlTHfeswQ7sMTct3fp6Td3+IOq7lxdGqjoaGt7iCOVtRGXrT7T1CW6ZLdb2RYerpYkbdxTkeKcdA3xNP97dvnOpPzw6gKnEZKMYAcx873p1NDkVv6+E9px8JSqz7Q+rkmXa01XeRtry95K3f3cei1419q2A+nuSMUZPbhws3YePJXqrCAJamqbdaahJdXZQDdEsIOYNTZ7JPErygrvrD8kqfvVLjyzbKdKK2s1+800e8wXUH3B8d318PIp2iPYQcyOnqhNTUO8KKS6gbIdWblH6xPwym/MiGpMSfhuIlKJDbutUwQ7SXK6tkmnTiemrwgzEtXPztETjIAN+wk+W8zf3d/8rEjPvb2LFwI6wT05dbrzvs9MdQa6A8MwdOez6yVJz915jXr1tO9uP1Pfoh5ZGd36pLI7K8s2rWICC34QvL+pdQiBg8dduvjrX4k7PcQmrY6rZDHxnbvjbvGx7103TVWfabJ1sPPwoi2SpEH9zklxTlp1lQbKiF/ZqTodP1mvKy8539J0Y7lBeDxeS/OQTnjKhK6Ix1jdRLKr1Ztb7Hux7+6sPJKsvHE+8GKennt7l/YcqootgW5ZHZB8ER+pW1AOBGRoj2AHtkYD5e6npNwVdxr0oAzYC8FOEnCtBEJLq0CC6gBT0qrMcBaHb6cIdtJYIkbiTRbuG/aVjKJN1NuD5jOQ2s3bwcmaBq3aWKL6xuRfx2INyFz1zTp20r5vmHbnw9q+LWVt4M1PiyxLK9k3j3T59UcD5fAOl5/R1v2V+uHYC9WzR0ZK8hDusAxsY5a09mZpctDWN7o1Y8k25V7aX//f1d9MdXYiWr62WF/p3UPfu/IbQdP/76tb5aprVknZmRTl7CyvYai0olZfP7+3MjPC/8b/3ZwvJEmP3z5WA/qmx0sWVkqPIzw1qNlJYweOnU51FtLC/tIanbbRqOHp4uFFW/TuhsP+3pvN6m4XzM6CrZraJq3ZfER1jcFDIMTz42LNliMqKT+jNz892Olyx0/W+YdnSabAr3bsZJ3e3VCi1z/c32E5V12zJKmgbeyxmDZgkTWbS/Xwoi36+4rdppYvPh5/u6+k624nZpQIdpKhWx6E1nzpfUeq9cTrX/r7KYo+F91y50el9ETkQUoTJU0qU1qFuckeP1mnslOhH208tWS7lnxSpAXvFoZNNtrBXd2eyMtXn2nS9Pl5uvu52M4LqzSaetSe/EJuX5SrN7f2f7TtwMmk58Ws2oYWzfvnbu0+xDhxiUCw002E+3V6uPyM3t90WO5Y+wUJex2z5tfZnpLOfxXGnG900OL2asPusrSrRVu18XDStxn4+POvi7fpgRfz5PF2PNZ87Tt2FIW+ib70XqHueuYL1UYx+KWZio1jBKid6pDHLvA0+41PirS5sFIzl6bZOHE2QbDTzb31WZHe/Oyg9h6Jsqo5gmQ0EdpVfEq/evIzrWn71WZGXkGF5r69S01tg5harfpMk5Z9flAnTzckJH1J+nL/CT32Sr4qa6zdxoovijX/3UI9+kp+p8tZWbThjpPAx0CNCSqrDiLcxd3u6O/y63aWyVXfoi92lplex9S5E2aZfUeq9dn2Y6a3lQztd2uo73ek4oyKjnbvx/ZVZ1I3nFB3QLCTBOn8KKWprfO/+ka3Pt56VMejfRMhbAPTODNmwosrCyRJSz4x35D7+Xf2KH/fCa3e0nmA9EHeEX25/0TUeXp2+U6t2nhYM5Zsj3rd9sLVxj27fJcOHnfppVXhH53EYntbFX+VK3k1O4k+TtbvKtPu4vR6LPDmZ0Va8G5B2PI106g+3DJ/XbxNr3ywT/tLa+LJYtI99NIW/eW11gbNXaLqqM2BozUJSXftjuNa8vGB6BrnmwiSu0AFV8IQ7HR3bUf/x1uP6vUP92v6/DyLEk6PC1a4m0JtffjHCofKXHrj0yI9u3xX1Ns71PbmSWV1fLUuXq+hRxbl65llO8MuU5fEV3rLTtXpzws2a8veyqSUrBVvYJWdqtOCVYWa+UbwYwGvN0zacVRHRpPb9zcd0frd5ToWZhBdK2pFT1hc6xePaPZNKhpch2Qy0+VV9QnZ/KL392rNllLtPVJjabrpcVVODYKdbiLc2yG+qUWxvvmV4DY78YqlVu10bXMCchKdkvIzOlxxJkKDyuRduhauKtTRE7Wm32YJx2sYev6dPVq1sSTuPFVU1euDvCNhH0nWhCnHB1/aHPe2Q4k2QHOHaAMkmXyTK4WnVyIfURcdO60WC9rhtc9jwrIcNm62Zoup6KPIruhnp5vzn5LdLORP96+bbo8+G6JoN+P2eOXxGuqZ1bHvnsKSauUVVChP0g/HXRjyxhmqIXAof3hhk6TW2oCfff/bHRcIE3yEq1GJ9/GJVSUW7jbp9nhVUFKtb3/D+tHUW9weZWUmp6+l9t8vMEh8/cP9ev3DECt1snOrXI06XdeswYNyAtKML4/xRkex1EyGXiWKdKJY9IO8I9pUUK57/muUzu2VZX7FLoyanSRI60fQbXebmLPo6Jq9JX+89Wj4mWn2fSoSVFUeDUeYz6Hc9ex63fHU52puaQ2QDpef0Yp1xWpq8fin+bQ/N5avPahfz/hcx8O86h1KotpNxMNV16ymgO+6o+ikHly4WUcrTbxFFWYHv72uWLPf3KGn39pp+SG6fle56WWtvp6ZSa6zZe6Zu0GPvpxv7RtqaXbN9ni92lRQriqXNY2Y3/i0SEcqavV+XvLfdEwVana6ubgvmkaaB3M28OjL+Xr2zmsSt4Ew5Vfb0KLHX9uqqy8dYGbxoPUkqexUvb45sI8eXrRFUusjrMEDczpbVe9uaL34Lv+8OHK+w+TH6zXU4vbqTBSve0sKitpjqlkLWGXuit1Bv5j3tTUYfvbt4HZgrvpmbS6sNJX8uh2tb3TtL62xPNhpH4QmlUXXj+KyxHQEWNvQoqff3KH/d/ggXTfq60HzrLz0dfaj8eP8o1rySZF6ZmXo73dfa9k2Y3nDsKsi2LGZhia3nnt7l8ZcOkDXXPEv/ulh3/yI86q57cAJ/cvXeke1jmEYKq2s1Vd699CJmkZd9PWchA1nEctwEWlWsaP6JI2R1r4M1mw5orJT9VrxxSF9/fzoyjiU0oraiMGOTzwB9NvrDmntjuM6ZfFbZZE6B2wfIIXqW6d9G4y/vLo1Yl9RdY0tWrvjeFR99XQlZgLLVN6SV64v0cHjLh087uoQ7IQTy/Us1OHlm7brUJUkBdUWdtxo1JvsVniMlWb2HanW8+/sibljt/fzDqugpFqL3t9rcc7CbS/cK9zhL09b9lbqoZe26M5n1+svr23Vlr3mftnGwtSFtLtXTYW5SHpM9OQb96YDtt3YbF1QZzbQCSr6CMfBXxd/aT4tk0K9tde+OBauKuw4dETAjtu2/4QWvleoFncKa2faCaopirBfTO03w1D+3kr9aUGeqcdVVv52amqJ/rjsKteUEzUNembZTh3sBkMTEeykmb8u3qa8ggq9uqbjWDNmNDRGd8Ez+wukytUYdhT20EmETrfsVJ3m/XNP0LRIYwAl0uMvb9b0F/OCfl1bdaGM9fn6mi2l1mTArBRelwPvCe+sL0lZPsw4UhF/m5BYboK7237VBwo8RJ9Zvktf7CzTR/mdtEOzkJnzY3UUHX2aYaj10eCxE3V6/p2CiLVcUfcXZqFYa+A626/RXpLMXnu2F53UtgMn9dirW6NOv6sEdD4EO0kQyzFx0uJ+MjYXVmrjbvONEANVuRp1z9wNmvb0upDzQ32/cCfu7Dc7doV+yqJGd7HYsLNMpZW1CfllE+0FxGfJxwfkdid/GIwOReYIPS9RteUn4uybqDNmbn71TW7TN6rKmgZt2B3cK3Kqr/3hXrVPhcCatfa1q+13U7T77eiJWk17el2HIToCz5mWJJ0/ew5VBd30l68t1rSn12njnoqo0+p0P0R50oWrLbeqX6B75m7QW5+n7kdqLAh2kqAw2lF/25yssTYIePHdgg7TzPxK29/2tosnXGdsUThh8XdKZ/F0kBa247sA5VUN1jy6aDsGzA5Y2X6pXcWndN/fN2ifxUOOmGYi22baPa3eXKppT6/TU0u3RzzWj52o0/xOBv8049FFYYblMHFSpvINSHOHiRHyY8Rlo1jk7bXBjdg7qw0/XRdbIGgYhhqawp9jW/ZWauOesz8i391QEvTXKofMjMJuYjceP1nnH40+Xu9vMl979/baYr22Zp8l240VwU4S1DfGVq3ZaWM0tZ6I0Y6o3F6iGgan4lfuh/ml2rov+iEeuor9pTVa/NHZC7rb44259ijQqdONWr+rTGWnYvvVN+uNHTp5ulF/XbwtaHqk9lKfbjuWlt0W7DlUpXe+OBTDmpEPeq+JGCDWXVLdSTu/KlejqQDaKp2d/0s/LlJNQF7NXCtCLRLNt4n1evT0Wzsjtil867PYazhc9We7KAh1LviyHdhb+obdZfogbFvJjtoPHVJRnfyuLFZuKNEnXx5LybZ9eBsrCea+HV+vs+E89/Zufbn/hC75f76qqf92ib4e5VtRkjWPJELfsJIb7RypOKN/fHRAkrTw/olxphbdXjEMQx6vocyMxP12+CDviN74tOMYYFa0Iyk7Va8FEcfZsj4qeXX1PmX3iL8ju1OuRj3w4iZTy5ptZ2D5Y4izS0WdrlnbwozltudQlZ5aul2XXdhXd//XqIRt3+3xKjPDqRa3J6gX5Pbf+ItdZSqvrtcfp14Zcr5ZhmGErz216HDdeTDyuGqxxpCu+mb9bs4X6pHl1Ly7J5gOyHw1ildc3E+D+kW+5j/xenDD+gVx1kjGIxWP532o2enCfANV7iut0dMh2sKYEuKiUBdlTVTokzS5P9mtqpqNxdNv7dT/nrU26v0WqDHCY5ZQgU4yJbsGJpp2XLUNLaZqpSprGnTns+vjyVanTL5UZIloamQ/ym9t8L6nxPxjxj0lVVq4qjDicAWB3+ezbcfk9nj1m1nrtClCsBjURs7EPpn7dsdx6o6eqNPdz4Upzy7Qdrb4WOujqeaW2AIA36PZfUeqWwfxNXlIVCZ53LR0achMzY5NxDpSdah+aH47e50FtSPJtaek4xsrkSyPoYGd2+NV8XGXvvUvOf6aHN+vP7OjpAcO0ui7DsTTJ8yBozX69je+GvP64Zjpo+hYu4a/gRe2NLnG+b35aVFig+IYH8e01z6OMdvYNnC9eMdUemrJdklSds8M/fz7QyS1/gjasKtcXzm3R8h1Fn90QMVlrg79BkU6Dsx0D3E03BAf8D8+7tunZ0LSf/If2yIv1AVQs2MTsY6lFO0v9mQ896+pbfIHBGazt3rz2de1j5rsNn7FuujbZbz8/l498fqXWvpJ7DUtZm5eLVH0cfP4a1/qSMWZDtP3lFTp+Xf26Ey9tTf4MwEBw5/m51matlmHyzt+30jSIvhKUh7+8fGBuNvzSa3tuXwWvFuof3x8oEPXEYFC1eiE6jQxMGupKpdIbSnToUYimjwkasT4WF+w8Un9XmxFsGMXUR5RsV4IdxzsbBTuQLEf4nc9u16/n7dR9Y0tMaXy5wXxjWodKgAsPu7Spj3lWt/2+n6nY2tZINpap5OnOz72eWrJduUVVOiNT4vk9ni1ZvMRHTx+Wks/OaBDcXSt/3YnQaIVDabN8A1BYdbLH+y17LXbcMz1BJzIS3/wgRtNp5Dvbjwccdyu7UVmz31zfD+cUhVTbOikKw6P19DDL0V3jCXbhijGMzPLMIyEDh2SysCHx1hp6oiZAQNjVFLu0ox/bNcN134rbM3JnLd26rYfDVOvnsGHSOhaiY6HcLQH9cmaBu0rrdHYy86Ow5Ts19TdHq8OV5xp9yjGkMPh0P99JfRrwk1RjAYeSrhfblaWf5WrSas3H9GygPGmAmvCzDBbw1Zs5hXZFAg70rmFzPUEbM22rG5DVdvQoj8v3Bz142tXHLWGXsOQUw6l4hb46bZjWtz2QkMoRyrOJPQabIVPtx3TtSP/JfKCJq3dcTzqnvf3l9aoxe3VpRf2lTNwbDnDUF2ju3V8uDSp2iHYSbIdRSejHkvKavPfLVR9k1uvrdmvkRd/LeQy24tO6v28w7rhmouSkqf75m2UFPyKpZTcZs4vvVeojXsq9M2BffzTjAh5+D9zQne0mGxer6Ga2iZ99dzQz+0PHkvPIKS7abbobZTQ7amsv6tsO3BST/5jm6ZOGhJyfkOcbYOk1NwLX11tYZ8vKXzcVWXho6tYhhjyvek1ZfxgOZ0OXTfq6zonO0vPv7NHmwsr9fufj0pIe8JY8Bgryd787KA83tS9fheN2obYL2SxBil72z0fDryM/PKJT3Tv3MS9TeN73TioPUiE65hVN694zV2xW3c9uz7s4ymrH0Ggo1S8adVuzZBTPXFmrPBwtWa0NVhOhDRoGmPawePpNYZUunRT9fa6Q1r2ebFe/qA1iNxc2No30Qd5RxL86NY8gp0EC/XYJxGxTqyHUzJeKbZytOZTrqaIo0Rbqf3bRoG27kvcAKaxsuo5/r4j1cHHRozHSaTBPRvjfAyYXqy5qJva1SEWCnde7C6O/k3F9sI1fo3nRralsDJhI7m7Pd64BpYN9606a9fU2fWgockd08sd7Wu60128jZkTiWAngeoaW/Rf098LMSdxke7e0rMHW7yvn4YS6k2MshANPwNfpZ729Dqt23k8pu2FuvBHFey07epY3yLrrL+X5yzoLNLqX7VW/YoqajdWmJnX0EP5wwvmOvtDdJLZG3KivPhugWYu3Z6Qt57qGt36n5lrww5eHEpgjXssLyB0dj3weA09/npw430z5+qrq/clLCBMlpKy6N+cTASCnQTavv9kyJbtibxOBTbE/MfHkUdOt2K4iCde+zLiMq98ENsz8lC76v629j1meA1DJeUu/fqpz/XepsMxZSCRr6DuK62xNP143rLqTCx5fOeLQzqdRoNTJpoVpTj37V1ym7hAfL79mInU0j8gKik/k9BclkbRyPiQiZtypM4SOxNru7l/djJ0SeBLB+nIUPLe0IyEYCcFPt9m5kIVv/UmHmlY8RTLzAChHq+hNZvNj+ciSfPfLVCoC7ar3vwvHcOQXl29X26Pt9MxbFZtDB0IGTL0zvoSU9uKtZ+LZ5Z17B02Vol6LBTLmyk7THS1bydWxKz5+05oRcAAl+EeC6RLg/MX3uk4uHDUEhjtbN13QnsOmXuMZ+Z3375240zFan9pTaf9FQXqrJbJbJ9iqWJmuI1kIdhJgSUBHdKVV9VrV3F0B4RhdBzcLWadnOCfbTumXz7xiWUX1iUmOuIL7P/n2Mk6vbshdBByuPyMqepdr2GYuojtDndBNDr/ZRUolk4KJWsbD5edqo84cKEZyz4vjqnjvs7UdKNanngE3vvD9V5r5iWHnQdPdajpS1THc/FIZM3Oh/mlemrp9qBpiz8MXePtTOKYKE+8/qXpnrFjkcoxqNIVwU4CdVbjse9I6y+2P76wSbPe2KGDx0+r0uSIsCs3lHQY3E2S1u2Ivl1M2Jt8gER3oBcLs53KHSpzxdX3y/6jNTGvmyp/X7HbP5KyFHujQTPHRjSieaTQFSWzx10zw4s89/ZuPfpycP9QYceSavPnBXlRtXOxQlMcDYlj8VGY61k8nekdi6KGJRnHSTw9vMfD6zVUEMPQPclAsJNAnQU7f128LWhAtpKyM7r/+fgac75kop+E9idavJ3ipbt4a6Wi7XwvXdzx1OepzkIHXsPQM8vDP7Lr7M23riBUL9ZdzdETdTH9aIpHvNc9q/jGmIokVAeVfzLZa7urvjnqmvyupL7JndBuCuJBp4IJ1XkEH9jQ1upHBuEUH3fpeBrfVNLpGW+imGnj1B0lejiHRGtfi9JVxdsvT3cTTRvC3835IoE5QWeo2UmgaK4ZX+wqS1xGAsT0RhIstXBVYaqzAMDm6htb9GaKHmelI2p2EigrM/1iyTNR/ApBYhwOMUI5kC4iDQiKrmHRuwV6f2NJqrORNtLvbmwjFw7KSXUWOmjfWRwABNoYR18yiM3mwgrLex8+eKzG0vS6OoKdBGpx27vxL6KzdsdxnYljlGgA9mS2z51oHDzKD9tAPMZKoNlv7Eh1FpBGFr2/V4veT3UuAHQHvAgRrMvW7Hi9Xs2ZM0fjx4/XyJEjddttt6m0NL1eE6YTNQAAUq/LBjtz587V4sWL9eijj2rJkiXyer269dZb1dxMgAEAAM7qksFOc3OzFi5cqGnTpmnChAkaOnSoZs2apfLycq1ZsybV2QMAAGmkSwY7e/fuVV1dncaNG+eflpOTo2HDhmnLFnPDCAAAgO6hSzZQLi9vHc170KBBQdP79+/vnxeLzDTsFwcAADvwGkbK7rNdMthpaGgdU6pHjx5B03v27KnTp2N73c7pdKhv395x5w0AAHQ07OL+6tUzNWFHlwx2srOzJbW23fF9lqSmpib16tUrpjS9XkMul7Vj87z+4CT17t1TVTX18ri9kkNyOBQ0ZJZhSBkZDnk8hhxOyfC2LmNIcqg1EnY4HPJ6jdbphuR0OFpnGpLD2frXl6SjLU05Wgf9NIzWQM7haP2OTqfDv12Hbx2HQ26P19/js6dtWw61Tnc6HDJkyKG2dNryZBht05xqy19rxn35lKM1r75t+76TfLugLf++vPiG13A4JY/HUIYvr23/y8hwyOM1Wr9H2z7wpe/x+FZu/d4ZTqc8Xq9//xre1nkO/06SMjKcysnppdOuBskw/N/BCCqg1v2RmeFsnd9WRq377WwZti7aun/98wPK0xtQFq3rGP6yNNS6XV96rVls3b8KOF48vu+ts8eIMyC/Xm9refjTaFvGaCsvH9+x5CvD1jRap2dlOv372OGQ//sEHmeB+9DpdPjz5fG2fnFfeoHHh2//e72GMjIccrYdc77lfeeAv7wlZWY41Senl864GuTxeJWZ4VSz2yOns3V9X3l5jbMngO8Y8eUnM8PZmq5v205H0LniO/b9x4jOHre+feBt+x5ewwg+99qOc98yzra8OJ0O/77xelvL2Wu0bduQfx/7jwMFnDNtyzqdDv/ybo/Xf2z4ytF3HPiOSSn42uErc9/h43CEmO8rm7bj0O3x+vOemelQi9vrL39f+fivA06HvpLTS3V1TWpuccshh3/fK+D7ONuOMd8+zMhoTdd3bqvt3PV9X69h+I+V1uPt7PHpP+wC9m1Gxtn94Dt2fEe6t+3E9J1zvv3mKyvfceHLr//4bttXXsMIKqvA67EvH7794V/AJyjD8l97vIHnou/6522X17Z0g4+T4E34jrvAl8uNtuOm9TzOUO9zs1Vb2yhP23mW4XQEXSd9+8MISLz9uR6Yd6P9Z99+C9h3vm04nK3XMI+37f7Rdtw31jepsb5JVsrJ6aWMjMi1RV0y2PE9vqqsrNQFF1zgn15ZWalLLrkk5nTdbm/khaKUkeFUptMhBZxwQSeF78brm9+uzHwXNd9JHcTR7m+H6cEznOHyICkrw3n2hhGwXpb/IAq+YLRP3xlw8eqQvhGwTmf5D1jE2f77+vZT240xSOD+C8hXpjNgZ7Y/Fxyt6/TIylCm0yG3OyCQUPB+8uUlXBmdXTzE/LbPHb5/0L9D7BudvbH5kw11DATkN6h8w26r3bEUMC8jwxF6H3c8BM7+2zh7vATt7/bbdbbbthG8vOHtuI8ynA71zMpQvdMhw9t6EfevE3BMZfgCEIXIT8CxEfIcUsAx3i77vvX822l3XAT+9S3jDCyzgB8m/nUdvqDUt4vapRm4bNu9OKuTC3nQcR+4WKjzLdz8to/+7bTd9Pz7uu3m17qttn2b6VR2z0w11DcFlUlGiHQdDsfZ/WJ0/D7OgGPCKUe74y38dSPDefZuH+r6EvKa49tG+2tG+00528qh3Xdydvh+4c650J87rh+wrXbpGt62vIbaTKjN+o7BtsCxV89MNdY7JO/Z/dvhO6vdMdj+XA93mQqXn3bb8B8bbce9LyhNhS7ZSGXo0KE699xzlZeX55/mcrlUUFCg3NzcFOYMAACkmy5Zs9OjRw9NnTpVM2bM0Hnnnaevf/3revLJJzVw4EBNmjQp1dkDAABppEsGO5I0bdo0ud1uTZ8+XY2NjcrNzdWCBQuUlZWV6qwBAIA04jD8rZO6N4/Hq6qqOkvTzMx0qm/f3qqurktIeyDEh/JJb5RP+qJs0lt3Kp/zzuttqoFyl2yzAwAAYBbBDgAAsDWCHQAAYGsEOwAAwNYIdgAAgK0R7AAAAFsj2AEAALZGsAMAAGyNYAcAANgaPSi3MYzEjMiakeGUx2PvHiy7MsonvVE+6YuySW/dpXycTkfwyO1hEOwAAABb4zEWAACwNYIdAABgawQ7AADA1gh2AACArRHsAAAAWyPYAQAAtkawAwAAbI1gBwAA2BrBDgAAsDWCHQAAYGsEOwAAwNYIdgAAgK0R7AAAAFsj2EkQr9erOXPmaPz48Ro5cqRuu+02lZaWpjpbtlNTU6M///nPuuaaazR69Gj97Gc/U35+vn/+xo0bdcMNN+iKK67Q9ddfr1WrVgWt39TUpIcffljjxo3TqFGjdPfdd6uqqipomUhpwJxDhw5p1KhRWr58uX9aYWGhpk6dqpEjR2rixIl65ZVXgtYxcx5FSgOdW7FihX7wgx9o+PDh+uEPf6j333/fP+/o0aO6/fbbNXr0aH33u9/V7Nmz5fF4gtZ//fXX9b3vfU8jRozQz3/+cxUUFATNN5MGQnO73Xr66ad13XXXadSoUbrpppu0fft2/3zOnygYSIhnnnnGuPrqq41PP/3UKCwsNH75y18akyZNMpqamlKdNVu5+eabjcmTJxtbtmwxiouLjYcfftgYMWKEcfDgQaOoqMgYPny4MXPmTKOoqMiYP3++MWzYMGPDhg3+9e+//37j+9//vrFlyxZjx44dxr//+78bN910k3++mTQQWXNzs3HDDTcYQ4YMMZYtW2YYhmFUVVUZV199tfGHP/zBKCoqMt566y1j+PDhxltvveVfL9J5ZCYNhLdixQpj2LBhxmuvvWYcPnzYmDt3rjF06FDjyy+/NJqbm41JkyYZv/rVr4x9+/YZH374oTFmzBjj6aef9q+/fPlyY8SIEcY///lP48CBA8a9995rjBkzxjh16pRhGIapNBDenDlzjO985zvGunXrjJKSEuOBBx4wrrzySqOiooLzJ0oEOwnQ1NRkjBo1ynj99df9006fPm2MGDHCWLlyZQpzZi8lJSXGkCFDjPz8fP80r9drfP/73zdmz55t/OlPfzL+4z/+I2idu+66y/jlL39pGIZhlJeXG0OHDjU+++wz//zi4mJjyJAhxpdffmkYhhExDZjz1FNPGf/rf/2voGBn3rx5xne/+12jpaUlaLlJkyYZhmHuPIqUBsLzer3GddddZzzxxBNB03/5y18a8+bNM1auXGlcfvnlRk1NjX/ekiVLjNGjR/tvlpMmTTL+9re/+ee3tLQY1157rTFv3jzDMAxTaSC8H//4x8bjjz/u//eZM2eMIUOGGKtXr+b8iRKPsRJg7969qqur07hx4/zTcnJyNGzYMG3ZsiWFObOXvn376oUXXtDw4cP90xwOhxwOh1wul/Lz84PKQJLGjh2rrVu3yjAMbd261T/NZ/DgwRowYIC/nCKlgci2bNmipUuX6oknngianp+frzFjxigzM9M/bezYsSopKdHJkydNnUeR0kB4hw4d0rFjx/SjH/0oaPqCBQt0++23Kz8/X5dddpm+8pWv+OeNHTtWtbW1Kiws1KlTp1RSUhJUPpmZmbrqqquCyqezNNC5fv366dNPP9XRo0fl8Xi0dOlS9ejRQ0OHDuX8iRLBTgKUl5dLkgYNGhQ0vX///v55iF9OTo6uvfZa9ejRwz9t9erVOnz4sMaPH6/y8nINHDgwaJ3+/furoaFB1dXVqqioUN++fdWzZ88Oy/jKKVIa6JzL5dJ9992n6dOndzgfwu1bSSorKzN1HkVKA+EdOnRIklRfX69bbrlF48aN009+8hN98sknkiifdPDAAw8oKytL3/ve9zR8+HDNmjVLc+bM0QUXXED5RIlgJwEaGhokKegmLEk9e/ZUU1NTKrLULXz55Zf6wx/+oEmTJmnChAlqbGzsUAa+fzc3N6uhoaHDfCm4nCKlgc499NBDGjVqVIfaAyn0vvUFnk1NTabOo0hpILza2lpJ0u9//3tNnjxZCxcu1He+8x39z//8jzZu3Ej5pIGioiL16dNHzz33nJYuXaobbrhB99xzjwoLCymfKGVGXgTRys7OltR6M/R9lloPnl69eqUqW7b20Ucf6Z577tHo0aM1Y8YMSa0nbfuAxPfvXr16KTs7O2TAElhOkdJAeCtWrFB+fr5WrlwZcn6o/e+7wJ5zzjmmzqNIaSC8rKwsSdItt9yiKVOmSJIuvfRSFRQU6KWXXoqqfNovQ/nEr6ysTHfffbcWLVqkq666SpI0fPhwFRUV6ZlnnuH8iRI1OwngqzasrKwMml5ZWakBAwakIku29tprr+m3v/2trrvuOs2bN8//y2TQoEEhy+Ccc85Rnz59NHDgQNXU1HQ42QPLKVIaCG/ZsmU6deqUJkyYoFGjRmnUqFGSpAcffFC33nqrBg4cGHLfStKAAQNMnUeR0kB4vv0zZMiQoOkXX3yxjh49Svmk2I4dO9TS0hLUJlGSrrjiCh0+fJjyiRLBTgIMHTpU5557rvLy8vzTXC6XCgoKlJubm8Kc2c/ixYv16KOP6qabbtLMmTODqmSvuuoqbd68OWj5TZs2afTo0XI6nbryyivl9Xr9DZWl1nYMFRUV/nKKlAbCmzFjht577z2tWLHC/58kTZs2TY899phyc3O1devWoD5XNm3apMGDB6tfv36mzqNIaSC8yy67TL1799aOHTuCpu/fv18XXHCBcnNzVVBQ4H/cJbXu2969e2vo0KHq16+fBg8eHFQ+brdb+fn5QeXTWRoIz9eWZt++fUHT9+/frwsvvJDzJ1qpfh3MrmbOnGmMGTPG+Oijj4L6N2hubk511myjuLjYuOyyy4zf/OY3RmVlZdB/LpfL2L9/v3HZZZcZTz75pFFUVGQsWLCgQx85d911lzFx4kRj06ZN/n52pk6d6p9vJg2YF/jq+cmTJ43c3Fzj97//vXHgwAFj2bJlxvDhw43ly5f7l490HplJA+E999xzxqhRo4yVK1cG9bOzadMmo7Gx0fj+979v3HLLLUZhYaG/j5xnnnnGv/7SpUuNESNGGMuXL/f3s3P11Vf7+9kxkwZC83g8xs9+9jPj+uuvNzZu3GgcOnTImDVrlnHppZca27dv5/yJEsFOgrjdbuNvf/ubMXbsWGPkyJHGbbfdZpSWlqY6W7by97//3RgyZEjI/37/+98bhmEYn3/+uTF58mTj8ssvN66//npj1apVQWnU1dUZDzzwgHHVVVcZV111lXHXXXcZVVVVQctESgPmBQY7hmEYO3bsMH76058al19+uXHdddcZr776atDyZs6jSGmgcwsXLjQmTpxoXHbZZcaPf/xj48MPP/TPKykpMW6++WZj+PDhxne/+11j9uzZhsfjCVp//vz5xjXXXGOMGDHC+PnPf24UFBQEzTeTBkKrqakxHnroIWPChAnGqFGjjP/8z/808vLy/PM5f8xzGAadhQAAAPui0QEAALA1gh0AAGBrBDsAAMDWCHYAAICtEewAAABbI9gBAAC2RrADAABsjWAHAADYGsEOAACwNYIdAABgawQ7AADA1v5/L/+/V+GF8MIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['ShoppingMall'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c259e4d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CryoSleep       217\n",
       "ShoppingMall    208\n",
       "VIP             203\n",
       "HomePlanet      201\n",
       "Cabin           199\n",
       "VRDeck          188\n",
       "FoodCourt       183\n",
       "Spa             183\n",
       "Destination     182\n",
       "RoomService     181\n",
       "Age             179\n",
       "Transported       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "761177bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/l4/t3mpr25116j0g9zz4jw97yfh0000gq/T/ipykernel_83686/1665899112.py:1: FutureWarning: The default value of numeric_only in DataFrame.skew is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  df.skew()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "CryoSleep        0.591110\n",
       "Age              0.419097\n",
       "VIP              6.300900\n",
       "RoomService      6.333014\n",
       "FoodCourt        7.102228\n",
       "ShoppingMall    12.627562\n",
       "Spa              7.636020\n",
       "VRDeck           7.819732\n",
       "Transported     -0.014497\n",
       "dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "fb588400",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.barplot(x=\"Transported\", y=\"HomePlanet\", data=df, errorbar=('ci', 95));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25b73a71",
   "metadata": {},
   "source": [
    "Converting 30% of the records where home planet is Mars to be Belt so there are two smaller groups of origin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a7198c7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# count the number of Europa categories\n",
    "num_europa = df['HomePlanet'].value_counts()['Mars']\n",
    "\n",
    "# randomly select 30% of the Europa categories\n",
    "num_convert = int(np.ceil(0.3 * num_europa))\n",
    "europa_idx = df.loc[df['HomePlanet'] == 'Mars'].sample(n=num_convert).index\n",
    "\n",
    "# convert the selected Europa categories to Belt\n",
    "df.loc[europa_idx, 'HomePlanet'] = 'Belt'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6a7732f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HomePlanet      201\n",
      "CryoSleep       217\n",
      "Destination     182\n",
      "Age             179\n",
      "VIP             203\n",
      "RoomService     181\n",
      "FoodCourt       183\n",
      "ShoppingMall    208\n",
      "Spa             183\n",
      "VRDeck          188\n",
      "Transported       0\n",
      "dtype: int64\n",
      "Age                          0\n",
      "VIP                          0\n",
      "RoomService                  0\n",
      "FoodCourt                    0\n",
      "ShoppingMall                 0\n",
      "Spa                          0\n",
      "VRDeck                       0\n",
      "HomePlanet_Earth             0\n",
      "HomePlanet_Europa            0\n",
      "HomePlanet_Mars              0\n",
      "Destination_55 Cancri e      0\n",
      "Destination_PSO J318.5-22    0\n",
      "Destination_TRAPPIST-1e      0\n",
      "CryoSleep_False              0\n",
      "CryoSleep_True               0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# load the training data\n",
    "train_df = pd.read_csv('train.csv')\n",
    "\n",
    "# drop PassengerId and Name columns\n",
    "train_df.drop(['PassengerId', 'Name', 'Cabin'], axis=1, inplace=True)\n",
    "\n",
    "# check for null values\n",
    "print(train_df.isnull().sum())\n",
    "\n",
    "\n",
    "# fill null values with the median for numeric columns\n",
    "imputer = SimpleImputer(strategy='median')\n",
    "train_df[['Age', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'VIP']] = imputer.fit_transform(train_df[['Age', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck','VIP']])\n",
    "\n",
    "# fill null values with the most frequent value for categorical columns\n",
    "imputer = SimpleImputer(strategy='most_frequent')\n",
    "train_df[['HomePlanet','Destination', 'CryoSleep']] = imputer.fit_transform(train_df[['HomePlanet', 'Destination', 'CryoSleep']])\n",
    "\n",
    "# encode categorical columns as numerical values\n",
    "train_df = pd.get_dummies(train_df, columns=['HomePlanet', 'Destination', 'CryoSleep'])\n",
    "\n",
    "\n",
    "# split data into features and target\n",
    "X = train_df.drop('Transported', axis=1)\n",
    "y = train_df['Transported']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99103bfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(X.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3382f8db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9298447383553766\n"
     ]
    }
   ],
   "source": [
    "# Assuming X and y are the input features and target variable respectively\n",
    "# Create a pipeline with SimpleImputer and DecisionTreeClassifier\n",
    "# split data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('clf', DecisionTreeClassifier())\n",
    "])\n",
    "\n",
    "# Fit the pipeline to the data\n",
    "pipe.fit(X, y)\n",
    "\n",
    "# Use the pipeline to predict\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "# calculate accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print('Accuracy:', accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "292d3563",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HomePlanet      0\n",
      "CryoSleep       0\n",
      "Destination     0\n",
      "Age             0\n",
      "VIP             0\n",
      "RoomService     0\n",
      "FoodCourt       0\n",
      "ShoppingMall    0\n",
      "Spa             0\n",
      "VRDeck          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(test_df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2e920cf5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HomePlanet       87\n",
      "CryoSleep        93\n",
      "Destination      92\n",
      "Age              91\n",
      "VIP              93\n",
      "RoomService      82\n",
      "FoodCourt       106\n",
      "ShoppingMall     98\n",
      "Spa             101\n",
      "VRDeck           80\n",
      "dtype: int64\n",
      "Age                          0\n",
      "VIP                          0\n",
      "RoomService                  0\n",
      "FoodCourt                    0\n",
      "ShoppingMall                 0\n",
      "Spa                          0\n",
      "VRDeck                       0\n",
      "HomePlanet_Earth             0\n",
      "HomePlanet_Europa            0\n",
      "HomePlanet_Mars              0\n",
      "Destination_55 Cancri e      0\n",
      "Destination_PSO J318.5-22    0\n",
      "Destination_TRAPPIST-1e      0\n",
      "CryoSleep_False              0\n",
      "CryoSleep_True               0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "test_df = pd.read_csv('test.csv')\n",
    "# drop PassengerId and Name columns\n",
    "\n",
    "test_df_original = test_df.copy()\n",
    "\n",
    "test_df.drop(['PassengerId', 'Name', 'Cabin'], axis=1, inplace=True)\n",
    "\n",
    "# check for null values\n",
    "print(test_df.isnull().sum())\n",
    "\n",
    "# fill null values with the median for numeric columns\n",
    "imputer = SimpleImputer(strategy='median')\n",
    "test_df[['Age', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'VIP']] = imputer.fit_transform(test_df[['Age', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck','VIP']])\n",
    "\n",
    "# fill null values with the most frequent value for categorical columns\n",
    "imputer = SimpleImputer(strategy='most_frequent')\n",
    "test_df[['HomePlanet','Destination', 'CryoSleep']] = imputer.fit_transform(test_df[['HomePlanet', 'Destination', 'CryoSleep']])\n",
    "\n",
    "# encode categorical columns as numerical values\n",
    "test_df = pd.get_dummies(test_df, columns=['HomePlanet', 'Destination', 'CryoSleep'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28b0ffca",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(test_df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d684333c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>HomePlanet</th>\n",
       "      <th>CryoSleep</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Age</th>\n",
       "      <th>VIP</th>\n",
       "      <th>RoomService</th>\n",
       "      <th>FoodCourt</th>\n",
       "      <th>ShoppingMall</th>\n",
       "      <th>Spa</th>\n",
       "      <th>VRDeck</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0013_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>True</td>\n",
       "      <td>G/3/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>27.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Nelly Carsoning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0018_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>F/4/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>19.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2823.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Lerome Peckers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0019_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>True</td>\n",
       "      <td>C/0/S</td>\n",
       "      <td>55 Cancri e</td>\n",
       "      <td>31.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sabih Unhearfus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0021_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>C/1/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>38.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6652.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>585.0</td>\n",
       "      <td>Meratz Caltilter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0023_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>F/5/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>20.0</td>\n",
       "      <td>False</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>635.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Brence Harperez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4272</th>\n",
       "      <td>9266_02</td>\n",
       "      <td>Earth</td>\n",
       "      <td>True</td>\n",
       "      <td>G/1496/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>34.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Jeron Peter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4273</th>\n",
       "      <td>9269_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>42.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>847.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>Matty Scheron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4274</th>\n",
       "      <td>9271_01</td>\n",
       "      <td>Mars</td>\n",
       "      <td>True</td>\n",
       "      <td>D/296/P</td>\n",
       "      <td>55 Cancri e</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Jayrin Pore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4275</th>\n",
       "      <td>9273_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>D/297/P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2680.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>523.0</td>\n",
       "      <td>Kitakan Conale</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4276</th>\n",
       "      <td>9277_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>True</td>\n",
       "      <td>G/1498/S</td>\n",
       "      <td>PSO J318.5-22</td>\n",
       "      <td>43.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Lilace Leonzaley</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4277 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId HomePlanet CryoSleep     Cabin    Destination   Age    VIP  \\\n",
       "0        0013_01      Earth      True     G/3/S    TRAPPIST-1e  27.0  False   \n",
       "1        0018_01      Earth     False     F/4/S    TRAPPIST-1e  19.0  False   \n",
       "2        0019_01     Europa      True     C/0/S    55 Cancri e  31.0  False   \n",
       "3        0021_01     Europa     False     C/1/S    TRAPPIST-1e  38.0  False   \n",
       "4        0023_01      Earth     False     F/5/S    TRAPPIST-1e  20.0  False   \n",
       "...          ...        ...       ...       ...            ...   ...    ...   \n",
       "4272     9266_02      Earth      True  G/1496/S    TRAPPIST-1e  34.0  False   \n",
       "4273     9269_01      Earth     False       NaN    TRAPPIST-1e  42.0  False   \n",
       "4274     9271_01       Mars      True   D/296/P    55 Cancri e   NaN  False   \n",
       "4275     9273_01     Europa     False   D/297/P            NaN   NaN  False   \n",
       "4276     9277_01      Earth      True  G/1498/S  PSO J318.5-22  43.0  False   \n",
       "\n",
       "      RoomService  FoodCourt  ShoppingMall     Spa  VRDeck              Name  \n",
       "0             0.0        0.0           0.0     0.0     0.0   Nelly Carsoning  \n",
       "1             0.0        9.0           0.0  2823.0     0.0    Lerome Peckers  \n",
       "2             0.0        0.0           0.0     0.0     0.0   Sabih Unhearfus  \n",
       "3             0.0     6652.0           0.0   181.0   585.0  Meratz Caltilter  \n",
       "4            10.0        0.0         635.0     0.0     0.0   Brence Harperez  \n",
       "...           ...        ...           ...     ...     ...               ...  \n",
       "4272          0.0        0.0           0.0     0.0     0.0       Jeron Peter  \n",
       "4273          0.0      847.0          17.0    10.0   144.0     Matty Scheron  \n",
       "4274          0.0        0.0           0.0     0.0     0.0       Jayrin Pore  \n",
       "4275          0.0     2680.0           0.0     0.0   523.0    Kitakan Conale  \n",
       "4276          0.0        0.0           0.0     0.0     0.0  Lilace Leonzaley  \n",
       "\n",
       "[4277 rows x 13 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the pipeline to predict\n",
    "test_df_original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "37c1c79c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dataframe for submission\n",
    "submission_df = pd.DataFrame({'PassengerId': test_df_original['PassengerId'], 'Transported': y_pred})\n",
    "\n",
    "# Save submission file\n",
    "submission_df.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "36c485f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3278a277",
   "metadata": {},
   "source": [
    "## Bias identification and Mitigation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "6f1739e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HomePlanet      201\n",
      "CryoSleep       217\n",
      "Destination     182\n",
      "Age             179\n",
      "VIP             203\n",
      "RoomService     181\n",
      "FoodCourt       183\n",
      "ShoppingMall    208\n",
      "Spa             183\n",
      "VRDeck          188\n",
      "Transported       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# load the training data\n",
    "train_df = pd.read_csv('train.csv')\n",
    "\n",
    "target_variable = \"Transported\"\n",
    "demographic = [\"HomePlanet\", \"Age\"]\n",
    "sensitive = [\"HomePlanet\"]\n",
    "\n",
    "train_df.drop(['PassengerId', 'Name', 'Cabin'], axis=1, inplace=True)\n",
    "\n",
    "# check for null values\n",
    "print(train_df.isnull().sum())\n",
    "\n",
    "# fill null values with the median for numeric columns\n",
    "imputer = SimpleImputer(strategy='median')\n",
    "train_df[['Age', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'VIP']] = imputer.fit_transform(train_df[['Age', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck','VIP']])\n",
    "\n",
    "# fill null values with the most frequent value for categorical columns\n",
    "imputer = SimpleImputer(strategy='most_frequent')\n",
    "train_df[['HomePlanet','Destination', 'CryoSleep']] = imputer.fit_transform(train_df[['HomePlanet', 'Destination', 'CryoSleep']])\n",
    "\n",
    "\n",
    "# drop PassengerId and Name columns\n",
    "X = train_df.drop('Transported', axis=1)\n",
    "Y, A = train_df.loc[:, target_variable], train_df.loc[:, sensitive]\n",
    "\n",
    "# encode categorical columns as numerical values\n",
    "X = pd.get_dummies(X, columns=['HomePlanet', 'Destination', 'CryoSleep'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "80108378",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age                          0\n",
      "VIP                          0\n",
      "RoomService                  0\n",
      "FoodCourt                    0\n",
      "ShoppingMall                 0\n",
      "Spa                          0\n",
      "VRDeck                       0\n",
      "HomePlanet_Earth             0\n",
      "HomePlanet_Europa            0\n",
      "HomePlanet_Mars              0\n",
      "Destination_55 Cancri e      0\n",
      "Destination_PSO J318.5-22    0\n",
      "Destination_TRAPPIST-1e      0\n",
      "CryoSleep_False              0\n",
      "CryoSleep_True               0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# check for null values\n",
    "print(X.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "513108dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "random_seed = 445\n",
    "np.random.seed(random_seed)\n",
    "\n",
    "X_train, X_test, Y_train, Y_test, A_train, A_test, df_train, df_test = train_test_split(\n",
    "    X,\n",
    "    Y,\n",
    "    A,\n",
    "    train_df,\n",
    "    test_size=0.50,\n",
    "    stratify=Y,\n",
    "    random_state=random_seed\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "84142954",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HomePlanet      0\n",
      "CryoSleep       0\n",
      "Destination     0\n",
      "Age             0\n",
      "VIP             0\n",
      "RoomService     0\n",
      "FoodCourt       0\n",
      "ShoppingMall    0\n",
      "Spa             0\n",
      "VRDeck          0\n",
      "Transported     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# check for null values\n",
    "print(df_train.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "176260aa",
   "metadata": {},
   "source": [
    "Downsampling the dataset to match have equal number of positive and negative Y values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "2750833b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def resample_dataset(X_train, Y_train, A_train):\n",
    "  negative_ids = Y_train[Y_train == 0].index\n",
    "  positive_ids = Y_train[Y_train == 1].index\n",
    "  balanced_ids = positive_ids.union(np.random.choice(a=negative_ids, size=len(positive_ids)))\n",
    "\n",
    "  X_train = X_train.loc[balanced_ids, :]\n",
    "  Y_train = Y_train.loc[balanced_ids]\n",
    "  A_train = A_train.loc[balanced_ids, :]\n",
    "  return X_train, Y_train, A_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "9afd970a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_bal, Y_train_bal, A_train_bal = resample_dataset(X_train, Y_train, A_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "0031e4b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>VIP</th>\n",
       "      <th>RoomService</th>\n",
       "      <th>FoodCourt</th>\n",
       "      <th>ShoppingMall</th>\n",
       "      <th>Spa</th>\n",
       "      <th>VRDeck</th>\n",
       "      <th>HomePlanet_Earth</th>\n",
       "      <th>HomePlanet_Europa</th>\n",
       "      <th>HomePlanet_Mars</th>\n",
       "      <th>Destination_55 Cancri e</th>\n",
       "      <th>Destination_PSO J318.5-22</th>\n",
       "      <th>Destination_TRAPPIST-1e</th>\n",
       "      <th>CryoSleep_False</th>\n",
       "      <th>CryoSleep_True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>19.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>655.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3262.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>295.0</td>\n",
       "      <td>1374.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2383</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>8260</th>\n",
       "      <td>25.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>1359.0</td>\n",
       "      <td>492.0</td>\n",
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       "    <tr>\n",
       "      <th>4039</th>\n",
       "      <td>20.0</td>\n",
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       "    <tr>\n",
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       "      <td>57.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>295.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4662</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4346 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age  VIP  RoomService  FoodCourt  ShoppingMall    Spa  VRDeck  \\\n",
       "5509  47.0  0.0          0.0        0.0           0.0    0.0     0.0   \n",
       "7755  32.0  0.0          0.0        0.0           0.0    0.0     0.0   \n",
       "1254  19.0  0.0       1507.0        0.0         260.0    0.0     0.0   \n",
       "4572  32.0  0.0        654.0        0.0         655.0    0.0     0.0   \n",
       "999   38.0  0.0          0.0     3262.0           0.0  295.0  1374.0   \n",
       "...    ...  ...          ...        ...           ...    ...     ...   \n",
       "2383   0.0  0.0          0.0        0.0           0.0    0.0     0.0   \n",
       "8260  25.0  0.0          0.0      145.0        1359.0  492.0   205.0   \n",
       "4039  20.0  0.0          0.0        0.0           0.0    0.0     0.0   \n",
       "1815  57.0  1.0        975.0        0.0         295.0   39.0     0.0   \n",
       "4662   3.0  0.0          0.0        0.0           0.0    0.0     0.0   \n",
       "\n",
       "      HomePlanet_Earth  HomePlanet_Europa  HomePlanet_Mars  \\\n",
       "5509                 0                  1                0   \n",
       "7755                 0                  0                1   \n",
       "1254                 0                  0                1   \n",
       "4572                 0                  0                1   \n",
       "999                  1                  0                0   \n",
       "...                ...                ...              ...   \n",
       "2383                 1                  0                0   \n",
       "8260                 0                  1                0   \n",
       "4039                 1                  0                0   \n",
       "1815                 0                  0                1   \n",
       "4662                 1                  0                0   \n",
       "\n",
       "      Destination_55 Cancri e  Destination_PSO J318.5-22  \\\n",
       "5509                        1                          0   \n",
       "7755                        0                          0   \n",
       "1254                        0                          0   \n",
       "4572                        0                          0   \n",
       "999                         0                          0   \n",
       "...                       ...                        ...   \n",
       "2383                        0                          1   \n",
       "8260                        1                          0   \n",
       "4039                        0                          0   \n",
       "1815                        0                          0   \n",
       "4662                        1                          0   \n",
       "\n",
       "      Destination_TRAPPIST-1e  CryoSleep_False  CryoSleep_True  \n",
       "5509                        0                0               1  \n",
       "7755                        1                0               1  \n",
       "1254                        1                1               0  \n",
       "4572                        1                1               0  \n",
       "999                         1                1               0  \n",
       "...                       ...              ...             ...  \n",
       "2383                        0                0               1  \n",
       "8260                        0                1               0  \n",
       "4039                        1                1               0  \n",
       "1815                        1                1               0  \n",
       "4662                        0                0               1  \n",
       "\n",
       "[4346 rows x 15 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "6cd79eb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "unmitigated_pipeline = Pipeline(steps=[\n",
    "    (\"preprocessing\", StandardScaler()),\n",
    "    ('clf', DecisionTreeClassifier())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "5a88f74a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;, StandardScaler()),\n",
       "                (&#x27;clf&#x27;, DecisionTreeClassifier())])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;, StandardScaler()),\n",
       "                (&#x27;clf&#x27;, DecisionTreeClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessing', StandardScaler()),\n",
       "                ('clf', DecisionTreeClassifier())])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unmitigated_pipeline.fit(X_train_bal, Y_train_bal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "d69d8ab2",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred_proba = unmitigated_pipeline.predict_proba(X_test)[:,1]\n",
    "Y_pred = unmitigated_pipeline.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "d2e6b8f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr, tpr, thresholds = roc_curve(Y_test, Y_pred)\n",
    "roc_auc = roc_auc_score(Y_test, Y_pred)\n",
    "\n",
    "# Plot ROC curve\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], 'k--')  # plot the diagonal line representing chance level\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "842542a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6945941689236477"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balanced_accuracy_score(Y_test, Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "07d6cc92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HomePlanet      0\n",
      "CryoSleep       0\n",
      "Destination     0\n",
      "Age             0\n",
      "VIP             0\n",
      "RoomService     0\n",
      "FoodCourt       0\n",
      "ShoppingMall    0\n",
      "Spa             0\n",
      "VRDeck          0\n",
      "Transported     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df_test.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "d78a0ec2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HomePlanet\n",
       "Earth     0.360544\n",
       "Europa    0.078621\n",
       "Mars      0.131034\n",
       "Name: false_negative_rate, dtype: object"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In its simplest form MetricFrame takes four arguments:\n",
    "#    metric_function with signature metric_function(y_true, y_pred)\n",
    "#    y_true: array of labels\n",
    "#    y_pred: array of predictions\n",
    "#    sensitive_features: array of sensitive feature values\n",
    "\n",
    "mf1 = MetricFrame(metrics=false_negative_rate,\n",
    "                  y_true=Y_test,\n",
    "                  y_pred=Y_pred,\n",
    "                  sensitive_features= df_test['HomePlanet'])\n",
    "\n",
    "# The disaggregated metrics are stored in a pandas Series mf1.by_group:\n",
    "\n",
    "mf1.by_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "064ac168",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference: 0.282\n",
      "ratio: 0.218\n",
      "max across groups: 0.361\n"
     ]
    }
   ],
   "source": [
    "# The largest difference, smallest ratio and worst-case performance are accessed as\n",
    "#   mf1.difference(), mf1.ratio(), mf1.group_max()\n",
    "\n",
    "print(f\"difference: {mf1.difference():.3}\\n\"\n",
    "      f\"ratio: {mf1.ratio():.3}\\n\"\n",
    "      f\"max across groups: {mf1.group_max():.3}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "ed65df9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>selection_rate</th>\n",
       "      <th>false_negative_rate</th>\n",
       "      <th>balanced_accuracy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HomePlanet</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Earth</th>\n",
       "      <td>0.527318</td>\n",
       "      <td>0.360544</td>\n",
       "      <td>0.597666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europa</th>\n",
       "      <td>0.701465</td>\n",
       "      <td>0.078621</td>\n",
       "      <td>0.827175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mars</th>\n",
       "      <td>0.600715</td>\n",
       "      <td>0.131034</td>\n",
       "      <td>0.778542</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           selection_rate false_negative_rate balanced_accuracy\n",
       "HomePlanet                                                     \n",
       "Earth            0.527318            0.360544          0.597666\n",
       "Europa           0.701465            0.078621          0.827175\n",
       "Mars             0.600715            0.131034          0.778542"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You can also evaluate multiple metrics by providing a dictionary\n",
    "\n",
    "metrics_dict = {\n",
    "    \"selection_rate\": selection_rate,\n",
    "    \"false_negative_rate\": false_negative_rate,\n",
    "    \"balanced_accuracy\": balanced_accuracy_score,\n",
    "}\n",
    "\n",
    "metricframe_unmitigated = MetricFrame(metrics=metrics_dict,\n",
    "                  y_true=Y_test,\n",
    "                  y_pred=Y_pred,\n",
    "                  sensitive_features=df_test['HomePlanet'])\n",
    "\n",
    "# The disaggregated metrics are then stored in a pandas DataFrame:\n",
    "\n",
    "metricframe_unmitigated.by_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "a54a0007",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "selection_rate         0.174147\n",
       "false_negative_rate    0.281924\n",
       "balanced_accuracy      0.229509\n",
       "dtype: object"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The largest difference, smallest ratio, and the maximum and minimum values\n",
    "# across the groups are then all pandas Series, for example:\n",
    "\n",
    "metricframe_unmitigated.difference()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "dbcde7f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>selection_rate</th>\n",
       "      <th>false_negative_rate</th>\n",
       "      <th>balanced_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>difference</th>\n",
       "      <td>0.174147</td>\n",
       "      <td>0.281924</td>\n",
       "      <td>0.229509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratio</th>\n",
       "      <td>0.751738</td>\n",
       "      <td>0.218061</td>\n",
       "      <td>0.722539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_min</th>\n",
       "      <td>0.527318</td>\n",
       "      <td>0.078621</td>\n",
       "      <td>0.597666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_max</th>\n",
       "      <td>0.701465</td>\n",
       "      <td>0.360544</td>\n",
       "      <td>0.827175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           selection_rate false_negative_rate balanced_accuracy\n",
       "difference       0.174147            0.281924          0.229509\n",
       "ratio            0.751738            0.218061          0.722539\n",
       "group_min        0.527318            0.078621          0.597666\n",
       "group_max        0.701465            0.360544          0.827175"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You'll probably want to view them transposed:\n",
    "\n",
    "pd.DataFrame({'difference': metricframe_unmitigated.difference(),\n",
    "              'ratio': metricframe_unmitigated.ratio(),\n",
    "              'group_min': metricframe_unmitigated.group_min(),\n",
    "              'group_max': metricframe_unmitigated.group_max()}).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "0bf8c066",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# You can also easily plot all of the metrics using DataFrame plotting capabilities\n",
    "\n",
    "metricframe_unmitigated.by_group.plot.bar(subplots=True, layout= [1,3], figsize=(12, 4),\n",
    "                      legend=False, rot=-45, position=1.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a0d2f3a",
   "metadata": {},
   "source": [
    "## Threshold Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "812e2c06",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we instantite ThresholdOptimizer with the Decision Tree Classifier\n",
    "postprocess_est = ThresholdOptimizer(\n",
    "    estimator=unmitigated_pipeline,\n",
    "    constraints=\"false_negative_rate_parity\",\n",
    "    objective=\"balanced_accuracy_score\",\n",
    "    prefit=True,\n",
    "    predict_method='predict_proba'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "58ef09fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ThresholdOptimizer(constraints=&#x27;false_negative_rate_parity&#x27;,\n",
       "                   estimator=Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
       "                                              StandardScaler()),\n",
       "                                             (&#x27;clf&#x27;,\n",
       "                                              DecisionTreeClassifier())]),\n",
       "                   objective=&#x27;balanced_accuracy_score&#x27;,\n",
       "                   predict_method=&#x27;predict_proba&#x27;, prefit=True)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ThresholdOptimizer</label><div class=\"sk-toggleable__content\"><pre>ThresholdOptimizer(constraints=&#x27;false_negative_rate_parity&#x27;,\n",
       "                   estimator=Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
       "                                              StandardScaler()),\n",
       "                                             (&#x27;clf&#x27;,\n",
       "                                              DecisionTreeClassifier())]),\n",
       "                   objective=&#x27;balanced_accuracy_score&#x27;,\n",
       "                   predict_method=&#x27;predict_proba&#x27;, prefit=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;, StandardScaler()),\n",
       "                (&#x27;clf&#x27;, DecisionTreeClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ThresholdOptimizer(constraints='false_negative_rate_parity',\n",
       "                   estimator=Pipeline(steps=[('preprocessing',\n",
       "                                              StandardScaler()),\n",
       "                                             ('clf',\n",
       "                                              DecisionTreeClassifier())]),\n",
       "                   objective='balanced_accuracy_score',\n",
       "                   predict_method='predict_proba', prefit=True)"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "postprocess_est.fit(X_train_bal, Y_train_bal, sensitive_features=A_train_bal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "de1225bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Record and evaluate the output of the trained ThresholdOptimizer on test data\n",
    "Y_pred_postprocess = postprocess_est.predict(X_test, sensitive_features=A_test)\n",
    "metricframe_postprocess = MetricFrame(\n",
    "    metrics=metrics_dict,\n",
    "    y_true=Y_test,\n",
    "    y_pred=Y_pred_postprocess,\n",
    "    sensitive_features=A_test\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "a38cf4cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead 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=\"3\" halign=\"left\">Unmitigated</th>\n",
       "      <th colspan=\"3\" halign=\"left\">ThresholdOptimizer</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>selection_rate</th>\n",
       "      <th>false_negative_rate</th>\n",
       "      <th>balanced_accuracy</th>\n",
       "      <th>selection_rate</th>\n",
       "      <th>false_negative_rate</th>\n",
       "      <th>balanced_accuracy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HomePlanet</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>Earth</th>\n",
       "      <td>0.527318</td>\n",
       "      <td>0.360544</td>\n",
       "      <td>0.597666</td>\n",
       "      <td>0.602235</td>\n",
       "      <td>0.24587</td>\n",
       "      <td>0.632292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europa</th>\n",
       "      <td>0.701465</td>\n",
       "      <td>0.078621</td>\n",
       "      <td>0.827175</td>\n",
       "      <td>0.690476</td>\n",
       "      <td>0.093793</td>\n",
       "      <td>0.820951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mars</th>\n",
       "      <td>0.600715</td>\n",
       "      <td>0.131034</td>\n",
       "      <td>0.778542</td>\n",
       "      <td>0.586412</td>\n",
       "      <td>0.149425</td>\n",
       "      <td>0.774297</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Unmitigated                                        \\\n",
       "           selection_rate false_negative_rate balanced_accuracy   \n",
       "HomePlanet                                                        \n",
       "Earth            0.527318            0.360544          0.597666   \n",
       "Europa           0.701465            0.078621          0.827175   \n",
       "Mars             0.600715            0.131034          0.778542   \n",
       "\n",
       "           ThresholdOptimizer                                        \n",
       "               selection_rate false_negative_rate balanced_accuracy  \n",
       "HomePlanet                                                           \n",
       "Earth                0.602235             0.24587          0.632292  \n",
       "Europa               0.690476            0.093793          0.820951  \n",
       "Mars                 0.586412            0.149425          0.774297  "
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([metricframe_unmitigated.by_group,\n",
    "           metricframe_postprocess.by_group],\n",
    "           keys=['Unmitigated', 'ThresholdOptimizer'],\n",
    "           axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62cd44c1",
   "metadata": {},
   "source": [
    "pd.concat([metricframe_unmitigated.difference(),\n",
    "           metricframe_postprocess.difference()],\n",
    "          keys=['Unmitigated: difference', 'ThresholdOptimizer: difference'],\n",
    "          axis=1).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "0adc753d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'selection_rate'}, xlabel='HomePlanet'>,\n",
       "        <Axes: title={'center': 'false_negative_rate'}, xlabel='HomePlanet'>,\n",
       "        <Axes: title={'center': 'balanced_accuracy'}, xlabel='HomePlanet'>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oNFi5ciVWrlypt+3tBXaV69evAwDee+89vPfee3rrCwoKtO1atGhR57iLiooAAC1bttRZbmNjAxcXF9y4cUO77M4ZnqsS5Z3P39TG7b/YgMpfSu+88w527doFiUSC9u3bo0ePHgBqfkevUqk0eFyJyDAajQYLFy7EunXrUFxcjDZt2iAgIOA/v19RUVFo2rQpNm3ahISEBMTHx+Ohhx5CXFwcHnnkEVy/fh1CCHTv3r3a7QsKCgwq0u+Wm65fv44LFy7Az8+v2u1LSkpw7dq1anPpncv27t2Ljz76COfOnUPTpk3h4+OjzWc15ao7SSQSPPnkk9i4cSPi4uKwe/duFBcX48knn9TGC0BbtN+pKv8b4s6ce+LECbz33ns4ceIEHBwc0LFjR9x///3/uR+miIvIWt153CSVSuHi4gKlUomLFy9i9uzZ2L9/P2xtbfHggw/Cx8cHQM3fL0O+k7fnPKlUqtPmbseFVd/jOy/OVMnPz4erqysAwMXF5T/3ubZWrVqFZcuW4fr167jvvvvg7+8PBwcH7TFmVUxVn3unoqIiCCHqdbx7u7ocg9bmeHvEiBFYtmwZ0tPT8cgjj2D//v1ISEgwSsxkOBbppPXggw8iPj4earUaf/zxB7Zt24Yvv/wSrVu3hkQiwdixY6tNitW93sjZ2RkAMGPGDAQFBemtb9asGQDAycmp2knS9uzZU6uD3Kp+rly5And3d+3y8vJyXLt2TS9Bm8r06dNx7tw5fP755+jWrRvkcjlKSkrw9ddf17hN06ZNDR5XIjLMihUr8Pnnn+O9995DeHg4nJycAAAjR46scRupVIrnn38ezz//PK5evYo9e/Zg2bJlmDJlCvbt2wcnJyc0adIEq1evrnb79u3bG3UfnJycEBQUhBkzZlS7Xi6Xo3Xr1vjnn3/01l29elX754sXL2Ly5MkYMGAAli9fjrZt20IikWDdunXYu3evQTE9/fTTWL58OTIyMpCWlobAwEBtDq7K/wkJCXjggQf0tr3vvvsM+qw73bx5E1FRUfD29sZ3332HBx98EFKpFHv27NG5KnQnU8dFZE2qCssqarVae9wUHR0NW1tbfPPNN+jUqRNsbGxw5swZbNu2rdq+6vqdrI6Tk1O1d1EePXoUzZo1036Pv/jiCzRt2lSv3f3336+djO32/FfdPtfGt99+i3nz5iEmJgbDhw/XFuKvvfaa9mp1VUyFhYV48MEHtdtevnwZFy9ehL+/PyQSid7xbllZGQ4cOIAuXbpoT86q1WrIZDIAlVfGa6M2x6B3G1dPT0+0bdsWQUFB2LlzJ65fvw5HR0cMGDCgtkNFRsbZ3QlA5XtxH3nkEVy5cgUymQzdunXDu+++C2dnZ1y9ehW+vr44d+4cOnfurP156KGHsHjxYu0tULd78MEH0aJFC+Tk5Ohs07p1ayxYsEA7K3GPHj2wb98+nVuhMjMzER0djb/++kubqGpSdQLgu+++01n+3XffQa1W4+GHH67v0NTKkSNHEB4ejp49e0IulwOAdnbmqqthd+6Lo6OjweNKRIY5cuQIOnbsiBEjRmgL9Pz8fPz999813kUzatQo7SzlLVq0wPDhw/H8889DqVTi5s2bCAoKQnFxMYQQOt/dv//+G59++qnOLZjGEBQUhPPnz6NDhw46n7dt2zZ88803kMlkCAoKQk5OjnaWdqDyFsvbi+8///wTZWVliI6ORrt27bQHhVVtqq64VF3Z+i+enp7w8/PDd999hz179uCpp57SruvSpQtsbW2Rn5+vE6+NjQ0WLlx418eY7ubcuXO4fv06xowZg44dO2rjvTPn3rkfpo6LyJrs27dPJ1f973//Q0VFBTp16oTz589j5MiR2u8HoP/9ul1tv5O10aNHD2RnZ+P06dPaZWVlZZgyZQq++eYb7RXia9eu6XyPCwsLsWjRIly/fh0PPPAA2rRpg++//16n76rb7w1x5MgRODs7IyoqSlug37p1C0eOHNHuV0BAAGxtbfX6T01NxbRp09CkSRN06tRJb/0vv/yC6OhoFBQUwNHREQCQl5en89m1jfFux6B3G9cqI0eOxG+//YYdO3Zg0KBBvKvTjHglnQAA3bt3h0ajweTJkxEdHY2mTZti586duHHjBsLDw9GvXz9ER0fjjTfewFNPPQW1Wo3U1FQcP34ckyZN0utPJpNh6tSpmD17NmQyGfr27QulUonk5GTk5+drb9ucNGkSnnvuObz00ksYM2YMSktL8cknnyAgIAC9evXSFu/79++Hp6cnunTpovM5HTt2xLBhw5CUlISSkhIEBgbi5MmTWLJkCXr27InQ0FDTDx4qE/S3334LPz8/uLm54ejRo1ixYgUkEon2udKqAuH2fZk2bZpB40pEhgkICEBycjJWrFiBrl274sKFC1i+fDlUKlWN81EEBgYiNTUV9913H7p164b8/HysWrUKQUFBcHV1RZ8+fRAYGIhJkyZh0qRJ8PT0xB9//IGkpCSEhobWeMtjXY0dOxbbtm3D2LFjMX78eLi4uCAtLQ1ff/219jVjQ4YMwYoVKzB58mS89tprcHZ2xqpVq3D16lXtLad+fn6wsbFBfHw8xo8fD5VKhc2bN+Pnn38G8H/PhFZdFdqxYwe6dOmCtm3bVhvX008/jY8//hg2NjZ4/PHHtctdXFwQFRWFRYsW4ebNm+jZsyfy8/OxaNEiSCQS7W2zddWhQwc4Ojpi2bJlsLGxgY2NDf73v/9pDzSr/r9W7ccPP/yA3r17w9PT06RxEVmTK1euYMqUKXjhhRfwzz//YOHChejVqxeeeOIJJCQkYN26dXBzc4OzszP27t2rvXOourxZ2+9kbQwfPhxr1qzBK6+8AoVCARcXF6xevRrl5eUYPXo02rZti6eeegpvv/02Ll26BH9/f5w/fx6JiYnw8PDAAw88AIlEgunTp+ONN95AXFwcHn/8cfz+++/48ssvDR6ngIAAfPnll5g3bx769u2LgoICpKSk4N9//9Xezenq6ooxY8bg888/h1wuR1BQEI4fP44vv/wSM2bMgFQqhUKhwCuvvIJp06Zh6NCh+Pfff7Fw4UIMGDAAXl5esLW1xdy5czF79mxMmDABubm5+PTTT6u9W6C6GO92DHq3ca0ycOBAzJkzB3/88Qfefvttg8eLjMgcs9WRZTp+/LgYP368CAoKEp07d9a+bqLKb7/9JkaPHi0CAgLEww8/LMaMGaMzu291M1N+9913YtiwYcLf318EBQWJl19+WWRlZem0OXbsmIiMjBQBAQHi0UcfFbGxseLq1ava9XPnzhVdu3YVgYGBQqVS6b2CraKiQiQnJ4v+/fsLPz8/0bdvX7Fw4UKdGePv3EaI6mdbv5uatsnJyREvvfSSePjhh8XDDz8sRowYIbZt2yYmTJggRowYUeO+1GZcicgwt3/fy8rKxHvvvSd69eolAgICxMCBA0VSUpJYvHix8Pf3F0VFRXrf6/LycpGUlCQGDBgg/P39RXBwsHjrrbdEYWGh9jNu3bolPvroI9G7d2/h5+cn+vXrJxYsWKCTdwyNtUp1eebChQtCoVCIwMBAERAQIJ566imxceNGne0uX74sJk+eLLp27Sp69Ogh3n//fTFlyhQxZMgQbZudO3eKwYMHi86dO4uQkBDx6quvioMHDwpvb2/tGyXy8vLEiBEjhJ+fn3jnnXeEELozDFf5999/ha+vr1AoFNXu29q1a8WgQYOEn5+fePTRR8Ubb7whLl26ZND43DlDe5UDBw6I4cOHi4CAAO1rjA4fPiy6desmPv74YyFE5SuHxo4dK/z8/MTEiRONGheRNevbt6+YN2+eeOutt0TXrl1FUFCQePfdd7WzvZ88eVL7msegoCAxevRo8csvv4jHH39c+32/85ivNt/J2sxgLkRlDpo2bZro0aOH9hWaJ0+e1K4vLy8XS5Ys0R739e7dW7zzzjvi2rVrOv1+9913YvDgwcLf318MHz5c7Nixw+DZ3TUajVi0aJHo3bu36Ny5sxgwYICYM2eO+Oqrr3ReHafRaMRnn32m/b3x+OOPiy+//FKnr927d4sRI0YIf39/0bt3bzFv3jydGfa3bNkiwsPDhZ+fn3jqqafEr7/+qv2dJUT9j0HvNq5VXn31VTFo0KBajxGZhkSIWs4UQ0RERBbp9OnTOHfuHMLDw3VmDx45ciTc3NywZMkSM0ZHRETWoLS0FH369MGkSZPw4osvmjucexpvdydC5UQddztfJZFI7vqMPBGREAJqtfqu7WQymU5BXR/FxcV47bXXMHr0aDz22GNQq9VIS0vDn3/+ienTpxvlM4ypNs/tS6XSWj0fT0RkKB736bp06RK2bNmC3377DRKJBCNGjDB3SPc8XkknAvDCCy9o37leE3d3d+3724mIapKRkYExY8bctd3cuXMxfPhwo33u999/j5SUFJw9exZCCPj6+uKVV15BSEiI0T7DWLy9ve/aZtiwYZg3b14DRENE9xoe9+nKzc3F0KFD0bRpU3z44YcIDg42d0j3PBbpRKicmfRur7qQy+W1OrAkonvbzZs3cf78+bu28/DwaLDXRFqaqlcX/RcXFxd4eHg0QDREdK/hcR9ZOhbpRERERERERBaCD3sRERERERERWQgW6UREREREREQWgkU6ERERERERkYWwylewCSGg0fBR+oYglUo41g2EY90wpFKJ0V57ZYmYHxsOv7MNh2PdMJgfyVj4nW04HOuG05A50iqLdI1GoLDwv2dkpPqzsZHCxaUplMpiVFRozB1Oo8axbjiurk0hkzXeg1Dmx4bB72zD4Vg3HOZHMgZ+ZxsOx7phNWSO5O3uRERERERERBaCRToRERERERGRhWCRTkRERERERGQhWKQTEdWSRqNBUlISQkND0bVrV0ycOBHZ2dk1tj99+jSio6PRs2dPBAcHQ6FQ4PLly9r1arUaAQEB8Pb21vlZvHhxQ+wOEREREVkgg4t0Qw5SFy9erHfwWfUTGxtb7+CJiBpScnIy1q9fjzlz5mDDhg3QaDSIioqCSqXSa3vt2jWMGzcO9vb2WLNmDVauXInCwkJERUWhrKwMAPDPP/+grKwM27Ztw6+//qr9GT9+fEPvGhERERFZCIOLdEMOUsePH69z4Pnrr79iwoQJaNKkCcaOHWuM+ImIGoRKpUJqaioUCgXCwsLg4+ODxMRE5OXlIT09Xa/9rl27UFxcjPnz58PLywv+/v6Ij4/H2bNncfToUQDAqVOn4OjoCB8fH7Rs2VL707Rp04bePSIiIiKyEAa9gq3qIHX69OkICwsDACQmJiI0NBTp6ekYMmSITvumTZvqHGxmZmZi9erVmDNnDry9vesfPRFRA8nKysKtW7cQHBysXebs7AxfX18cOnRIL/8FBwcjOTkZ9vb22mVSaeV5UaVSCaCySPf09DR6rDY2fJLJ1GQyqc5/yXQ41g2nEb8inYjIqhhUpBt6kHqn999/Hz169MCwYcPqFu1teBBqejwwajgc64ZT14PQvLw8AECbNm10lrdq1Uq77nYeHh7w8PDQWbZixQrY29sjMDAQAPD333+joqICEyZMQFZWFlq3bo0XX3wRTz/9dN2CBCCVSuDiwivxDcXZ2cHcIdwzONZERHSvMKhIN/Qg9Xa7d+/GsWPHsHXrVsMirAYPQhsWD4waDsfacpWUlAAA5HK5znI7OzsUFRXddfs1a9Zg7dq1iIuLg6urK4DKieU0Gg0UCgXc3NywZ88exMbGory8HCNHjqxTnBqNgFJZXKdtqfZkMimcnR2gVJZArdaYO5xGjWPdcJo1c9De8UNEROZjUJFen4PUVatWoW/fvujUqZOBIerjQWjD4IFRw+FYN5y6HoRW3bauUql0bmEvKyuDg0PNJ1eEEFi0aBGWLl2KV155BS+88IJ23Y4dO6BWq7WPBfn4+ODy5ctISUmpc5EOABUV/DfUUNRqDce7gXCsTU8Ic0dARESAgUV6XQ9SL1++jIyMDKxYsaKOYerjL+qGwwOjhsOxNr26HoRW3UFUUFCAdu3aaZcXFBTUOMdGeXk5YmNjsWPHDsTGxupNmHl7Hq3i5eWF7du31y1IIiIiIrJ6BhXpdTlIBSpnOXZ1dUWvXr3qGCY1RlKpBFKpdc1So9EIaDS81HAv8vHxgaOjIzIyMrT5T6lUIjMzE5GRkdVuM2PGDPzwww9YsGABBg8erLNOqVRiwIABmDVrFoYPH65dfuLECTz00EOm25F64HeWiIjMzRp/FwH8fUSGMahIr8tBKgAcPnwYQUFBsLEx6OOoEZNKJWjevInVTZSmVmtw/Xoxk+w9SC6XIzIyEgkJCXB1dYW7uzvi4+Ph5uaG8PBwqNVqFBYWwsnJCfb29ti8eTPS0tIwY8YMBAUF4cqVK9q+nJyc4OzsjEceeQSJiYlo0aIF2rdvj/T0dGzfvh3Lly83455WTyqVwKW5A6QymblDMYhGrca16yX8zhIRNQJSqQTNXRwgk1rX7yIAUGvUuH6Nv4+odgyqmg09SK2SmZmJESNGGD14sl5SqQQymRQJ644gJ/+GucOpFY/WTpj+/MOQSiVMsPcohUKBiooKxMXFobS0FIGBgUhJSYGtrS1ycnLQv39/zJ07F8OHD8eOHTsAAPPnz8f8+fN1+qlq89FHH2Hx4sV45513cPXqVXh6eiIpKQmhoaHm2L3/JJVKIJXJULD1E6iu5pg7nFqRt/BAq6Gv8ztLRNRISKUSyKQyJB1IxSXlf09abUncnd2geGQ8fx9RrRl8aduQg9QqV65cQfPmzY0ZNzUSOfk3cPbS3WfGJrIEMpkMMTExiImJ0Vvn4eGBU6dOaf+empp61/4cHR0RGxuL2NhYo8ZpSqqrOVDlnTd3GEREdA+7pMzD+WvZ5g6DyGQMLtINOUitcvz48bpFR0RERERWT6PRYMmSJdi4cSNu3LiBwMBAzJ49G23btq22/dWrV/HRRx9h3759EELg0UcfxaxZs9C6desGjpyIqOFZ1wPBRERERGR1kpOTsX79esyZMwcbNmyARqNBVFQUVCpVte1ff/11XL58GatWrcKqVatw+fJlTJ48uYGjJiIyDxbpRERERGQyKpUKqampUCgUCAsLg4+PDxITE5GXl4f09HS99kqlEgcPHsTEiRPRqVMn+Pr6Ijo6GidOnMD169cbfgeIiBoYi3QiIiIiMpmsrCzcunULwcHB2mXOzs7w9fXFoUOH9Nrb29ujadOm2Lp1K27evImbN29i27Zt6NChA5ydnRsydCIis+A70YiIiIjIZPLyKmfhbtOmjc7yVq1aadfdTi6XY968eZg9ezZ69OgBiUSCVq1aYe3atZBK63d9ycaG16dMrer1uqZ4za61vbr3TsaO35RjTfokkob7LBbpRERERGQyJSUlACqL79vZ2dmhqEj/DS9CCJw8eRLdunVDVFQU1Go1EhMTMWnSJHz55ZdwdHSsUxxSqQQuLk3rtC0ZztnZwdwhWBxTjQnHuvFhkU5EREREJmNvbw+g8tn0qj8DQFlZGRwc9IuLnTt3Yu3atdi9e7e2IF+2bBn69u2Lb775BmPHjq1THBqNgFJZXKdtqfZkMimcnR2gVJZArdaYpG9rZewxMeVYk75mzRzqfTdPbbFIJyIiIiKTqbrNvaCgAO3atdMuLygogLe3t177w4cPo0OHDjpXzJs1a4YOHTrgwoUL9YqlooKFTENRqzUc7zuYakw41g1DiIb7LD7AQEREREQm4+PjA0dHR2RkZGiXKZVKZGZmIjAwUK+9m5sbLly4gLKyMu2y4uJi5OTk4IEHHmiIkImIzIpFOhERERGZjFwuR2RkJBISEvDjjz8iKysLU6dOhZubG8LDw6FWq3HlyhWUlpYCAIYOHQqg8l3pWVlZyMrKwrRp02BnZ4fhw4ebcU+IiBoGi3QiIiIiMimFQoGRI0ciLi4OERERkMlkSElJga2tLXJzcxESEoK0tDQAlbO+r1+/HkIIvPjiixg3bhxsbW2xfv16ODk5mXlPiIhMj8+kExEREZFJyWQyxMTEICYmRm+dh4cHTp06pbPM09MTy5Yta6jwiIgsCq+kExEREREREVkIXkknIiIiIiIiHVKpBFKpxNxhGEyjEdBoGnAqdhNgkU5ERERERERaUqkEzV0cIJPKzB2KwdQaNa5fK7HqQp1FOhEREREREWlJpRLIpDIkHUjFJWWeucOpNXdnNygeGQ+pVMIinYiIiIiIiBqXS8o8nL+Wbe4w7jmcOI6IiIiIiIjIQrBIJyIiIiIiIrIQLNKJiIiIiIiILASLdCIiIiIiIiILwSKdiIiIiIiIyEKwSCciIiIiIiKyECzSiYiIiIiIiCwEi3QiIiIiIiIiC8EinYiIiIiIiMhCsEgnIiIiIiIishAs0omIiIiIiIgsBIt0IiIiIiIiIgvBIp2IiIiIiIjIQrBIJyIiIiIiIrIQBhfpGo0GSUlJCA0NRdeuXTFx4kRkZ2fX2L68vBwLFizQto+MjMTJkyfrFTQRERERERFRY2RwkZ6cnIz169djzpw52LBhAzQaDaKioqBSqapt/+6772Lz5s346KOPsGnTJri6umLixIm4ceNGvYMnIiIiIiIiakwMKtJVKhVSU1OhUCgQFhYGHx8fJCYmIi8vD+np6Xrts7OzsWnTJnz44YcIDQ2Fp6cnPvjgA8jlcvz5559G2wkiIiIiIiKixsDGkMZZWVm4desWgoODtcucnZ3h6+uLQ4cOYciQITrt9+3bBycnJ/Tu3Vun/U8//VTPsE1DKpVAKpWYOwyDaTQCGo0wdxhERERERERUTwYV6Xl5eQCANm3a6Cxv1aqVdt3tzp8/j7Zt2yI9PR0rVqxAfn4+fH19MWvWLHh6etYjbMDGxrhz3kkkEjg52UMms7659NRqDW7cKIUQxi3Uq8bCFGNijeNcxZTjYc3jYi0k1ncejoiIiIjuIQYV6SUlJQAAuVyus9zOzg5FRUV67W/evIkLFy4gOTkZM2bMgLOzM5YuXYrRo0cjLS0NLVq0qFPQUqkELi5N67Tt3SSsO4KcfOt5Xt6jtROmP/8wmjdvYrLPcHZ2MFnf1siU48GxJiKixkij0WDJkiXYuHEjbty4gcDAQMyePRtt27bVa7t48WIsWbKk2n6GDx+OuXPnmjpcIiKzMqhIt7e3B1D5bHrVnwGgrKwMDg76xYWNjQ1u3ryJxMRE7ZXzxMRE9OnTB1u2bEFUVFSdgtZoBJTK4jptWxOZTApnZwfk5N/A2Uv6JxwsnVJZArVaY9Q+q8bElH1bI2sba9LVrJkDpFLesUBE1JCqJh6eN28e3NzcEB8fj6ioKHz77bd6F3/Gjx+PUaNG6SxbtWoVvvzyS4wdO7YBoyYiMg+DivSq29wLCgrQrl077fKCggJ4e3vrtXdzc4ONjY3Ore329vZo27YtcnJy6hozAKCigoXM7dRqjcnGxJR9WyOOtXUz8lMhRER0F1UTD0+fPh1hYWEAKi/ahIaGIj09XW9Oo6ZNm6Jp0/+7YzIzMxOrV6/GnDlzqj3eJCJqbAwq0n18fODo6IiMjAxtka5UKpGZmYnIyEi99oGBgaioqMCJEyfQuXNnAEBpaSmys7MxePBgI4RPRERERJbM0ImH7/T++++jR48eGDZsWL1jMfacRqSPcxrVzNjxc6xrZor4G3JeI4OKdLlcjsjISCQkJMDV1RXu7u6Ij4+Hm5sbwsPDoVarUVhYCCcnJ9jb26NHjx549NFHMXPmTLz//vto3rw5kpKSIJPJ8PTTT5tqn4joDtb45gK+tYCIqHEwdOLh2+3evRvHjh3D1q1b6x2HKec0In3W+lijKZlqTDjW+qx9TAwq0gFAoVCgoqICcXFxKC0tRWBgIFJSUmBra4ucnBz0798fc+fOxfDhwwFUTv6RkJCAV199FaWlpejevTtWr14NV1dXo+8MEemTSiVo3ryJ1Z0RVas1uH69mIU6EZGVM3Ti4dutWrUKffv2RadOneodhynmNCJ9nNOoZsYeE451zUwxJg05r5HBRbpMJkNMTAxiYmL01nl4eODUqVM6yxwdHfHuu+/i3XffrXOQRFR3UqkEMpnUqt5cUPXWAqlUwiKdiMjKGTrxcJXLly8jIyMDK1asMFosnPel4XCeHX2mGhOOtT5TjElDzmtkcJFORNbJWt9cQERE1s3QiYer7Nq1C66urujVq5fJYyQisiTWdf8rEREREVmV2ycerlI18XBgYGCN2x0+fBhBQUGwseE1JSK6tzDrEREREZHJGDrxcJXMzEyMGDHCjJETEZkHr6QTERERkUkpFAqMHDkScXFxiIiIgEwm0048nJubi5CQEKSlpelsc+XKFTRv3tw8ARMRmRGvpBMR1ZJGo8GSJUuwceNG3LhxA4GBgZg9ezbatm1bbfvTp08jPj4ex48fh1QqRWBgIGbNmoX7779f22bdunVITU3FlStX4O/vj7i4OPj6+jbULhERNQhDJx4GgOPHjzdEaEREFodX0omIaik5ORnr16/HnDlzsGHDBmg0GkRFRUGlUum1vXbtGsaNGwd7e3usWbMGK1euRGFhIaKiolBWVgYA2LJlC+bPn4/XXnsNmzdvhoeHB8aNG4fCwsKG3jUiIiIishAs0omIakGlUiE1NRUKhQJhYWHw8fFBYmIi8vLykJ6ertd+165dKC4uxvz58+Hl5QV/f3/Ex8fj7NmzOHr0KABg2bJliIyMxFNPPYWOHTvio48+goODAzZu3NjQu0dEREREFoJFOhFRLWRlZeHWrVsIDg7WLnN2doavry8OHTqk1z44OBjJyck6kyBJpZUpV6lU4urVq/jnn390+rOxsUGPHj2q7Y+IiIiI7g18Jp2IqBby8vIA/N/7fqu0atVKu+52Hh4e8PDw0Fm2YsUK2NvbIzAwELm5uTX2l5WVVa9YbWyMf/5VJrPec7qmiL2qT2seF2vBsW44Eom5IyAiIoBFOhFRrZSUlACofJXQ7ezs7FBUVHTX7desWYO1a9ciLi4Orq6uOHfuXI39VT2zXhdSqQQuLk3rvH1j5OzsYJV9ky6ONRER3StYpBMR1ULVbesqlUrnFvaysjI4ONRcPAghsGjRIixduhSvvPIKXnjhBb3+bne3/u5GoxFQKovrvH1NZDKp1RZJSmUJ1GqNUfusGg9T9E26ONYNp1kzB+1jOUREZD4s0omIaqHqtvSCggK0a9dOu7ygoADe3t7VblNeXo7Y2Fjs2LEDsbGxGDt2bLX9eXp66vTXunXresVaUcFC5nZqtcZkY2LKvkkXx9r0hDB3BEREBHDiOCKiWvHx8YGjoyMyMjK0y5RKJTIzMxEYGFjtNjNmzMD333+PBQsW6BToANCiRQt06NBBp7+KigocPny4xv6IiIiIqPHjlXQiolqQy+WIjIxEQkICXF1d4e7ujvj4eLi5uSE8PBxqtRqFhYVwcnKCvb09Nm/ejLS0NMyYMQNBQUG4cuWKtq+qNuPHj8eHH36I9u3bo3PnzlixYgVKS0sxcuRIM+4pEREREZkTi3QiolpSKBSoqKhAXFwcSktLERgYiJSUFNja2iInJwf9+/fH3LlzMXz4cOzYsQMAMH/+fMyfP1+nn6o2zz77LG7cuIFPPvkE169fh7+/P1atWgVXV1dz7B4RERERWQAW6UREtSSTyRATE4OYmBi9dR4eHjh16pT276mpqbXqc8KECZgwYYLRYiQiIiIi68Zn0omIiIiIiIgsBIt0IiIiIiIiIgvBIp2IiIiIiIjIQrBIJyIiIiIiIrIQLNKJiIiIiIiILARndyciIiIiqgepVAKpVGLuMAyi0QhoNMLcYRBRNVikExERERHVkVQqQXMXB8ikMnOHYhC1Ro3r10pYqBNZIBbpRERERER1JJVKIJPKkHQgFZeUeeYOp1bcnd2geGQ8pFIJi3QiC8QinYiIiIioni4p83D+Wra5wyCiRoATxxERERERERFZCBbpRERERERERBaCRToRERERERGRhWCRTkRERERERGQhWKQTERERkUlpNBokJSUhNDQUXbt2xcSJE5GdXfMka+Xl5ViwYIG2fWRkJE6ePNmAERMRmY/BRbqhSXb79u3w9vbW+8nJyalX4ERERERkHZKTk7F+/XrMmTMHGzZsgEajQVRUFFQqVbXt3333XWzevBkfffQRNm3aBFdXV0ycOBE3btxo4MiJiBqewUW6oUn21KlTCAoKwq+//qrz06ZNm3oHT0RERESWTaVSITU1FQqFAmFhYfDx8UFiYiLy8vKQnp6u1z47OxubNm3Chx9+iNDQUHh6euKDDz6AXC7Hn3/+aYY9ICJqWAa9J70qyU6fPh1hYWEAgMTERISGhiI9PR1DhgzR2+bvv/+Gt7c3WrZsaZSAiYiIiMh6ZGVl4datWwgODtYuc3Z2hq+vLw4dOqR3/Lhv3z44OTmhd+/eOu1/+umnesdiY2P8Jz1lMut9etQUsVf1acq+rZWx4+dY18wU8UskRu+yRgYV6YYmWaDySnq/fv3qHykRERERWZ28vDwA0LuLslWrVtp1tzt//jzatm2L9PR0rFixAvn5+fD19cWsWbPg6elZ5zikUglcXJrWefvGyNnZwSr7tlamGhOOtT5rHxODinRDk2xRURHy8/Nx+PBhrF+/HteuXUNAQABiYmLQoUOHeoRt/DOhPFtUc588O6eLY91wrP0sKBERASUlJQAAuVyus9zOzg5FRUV67W/evIkLFy4gOTkZM2bMgLOzM5YuXYrRo0cjLS0NLVq0qFMcGo2AUllcp23/i0wmtdqCQKksgVqtMWqfVeNhyr6tlbHHhGNdM1OMSbNmDpBKG+a42qAi3dAke/r0aQCAEAJz585FaWmpNsl+++23uO++++oUNM+E6uOZ0IbDsW44HA8iIutnb28PoPKxyao/A0BZWRkcHPTzvI2NDW7evInExETtlfPExET06dMHW7ZsQVRUVJ1jqagw7kG7tVOrNSYbE1P2ba1MNSYca32mGBMhjNrdfzKoSDc0yfbo0QP79++Hi4sLJP//8tWSJUsQFhaGzZs3Izo6uk5Bm+JMKM8W6ePZuepxrBuOtZ8FJSKi/7sDs6CgAO3atdMuLygogLe3t157Nzc32NjY6Nzabm9vj7Zt2/LtQER0TzCoSDc0yQKAq6urzt8dHBzg4eGB/Px8Q2PVwbNFungmtOFwrBuOtZ8FJSIiwMfHB46OjsjIyNAePyqVSmRmZiIyMlKvfWBgICoqKnDixAl07twZAFBaWors7GwMHjy4QWMnIjIHgy4n3Z5kq1Ql2cDAQL32X331FXr27Ini4v+76n3z5k38888/6NixYz3CJiIiIiJrIJfLERkZiYSEBPz444/IysrC1KlT4ebmhvDwcKjValy5cgWlpaUAKu/EfPTRRzFz5kwcPnwYZ86cwYwZMyCTyfD000+beW+IiEzPoCLd0CTbu3dvaDQazJgxA6dPn8aJEycwZcoUuLq6Yvjw4SbZISIiIiKyLAqFAiNHjkRcXBwiIiIgk8mQkpICW1tb5ObmIiQkBGlpadr2ixcvRlBQEF599VWMHDkSN2/exOrVq/Xu0CQiaowMut0dqEyyFRUViIuLQ2lpKQIDA7VJNicnB/3798fcuXMxfPhwtGnTBp9//jkWLFiAiIgICCHQq1cvrF69GnZ2dqbYHyIiIiKyMDKZDDExMYiJidFb5+HhgVOnTuksc3R0xLvvvot33323gSIkIrIcBhfphiZZPz8/pKam1j1CIiIiIiIionsEpzgmIiIiIiIishAs0omIiIiIiIgsBIt0IiIiIiIiIgvBIp2IiIiIiIjIQrBIJyIiIiIiIrIQLNKJiIiIiIiILASLdCIiIiIiIiILwSKdiIiIiIiIyEKwSCciIiIiIiKyECzSiYiIiIiIiCwEi3QiIiIiIiIiC8EinYiIiIiIiMhCsEgnIiIiIiIishAs0omIiIiIiIgsBIt0IiIiIiIiIgvBIp2IiIiIiIjIQrBIJyIiIiIiIrIQLNKJiIiIiIiILASLdCIiIiIiIiILwSKdiIiIiIiIyEKwSCciIiIiIiKyECzSiYiIiIiIiCwEi3QiolrQaDRISkpCaGgounbtiokTJyI7O7tW20VFRWHx4sV668LDw+Ht7a3zM2vWLFOET0RkVobm0O3bt+vlR29vb+Tk5DRg1ERE5mFj7gCIiKxBcnIy1q9fj3nz5sHNzQ3x8fGIiorCt99+C7lcXu02KpUKs2fPxt69e9GlSxeddcXFxcjOzsby5cvh5+enXW5vb2/S/SAiMgdDc+ipU6cQFBSEhQsX6ix3dXVtqJCJiMyGV9KJiO5CpVIhNTUVCoUCYWFh8PHxQWJiIvLy8pCenl7tNkePHsXw4cNx+PBhODs7660/c+YMNBoNunXrhpYtW2p/nJycTL07REQNqi459O+//4a3t7dOfmzZsiVkMlkDR09E1PBYpBMR3UVWVhZu3bqF4OBg7TJnZ2f4+vri0KFD1W6zZ88ehIaGYuvWrdUW3qdOncJ9992HZs2amSxuIiJLUJcceurUKXh6ejZUiEREFoW3uxMR3UVeXh4AoE2bNjrLW7VqpV13p6lTp/5nn6dOnUKTJk2gUChw9OhRuLi4YMSIERgzZgyk0vqdP7WxMf75V5nMes/pmiL2qj6teVyshanHWiKRQCqVmKRvU9JoBIQQRu1TYqJhMDSHFhUVIT8/H4cPH8b69etx7do1BAQEICYmBh06dKhXLMyPuqwtP1rzWAPGj59jXTNTxG+qHFkdFulERHdRUlICAHrPTdrZ2aGoqKhOfZ4+fRpKpRIDBw7E5MmTceTIEcTHx6OoqAivvfZanWOVSiVwcWla5+0bI2dnB6vsm3SZaqyFRgNJPU+MmYM1xW1oDj19+jQAQAiBuXPnorS0FEuXLsXo0aPx7bff4r777qtTHMyP+pgfG5apxoRjrc/ax4RFOhHRXVRN5qZSqXQmdisrK4ODQ91+CaxcuRJlZWXaW+G9vb1x8+ZNLF26FFOmTKnz1XSNRkCpLK7Ttv9FJpNa7S88pbIEarXGqH1WjYcp+iZdphzrqr4Ltn4C1VXrmTVc3sIDrYa+bvQxadbMod538lTH0Bzao0cP7N+/Hy4uLpD8/0tXS5YsQVhYGDZv3ozo6Og6xcH8qM/a8qM1jzVg/PHmWNfMFGNiqhxZHRbpRER3UXWLZkFBAdq1a6ddXlBQAG9v7zr1KZfL9a4qeXl5obi4GEVFRXBxcalzvBUVLBpvp1ZrTDYmpuybdJlyrFVXc6DKO2+Svk3J2GNi5LvnteqSQ++cxd3BwQEeHh7Iz8+vVyz8vupifmxYphoTjrU+U4yJqXJkdazjPikiIjPy8fGBo6MjMjIytMuUSiUyMzMRGBhocH9CCAwYMABLlizRWX7ixAm0bNmyXgU6EZGlMTSHfvXVV+jZsyeKi//vqvfNmzfxzz//oGPHjg0SMxGRORlcpGs0GiQlJSE0NBRdu3bFxIkTkZ2dXattt2/fDm9vb+TkWM8tZUREcrkckZGRSEhIwI8//oisrCxMnToVbm5uCA8Ph1qtxpUrV1BaWlqr/iQSCR577DGkpKQgLS0NFy9exFdffYXPPvsMCoXCxHtDRNSwDM2hvXv3hkajwYwZM3D69GmcOHECU6ZMgaurK4YPH27mvSEiMj2Di/Tk5GSsX78ec+bMwYYNG6DRaBAVFQWVSvWf2126dAnvv/9+nQMlIjInhUKBkSNHIi4uDhEREZDJZEhJSYGtrS1yc3MREhKCtLS0Wvf3xhtvICoqCgsXLsSgQYOwatUqvPXWW3j22WdNuBdEROZhSA5t06YNPv/8cxQXFyMiIgJjx46Fk5MTVq9eDTs7OzPvCRGR6Rn0TLpKpUJqaiqmT5+OsLAwAEBiYiJCQ0ORnp6OIUOGVLudRqNBTEwM/Pz8cODAgXoHTUTU0GQyGWJiYhATE6O3zsPDA6dOnapx259++klvmY2NDSZPnozJkycbNU4iIktkaA718/NDampqQ4VHRGRRDLqSnpWVhVu3biE4OFi7zNnZGb6+vjh06FCN2y1btgzl5eV46aWX6h4pERERERERUSNn0JX0vLw8AP83S2eVVq1aadfd6Y8//kBqaiq++eabes/IeTsbG+POeWeKF943JFPEX9WnKfu2RhzrhmOK2P//23yIiIiIiCySQUV6SUkJAOi9NsjOzg5FRUV67YuLizF9+nRMnz4dDzzwgNGKdKlUAheXpkbpq7Ew5XsMrfkdiabAsW44HA8iIiIiutcYVKTb29sDqHw2verPAFBWVgYHB/2D6Q8++AAdOnTAqFGj6hmmLo1GQKksvntDA8hkUqsuCJTKEqjVxn0XYNWYmLJva8SxbjimGI9mzRwglVrv3QVERERE1LgZVKRX3eZeUFCAdu3aaZcXFBTA29tbr/2mTZsgl8vRrVs3AIBarQYADBkyBC+//DJefvnlOgdu7JfTWzu1WmOyMTFl39aIY91wTDEeQhi1OyIiIiIiozKoSPfx8YGjoyMyMjK0RbpSqURmZiYiIyP12qenp+v8/fjx44iJicGKFSvg5eVVj7CJiIiIiIiIGh+DinS5XI7IyEgkJCTA1dUV7u7uiI+Ph5ubG8LDw6FWq1FYWAgnJyfY29ujffv2OttXTS53//33o3nz5kbbCSIiIiIiIqLGwOAHMxUKBUaOHIm4uDhERERAJpMhJSUFtra2yM3NRUhICNLS0kwRKxEREREREVGjZtCVdACQyWSIiYlBTEyM3joPDw+cOnWqxm179uz5n+uJiIiIiIiI7mWc4piIiIiIiIjIQrBIJyIiIiIiIrIQLNKJiIiIiIiILASLdCIiIiIiIiILwSKdiIiIiIiIyEKwSCciIiIiIiKyECzSiYiIiIiIiCwEi3QiIiIiIiIiC8EinYiIiIiIiMhCsEgnIiIiIiIishAs0omIiIiIiIgsBIt0IiIiIiIiIgvBIp2IiIiIiIjIQrBIJyIiIiIiIrIQLNKJiIiIiIiILASLdCIiIiIyKY1Gg6SkJISGhqJr166YOHEisrOza7Xt9u3b4e3tjZycHBNHSURkGVikExEREZFJJScnY/369ZgzZw42bNgAjUaDqKgoqFSq/9zu0qVLeP/99xsoSiIiy8AinYiIiIhMRqVSITU1FQqFAmFhYfDx8UFiYiLy8vKQnp5e43YajQYxMTHw8/NrwGiJiMyPRToRERERmUxWVhZu3bqF4OBg7TJnZ2f4+vri0KFDNW63bNkylJeX46WXXmqIMImILIaNuQMgIiIiosYrLy8PANCmTRud5a1atdKuu9Mff/yB1NRUfPPNN8jPzzdaLDY2xr8+JZNZ7zUvU8Re1acp+7ZWxo6fY10zU8QvkRi9yxqxSCciIiIikykpKQEAyOVyneV2dnYoKirSa19cXIzp06dj+vTpeOCBB4xWpEulEri4NDVKX42Fs7ODVfZtrUw1JhxrfdY+JizSiYiIiMhk7O3tAVQ+m171ZwAoKyuDg4P+gfQHH3yADh06YNSoUUaNQ6MRUCqLjdonUHnFzloLAqWyBGq1xqh9Vo2HKfu2VsYeE451zUwxJs2aOUAqbZg7DFikExEREZHJVN3mXlBQgHbt2mmXFxQUwNvbW6/9pk2bIJfL0a1bNwCAWq0GAAwZMgQvv/wyXn755TrHUlFh3IN2a6dWa0w2Jqbs21qZakw41vpMMSZCGLW7/8QinYiIiIhMxsfHB46OjsjIyNAW6UqlEpmZmYiMjNRrf+eM78ePH0dMTAxWrFgBLy+vBomZiMicWKQTERERkcnI5XJERkYiISEBrq6ucHd3R3x8PNzc3BAeHg61Wo3CwkI4OTnB3t4e7du319m+anK5+++/H82bNzfDHhARNSzrnraPiIiIiCyeQqHAyJEjERcXh4iICMhkMqSkpMDW1ha5ubkICQlBWlqaucMkIrIIvJJORERERCYlk8kQExODmJgYvXUeHh44depUjdv27NnzP9cTETU2vJJOREREREREZCFYpBMRERERERFZCBbpRERERERERBbC4CJdo9EgKSkJoaGh6Nq1KyZOnIjs7Owa2//111948cUX0a1bNzzyyCOYPXs2bty4Ua+giYiIiIiIiBojg4v05ORkrF+/HnPmzMGGDRug0WgQFRUFlUql1/bff//FuHHj4O7ujs2bNyM5ORlHjhzBrFmzjBI8ERERERERUWNiUJGuUqmQmpoKhUKBsLAw+Pj4IDExEXl5eUhPT9drf+nSJYSEhOD9999Hhw4d0L17dzz77LPYt2+f0XaAiIiIiIiIqLEwqEjPysrCrVu3EBwcrF3m7OwMX19fHDp0SK99ly5dsHDhQtjYVL7p7ezZs9i2bRt69epVz7CJiIiIiIiIGh+D3pOel5cHAGjTpo3O8latWmnX1WTgwIH4559/4O7ujiVLlhgYpj4bG+POeSeTWfcceqaIv6pPU/ZtjTjWDccUsUskRu+SiIiIiMhoDCrSS0pKAAByuVxnuZ2dHYqKiv5z24SEBJSUlCA+Ph5jxozBtm3b0LRpUwPDrSSVSuDiUrdtGytnZwer7NsacawbDseDiIiIiO41BhXp9vb2ACqfTa/6MwCUlZXBweG/D6Y7d+4MAFiyZAn69OmDH374AUOHDjUw3EoajYBSWVynbWsik0mtuiBQKkugVmuM2mfVmJiyb2vEsW44phiPZs0cIJVa790FRERERNS4GVSkV93mXlBQgHbt2mmXFxQUwNvbW6/9uXPncPHiRYSFhWmXtW7dGs2bN0d+fn4dQ65UUWHcA3drp1ZrTDYmpuzbGnGsG44pxkMIo3ZHRERERGRUBl1O8vHxgaOjIzIyMrTLlEolMjMzERgYqNf+t99+g0KhgFKp1C67ePEirl27Bk9Pz3qETURERERERNT4GFSky+VyREZGIiEhAT/++COysrIwdepUuLm5ITw8HGq1GleuXEFpaSkAYMiQIWjevDliYmJw+vRpHD58GAqFAgEBAejbt69JdoiIyBQ0Gg2SkpIQGhqKrl27YuLEicjOzq7VdlFRUVi8eLHeup07d2LQoEEICAjA0KFDsX//flOETkRERERWxOAHMxUKBUaOHIm4uDhERERAJpMhJSUFtra2yM3NRUhICNLS0gAAzZs3xxdffAEAiIiIwOTJk+Hr64uUlBTIZDLj7gkRkQklJydj/fr1mDNnDjZs2KAtvlUqVY3bqFQqvPnmm9i7d6/eugMHDiAmJgajRo3Cli1bEBwcjOjoaJw9e9aUu0FEREREFs6gZ9IBQCaTISYmBjExMXrrPDw8cOrUKZ1lHTp0wPLly+seIRGRmalUKqSmpmL69OnaOTYSExMRGhqK9PR0DBkyRG+bo0ePYvbs2SgtLYWzs7Pe+pUrV2LAgAEYM2YMAGDmzJk4duwYvvjiC7z//vsm3R8iIiIislwGF+lERPearKws3Lp1C8HBwdplzs7O8PX1xaFDh6ot0vfs2YPQ0FBMnjwZTz31lM46jUaDo0ePYtasWTrLe/bsifT09HrHa2Nj/NnrTfHO+oZiitir+rTmcbEWphxra///Z+z4JRKjdkdERHXEIp2I6C7y8vIA/N8bLqq0atVKu+5OU6dOrbE/pVKJ4uJiuLm51bq/2pJKJXBxaVqvPhobU76C0Fpfb2iNONb6OCZERI0Ti3QiorsoKSkBUDl55u3s7OxQVFRkcH9Vk2tW119ZWVkdo6yk0QgolcX16qM6MpnUagsCpbIEarVxX+VXNR6m6Jt0mXKsrfnfNWD8f9vNmjlAKrXuuwuIiBoDFulERHdhb28PoPLZ9Ko/A0BZWRkcHAw/wLezs9P2d7u69ncnY79b3tqp1RqTjYkp+yZdHGt9xh4TIYzWFRER1QNPlxIR3UXVbe4FBQU6ywsKCtC6dWuD+2vevDmaNGlitP6IiIiIqPFgkU5EdBc+Pj5wdHRERkaGdplSqURmZiYCAwMN7k8ikaB79+44ePCgzvKMjAz06NGj3vESERERkfXi7e5ERHchl8sRGRmJhIQEuLq6wt3dHfHx8XBzc0N4eDjUajUKCwvh5OSkczv8fxk3bhyio6Ph6+uL3r17Y9OmTTh58iQ+/PBDE+8NEREREVkyXkknIqoFhUKBkSNHIi4uDhEREZDJZEhJSYGtrS1yc3MREhKCtLS0WvcXEhKCjz76CF9++SWGDRuGAwcOYNmyZfD09DThXhARERGRpeOVdCKiWpDJZIiJiUFMTIzeOg8PD5w6darGbX/66adqlw8dOhRDhw41VojUiEilEkil1vXSao1GQKPhzGNERET1xSKdiIjIgkilErg0d4BUJjN3KAbRqNW4dr2EhTpVS6PRYMmSJdi4cSNu3LiBwMBAzJ49G23btq22/V9//YX58+fjjz/+gJ2dHcLDwxETEwMnJ6cGjpyIqOGxSCciIrIgUqkEUpkMBVs/gepqjrnDqRV5Cw+0Gvo6pFIJi3SqVnJyMtavX4958+bBzc0N8fHxiIqKwrfffgu5XK7T9t9//8W4ceMwYMAAvPvuu7h27RrefvttzJo1C59++qmZ9oCIqOGwSCciIrJAqqs5UOWdN3cYRPWmUqmQmpqK6dOnIywsDACQmJiI0NBQpKenY8iQITrtL126hJCQELz//vuwsbFBhw4d8OyzzyIxMdEM0RMRNTwW6URERERkMllZWbh16xaCg4O1y5ydneHr64tDhw7pFeldunTBwoULtX8/e/Ystm3bhl69etU7Fhsb48+ZLJNZ7zzMpoi9qk9T9m2tjB0/x7pmpohf0oBTxbBIJyIiIiKTycvLAwC0adNGZ3mrVq2062oycOBA/PPPP3B3d8eSJUvqFYdUKoGLS9N69dHYODs7WGXf1spUY8Kx1mftY8IinYiIiIhMpqSkBAD0nj23s7NDUVHRf26bkJCAkpISxMfHY8yYMdi2bRuaNq1boa3RCCiVxXXa9r/IZFKrLQiUyhKo1Rqj9lk1Hqbs21oZe0w41jUzxZg0a+YAqbRh7jBgkU5EREREJmNvbw+g8tn0qj8DQFlZGRwc/rsI6Ny5MwBgyZIl6NOnD3744Yd6vbqyosK4B+3WTq3WmGxMTNm3tTLVmHCs9ZliTEQDzotq3Q8bEBEREZFFq7rNvaCgQGd5QUEBWrdurdf+3Llz+Pnnn3WWtW7dGs2bN0d+fr7J4iQishQs0omIiIjIZHx8fODo6IiMjAztMqVSiczMTAQGBuq1/+2336BQKKBUKrXLLl68iGvXrsHT07NBYiYiMicW6URERERkMnK5HJGRkUhISMCPP/6IrKwsTJ06FW5ubggPD4darcaVK1dQWloKABgyZAiaN2+OmJgYnD59GocPH4ZCoUBAQAD69u1r5r0hIjI9FulEREREZFIKhQIjR45EXFwcIiIiIJPJkJKSAltbW+Tm5iIkJARpaWkAgObNm+OLL74AAERERGDy5Mnw9fVFSkoKZDKZOXeDiKhBcOI4IiIiIjIpmUyGmJgYxMTE6K3z8PDAqVOndJZ16NABy5cvb6jwiIgsCq+kExEREREREVkIFulEREREREREFoJFOhEREREREZGFYJFOREREREREZCFYpBMRERERERFZCBbpRERERERERBaCRToRERERERGRhWCRTkRERERERGQhWKQTERERERERWQgW6UREREREREQWwuAiXaPRICkpCaGhoejatSsmTpyI7OzsGtufPn0a0dHR6NmzJ4KDg6FQKHD58uV6BU1ERERERETUGBlcpCcnJ2P9+vWYM2cONmzYAI1Gg6ioKKhUKr22165dw7hx42Bvb481a9Zg5cqVKCwsRFRUFMrKyoyyA0RERERERESNhUFFukqlQmpqKhQKBcLCwuDj44PExETk5eUhPT1dr/2uXbtQXFyM+fPnw8vLC/7+/oiPj8fZs2dx9OhRo+0EERERERERUWNgUJGelZWFW7duITg4WLvM2dkZvr6+OHTokF774OBgJCcnw97e/v8+UFr5kUqlsq4xExERERERETVKNoY0zsvLAwC0adNGZ3mrVq20627n4eEBDw8PnWUrVqyAvb09AgMDDY1Vh42Ncee8k8msew49U8Rf1acp+7ZGHOuGY4rYJRKjd0lEREREZDQGFeklJSUAALlcrrPczs4ORUVFd91+zZo1WLt2LeLi4uDq6mrIR+uQSiVwcWla5+0bI2dnB6vs2xpxrBsOx4OIiIiI7jUGFelVt62rVCqdW9jLysrg4FDzwbQQAosWLcLSpUvxyiuv4IUXXqhjuJU0GgGlsrhefdxJJpNadUGgVJZArdYYtc+qMTFl39aIY91wTDEezZo5aB+7ISIiIiKyNAYV6VW3uRcUFKBdu3ba5QUFBfD29q52m/LycsTGxmLHjh2IjY3F2LFj6x7tbSoqjHvgbu3Uao3JxsSUfVsjjnXDMcV4CGHU7oiIiIiIjMqgy0k+Pj5wdHRERkaGdplSqURmZmaNz5jPmDED33//PRYsWGC0Ap2IiIiIiIioMTLoSrpcLkdkZCQSEhLg6uoKd3d3xMfHw83NDeHh4VCr1SgsLISTkxPs7e2xefNmpKWlYcaMGQgKCsKVK1e0fVW1ISIiIiIiIqJKBj+YqVAoMHLkSMTFxSEiIgIymQwpKSmwtbVFbm4uQkJCkJaWBgDYsWMHAGD+/PkICQnR+alqQ0RERERERESVDLqSDgAymQwxMTGIiYnRW+fh4YFTp05p/56amlq/6IiIiIiIiIjuIZzimIiIiIiIiMhCsEgnIiIiIpPSaDRISkpCaGgounbtiokTJyI7O7vG9qdPn0Z0dDR69uyJ4OBgKBQKXL58uQEjJiIyHxbpRERERGRSycnJWL9+PebMmYMNGzZAo9EgKioKKpVKr+21a9cwbtw42NvbY82aNVi5ciUKCwsRFRWFsrIyM0RPRNSwWKQTERERkcmoVCqkpqZCoVAgLCwMPj4+SExMRF5eHtLT0/Xa79q1C8XFxZg/fz68vLzg7++P+Ph4nD17FkePHjXDHhARNSwW6URERERkMllZWbh16xaCg4O1y5ydneHr64tDhw7ptQ8ODkZycrLOq3ql0spDVqVSafqAiYjMzODZ3YmIiIiIaisvLw8A0KZNG53lrVq10q67nYeHBzw8PHSWrVixAvb29ggMDKxXLDY2xr8+JZNZ7zUvU8Re1acp+7ZWxo6fY10zU8QvkRi9yxqxSCciIiIikykpKQEAyOVyneV2dnYoKiq66/Zr1qzB2rVrERcXB1dX1zrHIZVK4OLStM7bN0bOzg5W2be1MtWYcKz1WfuYsEgnIiIiIpOpum1dpVLp3MJeVlYGB4eaD6SFEFi0aBGWLl2KV155BS+88EK94tBoBJTK4nr1UR2ZTGq1BYFSWQK1WmPUPqvGw5R9WytjjwnHumamGJNmzRy0j96YGot0IiIiIjKZqtvcCwoK0K5dO+3ygoICeHt7V7tNeXk5YmNjsWPHDsTGxmLs2LFGiaWiwrgH7dZOrdaYbExM2be1MtWYcKz1mWJMhDBqd//Juh82ICIiIiKL5uPjA0dHR2RkZGiXKZVKZGZm1viM+YwZM/D9999jwYIFRivQiYisBa+kExEREZHJyOVyREZGIiEhAa6urnB3d0d8fDzc3NwQHh4OtVqNwsJCODk5wd7eHps3b0ZaWhpmzJiBoKAgXLlyRdtXVRsiosaMV9KJiIiIyKQUCgVGjhyJuLg4REREQCaTISUlBba2tsjNzUVISAjS0tIAADt27AAAzJ8/HyEhITo/VW2IiBozXkknIiIiIpOSyWSIiYlBTEyM3joPDw+cOnVK+/fU1NSGDI2IyOLwSjoRUS1oNBokJSUhNDQUXbt2xcSJE5GdnV1j+2vXruGNN95AYGAggoKC8N5772lfQ1QlPDwc3t7eOj+zZs0y9a4QERERkQXjlXQiolpITk7G+vXrMW/ePLi5uSE+Ph5RUVH49ttv9d79C1Te2llSUoLPP/8cSqUSb731FoqLi/Hxxx8DAIqLi5GdnY3ly5fDz89Pux2ftSQiIiK6t/FKOhHRXahUKqSmpkKhUCAsLAw+Pj5ITExEXl4e0tPT9dofO3YMBw8exMcffww/Pz8EBwfj/fffx7Zt25Cfnw8AOHPmDDQaDbp164aWLVtqf5ycnBp694iIiIjIgrBIJyK6i6ysLNy6dQvBwcHaZc7OzvD19cWhQ4f02h8+fBgtW7aEp6endllQUBAkEgmOHDkCADh16hTuu+8+NGvWzPQ7QERERERWg7e7ExHdRV5eHgCgTZs2OstbtWqlXXe7/Px8vbZyuRzNmzdHbm4ugMoivUmTJlAoFDh69ChcXFwwYsQIjBkzBlJp/c6f2tgY//yrTGa953RNEXtVn6bs2xpxrBuWseOXSIzaHRER1RGLdCKiu6ia8O3OZ8/t7OxQVFRUbfvqnlO3s7NDWVkZAOD06dNQKpUYOHAgJk+ejCNHjiA+Ph5FRUV47bXX6hyrVCqBi0vTOm/fGDk7O1hl39aIY92wOCZERI0Ti3QioruomsxNpVLpTOxWVlYGBwf9g2R7e3uoVCq95WVlZWjSpAkAYOXKlSgrK9M+g+7t7Y2bN29i6dKlmDJlSp2vpms0AkplcZ22/S8ymdRqCwKlsgRqtcaofVaNhyn7tkYc64Zl7DFp1syh3nfyEBFR/bFIJyK6i6pb1wsKCtCuXTvt8oKCAnh7e+u1d3Nzw65du3SWqVQqXL9+Ha1atQJQeVX+zqvtXl5eKC4uRlFREVxcXOocb0WFcQsZa6dWa0w2Jqbs2xpxrBuWscdECKN1RURE9cDTpUREd+Hj4wNHR0dkZGRolymVSmRmZiIwMFCvfWBgIPLy8nDhwgXtsoMHDwIAHn74YQghMGDAACxZskRnuxMnTqBly5b1KtCJiIiIyLrxSjoR0V3I5XJERkYiISEBrq6ucHd3R3x8PNzc3BAeHg61Wo3CwkI4OTnB3t4eXbp0Qffu3TF16lS8++67KC4uxuzZszF06FC0bt0aAPDYY48hJSUFDz74IPz9/bF//3589tlneOutt8y8t0RERERkTizSiYhqQaFQoKKiAnFxcSgtLUVgYCBSUlJga2uLnJwc9O/fH3PnzsXw4cMhkUiwZMkSvPfee3jxxRdhZ2eHxx9/HLGxsdr+3njjDTg6OmLhwoXIy8uDh4cH3nrrLTz77LNm3EsiIiIiMjcW6UREtSCTyRATE4OYmBi9dR4eHjh16pTOshYtWiApKanG/mxsbDB58mRMnjzZ6LESERERkfXiM+lEREREREREFoJFOhEREREREZGFYJFOREREREREZCFYpBMRERERERFZCBbpRERERERERBbC4CJdo9EgKSkJoaGh6Nq1KyZOnIjs7OxabRcVFYXFixfXKVAiIiIiIiKixs7gIj05ORnr16/HnDlzsGHDBm3xrVKpatxGpVLhzTffxN69e+sVLBEREREREVFjZlCRrlKpkJqaCoVCgbCwMPj4+CAxMRF5eXlIT0+vdpujR49i+PDhOHz4MJydnY0SNBEREREREVFjZFCRnpWVhVu3biE4OFi7zNnZGb6+vjh06FC12+zZswehoaHYunUrnJyc6hctERERERERUSNmY0jjvLw8AECbNm10lrdq1Uq77k5Tp06tY2j/zcbGuHPeyWTWPYeeKeKv6tOUfVsjjnXDMUXsEonRuyQiIiIiMhqDivSSkhIAgFwu11luZ2eHoqIi40V1F1KpBC4uTRvs86yBs7ODVfZtjTjWDYfjQURERET3GoOKdHt7ewCVz6ZX/RkAysrK4ODQcAfTGo2AUlls1D5lMqlVFwRKZQnUao1R+6waE1P2bY041g3HFOPRrJkDpFLrvbuAiIiIiBo3g4r0qtvcCwoK0K5dO+3ygoICeHt7Gzeyu6ioMO6Bu7VTqzUmGxNT9m2NONYNxxTjIYRRuyMiolrQaDRYsmQJNm7ciBs3biAwMBCzZ89G27Zt77pddHQ0unTpgilTpjRQtERE5mXQ5SQfHx84OjoiIyNDu0ypVCIzMxOBgYFGD46IiIiIrB9f4UtEVHsGFelyuRyRkZFISEjAjz/+iKysLEydOhVubm4IDw+HWq3GlStXUFpaaqp4iYiIiMiK8BW+RESGMfjBTIVCgZEjRyIuLg4RERGQyWRISUmBra0tcnNzERISgrS0NFPESkRERERWhq/wJSIyjEHPpAOATCZDTEwMYmJi9NZ5eHjg1KlTNW77008/GfpxRERERGTFGvMrfAG+6rSmPvlaWX3Gjp9jXTNrf42vwUU6EREREVFt8RW+louvlW1YphoTjrU+ax8TFulEREREZDKN+RW+AF91eie+VrZmxh4TjnXNrP01vizSiYiIiMhk+Apfy8XXyjYsU40Jx1qftb/G17ofNiAiIiIii8ZX+BIRGYZX0omIiIjIZG5/ha+rqyvc3d0RHx+v8wrfwsJCODk56dwOT0R0r+KVdCIiIiIyKb7Cl4io9nglnYiIiIhMiq/wJSKqPV5JJyIiIiIiIrIQLNKJiIiIiIiILASLdCIiIiIiIiILwSKdiIiIiIiIyEKwSCciIiIiIiKyECzSiYiIiIiIiCwEi3QiIiIiIiIiC8EinYiIiIiIiMhCsEgnIiIiIiIishAs0omIiIiIiIgsBIt0IiIiIiIiIgvBIp2IiIiIiIjIQrBIJyIiIiIiIrIQLNKJiIiIiIiILASLdCIiIiIiIiILwSKdiIiIiIiIyEKwSCciIiIiIiKyECzSiYiIiIiIiCwEi3QiIiIiIiIiC8EinYiIiIiIiMhCsEgnIiIiIiIishAs0omIiIiIiIgsBIt0IiIiIiIiIgvBIp2IiIiIiIjIQhhcpGs0GiQlJSE0NBRdu3bFxIkTkZ2dXWP7a9eu4Y033kBgYCCCgoLw3nvvoaSkpF5BExE1NFPkvp07d2LQoEEICAjA0KFDsX//flPvBhGRWfD4kYio9gwu0pOTk7F+/XrMmTMHGzZsgEajQVRUFFQqVbXtFQoFLly4gM8//xyLFi3Cnj178O6779Y3biKiBmXs3HfgwAHExMRg1KhR2LJlC4KDgxEdHY2zZ8820B4RETUcHj8SEdWeQUW6SqVCamoqFAoFwsLC4OPjg8TEROTl5SE9PV2v/bFjx3Dw4EF8/PHH8PPzQ3BwMN5//31s27YN+fn5RtsJIiJTMkXuW7lyJQYMGIAxY8bA09MTM2fOhJ+fH7744ouG3j0iIpPi8SMRkWEMKtKzsrJw69YtBAcHa5c5OzvD19cXhw4d0mt/+PBhtGzZEp6entplQUFBkEgkOHLkSD3CJiJqOMbOfRqNBkePHtXpDwB69uxZbX9ERNaMx49ERIaxMaRxXl4eAKBNmzY6y1u1aqVdd7v8/Hy9tnK5HM2bN0dubq6hsWpJpRK4ujat8/bVkUgq//vuxGBUqDVG7duUbGSV51maNXOAEMbtu2pMTNm3NY03x7rhmHKspVKJwdsYO/cplUoUFxfDzc2tVv0ZwhT5Efi/f0dtRr0Noa4wev+mIJFV/oqz1u8sx/r/982x1mOq8a5LfqyNxnz8CPzfv6M3e09BhcY6/h3ZSK37O2tNYw2Ybrw51vpM+W/bVDmyOgYV6VUTdsjlcp3ldnZ2KCoqqrb9nW2r2peVlRny0TokEglkMtMMUnMnO5P0a2pSqekm6jdl39Y43hzrhmPK8TCEsXNfaWlpjf3VJzcCps2PACBr2sxkfZuKtX5nOdYN17c1jjVgOTnybu6F40cAaGbvZLK+TcVav7PWONaA6caEY63PWvJjTQyK3t7eHgD0JvkoKyuDg4NDte2rmxCkrKwMTZo0MeSjiYjMxti5z87OzqD+iIisGY8fiYgMY1CRXnXrUUFBgc7ygoICtG7dWq+9m5ubXluVSoXr16+jVatWhsZKRGQWxs59zZs3R5MmTWrdHxGRNePxIxGRYQwq0n18fODo6IiMjAztMqVSiczMTAQGBuq1DwwMRF5eHi5cuKBddvDgQQDAww8/XNeYiYgalLFzn0QiQffu3bXLqmRkZKBHjx4m2gsiIvPg8SMRkWEMeiZdLpcjMjISCQkJcHV1hbu7O+Lj4+Hm5obw8HCo1WoUFhbCyckJ9vb26NKlC7p3746pU6fi3XffRXFxMWbPno2hQ4fyahERWQ1T5L5x48YhOjoavr6+6N27NzZt2oSTJ0/iww8/NPPeEhEZF48fiYgMIxHCsHnv1Go1Fi5ciM2bN6O0tBSBgYGYPXs2PDw8kJOTg/79+2Pu3LkYPnw4AODq1at47733sHfvXtjZ2eHxxx9HbGys9plMIiJrYIrct3XrViQnJyMvLw8dO3ZETEyM3mvZiIgaAx4/EhHVnsFFOhERERERERGZhnXPTU9ERERERETUiLBIJyIiIiIiIrIQLNKJiIiIiIiILASLdCIiIiIiIiILwSKdiIiIiIiIyEKwSCciIiIiIiKyECzSCYWFheYOgcgkCgoKzB0CNQLMkdRYMUdSfTE/UmNl7vzIIv0ed/LkSQwaNAiLFy82dyiN0uXLl80dwj3r+PHj6N27N5KTk80dClkx5kjTYX40L+ZIqi/mR9NhfjQvS8iPLNLvYSdPnsRbb70Fb29vrFixAh9++KG5Q2pUsrKy8PTTT+Oll14ydyj3nMOHD+OVV14BAPzyyy+4cuWKmSMia8QcaTrMj+bFHEn1xfxoOsyP5mUp+ZFF+j3q5MmTmDlzJjw8PPDee+9h586d2LJlCz766CNzh9YonDx5ErGxsXjkkUdw9uxZjBo1ytwh3TMOHz6M2NhY9O7dG3K5HH369EHLli3NHRZZGeZI02F+NC/mSKov5kfTYX40L0vKjyzS70GnTp3C9OnT0alTJ7z88st44IEH4OHhgTVr1mDLli2YO3euuUO0aidPnsSsWbPg4eGB1157Dbt27YJKpUJERIS5Q2v0Dh8+jGnTpmHQoEG4cuUKOnbsiOjoaACAEMLM0ZG1YI40HeZH82KOpPpifjQd5kfzsrT8yCL9HnPy5ElMnz4dFy9ehKOjI3x9fQEAKpUKnTp1wpo1a7B161a8+eabZo7UOp06dQrTpk1Dx44d8frrr6Njx44AgDVr1qCsrAyjR482c4SN15EjR6BQKDBo0CB06dIFEokEI0aMgEwmg1qthkQi4UEo3RVzpOkwP5oXcyTVF/Oj6TA/mpcl5kcW6feQqtuTOnbsiFmzZuG7777DihUrAAByuRwVFRXw8fFBSkoK8vPzUVxcbOaIrUvVL69///0XV65cgaenJ4DKX15NmzbFunXrUF5ejieeeMLMkTY+Vcl1yJAhmDp1Ki5evIizZ88iJCQEACCTyQAAEokE2dnZOHTokDnDJQvFHGk6zI/mxRxJ9cX8aDrMj+ZlsflR0D3hzz//FMOGDRNTpkwRJ06cEEIIsXfvXhEUFCSWL1+ubVdeXi6EEEKtVpslTmuVmZkpnnzySfH666+Ln376STz99NNiypQp2vUqlUoIIcTNmzdFbGysKCoqMleojc6BAwdEnz59xEcffSQKCgrE+fPnxaOPPiq+/vprnXanTp0S27dvF6GhocLb21usXbvWTBGTJWKONB3mR/NijqT6Yn40HeZH87Lk/CgRgvc2NXYqlQqjRo3C1atXsWrVKjz44IMQQkAikWDfvn14/fXXERUVpTOLZNV6urs///wTb7/9Nh544AGMHz8enTt3Rk5ODl555RV06NABSUlJACr/P8jlcu12HOP6KykpwbBhwxAUFIQJEyagffv2SE1Nxe7du7Fo0SI4OTmhoqIC8+fPR3FxMf755x/88ccfaNeuHZ555hm8+OKLsLW1NfdukJkxR5oO86N5MUdSfTE/mg7zo3lZen7k7e73ALlcjnnz5uHatWvYtWuXzrpevXohMTERaWlp+PvvvwFU/qMtLS0FwIlkauOzzz7D2bNnERUVhc6dO0Oj0cDDwwPLli3D2bNn8eqrrwKAToIFwARrBA4ODli7di0mTpyI9u3b48aNG/j888/Rv39/2NjYYOnSpYiOjsbp06fx1FNPQa1Wo2vXroiMjMSoUaN48EkAmCNNifnRvJgjqb6YH02H+dG8LD0/ski/R3h5eWH9+vVYvHgxFi1apP2CCyEQEhKCJUuWoH379pgzZw6effZZvPbaa9izZw8nkqmFhIQEuLu7Y+nSpQAAqVQKIQTc3d2xYsUK3LhxAwcOHAAAREVFYf369eYMt9G577770LZtW2g0Gixfvhw3btyAXC7HkCFDcOHCBYwaNQpr167Fjh07UFxcjMGDB+PZZ5+Fo6MjNBqNucMnC8EcaRrMj+bHHEn1xfxoGsyP5mfJ+ZG3u99j/vzzT0RERGDixIlQKBR66/fs2YPCwkJcunQJK1euRHx8PMLDw80QqXWoqKiAjY0NysvLMWTIELRr1w4rV64EoH87UmxsLDIyMjB69GjeQmgCJSUlGDNmDE6cOIGnnnoK3bt3175fNDY2FocOHUJkZCQCAwPh7OwMW1tbuLm58bYx0sEcaTzMj5aFOZLqi/nReJgfLYsl5kcW6fegP//8E5GRkYiIiMDMmTPx448/Yv369Wjbti369u2LwMBANGnSBKtWrUJKSgpSUlLg7e1t7rAt1p2Jtn379toZT6tMmzYNv//+OyZMmICnn34ajo6OOus1Gg2kUt7YUl///PMPbt68CXd3d7i4uAAAFAoFMjMzMXbsWAQEBGDNmjXYt28f3NzcMGnSJAwYMIAHoaSDOdJ4mB8tC3Mk1Rfzo/EwP1oWS8uP/L96D/L398cXX3yBAwcOYP/+/XjrrbcAADdv3sTmzZuxYMECFBcX47nnnkN5eTnOnj1r5ogtm42NDSoqKmBra4sdO3YgOztb532Wr7/+Ok6cOIHx48dj6NChOgl2z549AMAEayQPPPAA/P39dZJrbm4uIiIi0L9/fwQEBOD555/Hl19+iREjRmDq1KnYtWsXDz5JB3Ok8TA/WhbmSKov5kfjYX60LJaWH21M0itZvC5dumDNmjX4/fff0bRpU7zzzjto164dfv75Z/z66694/vnn0a5dOwDAgw8+CAAoLS2Fvb09z6hX4/ZEu23bNrz88ss4cuQIjh49iuPHj2PixIl46qmn0LRpU+02sbGx2LlzJ7744gt06dLFjNE3TrGxsTh27BhGjx6Nn3/+GTt37kSzZs0waNAgPPHEE3j++edx9epVxMfHo3PnzmjdurW5QyYLwhxpPMyPlok5kuqK+dF4mB8tkyXkR55+uYc5OjqiU6dOEELgyy+/BACEhYUhLi4OpaWl+OOPP/DSSy/B09MT+fn5SElJwdGjR5lca1CVaOVyOVJSUtClSxfk5+fDxcUFgwYN0jkD+tZbb+HgwYOYPHkyOnbsqJ0JFeBsqMby8ssvQ6FQoGnTprh8+TKeeOIJBAQE4MCBA5gzZw4KCgrg7u6O8vJyzkRL1WKONB7mR8vDHEn1wfxoPMyPlscS8iOvpN/jWrRogSVLlmD8+PG4desWnJ2dcfr0ady4cQPjxo3Dc889h7KyMnz11VdYsWIFsrKy0KRJE/j4+KC8vJyTV9zBxqbyKyWRSGBjY6P9ZdS8eXNtmzfffBO//fYbXnjhBURERKCiogLJyclo3749nn32WUgkEj5jZATt27dH+/btkZaWBplMhjFjxsDW1hZ//vknNm3ahIiICNjb26NJkyZo0aIFAKCoqEjn/xURc6TxMD9aFuZIqi/mR+NhfrQslpAf+X+R4Ovri6+++goeHh7YunUrjh8/jnHjxiEiIgJCCCxbtgzJycnw9/eHq6srIiMjsX//ftja2iIvLw/p6ekoLCw0925YpJkzZ+LWrVuYNm0aPvjgA7zwwgv48ccftQlWo9EgNTUVa9aswebNm7XvIGWCNR5/f3/cvHkTy5cvR3l5Ofz8/DBs2DBcv34d5eXlSE1NhaOjIw4cOIChQ4fi/PnzAHhGmv4Pc6RpMD9aBuZIqg/mR9NgfrQM5syPvJJOACrPGD333HM4evQomjdvjtGjR0OtVmPp0qVISUnB9OnTERUVBQAIDAzEokWLcO7cOSxYsAB+fn5o06YNXF1dzbwXlqVq1s60tDT89NNPWLhwIUpKSvDyyy9j1KhR0Gg0WLZsGT777DP06dMHPj4+eO+993Du3DlER0ebO/xGo127dkhJSYFCocCZM2eQm5uLy5cvw8HBAevWrUPLli2xf/9+vPPOO8jLy8OqVavQv39/9OnTx9yhkwVhjjQu5kfLwRxJ9cX8aFzMj5bDrPlREN0mJydHXLp0SZSUlIgFCxYIb29v8cUXX2jXV1RUCCGEWLVqlfD29hbh4eEiJydHqNVqIYQQhw4dMkvclqpqvDQajfjoo4/EJ598Im7duiVu3Lgh5s+fL7y9vcVnn32mbX/kyBHx7bff6vRRNbZUPzk5OeKXX34R3t7eYvDgwSIvL08IIcSvv/4q+vbtK5566ikxb9488dlnnwl/f3/x119/mTliskTMkcbD/GhZmCOpvpgfjYf50bKYIz+ySKdqLVq0SHh7e4s1a9Zol5WVlQkhhLh8+bIICQkRffv2Fd26dRNHjx4VQgjxySefCG9vb3H8+HGzxGypqpKkRqPRJtiEhAS9X15CCJGfny/OnDkj/vzzT3HmzBm9Pqh+/v33XxESEiKOHDkihPi/5PrSSy+J3377TQghRHl5ufjjjz9Edna2djuNRmOWeMlyMUcaB/OjZWGOJGNgfjQO5kfL0tD5USIEHyoifWfOnMHRo0fx7LPPAgBUKhXkcjny8vIwYsQIuLu746OPPsK1a9fQsmVL7Ny5E8nJyZg2bZp2MgX6P+K2V46sX78e77//PuLi4hAZGalts3fvXqxbtw779u2Dk5MTXFxcMHDgQCgUCr0+qO5u3LgBJycn/PLLL/jggw/g6emJqKgoPPzww9o2f/zxBzZu3IjmzZsjODgYjz76KMefdDBHGg/zo2VhjqT6Yn40HuZHy9Kg+bGOJxPoHlJ19jM3N1f06tVLPPPMM+L48ePaM0NffPGF8Pb2FqtWrTJjlNajvLxc7Ny5U2dZRkaGePbZZ8Xo0aPFd999Jy5fvix+/fVX8eijj4qVK1eaKdLGq6SkRDz55JMiMjJSe0a0ytWrV0Xfvn3FqFGjxNtvvy26desmvv/+ezNFStaAOdJ4mB8tA3MkGQvzo/EwP1qGhsqPnDiO7koul6OwsBAjR46Eu7s73nrrLXTu3BkSiQRLlizBkiVL4ODggIKCAuzbtw+9evXiKyBqUDUZyOOPP67z9/T0dNy6dQszZ85EaGgoAKBNmzZ48cUXsW/fPowZMwZyudycoTcq9vb2WL58OQoKCtClSxft8pKSEtjY2ECj0WD48OF45plnEBAQgLlz58LLywsdOnQwY9RkqZgjjYP50XIwR5KxMD8aB/Oj5Wio/MhvANXK/v37odFo8MYbbyAgIAASiQRJSUn49NNPERUVhRkzZsDNzQ2vvPIKfvzxRybXGlS9B/P2v9+4cQM7duxAeHi4NsGWl5cDABwdHXH69GncvHlTZzuNRtMwATdibdq00UmuBw8eRFJSEpydnfHwww8jJSUFSqUSQUFBkEqluH79uvmCJYvHHFl/zI+WhTmSjIX5sf6YHy1LQ+RHfguoVgICAlBeXo6cnBwAQFJSEpYuXYpZs2ZhypQpiIiIwJgxYzB06FCsXr1aLylQzezs7ODp6Yn77rsPQOWzW7a2tiguLsa2bdvQsWNH7atJDh8+DKDyPZiC00kY1ZYtW3DixAkAwOjRo+Hs7Iyvv/4aTZs2RYcOHVBaWmrmCMmSMUeaBvOj5WCOpLpifjQN5kfLYYr8yCKdaqVt27b45ptv0KVLFyxatAifffYZZs2ahVGjRsHOzk77hVepVMjPz2cCMIBcLkfr1q2Rnp6unVxFpVLhrbfewvXr1xEREQEAWLFiBd566y38/vvvAACJRMIzokbUoUMHyGQyAEDXrl3h4+ODtLQ0NGvWDImJiQgODjZzhGTJmCNNg/nRcjBHUl0xP5oG86PlMEV+ZJFOtda+fXu0bNkS69atw+TJkxEREQE7OzsAlV/49PR0bNu2DcOGDYOTk5OZo7UOVb+IPv74Y1y9ehWjR4/G+PHj0b9/f2RmZuKFF17AwIEDsWbNGqSmpqKgoACpqalYt24dAJ4RNabw8HCcPXsWixYtghAC/fr1Q2ZmJs6dOwdnZ2dzh0dWgDnSuJgfLQtzJNUH86NxMT9aFlPkR76CjQx2/fp1NGnSRGciiu+//x6vv/46nn32WUyePBmtW7c2Y4TWpWryj4qKCqxduxaXL1+GRqPBsGHD4Ofnh88//xybNm3C/fffj/79+6O8vByLFi3CmDFj8Oqrr5o7/EYlMzMTkydPhpubG44dO4bw8HAsWLAAtra25g6NrAhzpPEwP1oW5kiqL+ZH42F+tCzGzo8s0qne0tPToVAo8Mwzz+Dll1+Gu7u7uUOyOmq1WnubzO1Wr16NL7/8Ek888QSee+457S+urVu3YsOGDfj000/RokWLhg63UcvLy8OFCxdw69YtBAUFwdHR0dwhkZVjjqwf5kfLwhxJxsT8WD/Mj5bFmPmRr2Cjevnxxx+hUCjw3HPPITo6msm1jqoSbNVZUaDyGaLPPvsMDzzwACIjI7WTfwDAv//+i9zcXF69MAE3Nze4ubmZOwxqJJgj64/50bIwR5KxMD/WH/OjZTFmfmSRTvXSvn17vPDCCxgzZgyTaz1pNBptgl2+fDn+97//4b777oOPj49Ogi0sLERWVhbatGlT7fNEfL8okeVgjjQO5keixof50TiYHxsnFulULx07dsTMmTP13t9IhqtKjJ988gl2796NgIAAtG7dGpmZmSgtLYW9vT1yc3OxZs0a7NixA59++imaNWuG/fv3Iz8/Hx4eHujRowekUikTLZGFYI40DuZHosaH+dE4mB8bJ34rqN6YXI3n2rVrOHDgADw9PfH666+jRYsWGDRoEMaPHw+5XI7S0lL8+eefmDNnDvr374+LFy8iNTUVubm5KC8vR3h4ON544w0mWiILwhxpHMyPRI0P86NxMD82Ppw4jsjCFBQUQK1Wo02bNgCA8vJyrFmzBtnZ2dqk6+7ujvPnz6NVq1YoLS3F/fffj6NHj+KVV17B888/D4VCYea9ICIyPuZHIqLqMT82LizSiSyUEAJqtVrvLPOZM2fw1ltv4Z9//kGrVq3Qt29fDBkyBF5eXli7di1Wr16NlJQUtG3b1kyRExGZFvMjEVH1mB8bB95jQmShJBJJtbeB/f3337h48SLmzZsHtVqN/fv3Y8GCBRg7diwcHBzw77//QqVSmSFiIqKGwfxIRFQ95sfGgUU6kZW5desWmjZtipCQENja2qJz587YtWsXXn75ZWg0GoSHh8PT09PcYRIRNTjmRyKi6jE/WhfOCkBkZYYPHw4nJydMmTIF2dnZKC8vh6enJzQaDYYOHYq5c+cCqHyVBhHRvYT5kYioesyP1oXPpBNZEbVaDZlMhvLyckRFRcHFxQW//PILiouLMWDAACxcuBByudzcYRIRNTjmRyKi6jE/Wh8W6URWpqKiAjY2NhBC4NSpUxg6dCieffZZxMXFMcES0T2N+ZGIqHrMj9aFt7sTWRkbGxtoNBpIJBI89NBD2iTLBEtE9zrmRyKi6jE/WhdeSSeyUlW3LgGVzw9JpTznRkQEMD8SEdWE+dE6sEgnIiIiIiIishA8dUJERERERERkIVikExEREREREVkIFulEREREREREFoJFOhEREREREZGFYJFOREREREREZCFYpBMRERERERFZCBbpRGbENyASEVWP+ZGIqHrMj40fi3S6q1mzZqFfv341ru/Xrx9mzZrVgBHVLCMjA97e3jo/Pj4+6N69O0aNGoWffvpJ23bz5s3w9vZGTk6OWWL98ccfMXPmTLN8NhEZB/OjaTA/Elk/5kfTYH68N9iYOwAiU5g9ezb8/PwAVJ5tLCoqQmpqKiZNmoTly5ejT58+Zo4Q+Pzzz80dAhHdg5gfiYiqx/xIloJFOjVKHTt2RNeuXXWW9ejRA2FhYVi9erVFJFkiInNgfiQiqh7zI1kK3u5ORqVWq7Fu3To8+eSTCAgIQFhYGBISElBWVqZtM2vWLEyYMAFfffUVBgwYgICAAIwaNQrnz5/H7t278eSTT6JLly545plncPLkSZ3+Dx8+jMjISHTp0gVBQUGYOXMmCgsLaxWbo6MjOnTogMuXL9fYZuPGjRg+fDi6du2KgIAAPP3009i5c6d2/ebNm+Hr64vjx4/jueeeQ+fOndG3b1+kpKTo9FNWVob58+ejT58+8Pf3x5NPPom0tDTt+hdeeAEHDx7EwYMH4e3tjYyMjFrtAxFZL+bHSsyPRHQn5sdKzI9UhUU61VpFRUW1P7ebPXs25s6diwEDBmDp0qV4/vnnsXbtWkyaNElnkotjx45h7dq1mDVrFubOnYuzZ88iOjoac+fOxUsvvYSFCxciNzcX06dP125z6NAhjB07Fvb29vjkk0/w5ptv4uDBgxgzZgxKS0vvGr9KpUJOTg7atWtX7fp169Zh9uzZGDBgAJYvX46EhATI5XJMnz4deXl52nYajQavv/46Bg0ahBUrVqB79+6YP38+9u7dC6Dy9qjJkydjw4YNGDduHJYuXYpu3bph6tSp2Lp1KwDgnXfega+vL3x9ffHVV19pb60iIuvE/FiJ+ZGI7sT8WIn5kQzB292pVi5dunTXRHDmzBl88803eOONNxAdHQ0A6NWrF1q1aoUZM2bgl19+0d4mdOvWLXzyySfw9PQEABw8eBAbNmzA559/juDgYADAhQsX8PHHH0OpVMLZ2RkLFixAhw4dsHz5cshkMgBAly5dMHjwYGzatAnPP/+8NhaNRqP9BVBRUYFLly4hOTkZhYWFOu1ul52djQkTJmDSpEnaZe7u7hg+fDiOHDmCwYMHA6hMopMmTcIzzzwDAHj44Yfxww8/4Oeff0ZoaCh+++037N27F4mJiRg0aBAAIDQ0FCUlJUhISMCQIUPQsWNHODo6AoDebVVEZF2YH5kfiah6zI/Mj1Q3LNKpVlq2bImlS5dWu+6VV14BUJkoAWiTUZXBgwcjNjYWGRkZ2iTbrFkzbYIFgPvuuw9AZdKs0rx5cwCAUqmEra0tjh8/jgkTJkAIoU2gbdu2haenJ/bt26eTPMeOHasXZ4sWLRAXF4fevXtXux9VM4wqlUqcO3cOFy5c0N5GpFKpdNp269ZN+2e5XA5XV1cUFxcDAPbv3w+JRII+ffronCnu168ftm/fjtOnT6NTp07VxkBE1of5kfmRiKrH/Mj8SHXDIp1qRS6Xo3PnzjWuA4CioiIAlQn5djY2NnBxccGNGze0y6rOAt6pSZMm1S5XKpXQaDRYuXIlVq5cqbfezs5O5+/vvfee9sytTCZDs2bNcP/990MikVTbPwBcvHgRs2fPxv79+2Fra4sHH3wQPj4+APTfR2lvb6/zd6lUqm1z/fp1CCHQvXv3aj+noKCASZaoEWF+ZH4kouoxPzI/Ut2wSCejadasGQDgypUrcHd31y4vLy/HtWvX4OLiUue+mzZtColEgrFjx+qdaQUABwcHnb936NChxl8K1dFoNIiOjoatrS2++eYbdOrUCTY2Njhz5gy2bdtmUKxOTk5o0qQJVq9eXe369u3bG9QfEVk/5sdKzI9EdCfmx0rMj3Q7ThxHRhMUFAQA+O6773SWf/fdd1Cr1Xj44Yfr3LejoyN8fX1x7tw5dO7cWfvz0EMPYfHixfWe3fLatWs4f/48Ro4cic6dO8PGpvL81S+//AKgMgnXVlBQEIqLiyGE0In177//xqeffqq9hUkq5deP6F7B/FiJ+ZGI7sT8WIn5kW7HK+lkNB07dsSwYcOQlJSEkpISBAYG4uTJk1iyZAl69uyJ0NDQevU/bdo0REdH44033sBTTz0FtVqN1NRUHD9+XGeyjrpo0aIF3N3dsW7dOri5ucHZ2Rl79+7Vns0sKSmpdV99+vRBYGAgJk2ahEmTJsHT0xN//PEHkpKSEBoaCldXVwCAs7Mzjh07hv3798PX11d7JpmIGh/mx0rMj0R0J+bHSsyPdDueiiGj+vDDDzF58mR8++23iI6Oxrp16zBmzBisXLmy3mf+QkJCkJKSgry8PCgUCsyYMQMymQyrVq0yygyXycnJaN26NWbNmoXXX38dx48fx9KlS/Hggw/i8OHDte5HKpVixYoVGDx4MJYvX44JEyZoX6eRmJiobff888/D1tYWEydO1J5xJaLGi/mR+ZGIqsf8yPxIuiTizhkNiIiIiIiIiMgseCWdiIiIiIiIyEKwSCciIiIiIiKyECzSiYiIiIiIiCwEi3QiIiIiIiIiC8EinYiIiIiIiMhCsEgnIiIiIiIishAs0omIiIiIiIgsBIt0IiIiIiIiIgvBIp2IiIiIiIjIQrBIJyIiIiIiIrIQLNKJiIiIiIiILMT/A46aJxxdWsjKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metricframe_postprocess.by_group.plot.bar(subplots=True, layout=[1,3], figsize=(12, 4), legend=False, rot=-45, position=1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "e9f06ed7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_threshold_optimizer(postprocess_est, ax=None, show_plot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bdf46dc",
   "metadata": {},
   "source": [
    "### Randomized predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "64b31a85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 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-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 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-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 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-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 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-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>InterpolatedThresholder(estimator=Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
       "                                                   StandardScaler()),\n",
       "                                                  (&#x27;clf&#x27;,\n",
       "                                                   DecisionTreeClassifier())]),\n",
       "                        interpolation_dict={&#x27;Earth&#x27;: {&#x27;operation0&#x27;: [&gt;0.30952380952380953],\n",
       "                                                      &#x27;operation1&#x27;: [&gt;0.35416666666666663],\n",
       "                                                      &#x27;p0&#x27;: 0.9178571428571428,\n",
       "                                                      &#x27;p1&#x27;: 0.08214285714285718},\n",
       "                                            &#x27;Europa&#x27;: {&#x27;operation0&#x27;: [&gt;0.95],\n",
       "                                                       &#x27;operation1&#x27;: [&gt;inf],\n",
       "                                                       &#x27;p0&#x27;: 0.9945772113943029,\n",
       "                                                       &#x27;p1&#x27;: 0.005422788605697115},\n",
       "                                            &#x27;Mars&#x27;: {&#x27;operation0&#x27;: [&gt;0.6904761904761905],\n",
       "                                                     &#x27;operation1&#x27;: [&gt;0.7321428571428572],\n",
       "                                                     &#x27;p0&#x27;: 0.969,\n",
       "                                                     &#x27;p1&#x27;: 0.031000000000000028}},\n",
       "                        predict_method=&#x27;predict_proba&#x27;, prefit=True)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">InterpolatedThresholder</label><div class=\"sk-toggleable__content\"><pre>InterpolatedThresholder(estimator=Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
       "                                                   StandardScaler()),\n",
       "                                                  (&#x27;clf&#x27;,\n",
       "                                                   DecisionTreeClassifier())]),\n",
       "                        interpolation_dict={&#x27;Earth&#x27;: {&#x27;operation0&#x27;: [&gt;0.30952380952380953],\n",
       "                                                      &#x27;operation1&#x27;: [&gt;0.35416666666666663],\n",
       "                                                      &#x27;p0&#x27;: 0.9178571428571428,\n",
       "                                                      &#x27;p1&#x27;: 0.08214285714285718},\n",
       "                                            &#x27;Europa&#x27;: {&#x27;operation0&#x27;: [&gt;0.95],\n",
       "                                                       &#x27;operation1&#x27;: [&gt;inf],\n",
       "                                                       &#x27;p0&#x27;: 0.9945772113943029,\n",
       "                                                       &#x27;p1&#x27;: 0.005422788605697115},\n",
       "                                            &#x27;Mars&#x27;: {&#x27;operation0&#x27;: [&gt;0.6904761904761905],\n",
       "                                                     &#x27;operation1&#x27;: [&gt;0.7321428571428572],\n",
       "                                                     &#x27;p0&#x27;: 0.969,\n",
       "                                                     &#x27;p1&#x27;: 0.031000000000000028}},\n",
       "                        predict_method=&#x27;predict_proba&#x27;, prefit=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;, StandardScaler()),\n",
       "                (&#x27;clf&#x27;, DecisionTreeClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "InterpolatedThresholder(estimator=Pipeline(steps=[('preprocessing',\n",
       "                                                   StandardScaler()),\n",
       "                                                  ('clf',\n",
       "                                                   DecisionTreeClassifier())]),\n",
       "                        interpolation_dict={'Earth': {'operation0': [>0.30952380952380953],\n",
       "                                                      'operation1': [>0.35416666666666663],\n",
       "                                                      'p0': 0.9178571428571428,\n",
       "                                                      'p1': 0.08214285714285718},\n",
       "                                            'Europa': {'operation0': [>0.95],\n",
       "                                                       'operation1': [>inf],\n",
       "                                                       'p0': 0.9945772113943029,\n",
       "                                                       'p1': 0.005422788605697115},\n",
       "                                            'Mars': {'operation0': [>0.6904761904761905],\n",
       "                                                     'operation1': [>0.7321428571428572],\n",
       "                                                     'p0': 0.969,\n",
       "                                                     'p1': 0.031000000000000028}},\n",
       "                        predict_method='predict_proba', prefit=True)"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interpolated = postprocess_est.interpolated_thresholder_\n",
    "interpolated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "b46aad19",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_deterministic(interpolate_dict):\n",
    "  \"\"\"\n",
    "  Creates a deterministic interpolation_dictionary from a randomized\n",
    "  interpolation_dictionary. The determinstic thresholds are created by taking\n",
    "  the weighted combinations of the two randomized thresholds for each sensitive\n",
    "  group.\n",
    "  \"\"\"\n",
    "  deterministic_dict = {}\n",
    "  for (race, operations) in interpolate_dict.items():\n",
    "    op0, op1 = operations[\"operation0\"]._threshold, operations[\"operation1\"]._threshold\n",
    "    p0, p1 = operations[\"p0\"], operations[\"p1\"]\n",
    "    deterministic_dict[race] = Bunch(\n",
    "      p0=0.0,\n",
    "      p1=1.0,\n",
    "      operation0=ThresholdOperation(operator=\">\",threshold=(p0*op0 + p1*op1)),\n",
    "      operation1=ThresholdOperation(operator=\">\",threshold=(p0*op0 + p1*op1))\n",
    "    )\n",
    "  return deterministic_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "f59fd328",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Earth': {'p0': 0.0,\n",
       "  'p1': 1.0,\n",
       "  'operation0': [>0.3131909013605442],\n",
       "  'operation1': [>0.3131909013605442]},\n",
       " 'Europa': {'p0': 0.0, 'p1': 1.0, 'operation0': [>inf], 'operation1': [>inf]},\n",
       " 'Mars': {'p0': 0.0,\n",
       "  'p1': 1.0,\n",
       "  'operation0': [>0.6917678571428572],\n",
       "  'operation1': [>0.6917678571428572]}}"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deterministic_dict = create_deterministic(interpolated.interpolation_dict)\n",
    "deterministic_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "ac883500",
   "metadata": {},
   "outputs": [],
   "source": [
    "deterministic_thresholder = InterpolatedThresholder(estimator=interpolated.estimator,\n",
    "                                                 interpolation_dict=deterministic_dict,\n",
    "                                                 prefit=True,\n",
    "                                                 predict_method='predict_proba')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "944824d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 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-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 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-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 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-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 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-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>InterpolatedThresholder(estimator=Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
       "                                                   StandardScaler()),\n",
       "                                                  (&#x27;clf&#x27;,\n",
       "                                                   DecisionTreeClassifier())]),\n",
       "                        interpolation_dict={&#x27;Earth&#x27;: {&#x27;operation0&#x27;: [&gt;0.3131909013605442],\n",
       "                                                      &#x27;operation1&#x27;: [&gt;0.3131909013605442],\n",
       "                                                      &#x27;p0&#x27;: 0.0, &#x27;p1&#x27;: 1.0},\n",
       "                                            &#x27;Europa&#x27;: {&#x27;operation0&#x27;: [&gt;inf],\n",
       "                                                       &#x27;operation1&#x27;: [&gt;inf],\n",
       "                                                       &#x27;p0&#x27;: 0.0, &#x27;p1&#x27;: 1.0},\n",
       "                                            &#x27;Mars&#x27;: {&#x27;operation0&#x27;: [&gt;0.6917678571428572],\n",
       "                                                     &#x27;operation1&#x27;: [&gt;0.6917678571428572],\n",
       "                                                     &#x27;p0&#x27;: 0.0, &#x27;p1&#x27;: 1.0}},\n",
       "                        predict_method=&#x27;predict_proba&#x27;, prefit=True)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">InterpolatedThresholder</label><div class=\"sk-toggleable__content\"><pre>InterpolatedThresholder(estimator=Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
       "                                                   StandardScaler()),\n",
       "                                                  (&#x27;clf&#x27;,\n",
       "                                                   DecisionTreeClassifier())]),\n",
       "                        interpolation_dict={&#x27;Earth&#x27;: {&#x27;operation0&#x27;: [&gt;0.3131909013605442],\n",
       "                                                      &#x27;operation1&#x27;: [&gt;0.3131909013605442],\n",
       "                                                      &#x27;p0&#x27;: 0.0, &#x27;p1&#x27;: 1.0},\n",
       "                                            &#x27;Europa&#x27;: {&#x27;operation0&#x27;: [&gt;inf],\n",
       "                                                       &#x27;operation1&#x27;: [&gt;inf],\n",
       "                                                       &#x27;p0&#x27;: 0.0, &#x27;p1&#x27;: 1.0},\n",
       "                                            &#x27;Mars&#x27;: {&#x27;operation0&#x27;: [&gt;0.6917678571428572],\n",
       "                                                     &#x27;operation1&#x27;: [&gt;0.6917678571428572],\n",
       "                                                     &#x27;p0&#x27;: 0.0, &#x27;p1&#x27;: 1.0}},\n",
       "                        predict_method=&#x27;predict_proba&#x27;, prefit=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;, StandardScaler()),\n",
       "                (&#x27;clf&#x27;, DecisionTreeClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "InterpolatedThresholder(estimator=Pipeline(steps=[('preprocessing',\n",
       "                                                   StandardScaler()),\n",
       "                                                  ('clf',\n",
       "                                                   DecisionTreeClassifier())]),\n",
       "                        interpolation_dict={'Earth': {'operation0': [>0.3131909013605442],\n",
       "                                                      'operation1': [>0.3131909013605442],\n",
       "                                                      'p0': 0.0, 'p1': 1.0},\n",
       "                                            'Europa': {'operation0': [>inf],\n",
       "                                                       'operation1': [>inf],\n",
       "                                                       'p0': 0.0, 'p1': 1.0},\n",
       "                                            'Mars': {'operation0': [>0.6917678571428572],\n",
       "                                                     'operation1': [>0.6917678571428572],\n",
       "                                                     'p0': 0.0, 'p1': 1.0}},\n",
       "                        predict_method='predict_proba', prefit=True)"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deterministic_thresholder.fit(X_train_bal, Y_train_bal, sensitive_features=A_train_bal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "484f80ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_postprocess_deterministic = deterministic_thresholder.predict(X_test, sensitive_features=A_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "45a01236",
   "metadata": {},
   "outputs": [],
   "source": [
    "mf_deterministic = MetricFrame(\n",
    "    metrics=metrics_dict,\n",
    "    y_true=Y_test,\n",
    "    y_pred=y_pred_postprocess_deterministic,\n",
    "    sensitive_features=A_test\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "212863c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>selection_rate</th>\n",
       "      <th>false_negative_rate</th>\n",
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       "    <tr>\n",
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       "      <td>0.602649</td>\n",
       "      <td>0.24587</td>\n",
       "      <td>0.631932</td>\n",
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       "    <tr>\n",
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      "text/plain": [
       "           selection_rate false_negative_rate balanced_accuracy\n",
       "HomePlanet                                                     \n",
       "Earth            0.602649             0.24587          0.631932\n",
       "Europa                0.0                 1.0               0.5\n",
       "Mars             0.586412            0.149425          0.774297"
      ]
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     "execution_count": 176,
     "metadata": {},
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    "mf_deterministic.by_group"
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  {
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   "id": "7d3f4a99",
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       "      <th>Europa</th>\n",
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       "           selection_rate false_negative_rate balanced_accuracy\n",
       "HomePlanet                                                     \n",
       "Earth            0.602235             0.24587          0.632292\n",
       "Europa           0.690476            0.093793          0.820951\n",
       "Mars             0.586412            0.149425          0.774297"
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    "metricframe_postprocess.by_group"
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   "cell_type": "code",
   "execution_count": 183,
   "id": "49a5e9a9",
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   "outputs": [
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       "      <td>3879.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>6379</th>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>756.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>236.0</td>\n",
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       "    <tr>\n",
       "      <th>1901</th>\n",
       "      <td>42.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>399.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>771.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2980</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>799.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4449</th>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>706.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>583.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4347 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age  VIP  RoomService  FoodCourt  ShoppingMall     Spa  VRDeck  \\\n",
       "352   23.0  0.0         13.0      747.0           0.0     0.0   128.0   \n",
       "7945  47.0  0.0          0.0        0.0           0.0     0.0     0.0   \n",
       "8450  58.0  0.0          0.0        0.0           0.0     0.0     0.0   \n",
       "719   33.0  0.0          0.0     3879.0           0.0    48.0    67.0   \n",
       "6379  41.0  0.0       1203.0      756.0         140.0     0.0     0.0   \n",
       "...    ...  ...          ...        ...           ...     ...     ...   \n",
       "2042  30.0  0.0          0.0      236.0           0.0  1149.0     0.0   \n",
       "1901  42.0  0.0        399.0       75.0          38.0   771.0     0.0   \n",
       "6675  51.0  0.0          0.0        0.0           0.0     0.0     0.0   \n",
       "2980  27.0  0.0         16.0        0.0         799.0     0.0    37.0   \n",
       "4449  23.0  0.0        706.0        0.0           0.0     0.0   583.0   \n",
       "\n",
       "      HomePlanet_Earth  HomePlanet_Europa  HomePlanet_Mars  \\\n",
       "352                  1                  0                0   \n",
       "7945                 1                  0                0   \n",
       "8450                 1                  0                0   \n",
       "719                  0                  1                0   \n",
       "6379                 0                  0                1   \n",
       "...                ...                ...              ...   \n",
       "2042                 1                  0                0   \n",
       "1901                 1                  0                0   \n",
       "6675                 1                  0                0   \n",
       "2980                 1                  0                0   \n",
       "4449                 0                  0                1   \n",
       "\n",
       "      Destination_55 Cancri e  Destination_PSO J318.5-22  \\\n",
       "352                         0                          0   \n",
       "7945                        1                          0   \n",
       "8450                        0                          0   \n",
       "719                         0                          0   \n",
       "6379                        0                          0   \n",
       "...                       ...                        ...   \n",
       "2042                        0                          0   \n",
       "1901                        0                          0   \n",
       "6675                        0                          0   \n",
       "2980                        0                          0   \n",
       "4449                        0                          0   \n",
       "\n",
       "      Destination_TRAPPIST-1e  CryoSleep_False  CryoSleep_True  \n",
       "352                         1                1               0  \n",
       "7945                        0                0               1  \n",
       "8450                        1                0               1  \n",
       "719                         1                1               0  \n",
       "6379                        1                1               0  \n",
       "...                       ...              ...             ...  \n",
       "2042                        1                1               0  \n",
       "1901                        1                1               0  \n",
       "6675                        1                0               1  \n",
       "2980                        1                1               0  \n",
       "4449                        1                1               0  \n",
       "\n",
       "[4347 rows x 15 columns]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "c9bbc3cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "expgrad_est = ExponentiatedGradient(\n",
    "    estimator=DecisionTreeClassifier(random_state=random_seed),\n",
    "    constraints=TruePositiveRateParity(difference_bound=0.02)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "adc0c2bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:251: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
      "  lambda_event = (lambda_vec[\"+\"] - self.ratio * lambda_vec[\"-\"]).sum(level=_EVENT) / \\\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:186: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  expect_event = self.tags.groupby(_EVENT).mean()\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:251: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
      "  lambda_event = (lambda_vec[\"+\"] - self.ratio * lambda_vec[\"-\"]).sum(level=_EVENT) / \\\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:186: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  expect_event = self.tags.groupby(_EVENT).mean()\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:251: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
      "  lambda_event = (lambda_vec[\"+\"] - self.ratio * lambda_vec[\"-\"]).sum(level=_EVENT) / \\\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:186: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  expect_event = self.tags.groupby(_EVENT).mean()\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:251: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
      "  lambda_event = (lambda_vec[\"+\"] - self.ratio * lambda_vec[\"-\"]).sum(level=_EVENT) / \\\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:186: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  expect_event = self.tags.groupby(_EVENT).mean()\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:251: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
      "  lambda_event = (lambda_vec[\"+\"] - self.ratio * lambda_vec[\"-\"]).sum(level=_EVENT) / \\\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:186: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  expect_event = self.tags.groupby(_EVENT).mean()\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:251: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
      "  lambda_event = (lambda_vec[\"+\"] - self.ratio * lambda_vec[\"-\"]).sum(level=_EVENT) / \\\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:186: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  expect_event = self.tags.groupby(_EVENT).mean()\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:251: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
      "  lambda_event = (lambda_vec[\"+\"] - self.ratio * lambda_vec[\"-\"]).sum(level=_EVENT) / \\\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:186: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  expect_event = self.tags.groupby(_EVENT).mean()\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:251: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
      "  lambda_event = (lambda_vec[\"+\"] - self.ratio * lambda_vec[\"-\"]).sum(level=_EVENT) / \\\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:186: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  expect_event = self.tags.groupby(_EVENT).mean()\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:251: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
      "  lambda_event = (lambda_vec[\"+\"] - self.ratio * lambda_vec[\"-\"]).sum(level=_EVENT) / \\\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:186: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  expect_event = self.tags.groupby(_EVENT).mean()\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:251: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
      "  lambda_event = (lambda_vec[\"+\"] - self.ratio * lambda_vec[\"-\"]).sum(level=_EVENT) / \\\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:186: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  expect_event = self.tags.groupby(_EVENT).mean()\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:251: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
      "  lambda_event = (lambda_vec[\"+\"] - self.ratio * lambda_vec[\"-\"]).sum(level=_EVENT) / \\\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:186: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  expect_event = self.tags.groupby(_EVENT).mean()\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:251: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
      "  lambda_event = (lambda_vec[\"+\"] - self.ratio * lambda_vec[\"-\"]).sum(level=_EVENT) / \\\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:186: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  expect_event = self.tags.groupby(_EVENT).mean()\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:251: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
      "  lambda_event = (lambda_vec[\"+\"] - self.ratio * lambda_vec[\"-\"]).sum(level=_EVENT) / \\\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:186: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  expect_event = self.tags.groupby(_EVENT).mean()\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:251: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
      "  lambda_event = (lambda_vec[\"+\"] - self.ratio * lambda_vec[\"-\"]).sum(level=_EVENT) / \\\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:186: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  expect_event = self.tags.groupby(_EVENT).mean()\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:251: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
      "  lambda_event = (lambda_vec[\"+\"] - self.ratio * lambda_vec[\"-\"]).sum(level=_EVENT) / \\\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:186: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  expect_event = self.tags.groupby(_EVENT).mean()\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:251: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
      "  lambda_event = (lambda_vec[\"+\"] - self.ratio * lambda_vec[\"-\"]).sum(level=_EVENT) / \\\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:186: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  expect_event = self.tags.groupby(_EVENT).mean()\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:251: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
      "  lambda_event = (lambda_vec[\"+\"] - self.ratio * lambda_vec[\"-\"]).sum(level=_EVENT) / \\\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:186: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  expect_event = self.tags.groupby(_EVENT).mean()\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:251: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
      "  lambda_event = (lambda_vec[\"+\"] - self.ratio * lambda_vec[\"-\"]).sum(level=_EVENT) / \\\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:186: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  expect_event = self.tags.groupby(_EVENT).mean()\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:251: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
      "  lambda_event = (lambda_vec[\"+\"] - self.ratio * lambda_vec[\"-\"]).sum(level=_EVENT) / \\\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:186: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  expect_event = self.tags.groupby(_EVENT).mean()\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:251: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
      "  lambda_event = (lambda_vec[\"+\"] - self.ratio * lambda_vec[\"-\"]).sum(level=_EVENT) / \\\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:186: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  expect_event = self.tags.groupby(_EVENT).mean()\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:251: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
      "  lambda_event = (lambda_vec[\"+\"] - self.ratio * lambda_vec[\"-\"]).sum(level=_EVENT) / \\\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:186: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  expect_event = self.tags.groupby(_EVENT).mean()\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:251: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
      "  lambda_event = (lambda_vec[\"+\"] - self.ratio * lambda_vec[\"-\"]).sum(level=_EVENT) / \\\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:186: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  expect_event = self.tags.groupby(_EVENT).mean()\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:251: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
      "  lambda_event = (lambda_vec[\"+\"] - self.ratio * lambda_vec[\"-\"]).sum(level=_EVENT) / \\\n",
      "/Users/k2260077/opt/anaconda3/envs/AI_Fairness/lib/python3.10/site-packages/fairlearn/reductions/_moments/utility_parity.py:186: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  expect_event = self.tags.groupby(_EVENT).mean()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 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-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 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-9 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-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 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-9 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-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 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-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 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-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 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-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ExponentiatedGradient(constraints=&lt;fairlearn.reductions._moments.utility_parity.TruePositiveRateParity object at 0x7fb4801f27d0&gt;,\n",
       "                      estimator=DecisionTreeClassifier(random_state=445),\n",
       "                      nu=0.0017958446608152448)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExponentiatedGradient</label><div class=\"sk-toggleable__content\"><pre>ExponentiatedGradient(constraints=&lt;fairlearn.reductions._moments.utility_parity.TruePositiveRateParity object at 0x7fb4801f27d0&gt;,\n",
       "                      estimator=DecisionTreeClassifier(random_state=445),\n",
       "                      nu=0.0017958446608152448)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=445)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=445)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ExponentiatedGradient(constraints=<fairlearn.reductions._moments.utility_parity.TruePositiveRateParity object at 0x7fb4801f27d0>,\n",
       "                      estimator=DecisionTreeClassifier(random_state=445),\n",
       "                      nu=0.0017958446608152448)"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the exponentiated gradient model\n",
    "expgrad_est.fit(X_train_bal, Y_train_bal, sensitive_features=A_train_bal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "23249b63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>selection_rate</th>\n",
       "      <th>false_negative_rate</th>\n",
       "      <th>balanced_accuracy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HomePlanet</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Earth</th>\n",
       "      <td>0.543874</td>\n",
       "      <td>0.312925</td>\n",
       "      <td>0.62472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europa</th>\n",
       "      <td>0.674908</td>\n",
       "      <td>0.082759</td>\n",
       "      <td>0.860528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mars</th>\n",
       "      <td>0.622169</td>\n",
       "      <td>0.128736</td>\n",
       "      <td>0.758652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           selection_rate false_negative_rate balanced_accuracy\n",
       "HomePlanet                                                     \n",
       "Earth            0.543874            0.312925           0.62472\n",
       "Europa           0.674908            0.082759          0.860528\n",
       "Mars             0.622169            0.128736          0.758652"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Record and evaluate predictions on test data\n",
    "Y_pred_reductions = expgrad_est.predict(X_test, random_state=random_seed)\n",
    "metricframe_reductions = MetricFrame(\n",
    "    metrics=metrics_dict,\n",
    "    y_true=Y_test,\n",
    "    y_pred=Y_pred_reductions,\n",
    "    sensitive_features=A_test\n",
    ")\n",
    "metricframe_reductions.by_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "3ffe0151",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "selection_rate         0.131034\n",
       "false_negative_rate    0.230167\n",
       "balanced_accuracy      0.235808\n",
       "dtype: object"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the difference between the largest and smallest value of each metric\n",
    "metricframe_reductions.difference()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "f42fb48a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     DecisionTreeClassifier(random_state=445)\n",
       "1     DecisionTreeClassifier(random_state=445)\n",
       "2     DecisionTreeClassifier(random_state=445)\n",
       "3     DecisionTreeClassifier(random_state=445)\n",
       "4     DecisionTreeClassifier(random_state=445)\n",
       "5     DecisionTreeClassifier(random_state=445)\n",
       "6     DecisionTreeClassifier(random_state=445)\n",
       "7     DecisionTreeClassifier(random_state=445)\n",
       "8     DecisionTreeClassifier(random_state=445)\n",
       "9     DecisionTreeClassifier(random_state=445)\n",
       "10    DecisionTreeClassifier(random_state=445)\n",
       "11    DecisionTreeClassifier(random_state=445)\n",
       "12    DecisionTreeClassifier(random_state=445)\n",
       "13    DecisionTreeClassifier(random_state=445)\n",
       "dtype: object"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictors = expgrad_est.predictors_\n",
    "predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "5e75148f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Collect predictions by all predictors and calculate balanced error\n",
    "# as well as the false negative difference for all of them\n",
    "\n",
    "sweep_preds = [clf.predict(X_test) for clf in predictors]\n",
    "balanced_error_sweep = [1-balanced_accuracy_score(Y_test, Y_sweep) for Y_sweep in sweep_preds]\n",
    "fnr_diff_sweep = [false_negative_rate_difference(Y_test, Y_sweep, sensitive_features=A_test) for Y_sweep in sweep_preds]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "fcd55a08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the balanced error / fnr difference values of all predictors on a raster plot  \n",
    "\n",
    "plt.scatter(balanced_error_sweep, fnr_diff_sweep, label=\"ExponentiatedGradient - Iterations\")\n",
    "for i in range(len(predictors)):\n",
    "  plt.annotate(str(i), xy=(balanced_error_sweep[i]+0.001, fnr_diff_sweep[i]+0.001))\n",
    "\n",
    "# Also include in the plot the combined ExponentiatedGradient model\n",
    "# as well as the three previously fitted models\n",
    "\n",
    "plt.scatter(1-balanced_accuracy_score(Y_test, Y_pred_reductions),\n",
    "            false_negative_rate_difference(Y_test, Y_pred_reductions, sensitive_features=A_test),\n",
    "            label=\"ExponentiatedGradient - Combined model\")\n",
    "plt.scatter(1-balanced_accuracy_score(Y_test, Y_pred),\n",
    "            false_negative_rate_difference(Y_test, Y_pred, sensitive_features=A_test),\n",
    "            label=\"Unmitigated\")\n",
    "plt.scatter(1-balanced_accuracy_score(Y_test, Y_pred_postprocess),\n",
    "            false_negative_rate_difference(Y_test, Y_pred_postprocess, sensitive_features=A_test),\n",
    "            label=\"ThresholdOptimizer\")\n",
    "plt.scatter(1-balanced_accuracy_score(Y_test, y_pred_postprocess_deterministic),\n",
    "            false_negative_rate_difference(Y_test, y_pred_postprocess_deterministic, sensitive_features=A_test),\n",
    "            label=\"ThresholdOptimizer (DET)\")\n",
    "\n",
    "plt.xlabel(\"Balanced Error Rate\")\n",
    "plt.ylabel(\"False Negative Rate Difference\")\n",
    "plt.legend(bbox_to_anchor=(1.9,1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "abc680f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_technique_comparison(mf_dict, metric):\n",
    "  \"\"\"\n",
    "  Plots a specified metric for a given dictionary of MetricFrames.\n",
    "  \"\"\"\n",
    "  mf_dict = {k:v.by_group[metric] for (k,v) in mf_dict.items()}\n",
    "  comparison_df = pd.DataFrame.from_dict(mf_dict)\n",
    "  comparison_df.plot.bar(figsize=(12, 6), legend=False)\n",
    "  plt.title(metric)\n",
    "  plt.xticks(rotation=0, ha='center');\n",
    "  plt.legend(bbox_to_anchor=(1.01,1), loc='upper left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "21bfbad7",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dict = {\n",
    "    \"Reductions\": metricframe_reductions,\n",
    "    \"Unmitigated\": metricframe_unmitigated,\n",
    "    \"Postprocessing\": metricframe_postprocess\n",
    "    #\"Postprocessing (DET)\": mf_deterministic\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "1a728b70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_technique_comparison(test_dict, \"false_negative_rate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "87fa1784",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_technique_comparison(test_dict, \"balanced_accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "e20f9159",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_technique_comparison(test_dict, \"selection_rate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "1a8f0099",
   "metadata": {},
   "outputs": [],
   "source": [
    "overall_df = pd.DataFrame.from_dict({\n",
    "    \"Unmitigated\": metricframe_unmitigated.overall,\n",
    "    \"Postprocessing\": metricframe_postprocess.overall,\n",
    "    #\"Postprocessing (DET)\": mf_deterministic.overall,\n",
    "    \"Reductions\": metricframe_reductions.overall\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "57d12721",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>selection_rate</th>\n",
       "      <th>false_negative_rate</th>\n",
       "      <th>balanced_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Unmitigated</th>\n",
       "      <td>0.585231</td>\n",
       "      <td>0.221562</td>\n",
       "      <td>0.694594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Postprocessing</th>\n",
       "      <td>0.621348</td>\n",
       "      <td>0.176336</td>\n",
       "      <td>0.703769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reductions</th>\n",
       "      <td>0.591902</td>\n",
       "      <td>0.200091</td>\n",
       "      <td>0.7095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               selection_rate false_negative_rate balanced_accuracy\n",
       "Unmitigated          0.585231            0.221562          0.694594\n",
       "Postprocessing       0.621348            0.176336          0.703769\n",
       "Reductions           0.591902            0.200091            0.7095"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overall_df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "b63f7b3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "overall_df.transpose().plot.bar(subplots=True, layout= [1,3], figsize=(12, 5), legend=False, rot=-45, position=1.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "d3f2b964",
   "metadata": {},
   "outputs": [],
   "source": [
    "difference_df = pd.DataFrame.from_dict({\n",
    "    \"Unmitigated\": metricframe_unmitigated.difference(),\n",
    "    \"Postprocessing\": metricframe_postprocess.difference(),\n",
    "    #\"Postprocessing (DET)\": mf_deterministic.difference(),\n",
    "    \"Reductions\": metricframe_reductions.difference()\n",
    "}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "052e54b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>selection_rate</th>\n",
       "      <th>false_negative_rate</th>\n",
       "      <th>balanced_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Unmitigated</th>\n",
       "      <td>0.174147</td>\n",
       "      <td>0.281924</td>\n",
       "      <td>0.229509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Postprocessing</th>\n",
       "      <td>0.104064</td>\n",
       "      <td>0.152077</td>\n",
       "      <td>0.188659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reductions</th>\n",
       "      <td>0.131034</td>\n",
       "      <td>0.230167</td>\n",
       "      <td>0.235808</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               selection_rate false_negative_rate balanced_accuracy\n",
       "Unmitigated          0.174147            0.281924          0.229509\n",
       "Postprocessing       0.104064            0.152077          0.188659\n",
       "Reductions           0.131034            0.230167          0.235808"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "difference_df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "c54dde9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "difference_df.T.plot.bar(subplots=True, layout= [1,3], figsize=(12, 5), legend=False, rot=-45, position=1.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a99a913a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "AI_Fairness",
   "language": "python",
   "name": "ai_fairness"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}