Code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5c4e0db9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import shap\n",
    "from sklearn.inspection import PartialDependenceDisplay\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.tree import plot_tree\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "57349b01",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"recruitment.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "64ab5edc",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop([\"Id\", \"company\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1c196cdb",
   "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>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>nationality</th>\n",
       "      <th>sport</th>\n",
       "      <th>ind-university_grade</th>\n",
       "      <th>ind-debateclub</th>\n",
       "      <th>ind-programming_exp</th>\n",
       "      <th>ind-international_exp</th>\n",
       "      <th>ind-entrepeneur_exp</th>\n",
       "      <th>ind-languages</th>\n",
       "      <th>ind-exact_study</th>\n",
       "      <th>ind-degree</th>\n",
       "      <th>decision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>24</td>\n",
       "      <td>German</td>\n",
       "      <td>Swimming</td>\n",
       "      <td>70</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>phd</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>26</td>\n",
       "      <td>German</td>\n",
       "      <td>Golf</td>\n",
       "      <td>67</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>bachelor</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>23</td>\n",
       "      <td>Dutch</td>\n",
       "      <td>Running</td>\n",
       "      <td>67</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>master</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>24</td>\n",
       "      <td>Dutch</td>\n",
       "      <td>Cricket</td>\n",
       "      <td>70</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>master</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>female</td>\n",
       "      <td>26</td>\n",
       "      <td>German</td>\n",
       "      <td>Golf</td>\n",
       "      <td>59</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>master</td>\n",
       "      <td>True</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>3995</th>\n",
       "      <td>female</td>\n",
       "      <td>28</td>\n",
       "      <td>Dutch</td>\n",
       "      <td>Running</td>\n",
       "      <td>63</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>master</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3996</th>\n",
       "      <td>female</td>\n",
       "      <td>27</td>\n",
       "      <td>Dutch</td>\n",
       "      <td>Tennis</td>\n",
       "      <td>62</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>bachelor</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3997</th>\n",
       "      <td>male</td>\n",
       "      <td>24</td>\n",
       "      <td>Belgian</td>\n",
       "      <td>Rugby</td>\n",
       "      <td>60</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>bachelor</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3998</th>\n",
       "      <td>female</td>\n",
       "      <td>22</td>\n",
       "      <td>Dutch</td>\n",
       "      <td>Tennis</td>\n",
       "      <td>66</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>bachelor</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3999</th>\n",
       "      <td>male</td>\n",
       "      <td>28</td>\n",
       "      <td>Dutch</td>\n",
       "      <td>Football</td>\n",
       "      <td>58</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>bachelor</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender  age nationality     sport  ind-university_grade  ind-debateclub  \\\n",
       "0     female   24      German  Swimming                    70           False   \n",
       "1       male   26      German      Golf                    67           False   \n",
       "2     female   23       Dutch   Running                    67           False   \n",
       "3       male   24       Dutch   Cricket                    70           False   \n",
       "4     female   26      German      Golf                    59           False   \n",
       "...      ...  ...         ...       ...                   ...             ...   \n",
       "3995  female   28       Dutch   Running                    63           False   \n",
       "3996  female   27       Dutch    Tennis                    62           False   \n",
       "3997    male   24     Belgian     Rugby                    60            True   \n",
       "3998  female   22       Dutch    Tennis                    66           False   \n",
       "3999    male   28       Dutch  Football                    58           False   \n",
       "\n",
       "      ind-programming_exp  ind-international_exp  ind-entrepeneur_exp  \\\n",
       "0                   False                  False                False   \n",
       "1                    True                  False                False   \n",
       "2                    True                   True                False   \n",
       "3                    True                  False                False   \n",
       "4                   False                  False                False   \n",
       "...                   ...                    ...                  ...   \n",
       "3995                False                  False                False   \n",
       "3996                False                  False                 True   \n",
       "3997                False                  False                 True   \n",
       "3998                 True                  False                False   \n",
       "3999                False                  False                False   \n",
       "\n",
       "      ind-languages  ind-exact_study ind-degree  decision  \n",
       "0                 1             True        phd      True  \n",
       "1                 2             True   bachelor     False  \n",
       "2                 0             True     master     False  \n",
       "3                 1             True     master      True  \n",
       "4                 1            False     master      True  \n",
       "...             ...              ...        ...       ...  \n",
       "3995              0            False     master     False  \n",
       "3996              2             True   bachelor     False  \n",
       "3997              2            False   bachelor     False  \n",
       "3998              1             True   bachelor     False  \n",
       "3999              1            False   bachelor     False  \n",
       "\n",
       "[4000 rows x 13 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e3c54e69",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[data[\"gender\"] != \"other\"]\n",
    "data = data.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4d3ce170",
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_age(value):\n",
    "    if value > 0 and value < 1:\n",
    "        return \"Baby\"\n",
    "    elif value >= 1 and value < 4:\n",
    "        return \"Toddler\"\n",
    "    elif value  >= 4 and value < 12:\n",
    "        return \"PreTeen\"\n",
    "    elif value >= 12 and value < 18:\n",
    "        return \"Teen\"\n",
    "    elif value >= 18 and value < 25:\n",
    "        return \"Young-Adult\"\n",
    "    elif value >= 25 and value < 40:\n",
    "        return \"Adult\"\n",
    "    elif value >= 40 and value < 60:\n",
    "        return \"Midde-Aged\"\n",
    "    else:\n",
    "        return \"Old\"\n",
    "    \n",
    "data[\"age\"] = data[\"age\"].apply(replace_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "39eb8867",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.replace({ False: 0, True: 1})\n",
    "\n",
    "data[\"gender\"] = data[\"gender\"].replace({\"female\": 0, \"male\": 1, \"other\": -1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "04fc7ca1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=data[\"gender\"], hue = data[\"decision\"])\n",
    "plt.title(\"Distrubiton of Decison by Gender\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4ef3e0a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "phd_data = data[data[\"gender\"] == 0]\n",
    "gender_counts = phd_data[\"decision\"].value_counts()\n",
    "\n",
    "plt.pie(gender_counts, labels=gender_counts.index, autopct='%1.1f%%')\n",
    "plt.title(\"Rejected rate of Female\")\n",
    "plt.show()\n",
    "\n",
    "phd_data = data[data[\"gender\"] == 1]\n",
    "gender_counts = phd_data[\"decision\"].value_counts()\n",
    "\n",
    "plt.pie(gender_counts, labels=gender_counts.index, autopct='%1.1f%%')\n",
    "plt.title(\"Rejected rate of Male\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "81abdaea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3917 entries, 0 to 3916\n",
      "Data columns (total 13 columns):\n",
      " #   Column                 Non-Null Count  Dtype \n",
      "---  ------                 --------------  ----- \n",
      " 0   gender                 3917 non-null   int64 \n",
      " 1   age                    3917 non-null   object\n",
      " 2   nationality            3917 non-null   object\n",
      " 3   sport                  3917 non-null   object\n",
      " 4   ind-university_grade   3917 non-null   int64 \n",
      " 5   ind-debateclub         3917 non-null   int64 \n",
      " 6   ind-programming_exp    3917 non-null   int64 \n",
      " 7   ind-international_exp  3917 non-null   int64 \n",
      " 8   ind-entrepeneur_exp    3917 non-null   int64 \n",
      " 9   ind-languages          3917 non-null   int64 \n",
      " 10  ind-exact_study        3917 non-null   int64 \n",
      " 11  ind-degree             3917 non-null   object\n",
      " 12  decision               3917 non-null   int64 \n",
      "dtypes: int64(9), object(4)\n",
      "memory usage: 397.9+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a4658b28",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </tbody>\n",
       "</table>\n",
       "<p>3917 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender  ind-university_grade  ind-debateclub  ind-programming_exp  \\\n",
       "0          0                    70               0                    0   \n",
       "1          1                    67               0                    1   \n",
       "2          0                    67               0                    1   \n",
       "3          1                    70               0                    1   \n",
       "4          0                    59               0                    0   \n",
       "...      ...                   ...             ...                  ...   \n",
       "3912       0                    63               0                    0   \n",
       "3913       0                    62               0                    0   \n",
       "3914       1                    60               1                    0   \n",
       "3915       0                    66               0                    1   \n",
       "3916       1                    58               0                    0   \n",
       "\n",
       "      ind-international_exp  ind-entrepeneur_exp  ind-languages  \\\n",
       "0                         0                    0              1   \n",
       "1                         0                    0              2   \n",
       "2                         1                    0              0   \n",
       "3                         0                    0              1   \n",
       "4                         0                    0              1   \n",
       "...                     ...                  ...            ...   \n",
       "3912                      0                    0              0   \n",
       "3913                      0                    1              2   \n",
       "3914                      0                    1              2   \n",
       "3915                      0                    0              1   \n",
       "3916                      0                    0              1   \n",
       "\n",
       "      ind-exact_study  decision  age_Adult  ...  sport_Cricket  \\\n",
       "0                   1         1          0  ...              0   \n",
       "1                   1         0          1  ...              0   \n",
       "2                   1         0          0  ...              0   \n",
       "3                   1         1          0  ...              1   \n",
       "4                   0         1          1  ...              0   \n",
       "...               ...       ...        ...  ...            ...   \n",
       "3912                0         0          1  ...              0   \n",
       "3913                1         0          1  ...              0   \n",
       "3914                0         0          0  ...              0   \n",
       "3915                1         0          0  ...              0   \n",
       "3916                0         0          1  ...              0   \n",
       "\n",
       "      sport_Football  sport_Golf  sport_Rugby  sport_Running  sport_Swimming  \\\n",
       "0                  0           0            0              0               1   \n",
       "1                  0           1            0              0               0   \n",
       "2                  0           0            0              1               0   \n",
       "3                  0           0            0              0               0   \n",
       "4                  0           1            0              0               0   \n",
       "...              ...         ...          ...            ...             ...   \n",
       "3912               0           0            0              1               0   \n",
       "3913               0           0            0              0               0   \n",
       "3914               0           0            1              0               0   \n",
       "3915               0           0            0              0               0   \n",
       "3916               1           0            0              0               0   \n",
       "\n",
       "      sport_Tennis  ind-degree_bachelor  ind-degree_master  ind-degree_phd  \n",
       "0                0                    0                  0               1  \n",
       "1                0                    1                  0               0  \n",
       "2                0                    0                  1               0  \n",
       "3                0                    0                  1               0  \n",
       "4                0                    0                  1               0  \n",
       "...            ...                  ...                ...             ...  \n",
       "3912             0                    0                  1               0  \n",
       "3913             1                    1                  0               0  \n",
       "3914             0                    1                  0               0  \n",
       "3915             1                    1                  0               0  \n",
       "3916             0                    1                  0               0  \n",
       "\n",
       "[3917 rows x 25 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_encoded = pd.get_dummies(data)\n",
    "data_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a07dfd3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data_encoded.drop(\"decision\", axis = 1)\n",
    "y = data_encoded[\"decision\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b288308",
   "metadata": {},
   "source": [
    "## 2) Find a good black-box classification model to predict the target variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "575e892f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "clf.fit(X, y)\n",
    "\n",
    "importances = clf.feature_importances_\n",
    "\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "top_features = indices[:10]\n",
    "top_importances = importances[top_features]\n",
    "feature_names = X.columns[top_features]\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.title(\"Top 10 Feature Importances\")\n",
    "plt.barh(range(len(top_features)), top_importances, align=\"center\")\n",
    "plt.yticks(range(len(top_features)), feature_names)\n",
    "plt.xlabel(\"Importance\")\n",
    "plt.ylabel(\"Feature\")\n",
    "plt.gca().invert_yaxis()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "332294dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display = PartialDependenceDisplay.from_estimator(clf, X, features=[\"gender\"])\n",
    "plt.title(\"PDP (Initial)\")\n",
    "plt.xlabel(\"Gender\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3ba2f29b",
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_probabilities = clf.predict_proba(X)\n",
    "data_encoded[\"predictions\"]= predicted_probabilities[:, 1]\n",
    "prediction_bb = predicted_probabilities[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "77222a0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9527699770232321\n"
     ]
    }
   ],
   "source": [
    "y_pred = clf.predict(X)\n",
    "\n",
    "accuracy_bb = accuracy_score(y, y_pred)\n",
    "print(\"Accuracy:\", accuracy_bb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4506cd2d",
   "metadata": {},
   "source": [
    "## 3) Use a global surrogate model for a model agnostic explanation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9412a472",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7061526678580546\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "classifier = DecisionTreeClassifier(max_depth = 2)\n",
    "classifier.fit(X, y)\n",
    "y_pred = classifier.predict(X)\n",
    "accuracy = accuracy_score(y, y_pred)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plot_tree(classifier, feature_names=X.columns, class_names=\"decision\", filled=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6f29590d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Attempting to set identical bottom == top == 0.31707939749808256 results in singular transformations; automatically expanding.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display = PartialDependenceDisplay.from_estimator(classifier, X, features=[\"gender\"])\n",
    "plt.title(\"Partial Dependence Plot\")\n",
    "plt.xlabel(\"Gender\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "514cd5da",
   "metadata": {},
   "source": [
    "## 5)  Perform a pre or post-processing algorithm to reduce the bias in the algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "74072ca3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def apply_threshold(predictions, threshold):\n",
    "    predicted_labels = np.where(predictions >= threshold, 1, 0)\n",
    "    return predicted_labels\n",
    "\n",
    "def calculate_false_positive_rate(predictions, actual_values):\n",
    "    \n",
    "    true_negatives = 0\n",
    "    false_positives = 0\n",
    "    \n",
    "    for prediction, actual in zip(predictions, actual_values):\n",
    "        if prediction == 0 and actual == 0:\n",
    "            true_negatives += 1\n",
    "        elif prediction == 1 and actual == 0:\n",
    "            false_positives += 1\n",
    "    \n",
    "    if true_negatives + false_positives == 0:\n",
    "        return 0.0\n",
    "    \n",
    "    false_positive_rate = false_positives / (true_negatives + false_positives)\n",
    "    return false_positive_rate\n",
    "\n",
    "def positive_predictive_parity(predicted, actual):\n",
    "    \n",
    "    tp = sum((actual == 1) & (predicted == 1))\n",
    "    \n",
    "    pp = sum(predicted == 1)\n",
    "\n",
    "    ppp = tp / pp\n",
    "\n",
    "    return ppp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cfa6ddbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold of Female: 0.73\n",
      "Threshold of Male: 0.75\n"
     ]
    }
   ],
   "source": [
    "thresholds = np.arange(0.0, 1.0, 0.01) \n",
    "\n",
    "min_false_positive_group_0 = float(\"inf\") \n",
    "optimized_threshold_group_0 = None\n",
    "min_false_positive_group_1 = float(\"inf\") \n",
    "optimized_threshold_group_1 = None\n",
    "\n",
    "for threshold in thresholds:\n",
    "    predicted_labels_3 = apply_threshold(data_encoded[\"predictions\"], threshold)\n",
    "    \n",
    "    df = pd.DataFrame({\"Gender\": data_encoded[\"gender\"], \"Decision\": data_encoded[\"decision\"], \"Predicted Labels\": predicted_labels_3})\n",
    "\n",
    "    if (len(np.unique(df[\"Predicted Labels\"])) == 2 and \\\n",
    "            sum((df[\"Decision\"] == 0) & (df[\"Gender\"] == 0) & (df[\"Predicted Labels\"] == 1)) > 0 and \\\n",
    "            sum((df[\"Decision\"] == 0) & (df[\"Gender\"] == 1) & (df[\"Predicted Labels\"] == 1)) > 0):\n",
    "\n",
    "        false_positive_group_0 = calculate_false_positive_rate(df[\"Predicted Labels\"][df[\"Gender\"] == 0], df[\"Decision\"][df[\"Gender\"] == 0])\n",
    "        false_positive_group_1 = calculate_false_positive_rate(df[\"Predicted Labels\"][df[\"Gender\"] == 1], df[\"Decision\"][df[\"Gender\"] == 1])\n",
    "    \n",
    "    else:\n",
    "        false_positive_group_0 = float(\"inf\")\n",
    "        false_positive_group_1 = float(\"inf\")\n",
    "\n",
    "    if false_positive_group_0 < min_false_positive_group_0:\n",
    "        min_false_positive_group_0 = false_positive_group_0\n",
    "        optimized_threshold_group_0 = threshold\n",
    "\n",
    "    if false_positive_group_1 < min_false_positive_group_1:\n",
    "        min_false_positive_group_1 = false_positive_group_1\n",
    "        optimized_threshold_group_1 = threshold\n",
    "\n",
    "print(\"Threshold of Female:\", optimized_threshold_group_0)\n",
    "print(\"Threshold of Male:\", optimized_threshold_group_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b813381e",
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_labels_3 = []\n",
    "\n",
    "for i, gender in enumerate(data_encoded[\"gender\"]):\n",
    "    if gender == 0:\n",
    "        prediction = apply_threshold(data_encoded[\"predictions\"][i], optimized_threshold_group_0)\n",
    "    elif gender == 1:\n",
    "        prediction = apply_threshold(data_encoded[\"predictions\"][i], optimized_threshold_group_1)\n",
    "    elif gender == 2:\n",
    "        prediction = apply_threshold(data_encoded[\"predictions\"][i], optimized_threshold_group_2)\n",
    "    predicted_labels_3.append(int(prediction))\n",
    "\n",
    "predicted_labels_3 = np.array(predicted_labels_3)\n",
    "df_3 = pd.DataFrame({\"Gender\": data_encoded[\"gender\"],\n",
    "                     \"Decision\": data_encoded[\"decision\"],\n",
    "                     \"Predicted Labels\": predicted_labels_3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "aa8a4cec",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_encoded[\"predictions2\"] = predicted_labels_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c5f24b7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_ = data_encoded.drop([\"predictions\",\"decision\",\"predictions2\"], axis = 1)\n",
    "y_ = data_encoded[\"predictions2\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f803ead8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "clf.fit(X_, y_)\n",
    "\n",
    "importances = clf.feature_importances_\n",
    "\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "top_features = indices[:10]\n",
    "top_importances = importances[top_features]\n",
    "feature_names = X_.columns[top_features]\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.title(\"Top 10 Feature Importances\")\n",
    "plt.barh(range(len(top_features)), top_importances, align=\"center\")\n",
    "plt.yticks(range(len(top_features)), feature_names)\n",
    "plt.xlabel(\"Importance\")\n",
    "plt.ylabel(\"Feature\")\n",
    "plt.gca().invert_yaxis()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2d7972e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display = PartialDependenceDisplay.from_estimator(clf, X_, features=[\"gender\"])\n",
    "plt.title(\"PDP (with Threshold)\")\n",
    "plt.xlabel(\"Gender\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}