ull_no_fair_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from fairness.pre_processing import categorical_to_numeric_converter\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('./dataset/ull/ULL_dataset.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = categorical_to_numeric_converter(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.columns = [x.lower() for x in dataset.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['unnamed: 0', 'scores', 'score_mat', 'level_mat', 'score_len',\n",
       "       'level_len', 'score_ing', 'level_ing', 'a1', 'a2',\n",
       "       ...\n",
       "       'p331a', 'p331b', 'p331c', 'p331d', 'p331e', 'p331f', 'p331g', 'p331j',\n",
       "       'pfc', 'rep'],\n",
       "      dtype='object', length=549)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_column = 'level_mat'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.drop(columns=['unnamed: 0', 'scores'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "random_forest_classifier = RandomForestClassifier()\n",
    "decision_tree_classifier = DecisionTreeClassifier()\n",
    "\n",
    "models = [\n",
    "    random_forest_classifier,\n",
    "    decision_tree_classifier\n",
    "]\n",
    "\n",
    "random_forest_params = {\n",
    "    'min_samples_leaf': [5, 7, 9, 11],\n",
    "    'n_estimators': [200, 500],\n",
    "    'max_depth': [10, 20, 50, 80, 100, 150],\n",
    "    'criterion': ['gini', 'entropy']\n",
    "}\n",
    "\n",
    "decision_tree_params = {\n",
    "    'min_samples_leaf': [5, 7, 9, 11],\n",
    "    'max_depth': [10, 20, 50, 80, 100, 150],\n",
    "    'criterion': ['gini', 'entropy']\n",
    "}\n",
    "\n",
    "\n",
    "model_params = [random_forest_params, decision_tree_params]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model:  RandomForestClassifier()\n",
      "Model:  DecisionTreeClassifier()\n",
      "OUTPUT COLUMN:  level_mat\n",
      "Best model:  RandomForestClassifier()\n",
      "Best_params:  {'min_samples_leaf': [5, 7, 9, 11], 'n_estimators': [200, 500], 'max_depth': [10, 20, 50, 80, 100, 150], 'criterion': ['gini', 'entropy']}\n",
      "Accuracy:  0.9088280995916598\n"
     ]
    }
   ],
   "source": [
    "X = dataset.loc[:, dataset.columns!=output_column]\n",
    "y = dataset[output_column]\n",
    "y = np.ravel(y)\n",
    "    \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
    "    \n",
    "standard_scaler_train = StandardScaler()\n",
    "X_train = standard_scaler_train.fit_transform(X_train)\n",
    "\n",
    "standard_scaler_test = StandardScaler()\n",
    "X_test = standard_scaler_test.fit_transform(X_test)\n",
    "\n",
    "best_params = None\n",
    "max_accuracy = 0\n",
    "best_model = None\n",
    "    \n",
    "for model_index in range(0, len(models)):\n",
    "        \n",
    "    print(\"Model: \", models[model_index])\n",
    "        \n",
    "    grid_search = GridSearchCV(\n",
    "    estimator=models[model_index],\n",
    "    param_grid=model_params[model_index],\n",
    "    scoring='accuracy', \n",
    "    return_train_score=True,\n",
    "    cv=3\n",
    "    )\n",
    "\n",
    "    grid_search_classifier = grid_search.fit(X_train, y_train)\n",
    "    y_pred = grid_search_classifier.predict(X_test)\n",
    "    if accuracy_score(y_test, y_pred) > max_accuracy:\n",
    "        max_accuracy = accuracy_score(y_test, y_pred)\n",
    "        best_model = models[model_index]\n",
    "        best_params = grid_search_classifier.best_params_\n",
    "    \n",
    "print(\"OUTPUT COLUMN: \", output_column)\n",
    "print(\"Best model: \", best_model)\n",
    "print(\"Best_params: \", best_params)\n",
    "print(\"Accuracy: \", max_accuracy)"
   ]
  }
 ],
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