MLPproject/Decision_tree.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "946e852d",
   "metadata": {},
   "source": [
    "### Toymodel as an initial test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0952f099",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Poor       0.87      0.87      0.87      4524\n",
      "        Rich       0.62      0.62      0.62      1509\n",
      "\n",
      "    accuracy                           0.81      6033\n",
      "   macro avg       0.74      0.75      0.74      6033\n",
      "weighted avg       0.81      0.81      0.81      6033\n",
      "\n",
      "Index(['age', 'workclass', 'education.num', 'marital.status', 'occupation',\n",
      "       'relationship', 'race', 'sex', 'capital.gain', 'capital.loss',\n",
      "       'hours.per.week', 'native.country'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split, RandomizedSearchCV, KFold, StratifiedKFold, GridSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error , r2_score\n",
    "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
    "from scipy.stats import randint\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Load dataset\n",
    "df = pd.read_csv('./Datasets/adult.csv', comment = '#')\n",
    "\n",
    "# First, create a copy of the dataframe to avoid modifying the original\n",
    "df_encoded = df.copy()\n",
    "df_encoded.drop(['fnlwgt', 'education'], axis=1, inplace=True)\n",
    "\n",
    "# drop all rows that contain '?'\n",
    "for column in df_encoded.columns:\n",
    "    df_encoded = df_encoded[df_encoded[column] != '?']\n",
    "\n",
    "# Apply label encoding to categorical columns\n",
    "label_encoder = LabelEncoder()\n",
    "categorical_columns = ['workclass', 'marital.status', 'occupation', \n",
    "                      'relationship', 'race', 'sex', 'native.country', 'income']\n",
    "\n",
    "for column in categorical_columns:\n",
    "    df_encoded[column] = label_encoder.fit_transform(df_encoded[column])\n",
    "\n",
    "# Now properly separate features and target\n",
    "X = df_encoded.drop(columns=['income'])\n",
    "y = df_encoded['income']\n",
    "\n",
    "# Split the data\n",
    "X_train_val, X_test, y_train_val, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train_val, y_train_val, test_size=0.25, random_state=42)\n",
    "\n",
    "\n",
    "# Build pipeline\n",
    "model = Pipeline([\n",
    "    ('full_dt_classifier', DecisionTreeClassifier(random_state=42))  # Train Decision Tree Regressor\n",
    "])\n",
    "\n",
    "# Train the model\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_val)\n",
    "\n",
    "# Visualize the decision tree\n",
    "plt.figure(figsize=(12, 12))\n",
    "plot_tree(\n",
    "    model.named_steps['full_dt_classifier'],\n",
    "    feature_names=X.columns,\n",
    "    class_names=[\"Poor\", \"Rich\"],\n",
    "    filled=True,\n",
    "    rounded=True,\n",
    "    max_depth=5,       # Keep tree shallow for readability\n",
    "    fontsize=3,\n",
    "    precision=2        # Limit decimals\n",
    ")\n",
    "\n",
    "#plt.savefig('decision_tree.pdf', format='pdf', dpi=300)\n",
    "plt.show()\n",
    "\n",
    "CM = confusion_matrix(y_val, y_pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=CM, display_labels=[\"Poor\", \"Rich\"])\n",
    "disp.plot(cmap=\"Blues\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()  \n",
    "\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(y_val, y_pred, target_names=[\"Poor\", \"Rich\"]))\n",
    "\n",
    "print(X_train_val.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfaae28c",
   "metadata": {},
   "source": [
    "### Hyperparameter tuning\n",
    "I think this section is pretty unecessary. It can probably be removed, but I'm going to keep it for now, just in case. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e567e4e9",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mKeyboardInterrupt\u001b[39m                         Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 8\u001b[39m\n\u001b[32m      6\u001b[39m skf = StratifiedKFold(n_splits=\u001b[32m10\u001b[39m, shuffle=\u001b[38;5;28;01mTrue\u001b[39;00m, random_state=\u001b[32m42\u001b[39m)\n\u001b[32m      7\u001b[39m grid_search = GridSearchCV(model, param_grid, scoring=\u001b[33m'\u001b[39m\u001b[33maccuracy\u001b[39m\u001b[33m'\u001b[39m, cv=skf, n_jobs=-\u001b[32m1\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m8\u001b[39m \u001b[43mgrid_search\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     11\u001b[39m \u001b[38;5;66;03m# Best model training\u001b[39;00m\n\u001b[32m     12\u001b[39m best_model = grid_search.best_estimator_\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/MLP/Projects/MLPproject/.venv/lib/python3.12/site-packages/sklearn/base.py:1365\u001b[39m, in \u001b[36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[39m\u001b[34m(estimator, *args, **kwargs)\u001b[39m\n\u001b[32m   1358\u001b[39m     estimator._validate_params()\n\u001b[32m   1360\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[32m   1361\u001b[39m     skip_parameter_validation=(\n\u001b[32m   1362\u001b[39m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[32m   1363\u001b[39m     )\n\u001b[32m   1364\u001b[39m ):\n\u001b[32m-> \u001b[39m\u001b[32m1365\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/MLP/Projects/MLPproject/.venv/lib/python3.12/site-packages/sklearn/model_selection/_search.py:1051\u001b[39m, in \u001b[36mBaseSearchCV.fit\u001b[39m\u001b[34m(self, X, y, **params)\u001b[39m\n\u001b[32m   1045\u001b[39m     results = \u001b[38;5;28mself\u001b[39m._format_results(\n\u001b[32m   1046\u001b[39m         all_candidate_params, n_splits, all_out, all_more_results\n\u001b[32m   1047\u001b[39m     )\n\u001b[32m   1049\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m results\n\u001b[32m-> \u001b[39m\u001b[32m1051\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_run_search\u001b[49m\u001b[43m(\u001b[49m\u001b[43mevaluate_candidates\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1053\u001b[39m \u001b[38;5;66;03m# multimetric is determined here because in the case of a callable\u001b[39;00m\n\u001b[32m   1054\u001b[39m \u001b[38;5;66;03m# self.scoring the return type is only known after calling\u001b[39;00m\n\u001b[32m   1055\u001b[39m first_test_score = all_out[\u001b[32m0\u001b[39m][\u001b[33m\"\u001b[39m\u001b[33mtest_scores\u001b[39m\u001b[33m\"\u001b[39m]\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/MLP/Projects/MLPproject/.venv/lib/python3.12/site-packages/sklearn/model_selection/_search.py:1605\u001b[39m, in \u001b[36mGridSearchCV._run_search\u001b[39m\u001b[34m(self, evaluate_candidates)\u001b[39m\n\u001b[32m   1603\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_run_search\u001b[39m(\u001b[38;5;28mself\u001b[39m, evaluate_candidates):\n\u001b[32m   1604\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"Search all candidates in param_grid\"\"\"\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1605\u001b[39m     \u001b[43mevaluate_candidates\u001b[49m\u001b[43m(\u001b[49m\u001b[43mParameterGrid\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mparam_grid\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/MLP/Projects/MLPproject/.venv/lib/python3.12/site-packages/sklearn/model_selection/_search.py:997\u001b[39m, in \u001b[36mBaseSearchCV.fit.<locals>.evaluate_candidates\u001b[39m\u001b[34m(candidate_params, cv, more_results)\u001b[39m\n\u001b[32m    989\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.verbose > \u001b[32m0\u001b[39m:\n\u001b[32m    990\u001b[39m     \u001b[38;5;28mprint\u001b[39m(\n\u001b[32m    991\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mFitting \u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[33m folds for each of \u001b[39m\u001b[38;5;132;01m{1}\u001b[39;00m\u001b[33m candidates,\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    992\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33m totalling \u001b[39m\u001b[38;5;132;01m{2}\u001b[39;00m\u001b[33m fits\u001b[39m\u001b[33m\"\u001b[39m.format(\n\u001b[32m    993\u001b[39m             n_splits, n_candidates, n_candidates * n_splits\n\u001b[32m    994\u001b[39m         )\n\u001b[32m    995\u001b[39m     )\n\u001b[32m--> \u001b[39m\u001b[32m997\u001b[39m out = \u001b[43mparallel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    998\u001b[39m \u001b[43m    \u001b[49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_fit_and_score\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    999\u001b[39m \u001b[43m        \u001b[49m\u001b[43mclone\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbase_estimator\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1000\u001b[39m \u001b[43m        \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1001\u001b[39m \u001b[43m        \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1002\u001b[39m \u001b[43m        \u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1003\u001b[39m \u001b[43m        \u001b[49m\u001b[43mtest\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1004\u001b[39m \u001b[43m        \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m=\u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1005\u001b[39m \u001b[43m        \u001b[49m\u001b[43msplit_progress\u001b[49m\u001b[43m=\u001b[49m\u001b[43m(\u001b[49m\u001b[43msplit_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_splits\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1006\u001b[39m \u001b[43m        \u001b[49m\u001b[43mcandidate_progress\u001b[49m\u001b[43m=\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcand_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_candidates\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1007\u001b[39m \u001b[43m        \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mfit_and_score_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1008\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1009\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mcand_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43msplit_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mproduct\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   1010\u001b[39m \u001b[43m        \u001b[49m\u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcandidate_params\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1011\u001b[39m \u001b[43m        \u001b[49m\u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcv\u001b[49m\u001b[43m.\u001b[49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mrouted_params\u001b[49m\u001b[43m.\u001b[49m\u001b[43msplitter\u001b[49m\u001b[43m.\u001b[49m\u001b[43msplit\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1012\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1013\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1015\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(out) < \u001b[32m1\u001b[39m:\n\u001b[32m   1016\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m   1017\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mNo fits were performed. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m   1018\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mWas the CV iterator empty? \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m   1019\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mWere there no candidates?\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m   1020\u001b[39m     )\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/MLP/Projects/MLPproject/.venv/lib/python3.12/site-packages/sklearn/utils/parallel.py:82\u001b[39m, in \u001b[36mParallel.__call__\u001b[39m\u001b[34m(self, iterable)\u001b[39m\n\u001b[32m     73\u001b[39m warning_filters = warnings.filters\n\u001b[32m     74\u001b[39m iterable_with_config_and_warning_filters = (\n\u001b[32m     75\u001b[39m     (\n\u001b[32m     76\u001b[39m         _with_config_and_warning_filters(delayed_func, config, warning_filters),\n\u001b[32m   (...)\u001b[39m\u001b[32m     80\u001b[39m     \u001b[38;5;28;01mfor\u001b[39;00m delayed_func, args, kwargs \u001b[38;5;129;01min\u001b[39;00m iterable\n\u001b[32m     81\u001b[39m )\n\u001b[32m---> \u001b[39m\u001b[32m82\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[34;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43miterable_with_config_and_warning_filters\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/MLP/Projects/MLPproject/.venv/lib/python3.12/site-packages/joblib/parallel.py:2072\u001b[39m, in \u001b[36mParallel.__call__\u001b[39m\u001b[34m(self, iterable)\u001b[39m\n\u001b[32m   2066\u001b[39m \u001b[38;5;66;03m# The first item from the output is blank, but it makes the interpreter\u001b[39;00m\n\u001b[32m   2067\u001b[39m \u001b[38;5;66;03m# progress until it enters the Try/Except block of the generator and\u001b[39;00m\n\u001b[32m   2068\u001b[39m \u001b[38;5;66;03m# reaches the first `yield` statement. This starts the asynchronous\u001b[39;00m\n\u001b[32m   2069\u001b[39m \u001b[38;5;66;03m# dispatch of the tasks to the workers.\u001b[39;00m\n\u001b[32m   2070\u001b[39m \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[32m-> \u001b[39m\u001b[32m2072\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.return_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/MLP/Projects/MLPproject/.venv/lib/python3.12/site-packages/joblib/parallel.py:1682\u001b[39m, in \u001b[36mParallel._get_outputs\u001b[39m\u001b[34m(self, iterator, pre_dispatch)\u001b[39m\n\u001b[32m   1679\u001b[39m     \u001b[38;5;28;01myield\u001b[39;00m\n\u001b[32m   1681\u001b[39m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m._backend.retrieval_context():\n\u001b[32m-> \u001b[39m\u001b[32m1682\u001b[39m         \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m._retrieve()\n\u001b[32m   1684\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mGeneratorExit\u001b[39;00m:\n\u001b[32m   1685\u001b[39m     \u001b[38;5;66;03m# The generator has been garbage collected before being fully\u001b[39;00m\n\u001b[32m   1686\u001b[39m     \u001b[38;5;66;03m# consumed. This aborts the remaining tasks if possible and warn\u001b[39;00m\n\u001b[32m   1687\u001b[39m     \u001b[38;5;66;03m# the user if necessary.\u001b[39;00m\n\u001b[32m   1688\u001b[39m     \u001b[38;5;28mself\u001b[39m._exception = \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/MLP/Projects/MLPproject/.venv/lib/python3.12/site-packages/joblib/parallel.py:1800\u001b[39m, in \u001b[36mParallel._retrieve\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m   1789\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.return_ordered:\n\u001b[32m   1790\u001b[39m     \u001b[38;5;66;03m# Case ordered: wait for completion (or error) of the next job\u001b[39;00m\n\u001b[32m   1791\u001b[39m     \u001b[38;5;66;03m# that have been dispatched and not retrieved yet. If no job\u001b[39;00m\n\u001b[32m   (...)\u001b[39m\u001b[32m   1795\u001b[39m     \u001b[38;5;66;03m# control only have to be done on the amount of time the next\u001b[39;00m\n\u001b[32m   1796\u001b[39m     \u001b[38;5;66;03m# dispatched job is pending.\u001b[39;00m\n\u001b[32m   1797\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m (nb_jobs == \u001b[32m0\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[32m   1798\u001b[39m         \u001b[38;5;28mself\u001b[39m._jobs[\u001b[32m0\u001b[39m].get_status(timeout=\u001b[38;5;28mself\u001b[39m.timeout) == TASK_PENDING\n\u001b[32m   1799\u001b[39m     ):\n\u001b[32m-> \u001b[39m\u001b[32m1800\u001b[39m         \u001b[43mtime\u001b[49m\u001b[43m.\u001b[49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[32;43m0.01\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m   1801\u001b[39m         \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[32m   1803\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m nb_jobs == \u001b[32m0\u001b[39m:\n\u001b[32m   1804\u001b[39m     \u001b[38;5;66;03m# Case unordered: jobs are added to the list of jobs to\u001b[39;00m\n\u001b[32m   1805\u001b[39m     \u001b[38;5;66;03m# retrieve `self._jobs` only once completed or in error, which\u001b[39;00m\n\u001b[32m   (...)\u001b[39m\u001b[32m   1811\u001b[39m     \u001b[38;5;66;03m# timeouts before any other dispatched job has completed and\u001b[39;00m\n\u001b[32m   1812\u001b[39m     \u001b[38;5;66;03m# been added to `self._jobs` to be retrieved.\u001b[39;00m\n",
      "\u001b[31mKeyboardInterrupt\u001b[39m: "
     ]
    }
   ],
   "source": [
    "param_grid = {\n",
    "                'full_dt_classifier__max_depth': [None, 10, 11, 12, 13, 14, 15, 16],\n",
    "                'full_dt_classifier__min_samples_split': [3, 4, 5, 6, 7, 8, 9],\n",
    "                'full_dt_classifier__min_samples_leaf': [1, 2, 3, 4, 5]\n",
    "            }\n",
    "skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)\n",
    "grid_search = GridSearchCV(model, param_grid, scoring='accuracy', cv=skf, n_jobs=-1)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "# Best model training\n",
    "best_model = grid_search.best_estimator_\n",
    "y_pred_best = best_model.predict(X_val)\n",
    "\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(y_val, y_pred_best, target_names=[\"Poor\", \"Rich\"]))\n",
    "\n",
    "best_max_depth = best_model.named_steps['full_dt_classifier'].max_depth\n",
    "best_min_samples_split = best_model.named_steps['full_dt_classifier'].min_samples_split\n",
    "best_min_samples_leaf = best_model.named_steps['full_dt_classifier'].min_samples_leaf\n",
    "\n",
    "print(f'Best max_depth: {best_max_depth}')\n",
    "print(f'Best min_samples_split: {best_min_samples_split}')\n",
    "print(f'Best min_samples_leaf: {best_min_samples_leaf}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a37f45b4",
   "metadata": {},
   "source": [
    "### Classifier comparison\n",
    "This is taken from the ensemble lab. A lot of models are excluded, but they are easy to implement into the class, for now It's kept short to minimize the execution time. We can also tune more parameters for each model, but of course that would also increase the execution time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e68b1ea9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV, StratifiedKFold\n",
    "import time\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import (accuracy_score, precision_score, \n",
    "                             recall_score, f1_score, \n",
    "                             confusion_matrix, ConfusionMatrixDisplay)\n",
    "from sklearn.ensemble import (RandomForestClassifier, BaggingClassifier, \n",
    "                              AdaBoostClassifier, GradientBoostingClassifier)\n",
    "from xgboost import XGBClassifier        # Requires installation of the package ; Not a native function in sklearn\n",
    "from lightgbm import LGBMClassifier      # Requires installation of the package; Not a native function in sklearn\n",
    "from catboost import CatBoostClassifier  # Requires installation of the package; Not a native function in sklearn\n",
    "\n",
    "class ClassifierComparisonOpt:\n",
    "    def __init__(self, X, y, test_size=0.25, use_bootstrap=True, random_state=42, cv_folds=10):\n",
    "        # Split data stratified by labels\n",
    "        X_train, X_test, y_train, y_test = train_test_split(\n",
    "            X, y, test_size=test_size, stratify=y, random_state=random_state)\n",
    "\n",
    "        # Scale features\n",
    "        #scaler = StandardScaler()\n",
    "        #self.X_train = scaler.fit_transform(X_train)\n",
    "        #self.X_test  = scaler.transform(X_test)\n",
    "\n",
    "        # Scaling not necessary for decision trees\n",
    "        self.X_train = X_train\n",
    "        self.X_test = X_test\n",
    "\n",
    "        self.y_train       = y_train\n",
    "        self.y_test        = y_test\n",
    "        self.use_bootstrap = use_bootstrap\n",
    "        self.cv_folds      = cv_folds\n",
    "        self.models        = {}\n",
    "        self.results       = {}\n",
    "        self.results_df    = None\n",
    "        self.feature_names = list(X.columns)\n",
    "\n",
    "    def get_models_with_params(self):\n",
    "        return {\n",
    "            'Decision Tree': (DecisionTreeClassifier(random_state=42), {\n",
    "                'max_depth': [None, 5, 10, 20],\n",
    "                'min_samples_split': [2, 5, 10],\n",
    "                #'min_samples_leaf': [2, 4, 6]\n",
    "            }),\n",
    "            'Random Forest': (RandomForestClassifier(bootstrap=self.use_bootstrap, random_state=42), {\n",
    "                'n_estimators': [50, 100, 200],\n",
    "                'max_depth': [None, 5, 10, 20],\n",
    "                'min_samples_split': [2, 5, 10],\n",
    "                #'min_samples_leaf': [2, 4, 6]\n",
    "            })\n",
    "        }\n",
    "\n",
    "    def fit_models(self, models_with_params=None):\n",
    "        # Set self.get_models_with_params() as a default parameter distribution\n",
    "        if models_with_params == None:\n",
    "            models_with_params = self.get_models_with_params()\n",
    "\n",
    "        results_list = []\n",
    "        cv = StratifiedKFold(n_splits=self.cv_folds, shuffle=True, random_state=42)\n",
    "\n",
    "        for name, (model, param_grid) in models_with_params.items():\n",
    "            print(f\"Tuning {name} ...\")\n",
    "            \n",
    "            grid_search = GridSearchCV(model, param_grid, scoring='accuracy', cv=cv, n_jobs=-1)\n",
    "            \n",
    "            start_train = time.time()\n",
    "            grid_search.fit(self.X_train, self.y_train)\n",
    "            end_train = time.time()\n",
    "\n",
    "            best_model = grid_search.best_estimator_\n",
    "            y_pred = best_model.predict(self.X_test)\n",
    "            end_pred = time.time()\n",
    "\n",
    "            self.models[name] = {\n",
    "                'model': best_model,\n",
    "                'confusion_matrix': confusion_matrix(self.y_test, y_pred)\n",
    "            }\n",
    "\n",
    "            metrics = {\n",
    "                'Model': name,\n",
    "                'Accuracy': accuracy_score(self.y_test, y_pred),\n",
    "                'Precision': precision_score(self.y_test, y_pred, average='weighted', zero_division=0),\n",
    "                'Recall': recall_score(self.y_test, y_pred, average='weighted'),\n",
    "                'F1 Score': f1_score(self.y_test, y_pred, average='weighted'),\n",
    "                'Best Params': grid_search.best_params_,\n",
    "                'Training Time (s)': (end_train - start_train),\n",
    "                'Prediction Time (s)': (end_pred - end_train),\n",
    "                'Total Time (s)': (end_pred - start_train)\n",
    "            }\n",
    "\n",
    "            results_list.append(metrics)\n",
    "\n",
    "        self.results_df = pd.DataFrame(results_list)\n",
    "\n",
    "    def print_summary(self):\n",
    "        print(\"\\n------ Results Sorted by Accuracy ------\")\n",
    "        print(self.results_df.sort_values(by='Accuracy', ascending=False).to_string(index=False))\n",
    "\n",
    "        print(\"\\n------ Results Sorted by Total Time ------\")\n",
    "        print(self.results_df.sort_values(by='Total Time (s)', ascending=True).to_string(index=False))\n",
    "\n",
    "    # Show feature importance for models that support it          \n",
    "    def show_feature_importance(self):\n",
    "        importance = {}\n",
    "        features = self.feature_names\n",
    "\n",
    "        for name, result in self.models.items():\n",
    "            model = result['model']\n",
    "            if hasattr(model, 'feature_importances_'):\n",
    "                importance[name] = model.feature_importances_\n",
    "            elif hasattr(model, 'coef_'):\n",
    "                coef = model.coef_\n",
    "                if coef.ndim == 1:\n",
    "                    importance[name] = np.abs(coef)\n",
    "                else:\n",
    "                    importance[name] = np.mean(np.abs(coef), axis=0)\n",
    "            else:\n",
    "                print(f\"Feature importance not available for model {name}\")\n",
    "\n",
    "        for name, imp in importance.items():\n",
    "            sorted_idx = np.argsort(imp)[::-1]\n",
    "            sorted_features = [features[i] for i in sorted_idx]\n",
    "            plt.figure()\n",
    "            plt.bar(range(len(imp)), imp[sorted_idx], align='center')\n",
    "            plt.xticks(range(len(imp)), sorted_features, rotation=70)\n",
    "            plt.title(f\"Feature importance for {name}\")\n",
    "            plt.xlabel(\"Feature index\")\n",
    "            plt.ylabel(\"Importance score\")\n",
    "            plt.grid(True, linestyle='--', alpha=0.6)\n",
    "            plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da377fc1",
   "metadata": {},
   "source": [
    "### Compare models with coarse hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d6fd1fee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuning Decision Tree ...\n",
      "Tuning Random Forest ...\n",
      "\n",
      "------ Results Sorted by Accuracy ------\n",
      "        Model  Accuracy  Precision   Recall  F1 Score                                                     Best Params  Training Time (s)  Prediction Time (s)  Total Time (s)\n",
      "Random Forest  0.858942   0.853479 0.858942  0.853443 {'max_depth': 20, 'min_samples_split': 10, 'n_estimators': 100}         160.295069             0.188931      160.484000\n",
      "Decision Tree  0.848334   0.844900 0.848334  0.846228                       {'max_depth': 10, 'min_samples_split': 5}          11.927628             0.006326       11.933954\n",
      "\n",
      "------ Results Sorted by Total Time ------\n",
      "        Model  Accuracy  Precision   Recall  F1 Score                                                     Best Params  Training Time (s)  Prediction Time (s)  Total Time (s)\n",
      "Decision Tree  0.848334   0.844900 0.848334  0.846228                       {'max_depth': 10, 'min_samples_split': 5}          11.927628             0.006326       11.933954\n",
      "Random Forest  0.858942   0.853479 0.858942  0.853443 {'max_depth': 20, 'min_samples_split': 10, 'n_estimators': 100}         160.295069             0.188931      160.484000\n"
     ]
    }
   ],
   "source": [
    "# optimized classifiers and find their hyperparameters\n",
    "clf_opt = ClassifierComparisonOpt(X_train_val, y_train_val)\n",
    "clf_opt.fit_models()\n",
    "clf_opt.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc529395",
   "metadata": {},
   "source": [
    "### Finetune and compare again\n",
    "After tuning with a coarse grid we can use the result to select a finer grid and find better hyperparameters. This will only slightly improve the models, you will generally get diminishing returns the finer you tune."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4185a428",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuning Decision Tree ...\n",
      "Tuning Random Forest ...\n",
      "\n",
      "------ Results Sorted by Accuracy ------\n",
      "        Model  Accuracy  Precision   Recall  F1 Score                                                     Best Params  Training Time (s)  Prediction Time (s)  Total Time (s)\n",
      "Random Forest  0.858942   0.853479 0.858942  0.853443 {'max_depth': 20, 'min_samples_split': 10, 'n_estimators': 100}         167.364476             0.126707      167.491183\n",
      "Decision Tree  0.849163   0.843062 0.849163  0.839388                        {'max_depth': 8, 'min_samples_split': 6}           2.160097             0.003498        2.163594\n",
      "\n",
      "------ Results Sorted by Total Time ------\n",
      "        Model  Accuracy  Precision   Recall  F1 Score                                                     Best Params  Training Time (s)  Prediction Time (s)  Total Time (s)\n",
      "Decision Tree  0.849163   0.843062 0.849163  0.839388                        {'max_depth': 8, 'min_samples_split': 6}           2.160097             0.003498        2.163594\n",
      "Random Forest  0.858942   0.853479 0.858942  0.853443 {'max_depth': 20, 'min_samples_split': 10, 'n_estimators': 100}         167.364476             0.126707      167.491183\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the classifiers and their hyperparameters in a finer grid\n",
    "def get_fine_grids(use_bootstrap=True):\n",
    "    return {\n",
    "        'Decision Tree': (DecisionTreeClassifier(random_state=42), {\n",
    "            'max_depth': [8, 10, 12],\n",
    "            'min_samples_split': [4, 5, 6],\n",
    "        }),\n",
    "        'Random Forest': (RandomForestClassifier(bootstrap=use_bootstrap, random_state=42), {\n",
    "            'n_estimators': [75, 100, 150], \n",
    "            'max_depth': [15, 20, 25],\n",
    "            'min_samples_split': [8, 10, 12],\n",
    "        })\n",
    "    }\n",
    "\n",
    "# optimized classifiers and find their hyperparameters\n",
    "clf_opt = ClassifierComparisonOpt(X_train_val, y_train_val)\n",
    "fine_grid = get_fine_grids()\n",
    "clf_opt.fit_models(fine_grid)\n",
    "clf_opt.print_summary()\n",
    "\n",
    "# Show feature importance for each model\n",
    "clf_opt.show_feature_importance()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01f5866d",
   "metadata": {},
   "source": [
    "### Remove redundant features\n",
    "This section is pretty bad :(. If we want to remove redunant features we need to select different features for different models, which I have not implemented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "776cdb17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuning Decision Tree ...\n",
      "\n",
      "------ Results Sorted by Accuracy ------\n",
      "        Model  Accuracy  Precision   Recall  F1 Score                                                      Best Params  Training Time (s)  Prediction Time (s)  Total Time (s)\n",
      "Decision Tree  0.846842   0.840202 0.846842  0.840706 {'max_depth': 10, 'min_samples_leaf': 1, 'min_samples_split': 5}          10.301274             0.007761       10.309036\n",
      "\n",
      "------ Results Sorted by Total Time ------\n",
      "        Model  Accuracy  Precision   Recall  F1 Score                                                      Best Params  Training Time (s)  Prediction Time (s)  Total Time (s)\n",
      "Decision Tree  0.846842   0.840202 0.846842  0.840706 {'max_depth': 10, 'min_samples_leaf': 1, 'min_samples_split': 5}          10.301274             0.007761       10.309036\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>age</th>\n",
       "      <th>education.num</th>\n",
       "      <th>relationship</th>\n",
       "      <th>capital.gain</th>\n",
       "      <th>capital.loss</th>\n",
       "      <th>hours.per.week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19841</th>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24329</th>\n",
       "      <td>50</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10002</th>\n",
       "      <td>43</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25682</th>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13800</th>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  education.num  relationship  capital.gain  capital.loss  \\\n",
       "19841   35             10             0             0             0   \n",
       "24329   50              9             4             0             0   \n",
       "10002   43              9             0             0             0   \n",
       "25682   23             10             3             0             0   \n",
       "13800   30             10             1             0             0   \n",
       "\n",
       "       hours.per.week  \n",
       "19841              45  \n",
       "24329              12  \n",
       "10002              45  \n",
       "25682              24  \n",
       "13800              40  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove redundant features\n",
    "X_filtered = X_train_val.drop(columns=['sex', 'native.country', 'marital.status', 'race', 'occupation', 'workclass'])\n",
    "\n",
    "# optimized classifiers and find their hyperparameters\n",
    "clf_opt = ClassifierComparisonOpt(X_filtered, y_train_val)\n",
    "clf_opt.fit_models()\n",
    "clf_opt.print_summary()\n",
    "\n",
    "# Show feature importance for each model\n",
    "clf_opt.show_feature_importance()\n",
    "X_filtered.head()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.12.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}