MLPproject/Decision_tree.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "946e852d",
   "metadata": {},
   "source": [
    "### Toymodel as an initial test\n",
    "This is only a quick test to see if the model is applicable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0952f099",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "cannot unpack non-iterable DecisionTreeClassifier object",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mTypeError\u001b[39m                                 Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[18]\u001b[39m\u001b[32m, line 46\u001b[39m\n\u001b[32m     41\u001b[39m model = Pipeline([\n\u001b[32m     42\u001b[39m     (DecisionTreeClassifier(random_state=\u001b[32m42\u001b[39m))  \u001b[38;5;66;03m# Train Decision Tree Regressor\u001b[39;00m\n\u001b[32m     43\u001b[39m ])\n\u001b[32m     45\u001b[39m \u001b[38;5;66;03m# Train the model\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m46\u001b[39m \u001b[43mmodel\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     47\u001b[39m y_pred = model.predict(X_val)\n\u001b[32m     49\u001b[39m \u001b[38;5;66;03m# Visualize the decision tree\u001b[39;00m\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/pipeline.py:654\u001b[39m, in \u001b[36mPipeline.fit\u001b[39m\u001b[34m(self, X, y, **params)\u001b[39m\n\u001b[32m    647\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _routing_enabled() \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m.transform_input \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    648\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m    649\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mThe `transform_input` parameter can only be set if metadata \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    650\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mrouting is enabled. You can enable metadata routing using \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    651\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33m`sklearn.set_config(enable_metadata_routing=True)`.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    652\u001b[39m     )\n\u001b[32m--> \u001b[39m\u001b[32m654\u001b[39m routed_params = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_check_method_params\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mfit\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprops\u001b[49m\u001b[43m=\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    655\u001b[39m Xt = \u001b[38;5;28mself\u001b[39m._fit(X, y, routed_params, raw_params=params)\n\u001b[32m    656\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m _print_elapsed_time(\u001b[33m\"\u001b[39m\u001b[33mPipeline\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28mself\u001b[39m._log_message(\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m.steps) - \u001b[32m1\u001b[39m)):\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/MLP/Projects/MLPproject/.venv/lib/python3.12/site-packages/sklearn/pipeline.py:454\u001b[39m, in \u001b[36mPipeline._check_method_params\u001b[39m\u001b[34m(self, method, props, **kwargs)\u001b[39m\n\u001b[32m    449\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m routed_params\n\u001b[32m    450\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    451\u001b[39m     fit_params_steps = Bunch(\n\u001b[32m    452\u001b[39m         **{\n\u001b[32m    453\u001b[39m             name: Bunch(**{method: {} \u001b[38;5;28;01mfor\u001b[39;00m method \u001b[38;5;129;01min\u001b[39;00m METHODS})\n\u001b[32m--> \u001b[39m\u001b[32m454\u001b[39m             \u001b[38;5;28;01mfor\u001b[39;00m name, step \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.steps\n\u001b[32m    455\u001b[39m             \u001b[38;5;28;01mif\u001b[39;00m step \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m    456\u001b[39m         }\n\u001b[32m    457\u001b[39m     )\n\u001b[32m    458\u001b[39m     \u001b[38;5;28;01mfor\u001b[39;00m pname, pval \u001b[38;5;129;01min\u001b[39;00m props.items():\n\u001b[32m    459\u001b[39m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33m__\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m pname:\n",
      "\u001b[31mTypeError\u001b[39m: cannot unpack non-iterable DecisionTreeClassifier object"
     ]
    }
   ],
   "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",
    "# 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": 4,
   "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",
    "            'XGBoost': (XGBClassifier(eval_metric='mlogloss', random_state=42), {\n",
    "                'n_estimators': [50, 100, 200],\n",
    "                'learning_rate': [0.01, 0.1, 0.2, 0.5, 1.0],\n",
    "                'max_depth': [3, 5, 10]\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=-2)\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": 5,
   "id": "d6fd1fee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuning Decision Tree ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuning Random Forest ...\n",
      "Tuning XGBoost ...\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",
      "      XGBoost  0.868059   0.863590 0.868059  0.863957     {'learning_rate': 0.2, 'max_depth': 5, 'n_estimators': 100}          25.874328             0.005939       25.880267\n",
      "Random Forest  0.858942   0.853479 0.858942  0.853443 {'max_depth': 20, 'min_samples_split': 10, 'n_estimators': 100}         125.898729             0.065410      125.964139\n",
      "Decision Tree  0.848334   0.844900 0.848334  0.846228                       {'max_depth': 10, 'min_samples_split': 5}           9.028944             0.001837        9.030781\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}           9.028944             0.001837        9.030781\n",
      "      XGBoost  0.868059   0.863590 0.868059  0.863957     {'learning_rate': 0.2, 'max_depth': 5, 'n_estimators': 100}          25.874328             0.005939       25.880267\n",
      "Random Forest  0.858942   0.853479 0.858942  0.853443 {'max_depth': 20, 'min_samples_split': 10, 'n_estimators': 100}         125.898729             0.065410      125.964139\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": 6,
   "id": "4185a428",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuning Decision Tree ...\n",
      "Tuning Random Forest ...\n",
      "Tuning XGBoost ...\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",
      "      XGBoost  0.868556   0.864049 0.868556  0.864181      {'learning_rate': 0.2, 'max_depth': 5, 'n_estimators': 75}          11.834481             0.006366       11.840847\n",
      "Random Forest  0.858942   0.853479 0.858942  0.853443 {'max_depth': 20, 'min_samples_split': 10, 'n_estimators': 100}         129.781899             0.075195      129.857094\n",
      "Decision Tree  0.849163   0.843062 0.849163  0.839388                        {'max_depth': 8, 'min_samples_split': 6}           1.778030             0.002314        1.780344\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}           1.778030             0.002314        1.780344\n",
      "      XGBoost  0.868556   0.864049 0.868556  0.864181      {'learning_rate': 0.2, 'max_depth': 5, 'n_estimators': 75}          11.834481             0.006366       11.840847\n",
      "Random Forest  0.858942   0.853479 0.858942  0.853443 {'max_depth': 20, 'min_samples_split': 10, 'n_estimators': 100}         129.781899             0.075195      129.857094\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"
    },
    {
     "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",
    "        'XGBoost': (XGBClassifier(eval_metric='mlogloss', random_state=42), {\n",
    "                'n_estimators': [75, 100, 150],\n",
    "                'learning_rate': [0.15, 0.2, 0.25],\n",
    "                'max_depth': [4, 5, 6]\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": 17,
   "id": "776cdb17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuning Decision Tree ...\n",
      "Tuning XGBoost ...\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",
      "      XGBoost  0.867893   0.863335 0.867893  0.863496    {'learning_rate': 0.15, 'max_depth': 5, 'n_estimators': 100}          20.872965             0.009083       20.882048\n",
      "Random Forest  0.858942   0.853479 0.858942  0.853443 {'max_depth': 20, 'min_samples_split': 10, 'n_estimators': 100}         140.562873             0.179120      140.741993\n",
      "Decision Tree  0.848666   0.843533 0.848666  0.837078                        {'max_depth': 8, 'min_samples_split': 4}           1.247936             0.001514        1.249449\n"
     ]
    }
   ],
   "source": [
    "# Remove redundant features for decision tree\n",
    "X_dt = X_train_val.drop(columns=['sex', 'native.country', 'marital.status', 'race', 'occupation', 'workclass'])\n",
    "\n",
    "# Remove redundant features for XGBoost\n",
    "X_xgb = X_train_val.drop(columns=['native.country', 'race'])\n",
    "\n",
    "# Train decision tree without redundant features\n",
    "cmp_dt = ClassifierComparisonOpt(X_dt, y_train_val)\n",
    "cmp_dt.fit_models({'Decision Tree': fine_grid['Decision Tree']})\n",
    "\n",
    "# Train XGBoost without redundant features\n",
    "cmp_xgb = ClassifierComparisonOpt(X_xgb, y_train_val)\n",
    "cmp_xgb.fit_models({'XGBoost': fine_grid['XGBoost']})\n",
    "\n",
    "# Train Random Forest, keep all features\n",
    "cmp_rf = ClassifierComparisonOpt(X_train_val, y_train_val)\n",
    "cmp_rf.fit_models({'Random Forest': fine_grid['Random Forest']})\n",
    "\n",
    "# Print results\n",
    "res = pd.concat([cmp_dt.results_df, cmp_rf.results_df, cmp_xgb.results_df])\n",
    "print(\"\\n------ Results Sorted by Accuracy ------\")\n",
    "print(res.sort_values(by='Accuracy', ascending=False).to_string(index=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fbac4c8",
   "metadata": {},
   "source": [
    "### Final Models\n",
    "I have not run this code yet. I'm still not sure if we should remove some of the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a470fd20",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ---------------------------- Decision Tree ----------------------------\n",
    "dt = Pipeline([\n",
    "    ('dt', DecisionTreeClassifier(max_depth=8, min_samples_split=4, random_state=42)) \n",
    "])\n",
    "\n",
    "# Train the model\n",
    "dt.fit(X_train_val, y_train_val)\n",
    "y_pred = dt.predict(X_test)\n",
    "\n",
    "# Classification Report\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(y_val, y_pred, target_names=[\"Poor\", \"Rich\"]))\n",
    "\n",
    "\n",
    "# ---------------------------- Random Forest ----------------------------\n",
    "rf = Pipeline([\n",
    "    ('rf', RandomForestClassifier(max_depth=20, min_samples_split=10, n_estimators=100, bootstrap=True, random_state=42))\n",
    "])\n",
    "\n",
    "# Train the model\n",
    "rf.fit(X_train_val, y_train_val)\n",
    "y_pred = rf.predict(X_test)\n",
    "\n",
    "# Classification Report\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(y_val, y_pred, target_names=[\"Poor\", \"Rich\"]))\n",
    "\n",
    "\n",
    "# ---------------------------- Random Forest ----------------------------\n",
    "xgb = Pipeline([\n",
    "    ('xgb', XGBClassifier(learning_rate=0.15, max_depth=5, n_estimators=100, random_state=42))\n",
    "])\n",
    "\n",
    "# Train the model\n",
    "xgb.fit(X_train_val, y_train_val)\n",
    "y_pred = cmp_xgb.predict(X_test)\n",
    "\n",
    "# Classification Report\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(y_val, y_pred, target_names=[\"Poor\", \"Rich\"]))"
   ]
  }
 ],
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