T_GBDT_unscaled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split, KFold, GridSearchCV\n",
    "from sklearn.metrics import root_mean_squared_error,mean_squared_error, mean_absolute_error, r2_score\n",
    "import lightgbm as lgb\n",
    "import xgboost as xgb\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.014644 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 71398, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1079.240443\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004309 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 71398, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1079.240443\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\iltergo\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\iltergo\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001807 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 71398, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.639464\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001722 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 71398, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.639464\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\iltergo\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\iltergo\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001467 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1519\n",
      "[LightGBM] [Info] Number of data points in the train set: 71398, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.931459\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001792 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1519\n",
      "[LightGBM] [Info] Number of data points in the train set: 71398, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.931459\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\iltergo\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\iltergo\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001836 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1520\n",
      "[LightGBM] [Info] Number of data points in the train set: 71399, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1079.449496\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001834 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1520\n",
      "[LightGBM] [Info] Number of data points in the train set: 71399, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1079.449496\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\iltergo\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\iltergo\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002439 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1516\n",
      "[LightGBM] [Info] Number of data points in the train set: 71399, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1079.703434\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001770 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1516\n",
      "[LightGBM] [Info] Number of data points in the train set: 71399, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1079.703434\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\iltergo\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\iltergo\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\iltergo\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\iltergo\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\iltergo\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\iltergo\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\iltergo\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\iltergo\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\iltergo\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\iltergo\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-Validation Results Summary:\n",
      "      Model RMSE (mean ± std) MAE (mean ± std)  R² (mean ± std)\n",
      "0  LightGBM   3.4902 ± 0.3318  2.1094 ± 0.0448  0.9997 ± 0.0001\n",
      "1   XGBoost   2.4287 ± 0.0455  1.4368 ± 0.0230  0.9999 ± 0.0000\n",
      "\n",
      "Final Test Results:\n",
      "              RMSE       MAE        R2\n",
      "LightGBM  3.176693  2.135881  0.999766\n",
      "XGBoost   2.387187  1.406654  0.999868\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\iltergo\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\iltergo\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\iltergo\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\iltergo\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "plt.rcParams.update({'font.size': 18})\n",
    "\n",
    "\n",
    "file_path = \"simulation_results_with_pressure_without_early_values.csv\"  # Replace with your file path\n",
    "df = pd.read_csv(file_path)\n",
    "\n",
    "\n",
    "filtered_df = df[~df['time'].isin([0, 1])]\n",
    "df = filtered_df\n",
    "\n",
    "\n",
    "simulation_data = df\n",
    "\n",
    "\n",
    "X = simulation_data.drop(columns=[\"volAverage(T)\", \"volAverage(Bu)\", \"rodPressure\"])\n",
    "y_temp = simulation_data[\"volAverage(T)\"]\n",
    "\n",
    "\n",
    "num_bins = 10\n",
    "y_temp_bins = pd.qcut(y_temp, q=num_bins, duplicates='drop', labels=False)\n",
    "\n",
    "\n",
    "X_train, X_temp, y_train, y_temp, y_bins_train, y_bins_temp = train_test_split(\n",
    "    X, y_temp, y_temp_bins, test_size=0.2, random_state=42, stratify=y_temp_bins\n",
    ")\n",
    "\n",
    "X_val, X_test, y_val, y_test, y_bins_val, y_bins_test = train_test_split(\n",
    "    X_temp, y_temp, y_bins_temp, test_size=0.5, random_state=42, stratify=y_bins_temp\n",
    ")\n",
    "\n",
    "\n",
    "scaler_X = StandardScaler()\n",
    "\n",
    "\n",
    "X_train_scaled = scaler_X.fit_transform(X_train)\n",
    "X_val_scaled = scaler_X.transform(X_val)\n",
    "X_test_scaled = scaler_X.transform(X_test)\n",
    "\n",
    "\n",
    "X_train = pd.DataFrame(X_train_scaled, columns=X.columns, index=X_train.index)\n",
    "X_val = pd.DataFrame(X_val_scaled, columns=X.columns, index=X_val.index)\n",
    "X_test = pd.DataFrame(X_test_scaled, columns=X.columns, index=X_test.index)\n",
    "\n",
    "\n",
    "def train_and_evaluate_model_with_tuning(model_name, model_class, param_grid, X_train, y_train):\n",
    "    kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
    "    train_rmse, val_rmse = [], []\n",
    "    train_mae, val_mae = [], []\n",
    "    train_r2, val_r2 = [], []\n",
    "    best_model = None\n",
    "    best_rmse = float('inf')\n",
    "    best_params = None\n",
    "\n",
    "    for train_index, val_index in kf.split(X_train):\n",
    "        X_t, X_v = X_train.iloc[train_index], X_train.iloc[val_index]\n",
    "        y_t, y_v = y_train.iloc[train_index], y_train.iloc[val_index]\n",
    "\n",
    "        # Hyperparameter tuning\n",
    "        base_model = model_class()\n",
    "        grid_search = GridSearchCV(\n",
    "            estimator=base_model,\n",
    "            param_grid=param_grid,\n",
    "            scoring='neg_root_mean_squared_error',\n",
    "            cv=3,\n",
    "            verbose=0,\n",
    "            n_jobs=-1\n",
    "        )\n",
    "        grid_search.fit(X_t, y_t)\n",
    "        fold_best_params = grid_search.best_params_\n",
    "\n",
    "\n",
    "        model = model_class(**fold_best_params)\n",
    "        model.fit(X_t, y_t)\n",
    "\n",
    "        # Evaluate training and validation performance on the fold  (RMSE, MAE, R²)\n",
    "        y_t_pred = model.predict(X_t)\n",
    "        y_v_pred = model.predict(X_v)\n",
    "\n",
    "    \n",
    "        train_rmse.append(mean_squared_error(y_t, y_t_pred, squared=False))\n",
    "        val_rmse.append(mean_squared_error(y_v, y_v_pred, squared=False))\n",
    "        train_mae.append(mean_absolute_error(y_t, y_t_pred))\n",
    "        val_mae.append(mean_absolute_error(y_v, y_v_pred))\n",
    "        train_r2.append(r2_score(y_t, y_t_pred))\n",
    "        val_r2.append(r2_score(y_v, y_v_pred))\n",
    "\n",
    "        # Save the best model\n",
    "        if val_rmse[-1] < best_rmse:\n",
    "            best_rmse = val_rmse[-1]\n",
    "            best_model = model\n",
    "            best_params = fold_best_params\n",
    "\n",
    "    return {\n",
    "        \"model\": best_model,\n",
    "        \"train_rmse\": train_rmse,\n",
    "        \"val_rmse\": val_rmse,\n",
    "        \"train_mae\": train_mae,\n",
    "        \"val_mae\": val_mae,\n",
    "        \"train_r2\": train_r2,\n",
    "        \"val_r2\": val_r2,\n",
    "        \"best_params\": best_params\n",
    "    }\n",
    "\n",
    "\n",
    "# Hyperparameter grids\n",
    "lgb_param_grid = {\n",
    "    \"num_leaves\": [20, 31, 40],\n",
    "    \"learning_rate\": [0.1, 0.05, 0.01],\n",
    "    \"n_estimators\": [50, 100, 200]\n",
    "}\n",
    "xgb_param_grid = {\n",
    "    \"max_depth\": [3, 5, 7],\n",
    "    \"learning_rate\": [0.1, 0.05, 0.01],\n",
    "    \"n_estimators\": [50, 100, 200]\n",
    "}\n",
    "\n",
    "\n",
    "lgb_results = train_and_evaluate_model_with_tuning(\"LightGBM\", lgb.LGBMRegressor, lgb_param_grid, X_train, y_train)\n",
    "xgb_results = train_and_evaluate_model_with_tuning(\"XGBoost\", xgb.XGBRegressor, xgb_param_grid, X_train, y_train)\n",
    "\n",
    "\n",
    "results_summary = {\n",
    "    \"Model\": [\"LightGBM\", \"XGBoost\"],\n",
    "    \"RMSE (mean ± std)\": [\n",
    "        f\"{np.mean(lgb_results['val_rmse']):.4f} ± {np.std(lgb_results['val_rmse']):.4f}\",\n",
    "        f\"{np.mean(xgb_results['val_rmse']):.4f} ± {np.std(xgb_results['val_rmse']):.4f}\"\n",
    "    ],\n",
    "    \"MAE (mean ± std)\": [\n",
    "        f\"{np.mean(lgb_results['val_mae']):.4f} ± {np.std(lgb_results['val_mae']):.4f}\",\n",
    "        f\"{np.mean(xgb_results['val_mae']):.4f} ± {np.std(xgb_results['val_mae']):.4f}\"\n",
    "    ],\n",
    "    \"R² (mean ± std)\": [\n",
    "        f\"{np.mean(lgb_results['val_r2']):.4f} ± {np.std(lgb_results['val_r2']):.4f}\",\n",
    "        f\"{np.mean(xgb_results['val_r2']):.4f} ± {np.std(xgb_results['val_r2']):.4f}\"\n",
    "    ]\n",
    "}\n",
    "\n",
    "\n",
    "results_df = pd.DataFrame(results_summary)\n",
    "print(\"Cross-Validation Results Summary:\")\n",
    "print(results_df)\n",
    "\n",
    "\n",
    "lgb_test_pred = lgb_results[\"model\"].predict(X_test)\n",
    "xgb_test_pred = xgb_results[\"model\"].predict(X_test)\n",
    "\n",
    "test_results = {\n",
    "    \"LightGBM\": {\n",
    "        \"RMSE\": mean_squared_error(y_test, lgb_test_pred, squared=False),\n",
    "        \"MAE\": mean_absolute_error(y_test, lgb_test_pred),\n",
    "        \"R2\": r2_score(y_test, lgb_test_pred)\n",
    "    },\n",
    "    \"XGBoost\": {\n",
    "        \"RMSE\": mean_squared_error(y_test, xgb_test_pred, squared=False),\n",
    "        \"MAE\": mean_absolute_error(y_test, xgb_test_pred),\n",
    "        \"R2\": r2_score(y_test, xgb_test_pred)\n",
    "    }\n",
    "}\n",
    "\n",
    "\n",
    "test_results_df = pd.DataFrame(test_results).T\n",
    "print(\"\\nFinal Test Results:\")\n",
    "print(test_results_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Hyperparameters:\n",
      "LightGBM: {'learning_rate': 0.1, 'n_estimators': 200, 'num_leaves': 40}\n",
      "XGBoost: {'learning_rate': 0.1, 'max_depth': 7, 'n_estimators': 200}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"Best Hyperparameters:\")\n",
    "print(f\"LightGBM: {lgb_results['best_params']}\")\n",
    "print(f\"XGBoost: {xgb_results['best_params']}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Average Losses Across Folds\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.plot(lgb_results[\"train_rmse\"], label=\"LightGBM Training Loss\", linestyle=\"--\", color=\"blue\")\n",
    "plt.plot(lgb_results[\"val_rmse\"], label=\"LightGBM Validation Loss\", linestyle=\"--\", color=\"orange\")\n",
    "plt.plot(xgb_results[\"train_rmse\"], label=\"XGBoost Training Loss\", linestyle=\"-\", color=\"green\")\n",
    "plt.plot(xgb_results[\"val_rmse\"], label=\"XGBoost Validation Loss\", linestyle=\"-\", color=\"red\")\n",
    "plt.xlabel(\"Cross-Validation Fold\")\n",
    "plt.ylabel(\"RMSE\")\n",
    "plt.title(\"Average Losses Across Folds for LightGBM and XGBoost\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "# Add Error Bars to Show Variance Across Folds\n",
    "mean_train_loss_lgb = np.mean(lgb_results[\"train_rmse\"])\n",
    "std_train_loss_lgb = np.std(lgb_results[\"train_rmse\"])\n",
    "mean_val_loss_lgb = np.mean(lgb_results[\"val_rmse\"])\n",
    "std_val_loss_lgb = np.std(lgb_results[\"val_rmse\"])\n",
    "\n",
    "mean_train_loss_xgb = np.mean(xgb_results[\"train_rmse\"])\n",
    "std_train_loss_xgb = np.std(xgb_results[\"train_rmse\"])\n",
    "mean_val_loss_xgb = np.mean(xgb_results[\"val_rmse\"])\n",
    "std_val_loss_xgb = np.std(xgb_results[\"val_rmse\"])\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.errorbar([\"LGB Train\", \"LGB Val\"], [mean_train_loss_lgb, mean_val_loss_lgb], \n",
    "             yerr=[std_train_loss_lgb, std_val_loss_lgb], fmt=\"o\", label=\"LightGBM\", color=\"blue\")\n",
    "plt.errorbar([\"XGB Train\", \"XGB Val\"], [mean_train_loss_xgb, mean_val_loss_xgb], \n",
    "             yerr=[std_train_loss_xgb, std_val_loss_xgb], fmt=\"o\", label=\"XGBoost\", color=\"orange\")\n",
    "plt.ylabel(\"RMSE\")\n",
    "plt.title(\"Loss with Variance Across Folds\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002552 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 89248, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1079.192861\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "best_params = lgb_results[\"best_params\"]  # For LightGBM as an example\n",
    "\n",
    "# Let's say we want to train for 500 trees to observe overfitting behavior\n",
    "long_run_params = best_params.copy()\n",
    "long_run_params[\"n_estimators\"] = 500  # Increase number of trees\n",
    "\n",
    "\n",
    "model = lgb.LGBMRegressor(**long_run_params)\n",
    "model.fit(\n",
    "    X_train, y_train,\n",
    "    eval_set=[(X_train, y_train), (X_test, y_test), (X_val, y_val)],\n",
    "    eval_names=[\"train\", \"test\", \"valid\"],\n",
    "    eval_metric=\"rmse\",\n",
    "    \n",
    ")\n",
    "\n",
    "\n",
    "evals_result_long = model.evals_result_\n",
    "\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.plot(evals_result_long[\"train\"][\"rmse\"], label=\"Train RMSE\", linestyle='--', color='blue')\n",
    "plt.plot(evals_result_long[\"test\"][\"rmse\"], label=\"Test RMSE\", linestyle=':', color='blue')\n",
    "plt.plot(evals_result_long[\"valid\"][\"rmse\"], label=\"Valid RMSE\", linestyle='-', color='blue')\n",
    "plt.xlabel(\"Epochs (Trees)\")\n",
    "plt.ylabel(\"RMSE\")\n",
    "plt.title(\"Extended Training to Observe Overfitting\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for LightGBM (Unscaled): 3.1767\n",
      "RMSE for XGBoost (Unscaled): 2.3872\n",
      "Real Temperature Predictions (Unscaled):\n",
      "      Actual  LGBM Prediction  XGBoost Prediction\n",
      "0  1009.1180      1011.508463         1010.257812\n",
      "1   924.2286       921.480618          923.594910\n",
      "2  1132.9700      1126.569857         1131.907104\n",
      "3   936.4719       940.192558          937.151611\n",
      "4  1320.0080      1324.489151         1322.286621\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "results_df = pd.DataFrame({\n",
    "    \"Actual\": y_test.to_numpy(),  # Actual values (unscaled)\n",
    "    \"LGBM Prediction\": lgb_test_pred,  # LightGBM predictions\n",
    "    \"XGBoost Prediction\": xgb_test_pred  # XGBoost predictions\n",
    "})\n",
    "\n",
    "\n",
    "rmse_lgb_real = root_mean_squared_error(y_test.to_numpy(), lgb_test_pred)\n",
    "rmse_xgb_real = root_mean_squared_error(y_test.to_numpy(), xgb_test_pred)\n",
    "\n",
    "\n",
    "print(f\"RMSE for LightGBM (Unscaled): {rmse_lgb_real:.4f}\")\n",
    "print(f\"RMSE for XGBoost (Unscaled): {rmse_xgb_real:.4f}\")\n",
    "\n",
    "\n",
    "print(\"Real Temperature Predictions (Unscaled):\")\n",
    "print(results_df.head())\n",
    "\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "\n",
    "\n",
    "plt.scatter(y_test, lgb_test_pred, label=\"LightGBM\", alpha=0.7, color=\"blue\")\n",
    "\n",
    "plt.scatter(y_test, xgb_test_pred, label=\"XGBoost\", alpha=0.7, color=\"orange\")\n",
    "\n",
    "\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], \n",
    "         label=\"Perfect Prediction\", linestyle=\"--\", color=\"black\")\n",
    "\n",
    "\n",
    "plt.xlim(y_test.min(), y_test.max())\n",
    "plt.ylim(y_test.min(), y_test.max())\n",
    "plt.axis(\"equal\")  # Ensures equal scaling\n",
    "\n",
    "plt.xlabel(\"Actual Temperature\")\n",
    "plt.ylabel(\"Predicted Temperature\")\n",
    "plt.title(\"Actual vs Predicted Temperatures\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_feature_importance(model, feature_names, model_name):\n",
    "    \"\"\"\n",
    "    Plot feature importance for a given model.\n",
    "    \"\"\"\n",
    "    if hasattr(model, \"feature_importances_\"):\n",
    "        importances = model.feature_importances_\n",
    "        sorted_indices = np.argsort(importances)[::-1]\n",
    "\n",
    "        plt.figure(figsize=(8, 8))\n",
    "        plt.bar(range(len(importances)), importances[sorted_indices], align=\"center\")\n",
    "        plt.xticks(range(len(importances)), [feature_names[i] for i in sorted_indices], rotation=90)\n",
    "        plt.xlabel(\"Feature\")\n",
    "        plt.ylabel(\"Importance\")\n",
    "        plt.title(f\"{model_name} Feature Importances\")\n",
    "        plt.show()\n",
    "    else:\n",
    "        print(f\"The model '{model_name}' does not have feature_importances_ attribute.\")\n",
    "\n",
    "\n",
    "plot_feature_importance(lgb_results[\"model\"], X_train.columns, \"LightGBM\")\n",
    "plot_feature_importance(xgb_results[\"model\"], X_train.columns, \"XGBoost\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000077 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 1513\n",
      "[LightGBM] [Info] Number of data points in the train set: 11157, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1079.696036\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000169 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1513\n",
      "[LightGBM] [Info] Number of data points in the train set: 11157, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1079.656043\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000277 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1513\n",
      "[LightGBM] [Info] Number of data points in the train set: 11157, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1079.905083\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1513\n",
      "[LightGBM] [Info] Number of data points in the train set: 11157, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1079.693618\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000287 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1513\n",
      "[LightGBM] [Info] Number of data points in the train set: 11157, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1080.498099\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "def add_gaussian_noise(data, column, noise_level=0.1):\n",
    "    \"\"\"\n",
    "    Add Gaussian noise to a specified column and reset index.\n",
    "    \"\"\"\n",
    "    noisy_data = data.copy()\n",
    "    std_dev = noisy_data[column].std() * noise_level\n",
    "    noise = np.random.normal(0, std_dev, size=noisy_data[column].shape)\n",
    "    noisy_data[column] += noise\n",
    "    return noisy_data.reset_index(drop=True)\n",
    "\n",
    "\n",
    "def add_outliers(data, column, num_outliers=10, scale=10):\n",
    "    \"\"\"\n",
    "    Add outliers to a specified column and reset index.\n",
    "    \"\"\"\n",
    "    outlier_data = data.copy().reset_index(drop=True)\n",
    "    for _ in range(num_outliers):\n",
    "        row_idx = np.random.randint(0, len(outlier_data))\n",
    "        outlier_data.loc[row_idx, column] += scale * outlier_data[column].std()\n",
    "    return outlier_data\n",
    "\n",
    "def calculate_rmse(y_true, y_pred):\n",
    "    \"\"\"\n",
    "    Calculate RMSE between true and predicted values.\n",
    "    \"\"\"\n",
    "    return np.sqrt(mean_squared_error(y_true, y_pred))\n",
    "\n",
    "import copy\n",
    "def noise_resistance_experiment(X, y, model, feature_noise_cols, noise_levels, outlier_levels=None, target_noise=False, outliers=False):\n",
    "    \"\"\"\n",
    "    Evaluate model's resistance to multiple levels of noise, outliers, and target noise.\n",
    "    Compute RMSE directly without inverse transformations (since y is in original scale).\n",
    "    \"\"\"\n",
    "    results = {}\n",
    "\n",
    "    \n",
    "    baseline_preds = model.predict(X)\n",
    "    baseline_rmse = np.sqrt(mean_squared_error(y, baseline_preds))\n",
    "    results[\"Baseline RMSE\"] = baseline_rmse\n",
    "\n",
    "    \n",
    "    for noise_level in noise_levels:\n",
    "        for col in feature_noise_cols:\n",
    "            noisy_X = add_gaussian_noise(X, column=col, noise_level=noise_level)\n",
    "            noisy_preds = model.predict(noisy_X)\n",
    "            noisy_rmse = np.sqrt(mean_squared_error(y, noisy_preds))\n",
    "            results[f\"Noise on {col} ({noise_level * 100:.0f}%)\"] = noisy_rmse\n",
    "\n",
    "    \n",
    "    if target_noise:\n",
    "        for noise_level in noise_levels:\n",
    "            noisy_y = y.copy() + np.random.normal(0, noise_level * y.std(), y.shape)\n",
    "\n",
    "            # Create a copy of the model for retraining\n",
    "            temp_model = copy.deepcopy(model)\n",
    "\n",
    "            # Retrain model with target noise\n",
    "            if isinstance(temp_model, xgb.XGBRegressor):\n",
    "                temp_model.fit(X, noisy_y, eval_set=[(X, noisy_y)], verbose=False)\n",
    "            else:\n",
    "                temp_model.fit(X, noisy_y)\n",
    "\n",
    "            noisy_target_preds = temp_model.predict(X)\n",
    "            noisy_rmse = np.sqrt(mean_squared_error(y, noisy_target_preds))\n",
    "            results[f\"Target Noise ({noise_level * 100:.0f}%)\"] = noisy_rmse\n",
    "\n",
    "    \n",
    "    if outliers:\n",
    "        for level in outlier_levels:\n",
    "            for col in feature_noise_cols:\n",
    "                num_outliers = int(level * len(X))\n",
    "                outlier_X = add_outliers(X, column=col, num_outliers=num_outliers, scale=10)\n",
    "                outlier_preds = model.predict(outlier_X)\n",
    "                outlier_rmse = np.sqrt(mean_squared_error(y, outlier_preds))\n",
    "                results[f\"Outliers in {col} ({level * 100:.0f}%)\"] = outlier_rmse\n",
    "\n",
    "    return results\n",
    "\n",
    "\n",
    "\n",
    "# Visualization Function for Noise Levels\n",
    "def compare_noise_levels(lgb_results, xgb_results, noise_levels, feature_noise_cols, experiment_title):\n",
    "    \"\"\"\n",
    "    Compare and visualize RMSE for different noise levels for LightGBM and XGBoost.\n",
    "    \"\"\"\n",
    "    for col in feature_noise_cols:\n",
    "        lgb_values = [lgb_results[f\"Noise on {col} ({noise_level * 100:.0f}%)\"] for noise_level in noise_levels]\n",
    "        xgb_values = [xgb_results[f\"Noise on {col} ({noise_level * 100:.0f}%)\"] for noise_level in noise_levels]\n",
    "\n",
    "        # Plot\n",
    "        plt.figure(figsize=(8, 8))\n",
    "        plt.plot(noise_levels, lgb_values, label=\"LightGBM\", marker='o', linestyle='--', color='blue')\n",
    "        plt.plot(noise_levels, xgb_values, label=\"XGBoost\", marker='s', linestyle='-', color='orange')\n",
    "\n",
    "        plt.xlabel(\"Noise Level (%)\")\n",
    "        plt.ylabel(\"RMSE\")\n",
    "        plt.title(f\"Resistance to Noise in Feature: \\n {col} ({experiment_title})\")\n",
    "        plt.legend()\n",
    "        plt.grid()\n",
    "        plt.show()\n",
    "\n",
    "# Visualization Function for Target Noise Levels\n",
    "def compare_target_noise_levels(lgb_results, xgb_results, noise_levels, experiment_title):\n",
    "    \"\"\"\n",
    "    Compare and visualize RMSE for different levels of target noise for LightGBM and XGBoost.\n",
    "    \"\"\"\n",
    "    lgb_values = [lgb_results[f\"Target Noise ({noise_level * 100:.0f}%)\"] for noise_level in noise_levels]\n",
    "    xgb_values = [xgb_results[f\"Target Noise ({noise_level * 100:.0f}%)\"] for noise_level in noise_levels]\n",
    "\n",
    "    # Plot\n",
    "    plt.figure(figsize=(8, 8))\n",
    "    plt.plot(noise_levels, lgb_values, label=\"LightGBM\", marker='o', linestyle='--', color='blue')\n",
    "    plt.plot(noise_levels, xgb_values, label=\"XGBoost\", marker='s', linestyle='-', color='orange')\n",
    "\n",
    "    plt.xlabel(\"Target Noise Level (%)\")\n",
    "    plt.ylabel(\"RMSE\")\n",
    "    plt.title(f\"Resistance to Target Noise \\n ({experiment_title})\")\n",
    "    plt.legend()\n",
    "    plt.grid()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# Visualization Function for Outlier Percentages\n",
    "def compare_outlier_impact(lgb_results, xgb_results, outlier_levels, feature_noise_cols, experiment_title):\n",
    "    \"\"\"\n",
    "    Compare and visualize RMSE for different percentages of outliers in selected features for LightGBM and XGBoost.\n",
    "    \"\"\"\n",
    "    for col in feature_noise_cols:\n",
    "        lgb_values = [lgb_results[f\"Outliers in {col} ({level * 100:.0f}%)\"] for level in outlier_levels]\n",
    "        xgb_values = [xgb_results[f\"Outliers in {col} ({level * 100:.0f}%)\"] for level in outlier_levels]\n",
    "\n",
    "        # Plot\n",
    "        plt.figure(figsize=(8, 8))\n",
    "        plt.plot(outlier_levels, lgb_values, label=\"LightGBM\", marker='o', linestyle='--', color='blue')\n",
    "        plt.plot(outlier_levels, xgb_values, label=\"XGBoost\", marker='s', linestyle='-', color='orange')\n",
    "\n",
    "        plt.xlabel(\"Outlier Percentage (%)\")\n",
    "        plt.ylabel(\"RMSE\")\n",
    "        plt.title(f\"Resistance to Outliers in Feature: \\n {col} ({experiment_title})\")\n",
    "        plt.legend()\n",
    "        plt.grid()\n",
    "        plt.show()\n",
    "\n",
    "\n",
    "\n",
    "noise_levels = [0, 0.05, 0.1, 0.2, 0.3]  # Noise levels for feature and target\n",
    "outlier_levels = [0.001, 0.005, 0.01, 0.05]  # Percentages of outliers\n",
    "\n",
    "feature_noise_columns = [\"lhgr\", \"coolant_temperature\"]  # Adjust to your dataset's column names\n",
    "\n",
    "\n",
    "\n",
    "lgb_resistance_results = noise_resistance_experiment(\n",
    "    X_test, y_test, lgb_results[\"model\"], feature_noise_cols=feature_noise_columns, \n",
    "     noise_levels=noise_levels, outlier_levels=outlier_levels, target_noise=True, outliers=True\n",
    ")\n",
    "\n",
    "\n",
    "xgb_resistance_results = noise_resistance_experiment(\n",
    "    X_test, y_test, xgb_results[\"model\"], feature_noise_cols=feature_noise_columns, \n",
    "     noise_levels=noise_levels, outlier_levels=outlier_levels, target_noise=True, outliers=True\n",
    ")\n",
    "\n",
    "\n",
    "compare_target_noise_levels(\n",
    "    lgb_resistance_results, xgb_resistance_results, \n",
    "    noise_levels=noise_levels, experiment_title=\"Temperature Prediction\"\n",
    ")\n",
    "\n",
    "\n",
    "compare_outlier_impact(\n",
    "    lgb_resistance_results, xgb_resistance_results, \n",
    "    outlier_levels=outlier_levels, feature_noise_cols=feature_noise_columns, \n",
    "    experiment_title=\"Temperature Prediction\"\n",
    ")\n",
    "\n",
    "compare_noise_levels(\n",
    "    lgb_resistance_results, xgb_resistance_results, \n",
    "    noise_levels=noise_levels, feature_noise_cols=feature_noise_columns, \n",
    "    experiment_title=\"Temperature Prediction\"\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001605 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53548, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.372469\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001225 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53548, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.372469\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000530 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 1519\n",
      "[LightGBM] [Info] Number of data points in the train set: 53549, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1079.560974\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001443 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1519\n",
      "[LightGBM] [Info] Number of data points in the train set: 53549, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1079.560974\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001736 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 53549, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.780629\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001549 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 53549, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.780629\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001355 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 53549, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1079.694368\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001331 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 53549, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1079.694368\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001399 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 53549, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1079.343559\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001272 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 53549, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1079.343559\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001305 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 53305, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.854812\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001219 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 53305, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.854812\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001470 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53305, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.701794\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001521 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53305, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.701794\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001587 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 53306, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.850491\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000291 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 53306, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.850491\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000973 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1521\n",
      "[LightGBM] [Info] Number of data points in the train set: 53306, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.525756\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001259 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1521\n",
      "[LightGBM] [Info] Number of data points in the train set: 53306, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.525756\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001494 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 53306, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.608258\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001476 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 53306, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.608258\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001288 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 53305, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.854812\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001325 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 53305, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.854812\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001293 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53305, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.701794\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001224 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53305, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.701794\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001094 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 53306, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.850491\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001328 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 53306, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.850491\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001265 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1521\n",
      "[LightGBM] [Info] Number of data points in the train set: 53306, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.525756\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000702 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1521\n",
      "[LightGBM] [Info] Number of data points in the train set: 53306, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.525756\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001279 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 53306, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.608258\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001447 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 53306, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.608258\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001573 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 53301, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.883363\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001571 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 53301, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.883363\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001620 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 53301, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.153485\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001590 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 53301, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.153485\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003567 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1516\n",
      "[LightGBM] [Info] Number of data points in the train set: 53302, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1079.018372\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001436 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1516\n",
      "[LightGBM] [Info] Number of data points in the train set: 53302, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1079.018372\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001555 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1516\n",
      "[LightGBM] [Info] Number of data points in the train set: 53302, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1079.102162\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001455 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1516\n",
      "[LightGBM] [Info] Number of data points in the train set: 53302, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1079.102162\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001542 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1520\n",
      "[LightGBM] [Info] Number of data points in the train set: 53302, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.862486\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000968 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1520\n",
      "[LightGBM] [Info] Number of data points in the train set: 53302, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.862486\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000838 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53282, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.173097\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001558 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53282, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.173097\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001256 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 53282, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.774301\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001201 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 53282, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.774301\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001115 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53282, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.869853\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000914 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53282, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.869853\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001209 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 53283, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.642477\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001187 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 53283, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.642477\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001513 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1519\n",
      "[LightGBM] [Info] Number of data points in the train set: 53283, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.149397\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001181 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1519\n",
      "[LightGBM] [Info] Number of data points in the train set: 53283, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.149397\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001724 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1520\n",
      "[LightGBM] [Info] Number of data points in the train set: 53280, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.405720\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000992 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1520\n",
      "[LightGBM] [Info] Number of data points in the train set: 53280, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.405720\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001285 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53281, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.335449\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001226 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53281, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.335449\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001789 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1519\n",
      "[LightGBM] [Info] Number of data points in the train set: 53281, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.990335\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001272 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1519\n",
      "[LightGBM] [Info] Number of data points in the train set: 53281, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.990335\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001324 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 53281, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.631388\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001422 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1518\n",
      "[LightGBM] [Info] Number of data points in the train set: 53281, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.631388\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001243 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1519\n",
      "[LightGBM] [Info] Number of data points in the train set: 53281, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1077.744272\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001749 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1519\n",
      "[LightGBM] [Info] Number of data points in the train set: 53281, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1077.744272\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001151 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1520\n",
      "[LightGBM] [Info] Number of data points in the train set: 53276, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1079.193328\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001292 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1520\n",
      "[LightGBM] [Info] Number of data points in the train set: 53276, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1079.193328\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001395 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53277, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.840544\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001232 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53277, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.840544\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001708 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53277, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1077.999876\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001168 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53277, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1077.999876\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001308 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53277, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.005681\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000997 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53277, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.005681\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001305 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53277, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.052506\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001310 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53277, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.052506\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001744 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1520\n",
      "[LightGBM] [Info] Number of data points in the train set: 53276, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1079.193328\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001308 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1520\n",
      "[LightGBM] [Info] Number of data points in the train set: 53276, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1079.193328\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001299 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53277, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.840544\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001167 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53277, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.840544\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001322 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53277, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1077.999876\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001290 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53277, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1077.999876\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001582 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53277, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.005681\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001387 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53277, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.005681\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001306 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53277, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.052506\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001341 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53277, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.052506\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000698 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1520\n",
      "[LightGBM] [Info] Number of data points in the train set: 53276, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1079.193328\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000835 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1520\n",
      "[LightGBM] [Info] Number of data points in the train set: 53276, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1079.193328\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001243 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53277, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.840544\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001618 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53277, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.840544\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000737 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53277, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1077.999876\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001185 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53277, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1077.999876\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001235 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53277, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.005681\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001267 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53277, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.005681\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001281 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53277, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.052506\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001210 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1517\n",
      "[LightGBM] [Info] Number of data points in the train set: 53277, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1078.052506\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def dilute_time_samples(data, time_col, dilution_factor):\n",
    "    \"\"\"\n",
    "    Dilutes the dataset by randomly removing a fraction of time samples for each unique feature combination.\n",
    "\n",
    "    Parameters:\n",
    "        data (pd.DataFrame): Input dataset.\n",
    "        time_col (str): The column name representing time.\n",
    "        dilution_factor (float): Fraction of time samples to remove (0 <= dilution_factor < 1).\n",
    "\n",
    "    Returns:\n",
    "        pd.DataFrame: Diluted dataset.\n",
    "    \"\"\"\n",
    "    diluted_data = []\n",
    "\n",
    "    # Group by feature combinations excluding the time column\n",
    "    feature_groups = data.groupby(data.columns.difference([time_col]).tolist())\n",
    "\n",
    "    for _, group in feature_groups:\n",
    "        n_samples = len(group)\n",
    "        n_to_keep = max(1, int((1 - dilution_factor) * n_samples))\n",
    "        diluted_group = group.sample(n=n_to_keep, random_state=42)\n",
    "        diluted_data.append(diluted_group)\n",
    "\n",
    "    return pd.concat(diluted_data, ignore_index=True)\n",
    "\n",
    "def evaluate_dilution_impact(dilution_factors, model_name, model_class, param_grid, X_train, X_val, X_test, y_train, y_val, y_test, time_col):\n",
    "    \"\"\"\n",
    "    Evaluate the impact of dilution on model performance.\n",
    "\n",
    "    Parameters:\n",
    "        dilution_factors (list): List of dilution factors to evaluate.\n",
    "        model_name (str): Name of the model (e.g., 'LightGBM').\n",
    "        model_class: Model class (e.g., lgb.LGBMRegressor).\n",
    "        param_grid (dict): Hyperparameter grid for tuning.\n",
    "        X_train, X_val, X_test (pd.DataFrame): Features.\n",
    "        y_train, y_val, y_test (pd.Series): Target variables.\n",
    "        time_col (str): The column name representing time.\n",
    "\n",
    "    Returns:\n",
    "        dict: RMSE values for each dilution factor.\n",
    "    \"\"\"\n",
    "    results = {}\n",
    "\n",
    "    for factor in dilution_factors:\n",
    "        \n",
    "        combined_X = pd.concat([X_train, X_val, X_test], axis=0)\n",
    "        combined_y = pd.concat([y_train, y_val, y_test], axis=0)\n",
    "        combined_data = pd.concat([combined_X, combined_y], axis=1)\n",
    "\n",
    "        \n",
    "        diluted_data = dilute_time_samples(combined_data, time_col, factor)\n",
    "        X_diluted = diluted_data[X_train.columns]\n",
    "        y_diluted = diluted_data[combined_y.name]\n",
    "\n",
    "        \n",
    "        X_train_diluted, X_temp, y_train_diluted, y_temp = train_test_split(\n",
    "            X_diluted, y_diluted, test_size=0.4, random_state=42\n",
    "        )\n",
    "        X_val_diluted, X_test_diluted, y_val_diluted, y_test_diluted = train_test_split(\n",
    "            X_temp, y_temp, test_size=0.5, random_state=42\n",
    "        )\n",
    "\n",
    "        \n",
    "        kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
    "        best_rmse = float(\"inf\")\n",
    "\n",
    "        for train_index, val_index in kf.split(X_train_diluted):\n",
    "            X_t, X_v = X_train_diluted.iloc[train_index], X_train_diluted.iloc[val_index]\n",
    "            y_t, y_v = y_train_diluted.iloc[train_index], y_train_diluted.iloc[val_index]\n",
    "\n",
    "            grid_search = GridSearchCV(\n",
    "                estimator=model_class(),\n",
    "                param_grid=param_grid,\n",
    "                scoring='neg_root_mean_squared_error',\n",
    "                cv=3,\n",
    "                verbose=0,\n",
    "                n_jobs=-1\n",
    "            )\n",
    "            grid_search.fit(X_t, y_t)\n",
    "            best_params = grid_search.best_params_\n",
    "\n",
    "            model = model_class(**best_params)\n",
    "            model.fit(X_t, y_t)\n",
    "            y_v_pred = model.predict(X_v)\n",
    "            rmse = root_mean_squared_error(y_v, y_v_pred)\n",
    "\n",
    "            best_rmse = min(best_rmse, rmse)\n",
    "\n",
    "        results[factor] = best_rmse\n",
    "\n",
    "    return results\n",
    "\n",
    "\n",
    "dilution_factors = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5,0.8, 0.9,0.95]  # 0.0 means no dilution\n",
    "\n",
    "time_col = 'time'\n",
    "\n",
    "\n",
    "lgb_param_grid = {\n",
    "    \"num_leaves\": [20, 31, 40],\n",
    "    \"learning_rate\": [0.1, 0.05, 0.01],\n",
    "    \"n_estimators\": [50, 100, 200]\n",
    "}\n",
    "lgb_results = evaluate_dilution_impact(\n",
    "    dilution_factors, \"LightGBM\", lgb.LGBMRegressor, lgb_param_grid,\n",
    "    X_train, X_val, X_test, y_train, y_val, y_test, time_col\n",
    ")\n",
    "\n",
    "\n",
    "xgb_param_grid = {\n",
    "    \"max_depth\": [3, 5, 7],\n",
    "    \"learning_rate\": [0.1, 0.05, 0.01],\n",
    "    \"n_estimators\": [50, 100, 200]\n",
    "}\n",
    "xgb_results = evaluate_dilution_impact(\n",
    "    dilution_factors, \"XGBoost\", xgb.XGBRegressor, xgb_param_grid,\n",
    "    X_train, X_val, X_test, y_train, y_val, y_test, time_col\n",
    ")\n",
    "\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.plot(dilution_factors, list(lgb_results.values()), label=\"LightGBM\", marker='o')\n",
    "plt.plot(dilution_factors, list(xgb_results.values()), label=\"XGBoost\", marker='o')\n",
    "plt.xlabel(\"Dilution Factor\")\n",
    "plt.ylabel(\"RMSE\")\n",
    "plt.title(\"RMSE vs Dilution Factor\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.11.4"
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 },
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