Chapter 8 - Predicting Room Bookings.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lightgbm import LGBMRegressor,LGBMClassifier\n",
    "from sklearn.linear_model import LinearRegression,LogisticRegression\n",
    "from xgboost import XGBRegressor,XGBClassifier\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error,f1_score,precision_score,recall_score\n",
    "\n",
    "from sklearnex import patch_sklearn,unpatch_sklearn\n",
    "\n",
    "from scipy.stats import f_oneway\n",
    "\n",
    "import gc\n",
    "import pickle\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from collections import Counter\n",
    "\n",
    "from Interaction_effect_features import get_data\n",
    "\n",
    "from MetaHeuristicsFS import FeatureSelection\n",
    "\n",
    "# from EvolutionaryFS import GeneticAlgorithmFS\n",
    "# from SimmulatedAnnealingFS import SimulatedAnnealingFS\n",
    "# from ACO import AntColonyOptimizationFS\n",
    "# from PSOFinal import ParticleSwarmOptimizationFS\n",
    "\n",
    "import shap\n",
    "\n",
    "np.seterr(divide = 'ignore')\n",
    "pd.options.mode.chained_assignment = None  # default='warn'\n",
    "import warnings\n",
    "# suppress warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_metrics(model,data_dict,problem_type,dependent_variable,features,cost_funct):\n",
    "    '''\n",
    "    problem_type = 'regression' or classification\n",
    "    '''\n",
    "    \n",
    "    test_tree_cost = {}\n",
    "    \n",
    "    external_tree_cost = []\n",
    "    external_tree_precision = []\n",
    "    external_tree_recall = []\n",
    "    \n",
    "    data_external_test = data_dict['data_external_test']\n",
    "    data_validation_test = data_dict['data_validation_test']\n",
    "    \n",
    "    #for each training data\n",
    "    for data_fold_index in data_dict['fold_dict'].keys():\n",
    "        fold_training = data_dict['fold_dict'][data_fold_index]['fold_training']\n",
    "        fold_test = data_dict['fold_dict'][data_fold_index]['fold_test']\n",
    "        \n",
    "        #based on regression/classification\n",
    "        if problem_type == 'regression':\n",
    "            cost_function = cost_funct\n",
    "        else:\n",
    "            cost_function = f1_score\n",
    "        \n",
    "        Y_train = fold_training[dependent_variable]\n",
    "        Y_test = fold_test[dependent_variable]\n",
    "        Y_validation = data_validation_test[dependent_variable]\n",
    "        Y_external = data_external_test[dependent_variable]\n",
    "        \n",
    "        model.fit(fold_training[features],Y_train)\n",
    "                \n",
    "        Y_test_tree = model.predict(fold_test[features])\n",
    "        Y_validation_tree = model.predict(data_validation_test[features])\n",
    "        Y_external_tree = model.predict(data_external_test[features])\n",
    "        \n",
    "        place_holder = np.mean([cost_funct(Y_test,Y_test_tree),cost_funct(Y_validation,Y_validation_tree)])\n",
    "        \n",
    "        #predict for test and calculate cost function\n",
    "        test_tree_cost['CV Sample-'+str(data_fold_index)] = place_holder\n",
    "\n",
    "        #predict for external test and calculate cost function\n",
    "        external_tree_cost.append(cost_funct(Y_external,Y_external_tree))\n",
    "        \n",
    "        if problem_type != 'regression':\n",
    "            external_tree_precision.append(precision_score(Y_external,Y_external_tree))\n",
    "            external_tree_recall.append(recall_score(Y_external,Y_external_tree))\n",
    "            \n",
    "    #return cost\n",
    "    if problem_type != 'regression':\n",
    "        #print('External Test precision: ' + str(round(np.mean(external_tree_precision),2)) + ', recall: ' + str(round(np.mean(external_tree_recall),2)))\n",
    "        print('External Test recall: ' + str(round(np.mean(external_tree_recall),2)))\n",
    "    return test_tree_cost,external_tree_cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_plot(metric,title,test_tree_cost,external_tree_cost):\n",
    "    sns.set(rc={'figure.figsize':(16,8)})\n",
    "    sns.set_context('paper', font_scale = 1.3)\n",
    "    if metric=='RMSE':\n",
    "        fig, ax = plt.subplots(1, 2)\n",
    "    else:\n",
    "        fig, ax = plt.subplots(1, 2, sharey=True)\n",
    "        \n",
    "    validation_name = [' External_Test','External_Test ',' External_Test ',' External_Test  ','  External_Test']\n",
    "\n",
    "    ax[0].bar(list(test_tree_cost.keys()),list(test_tree_cost.values()))\n",
    "    ax[0].set_title(metric + ' for test data and validation data')\n",
    "\n",
    "    ax[1].bar(validation_name,external_tree_cost)\n",
    "    ax[1].set_title(metric + ' for external test data')\n",
    "\n",
    "    #plt.title('')\n",
    "    \n",
    "def get_data_for_FS(data_dict,dependent_variable,features_all):\n",
    "    \n",
    "    data_dict_for_SA = {}\n",
    "\n",
    "#     x_validation = data_dict['data_external_test'][features_all]\n",
    "#     y_validation = data_dict['data_external_test'][dependent_variable]\n",
    "    \n",
    "    x_validation = data_dict['data_validation_test'][features_all]\n",
    "    y_validation = data_dict['data_validation_test'][dependent_variable]\n",
    "\n",
    "    #for each training data\n",
    "    for data_fold_index in data_dict['fold_dict'].keys():\n",
    "        \n",
    "        for feature in features_all:\n",
    "            if feature not in data_dict['fold_dict'][data_fold_index]['fold_training'].columns:\n",
    "                print(feature,'not present')\n",
    "\n",
    "        data_dict_for_SA[data_fold_index]={'x_train':data_dict['fold_dict'][data_fold_index]['fold_training'][features_all],\n",
    "                                           'y_train':data_dict['fold_dict'][data_fold_index]['fold_training'][dependent_variable],\n",
    "                                           'x_test':data_dict['fold_dict'][data_fold_index]['fold_test'][features_all],\n",
    "                                           'y_test':data_dict['fold_dict'][data_fold_index]['fold_test'][dependent_variable]}\n",
    "    \n",
    "#     for feature in features_all:\n",
    "#         if '_Encoded' not in feature:\n",
    "            \n",
    "    \n",
    "    return data_dict_for_SA,x_validation,y_validation,features_all\n",
    "\n",
    "def last_preprocessing(data_dict):\n",
    "    \n",
    "    \n",
    "    columns = ['AdjustedLeadTime_Quartile_countRankEncoded', 'CumulativeNumberOfRooms_Quartile_percentEncoded', 'CumulativeNumberOfRooms_Quartile_countRankEncoded', 'CumulativeNumberOfRooms_Quartile_MeanEncoded', 'CumulativeNumberOfRoomsSubstract_Quartile_percentEncoded', 'CumulativeNumberOfRoomsSubstract_Quartile_countRankEncoded', 'CumulativeNumberOfRoomsSubstract_Quartile_MeanEncoded', 'CumulativeNumberOfRoomsNet_Quartile_countEncoded', 'CumulativeNumberOfRoomsNet_Quartile_MeanEncoded', 'AdjustedLeadTimeCumulativeNumberOfRooms_Quartile_countEncoded', 'AdjustedLeadTimeCumulativeNumberOfRooms_Quartile_percentEncoded', 'AdjustedLeadTimeCumulativeNumberOfRooms_Quartile_countRankEncoded', 'AdjustedLeadTimeCumulativeNumberOfRooms_Quartile_MeanEncoded', 'AdjustedLeadTimeCumulativeNumberOfRoomsSubstract_Quartile_countRankEncoded', 'AdjustedLeadTimeCumulativeNumberOfRoomsSubstract_Quartile_MeanEncoded', 'AdjustedLeadTimeCumulativeNumberOfRoomsNet_Quartile_percentEncoded', 'CumulativeRevenue_Quartile_countEncoded', 'CumulativeRevenue_Quartile_percentEncoded', 'CumulativeRevenue_Quartile_countRankEncoded', 'CumulativeRevenue_Quartile_MeanEncoded', 'CumulativeRevenueSubstract_Quartile_percentEncoded', 'CumulativeRevenueSubstract_Quartile_countRankEncoded', 'CumulativeRevenueNet_Quartile_percentEncoded', 'CumulativeRevenueNet_Quartile_MeanEncoded', 'AdjustedLeadTimeCumulativeRevenue_Quartile_percentEncoded', 'AdjustedLeadTimeCumulativeRevenue_Quartile_countRankEncoded', 'AdjustedLeadTimeCumulativeRevenueSubstract_Quartile_countEncoded', 'AdjustedLeadTimeCumulativeRevenueSubstract_Quartile_countRankEncoded', 'AdjustedLeadTimeCumulativeRevenueSubstract_Quartile_MeanEncoded', 'AdjustedLeadTimeCumulativeRevenueNet_Quartile_countRankEncoded', 'AdjustedLeadTimeCumulativeRevenueNet_Quartile_MeanEncoded','AdjustedLeadTime_Quartile_percentEncoded', 'CumulativeNumberOfRoomsSubstract_Quartile_countEncoded', 'CumulativeNumberOfRoomsNet_Quartile_percentEncoded', 'AdjustedLeadTimeCumulativeNumberOfRoomsSubstract_Quartile_countEncoded', 'AdjustedLeadTimeCumulativeNumberOfRoomsNet_Quartile_countEncoded', 'AdjustedLeadTimeCumulativeNumberOfRoomsNet_Quartile_countRankEncoded', 'AdjustedLeadTimeCumulativeNumberOfRoomsNet_Quartile_MeanEncoded', 'AdjustedLeadTimeCumulativeRevenue_Quartile_MeanEncoded', 'AdjustedLeadTimeCumulativeRevenueSubstract_Quartile_percentEncoded','AdjustedLeadTime_Quartile_countEncoded', 'CumulativeNumberOfRoomsNet_Quartile_countRankEncoded', 'AdjustedLeadTimeCumulativeNumberOfRoomsSubstract_Quartile_percentEncoded', 'CumulativeRevenueSubstract_Quartile_countEncoded', 'CumulativeRevenueSubstract_Quartile_MeanEncoded', 'CumulativeRevenueNet_Quartile_countEncoded', 'AdjustedLeadTimeCumulativeRevenueNet_Quartile_countEncoded','AdjustedLeadTime_Quartile_MeanEncoded', 'CumulativeNumberOfRooms_Quartile_countEncoded', 'CumulativeRevenueNet_Quartile_countRankEncoded','AdjustedLeadTimeCumulativeRevenue_Quartile_countEncoded', 'AdjustedLeadTimeCumulativeRevenueNet_Quartile_percentEncoded']\n",
    "    \n",
    "    for feature in columns:\n",
    "        if feature in data_dict['data_external_test'].columns:\n",
    "            data_dict['data_external_test'][feature] = data_dict['data_external_test'][feature].astype(int)\n",
    "            data_dict['data_validation_test'][feature] = data_dict['data_validation_test'][feature].astype(int)\n",
    "\n",
    "            for index in data_dict['fold_dict'].keys():\n",
    "                data_dict['fold_dict'][index]['fold_training'][feature] = data_dict['fold_dict'][index]['fold_training'][feature].astype(int)\n",
    "                data_dict['fold_dict'][index]['fold_test'][feature] = data_dict['fold_dict'][index]['fold_test'][feature].astype(int)\n",
    "        \n",
    "    return data_dict\n",
    "\n",
    "\n",
    "def cost_funct(y_true,y_pred):\n",
    "    return np.sqrt(mean_squared_error(y_true,y_pred))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_features_corr(cutoff,features_all,data_dict_for_linear):\n",
    "\n",
    "    keep_cols = {}\n",
    "    common_list = []\n",
    "\n",
    "    for index in data_dict_for_linear.keys():\n",
    "\n",
    "        #calculate correlation of all features with dependent variables\n",
    "        fold_df = data_dict_for_linear[index]['x_train'][features_all]\n",
    "        fold_df['y_train'] = data_dict_for_linear[index]['y_train']\n",
    "        fold_corr = fold_df.corr()\n",
    "\n",
    "        #filter all features below the cutoff and save correlation acceptable features\n",
    "        keep_cols[index] = list(fold_corr[((fold_corr['y_train'] >=cutoff) | (fold_corr['y_train'] <=-cutoff)) & (fold_corr['y_train'] !=1)].index)\n",
    "\n",
    "    #find common set of features in all crossvalidation as features to be used\n",
    "    common_list = set(keep_cols[0]).intersection(set(keep_cols[1])).intersection(set(keep_cols[2])).intersection(set(keep_cols[3])).intersection(set(keep_cols[4]))\n",
    "    \n",
    "    return list(common_list)\n",
    "\n",
    "\n",
    "def filter_features_reg(cutoff,features_all,data_dict_for_linear):\n",
    "\n",
    "    keep_cols = {}\n",
    "    common_list = []\n",
    "    model = LinearRegression(n_jobs=-1)\n",
    "\n",
    "    for index in data_dict_for_linear.keys():\n",
    "\n",
    "        #calculate correlation of all features with dependent variables\n",
    "        X = data_dict_for_linear[index]['x_train'][features_all]\n",
    "        Y = data_dict_for_linear[index]['y_train']\n",
    "\n",
    "        #fit linear model\n",
    "        model.fit(X,Y)\n",
    "\n",
    "        #get beta of each feature, and create df of feature and beta\n",
    "        coefficients = pd.DataFrame({\"Feature\":X.columns,\"Coefficients\":np.abs(np.transpose(model.coef_).ravel())})\n",
    "\n",
    "        #cumsum of beta\n",
    "        coefficients.sort_values(by='Coefficients',inplace=True,ascending=False)\n",
    "        coefficients.reset_index(inplace=True,drop=True)\n",
    "\n",
    "        coefficients['cum_sum'] = coefficients['Coefficients'].cumsum()\n",
    "        coefficients['cum_perc'] = 100*coefficients['cum_sum']/coefficients['Coefficients'].sum()\n",
    "\n",
    "        #filter at cutoff cumsum, use for feature selection\n",
    "        keep_cols[index] = list(coefficients[coefficients['cum_perc']<=cutoff].Feature.values)\n",
    "\n",
    "    #find common set of features in all crossvalidation as features to be used\n",
    "    common_list = set(keep_cols[0]).intersection(set(keep_cols[1])).intersection(set(keep_cols[2])).intersection(set(keep_cols[3])).intersection(set(keep_cols[4]))\n",
    "\n",
    "\n",
    "def anova_fs(linear_features_all,data_dict,dependent_variable):\n",
    "    \n",
    "    common_list = []\n",
    "    \n",
    "    for feature in linear_features_all:\n",
    "        \n",
    "        p_counter = 0\n",
    "        \n",
    "        for index in data_dict['fold_dict'].keys():\n",
    "            \n",
    "            indx_df = data_dict['fold_dict'][index]['fold_training']\n",
    "            \n",
    "            class1 = indx_df[indx_df[dependent_variable[0]]==1][feature]\n",
    "            class2 = indx_df[indx_df[dependent_variable[0]]==0][feature]\n",
    "            \n",
    "            stat,p = f_oneway(class1, class2)\n",
    "            \n",
    "            if p <= 0.05:\n",
    "                p_counter += 1\n",
    "        \n",
    "        if p_counter == 5:\n",
    "            common_list.append(feature)\n",
    "        \n",
    "    return common_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict Room Booking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#'HotelCancellations', 'CouponRecommendation', 'PredictRoomBooking','CarSales'\n",
    "problem = 'PredictRoomBooking'\n",
    "data_dict,dependent_variable,categorical_linear,categorical_tree,categorical_features,numerical_features_linear,numerical_features_tree,ordinal_features,ordinal_features_engineered = get_data(problem)\n",
    "\n",
    "data_dict = last_preprocessing(data_dict)\n",
    "# data_dict_linear = last_preprocessing_linear(data_dict)\n",
    "\n",
    "linear_features_all = categorical_linear+numerical_features_linear+ordinal_features_engineered\n",
    "tree_features_all = categorical_tree+numerical_features_tree+ordinal_features_engineered\n",
    "\n",
    "#data_dict_for_linear,x_validation_linear,y_validation_linear = get_data_for_SA(data_dict,dependent_variable,features_all=linear_features_all)\n",
    "\n",
    "data_dict_for_tree,x_validation_tree,y_validation_tree,features_all = get_data_for_FS(data_dict,dependent_variable,features_all=tree_features_all)\n",
    "\n",
    "data_dict_for_linear,x_validation_linear,y_validation_linear,features_all_linear = get_data_for_FS(data_dict,dependent_variable,features_all=linear_features_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#filter by correlation coefficient\n",
    "cutoff_corr = 0.1\n",
    "common_list = filter_features_corr(cutoff = cutoff_corr,features_all = linear_features_all,data_dict_for_linear = data_dict_for_linear)\n",
    "\n",
    "#filter by regression weights\n",
    "# cutoff_reg = 0.95\n",
    "# common_list_reg = filter_features_reg(cutoff = cutoff_reg,features_all = linear_features_all,data_dict_for_linear = data_dict_for_linear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_use = LGBMRegressor(random_state=1,n_jobs=-1,n_estimators=200)\n",
    "fsObj = FeatureSelection(columns_list = tree_features_all,\n",
    "                         data_dict = data_dict_for_tree,\n",
    "                         x_validation_dataframe = x_validation_tree,\n",
    "                         y_validation_dataframe = y_validation_tree,\n",
    "                         model = model_use,\n",
    "                         cost_function = cost_funct,\n",
    "                         cost_function_improvement = 'decrease',\n",
    "                         average = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combined performance on test and validation data for iteration 0: 18.56686927892919\n",
      "Best combined performance on test and validation data for iteration 1: 16.516465920009534\n",
      "Best combined performance on test and validation data for iteration 2: 16.51461376306024\n",
      "Best combined performance on test and validation data for iteration 3: 16.43981040058953\n",
      "Best combined performance on test and validation data for iteration 4: 16.298331720284587\n",
      "Best combined performance on test and validation data for iteration 5: 16.29134887305198\n",
      "Best combined performance on test and validation data for iteration 6: 16.29134887305198\n",
      "Best combined performance on test and validation data for iteration 7: 16.29134887305198\n",
      "Best combined performance on test and validation data for iteration 8: 16.29134887305198\n",
      "Best combined performance on test and validation data for iteration 9: 16.29134887305198\n",
      "Best combined performance on test and validation data for iteration 10: 16.29134887305198\n",
      "Best combined performance on test and validation data for iteration 11: 16.29134887305198\n",
      "Best combined performance on test and validation data for iteration 12: 16.29134887305198\n",
      "Best combined performance on test and validation data for iteration 13: 16.29134887305198\n",
      "Best combined performance on test and validation data for iteration 14: 16.28910442365577\n",
      "Best combined performance on test and validation data for iteration 15: 16.28910442365577\n",
      "Best combined performance on test and validation data for iteration 16: 16.28910442365577\n",
      "Best combined performance on test and validation data for iteration 17: 16.28910442365577\n",
      "Best combined performance on test and validation data for iteration 18: 16.28910442365577\n",
      "Best combined performance on test and validation data for iteration 19: 16.28910442365577\n",
      "Best combined performance on test and validation data for iteration 20: 16.28910442365577\n",
      "Best combined performance on test and validation data for iteration 21: 16.28910442365577\n",
      "Best combined performance on test and validation data for iteration 22: 16.28910442365577\n",
      "Best combined performance on test and validation data for iteration 23: 16.28910442365577\n",
      "Best combined performance on test and validation data for iteration 24: 16.28910442365577\n",
      "================= Best result: 16.28910442365577 =================\n",
      "================= Execution time in minutes: 264.0 =================\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'best_columns_PSO_PredictRoomBookings_lightgbm' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [9], line 7\u001b[0m\n\u001b[0;32m      2\u001b[0m best_columns_PSO_PredictRoomBooking_lightgbm \u001b[38;5;241m=\u001b[39m fsObj\u001b[38;5;241m.\u001b[39mParticleSwarmOptimization(iterations \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m25\u001b[39m,                                                                   \n\u001b[0;32m      3\u001b[0m                                                                        swarmSize \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m75\u001b[39m,\n\u001b[0;32m      4\u001b[0m                                                                        run_time\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1200\u001b[39m)\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mD:\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mResults\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mResultPickle\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mbest_columns_PSO_PredictRoomBooking_lightgbm.pickle\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwb\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m output_file:\n\u001b[1;32m----> 7\u001b[0m     pickle\u001b[38;5;241m.\u001b[39mdump(\u001b[43mbest_columns_PSO_PredictRoomBookings_lightgbm\u001b[49m, output_file)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'best_columns_PSO_PredictRoomBookings_lightgbm' is not defined"
     ]
    }
   ],
   "source": [
    "#### Particle Swarm Optimization\n",
    "best_columns_PSO_PredictRoomBooking_lightgbm = fsObj.ParticleSwarmOptimization(iterations = 25,                                                                   \n",
    "                                                                       swarmSize = 75,\n",
    "                                                                       run_time=1200)\n",
    "\n",
    "with open(r\"D:\\\\Results\\\\ResultPickle\\\\best_columns_PSO_PredictRoomBooking_lightgbm.pickle\", \"wb\") as output_file:\n",
    "    pickle.dump(best_columns_PSO_PredictRoomBooking_lightgbm, output_file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combined performance on test and validation data for iteration 0: 16.06987591264096\n",
      "Best combined performance on test and validation data for iteration 1: 15.413262366988738\n",
      "Best combined performance on test and validation data for iteration 2: 15.06423487096231\n",
      "Best combined performance on test and validation data for iteration 3: 14.990016513349488\n",
      "Best combined performance on test and validation data for iteration 4: 14.981864989733216\n",
      "Best combined performance on test and validation data for iteration 5: 14.978191558616956\n",
      "Best combined performance on test and validation data for iteration 6: 14.978191558616956\n",
      "Best combined performance on test and validation data for iteration 7: 14.978191558616956\n",
      "Best combined performance on test and validation data for iteration 8: 14.978191558616956\n",
      "Best combined performance on test and validation data for iteration 9: 14.978191558616956\n",
      "Best combined performance on test and validation data for iteration 10: 14.978191558616956\n",
      "Best combined performance on test and validation data for iteration 11: 14.978191558616956\n",
      "Best combined performance on test and validation data for iteration 12: 14.978191558616956\n",
      "Best combined performance on test and validation data for iteration 13: 14.978191558616956\n",
      "Best combined performance on test and validation data for iteration 14: 14.978191558616956\n",
      "Best combined performance on test and validation data for iteration 15: 14.978191558616956\n",
      "Best combined performance on test and validation data for iteration 16: 14.978191558616956\n",
      "Best combined performance on test and validation data for iteration 17: 14.978191558616956\n",
      "Best combined performance on test and validation data for iteration 18: 14.978191558616956\n",
      "Best combined performance on test and validation data for iteration 19: 14.978191558616956\n",
      "Best combined performance on test and validation data for iteration 20: 14.978191558616956\n",
      "Best combined performance on test and validation data for iteration 21: 14.978191558616956\n",
      "Best combined performance on test and validation data for iteration 22: 14.978191558616956\n",
      "Best combined performance on test and validation data for iteration 23: 14.978191558616956\n",
      "Best combined performance on test and validation data for iteration 24: 14.978191558616956\n",
      "================= Best result: 14.978191558616956 =================\n",
      "================= Execution time in minutes: 245.0 =================\n"
     ]
    }
   ],
   "source": [
    "#### Ant Colony Optimization\n",
    "best_columns_ACO_PredictRoomBooking_lightgbm = fsObj.AntColonyOptimization(iterations = 25,\n",
    "                                                                N_ants = 75,\n",
    "                                                                run_time=1200,\n",
    "                                                                evaporation_rate=0.8,\n",
    "                                                                Q=0.2)\n",
    "\n",
    "with open(r\"D:\\\\Results\\\\ResultPickle\\\\best_columns_ACO_PredictRoomBooking_lightgbm.pickle\", \"wb\") as output_file:\n",
    "    pickle.dump(best_columns_ACO_PredictRoomBooking_lightgbm, output_file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combined performance on test and validation data for iteration 0: 19.633861051856037\n",
      "Best combined performance on test and validation data for iteration 1: 19.633861051856037\n",
      "Best combined performance on test and validation data for iteration 2: 21.66576605456466\n",
      "Best combined performance on test and validation data for iteration 3: 21.75050428967806\n",
      "Best combined performance on test and validation data for iteration 4: 25.341890921069922\n",
      "Best combined performance on test and validation data for iteration 5: 28.263056084667927\n",
      "Best combined performance on test and validation data for iteration 6: 18.650942698678218\n",
      "Best combined performance on test and validation data for iteration 7: 18.650942698678218\n",
      "Best combined performance on test and validation data for iteration 8: 20.60675056297895\n",
      "Best combined performance on test and validation data for iteration 9: 20.60675056297895\n",
      "Best combined performance on test and validation data for iteration 10: 21.433916759738487\n",
      "Best combined performance on test and validation data for iteration 11: 23.17967583458917\n",
      "Best combined performance on test and validation data for iteration 12: 25.13824805173031\n",
      "Best combined performance on test and validation data for iteration 13: 29.316509982849386\n",
      "Best combined performance on test and validation data for iteration 14: 36.973884498731096\n",
      "Best combined performance on test and validation data for iteration 15: 40.946111181575176\n",
      "Best combined performance on test and validation data for iteration 16: 29.771984243648596\n",
      "Best combined performance on test and validation data for iteration 17: 29.771984243648596\n",
      "Best combined performance on test and validation data for iteration 18: 32.798711055277344\n",
      "Best combined performance on test and validation data for iteration 19: 17.91299097054489\n",
      "Best combined performance on test and validation data for iteration 20: 17.91299097054489\n",
      "Best combined performance on test and validation data for iteration 21: 20.80088467478408\n",
      "Best combined performance on test and validation data for iteration 22: 20.62644431580207\n",
      "Best combined performance on test and validation data for iteration 23: 20.62644431580207\n",
      "Best combined performance on test and validation data for iteration 24: 20.951813710494513\n",
      "Best combined performance on test and validation data for iteration 25: 20.951813710494513\n",
      "Best combined performance on test and validation data for iteration 26: 21.293988549814195\n",
      "Best combined performance on test and validation data for iteration 27: 23.665183474435462\n",
      "Best combined performance on test and validation data for iteration 28: 18.93479699856789\n",
      "Best combined performance on test and validation data for iteration 29: 18.93479699856789\n",
      "Best combined performance on test and validation data for iteration 30: 19.00543026277898\n",
      "Best combined performance on test and validation data for iteration 31: 19.00543026277898\n",
      "Best combined performance on test and validation data for iteration 32: 20.28725807620646\n",
      "Best combined performance on test and validation data for iteration 33: 20.28725807620646\n",
      "Best combined performance on test and validation data for iteration 34: 21.38586749339037\n",
      "Best combined performance on test and validation data for iteration 35: 24.224984148075855\n",
      "Best combined performance on test and validation data for iteration 36: 28.84426388477999\n",
      "Best combined performance on test and validation data for iteration 37: 30.787254512408964\n",
      "Best combined performance on test and validation data for iteration 38: 30.787254512408964\n",
      "Best combined performance on test and validation data for iteration 39: 22.097694834863766\n",
      "Best combined performance on test and validation data for iteration 40: 22.097694834863766\n",
      "Best combined performance on test and validation data for iteration 41: 24.783407504957687\n",
      "Best combined performance on test and validation data for iteration 42: 24.53645611592101\n",
      "Best combined performance on test and validation data for iteration 43: 24.53645611592101\n",
      "Best combined performance on test and validation data for iteration 44: 25.087973039113912\n",
      "Best combined performance on test and validation data for iteration 45: 24.18259260400164\n",
      "Best combined performance on test and validation data for iteration 46: 19.31933769924538\n",
      "Best combined performance on test and validation data for iteration 47: 19.31933769924538\n",
      "Best combined performance on test and validation data for iteration 48: 19.737816384622924\n",
      "Best combined performance on test and validation data for iteration 49: 19.737816384622924\n",
      "================= Best result: 17.91299097054489 =================\n",
      "================= Execution time in minutes: 307.0 =================\n"
     ]
    }
   ],
   "source": [
    "#### Simulated Annealing\n",
    "best_columns_SA_PredictRoomBooking_lightgbm = fsObj.SimulatedAnnealing(temperature = 1500,\n",
    "                                                               iterations = 50,\n",
    "                                                               n_perturb = 75,\n",
    "                                                               run_time=1200,\n",
    "                                                               n_features_percent_perturb=1,\n",
    "                                                               alpha=0.9)\n",
    "\n",
    "with open(r\"D:\\\\Results\\\\ResultPickle\\\\best_columns_SA_PredictRoomBooking_lightgbm.pickle\", \"wb\") as output_file:\n",
    "    pickle.dump(best_columns_SA_PredictRoomBooking_lightgbm, output_file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combined performance on test and validation data for generation 0 : 17.4933658572529\n",
      "Best combined performance on test and validation data for generation 1 : 15.79591558694597\n",
      "Best combined performance on test and validation data for generation 2 : 15.266274645609474\n",
      "Best combined performance on test and validation data for generation 3 : 14.000314510582353\n",
      "Best combined performance on test and validation data for generation 4 : 14.242632343215869\n",
      "Best combined performance on test and validation data for generation 5 : 14.456724675685416\n",
      "Best combined performance on test and validation data for generation 6 : 14.263278887770706\n",
      "Best combined performance on test and validation data for generation 7 : 14.343453197524951\n",
      "Best combined performance on test and validation data for generation 8 : 11.707511389719157\n",
      "Best combined performance on test and validation data for generation 9 : 13.812486911465701\n",
      "Best combined performance on test and validation data for generation 10 : 11.173627091567434\n",
      "Best combined performance on test and validation data for generation 11 : 11.404484834624723\n",
      "Best combined performance on test and validation data for generation 12 : 11.927629665917602\n",
      "Best combined performance on test and validation data for generation 13 : 13.385674092307926\n",
      "Best combined performance on test and validation data for generation 14 : 12.234432634996429\n",
      "Best combined performance on test and validation data for generation 15 : 12.342353141038926\n",
      "Best combined performance on test and validation data for generation 16 : 12.591703706184113\n",
      "Best combined performance on test and validation data for generation 17 : 12.675208203267823\n",
      "Best combined performance on test and validation data for generation 18 : 11.446639936165724\n",
      "Best combined performance on test and validation data for generation 19 : 13.25477066531634\n",
      "================= Best result: 11.173627091567434 =================\n",
      "================= Execution time in minutes: 496.0 =================\n"
     ]
    }
   ],
   "source": [
    "#### Genetic Algorithm\n",
    "best_columns_GA_PredictRoomBooking_lightgbm = fsObj.GeneticAlgorithm(generations=20,\n",
    "                                                             population=75,\n",
    "                                                             run_time=1200)\n",
    "\n",
    "with open(r\"D:\\\\Results\\\\ResultPickle\\\\best_columns_GA_PredictRoomBooking_lightgbm.pickle\", \"wb\") as output_file:\n",
    "    pickle.dump(best_columns_GA_PredictRoomBooking_lightgbm, output_file)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Test and Validation Cost: 16.29 Average External Test Cost: 13.06\n",
      "Predict Room Booking dataset - Particle Swarm Optimization\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#model_use = XGBClassifier(n_estimators=500,random_state=1,n_jobs=-1,tree_method = 'gpu_hist')\n",
    "test_tree_cost,external_tree_cost = get_metrics(model=model_use,\n",
    "                                                  data_dict=data_dict,\n",
    "                                                  problem_type = 'regression',\n",
    "                                                  dependent_variable=dependent_variable,\n",
    "                                                  features=best_columns_PSO_PredictRoomBooking_lightgbm,\n",
    "                                                  cost_funct=cost_funct)\n",
    "print('Average Test and Validation Cost:',round(np.mean(list(test_tree_cost.values())),2),'Average External Test Cost:',round(np.mean(external_tree_cost),2))\n",
    "metric = 'RMSE'\n",
    "title = 'Predict Room Booking dataset - Particle Swarm Optimization'\n",
    "print(title)\n",
    "make_plot(metric=metric,\n",
    "          title=title,\n",
    "          test_tree_cost=test_tree_cost,\n",
    "          external_tree_cost=external_tree_cost)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Test and Validation Cost: 14.98 Average External Test Cost: 13.7\n",
      "Predict Room Booking dataset - Ant Colony Optimization\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#model_use = XGBClassifier(n_estimators=500,random_state=1,n_jobs=-1)#,tree_method = 'gpu_hist')\n",
    "test_tree_cost,external_tree_cost = get_metrics(model=model_use,\n",
    "                                                  data_dict=data_dict,\n",
    "                                                  problem_type = 'regression',\n",
    "                                                  dependent_variable=dependent_variable,\n",
    "                                                  features=best_columns_ACO_PredictRoomBooking_lightgbm,\n",
    "                                                  cost_funct=cost_funct)\n",
    "\n",
    "print('Average Test and Validation Cost:',round(np.mean(list(test_tree_cost.values())),2),'Average External Test Cost:',round(np.mean(external_tree_cost),2))\n",
    "metric = 'RMSE'\n",
    "title = 'Predict Room Booking dataset - Ant Colony Optimization'\n",
    "print(title)\n",
    "make_plot(metric=metric,\n",
    "          title=title,\n",
    "          test_tree_cost=test_tree_cost,\n",
    "          external_tree_cost=external_tree_cost)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Test and Validation Cost: 17.91 Average External Test Cost: 12.7\n",
      "Predict Room Booking dataset - Simulated Annealing\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#model_use = XGBClassifier(n_estimators=200,random_state=1,n_jobs=-1)\n",
    "test_tree_cost,external_tree_cost = get_metrics(model=model_use,\n",
    "                                                  data_dict=data_dict,\n",
    "                                                  problem_type = 'regression',\n",
    "                                                  dependent_variable=dependent_variable,\n",
    "                                                  features=best_columns_SA_PredictRoomBooking_lightgbm,\n",
    "                                                  cost_funct=cost_funct)\n",
    "\n",
    "print('Average Test and Validation Cost:',round(np.mean(list(test_tree_cost.values())),2),'Average External Test Cost:',round(np.mean(external_tree_cost),2))\n",
    "metric = 'RMSE'\n",
    "title = 'Predict Room Booking dataset - Simulated Annealing'\n",
    "print(title)\n",
    "make_plot(metric=metric,\n",
    "          title=title,\n",
    "          test_tree_cost=test_tree_cost,\n",
    "          external_tree_cost=external_tree_cost)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Test and Validation Cost: 11.17 Average External Test Cost: 11.19\n",
      "Predict Room Booking dataset - Genetic Algorithm\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#model_use = XGBClassifier(n_estimators=500,random_state=1,n_jobs=-1,tree_method = 'gpu_hist')\n",
    "test_tree_cost,external_tree_cost = get_metrics(model=model_use,\n",
    "                                                  data_dict=data_dict,\n",
    "                                                  problem_type = 'regression',\n",
    "                                                  dependent_variable=dependent_variable,\n",
    "                                                  features=best_columns_GA_PredictRoomBooking_lightgbm,\n",
    "                                                  cost_funct=cost_funct)\n",
    "\n",
    "print('Average Test and Validation Cost:',round(np.mean(list(test_tree_cost.values())),2),'Average External Test Cost:',round(np.mean(external_tree_cost),2))\n",
    "metric = 'RMSE'\n",
    "title = 'Predict Room Booking dataset - Genetic Algorithm'\n",
    "print(title)\n",
    "make_plot(metric=metric,\n",
    "          title=title,\n",
    "          test_tree_cost=test_tree_cost,\n",
    "          external_tree_cost=external_tree_cost)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model_use = LGBMRegressor(random_state=1,n_jobs=-1,n_estimators=100)\n",
    "model_use = XGBRegressor(n_estimators=200,random_state=1,n_jobs=-1,tree_method = 'gpu_hist')\n",
    "# model_use = RandomForestRegressor(random_state=1,n_jobs=-1,n_estimators=200)\n",
    "\n",
    "fsObj = FeatureSelection(columns_list = tree_features_all,\n",
    "                         data_dict = data_dict_for_tree,\n",
    "                         x_validation_dataframe = x_validation_tree,\n",
    "                         y_validation_dataframe = y_validation_tree,\n",
    "                         model = model_use,\n",
    "                         cost_function = cost_funct,\n",
    "                         cost_function_improvement = 'decrease',\n",
    "                         average = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combined performance on test and validation data for iteration 0: 15.566732273104183\n",
      "Best combined performance on test and validation data for iteration 1: 14.833210920102061\n",
      "Best combined performance on test and validation data for iteration 2: 14.396002107538658\n",
      "Best combined performance on test and validation data for iteration 3: 14.27478872315383\n",
      "Best combined performance on test and validation data for iteration 4: 13.388374944397901\n",
      "Best combined performance on test and validation data for iteration 5: 13.18649366516028\n",
      "Best combined performance on test and validation data for iteration 6: 12.839133486146716\n",
      "Best combined performance on test and validation data for iteration 7: 12.839098171767438\n",
      "Best combined performance on test and validation data for iteration 8: 12.839098171767438\n",
      "Best combined performance on test and validation data for iteration 9: 12.839098171767438\n",
      "Best combined performance on test and validation data for iteration 10: 12.839098171767438\n",
      "Best combined performance on test and validation data for iteration 11: 12.839098171767438\n",
      "Best combined performance on test and validation data for iteration 12: 12.839098171767438\n",
      "Best combined performance on test and validation data for iteration 13: 12.831290599571279\n",
      "Best combined performance on test and validation data for iteration 14: 12.831290599571279\n",
      "Best combined performance on test and validation data for iteration 15: 12.831290599571279\n",
      "Best combined performance on test and validation data for iteration 16: 12.803132051529772\n",
      "Best combined performance on test and validation data for iteration 17: 12.803132051529772\n",
      "Best combined performance on test and validation data for iteration 18: 12.803132051529772\n",
      "Best combined performance on test and validation data for iteration 19: 12.803132051529772\n",
      "Best combined performance on test and validation data for iteration 20: 12.803132051529772\n",
      "Best combined performance on test and validation data for iteration 21: 12.803132051529772\n",
      "Best combined performance on test and validation data for iteration 22: 12.803132051529772\n",
      "Best combined performance on test and validation data for iteration 23: 12.803132051529772\n",
      "Best combined performance on test and validation data for iteration 24: 12.803132051529772\n",
      "================= Best result: 12.803132051529772 =================\n",
      "================= Execution time in minutes: 280.0 =================\n"
     ]
    }
   ],
   "source": [
    "#### Particle Swarm Optimization\n",
    "best_columns_PSO_PredictRoomBooking_xgboost = fsObj.ParticleSwarmOptimization(iterations = 25,                                                                   \n",
    "                                                                       swarmSize = 75,\n",
    "                                                                       run_time=1200)\n",
    "\n",
    "with open(r\"D:\\\\Results\\\\ResultPickle\\\\best_columns_PSO_PredictRoomBooking_xgboost.pickle\", \"wb\") as output_file:\n",
    "    pickle.dump(best_columns_PSO_PredictRoomBooking_xgboost, output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combined performance on test and validation data for iteration 0: 14.970980171437194\n",
      "Best combined performance on test and validation data for iteration 1: 13.50770671394742\n",
      "Best combined performance on test and validation data for iteration 2: 12.5940434304615\n",
      "Best combined performance on test and validation data for iteration 3: 11.983068572978983\n",
      "Best combined performance on test and validation data for iteration 4: 11.970514839997673\n",
      "Best combined performance on test and validation data for iteration 5: 11.970514839997673\n",
      "Best combined performance on test and validation data for iteration 6: 11.970514839997673\n",
      "Best combined performance on test and validation data for iteration 7: 11.970514839997673\n",
      "Best combined performance on test and validation data for iteration 8: 11.970514839997673\n",
      "Best combined performance on test and validation data for iteration 9: 11.970514839997673\n",
      "Best combined performance on test and validation data for iteration 10: 11.970514839997673\n",
      "Best combined performance on test and validation data for iteration 11: 11.970514839997673\n",
      "Best combined performance on test and validation data for iteration 12: 11.970514839997673\n",
      "Best combined performance on test and validation data for iteration 13: 11.970514839997673\n",
      "Best combined performance on test and validation data for iteration 14: 11.970514839997673\n",
      "Best combined performance on test and validation data for iteration 15: 11.970514839997673\n",
      "Best combined performance on test and validation data for iteration 16: 11.970514839997673\n",
      "Best combined performance on test and validation data for iteration 17: 11.970514839997673\n",
      "Best combined performance on test and validation data for iteration 18: 11.970514839997673\n",
      "Best combined performance on test and validation data for iteration 19: 11.970514839997673\n",
      "Best combined performance on test and validation data for iteration 20: 11.970514839997673\n",
      "Best combined performance on test and validation data for iteration 21: 11.970514839997673\n",
      "Best combined performance on test and validation data for iteration 22: 11.970514839997673\n",
      "Best combined performance on test and validation data for iteration 23: 11.970514839997673\n",
      "Best combined performance on test and validation data for iteration 24: 11.970514839997673\n",
      "================= Best result: 11.970514839997673 =================\n",
      "================= Execution time in minutes: 286.0 =================\n"
     ]
    }
   ],
   "source": [
    "#### Ant Colony Optimization\n",
    "best_columns_ACO_PredictRoomBooking_xgboost = fsObj.AntColonyOptimization(iterations = 25,\n",
    "                                                                N_ants = 75,\n",
    "                                                                run_time=1200,\n",
    "                                                                evaporation_rate=0.8,\n",
    "                                                                Q=0.2)\n",
    "\n",
    "with open(r\"D:\\\\Results\\\\ResultPickle\\\\best_columns_ACO_PredictRoomBooking_xgboost.pickle\", \"wb\") as output_file:\n",
    "    pickle.dump(best_columns_ACO_PredictRoomBooking_xgboost, output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combined performance on test and validation data for iteration 0: 31.883395531474434\n",
      "Best combined performance on test and validation data for iteration 1: 31.883395531474434\n",
      "Best combined performance on test and validation data for iteration 2: 31.180984401644043\n",
      "Best combined performance on test and validation data for iteration 3: 27.626714611808257\n",
      "Best combined performance on test and validation data for iteration 4: 27.626714611808257\n",
      "Best combined performance on test and validation data for iteration 5: 24.554031329297676\n",
      "Best combined performance on test and validation data for iteration 6: 24.554031329297676\n",
      "Best combined performance on test and validation data for iteration 7: 21.99697640844301\n",
      "Best combined performance on test and validation data for iteration 8: 21.99697640844301\n",
      "Best combined performance on test and validation data for iteration 9: 24.749103695347863\n",
      "Best combined performance on test and validation data for iteration 10: 27.1344718563946\n",
      "Best combined performance on test and validation data for iteration 11: 23.329028294597236\n",
      "Best combined performance on test and validation data for iteration 12: 22.81170210219246\n",
      "Best combined performance on test and validation data for iteration 13: 22.81170210219246\n",
      "Best combined performance on test and validation data for iteration 14: 23.05725813559989\n",
      "Best combined performance on test and validation data for iteration 15: 30.05545399165344\n",
      "Best combined performance on test and validation data for iteration 16: 32.248839071257734\n",
      "Best combined performance on test and validation data for iteration 17: 33.98468213969806\n",
      "Best combined performance on test and validation data for iteration 18: 27.402906401657624\n",
      "Best combined performance on test and validation data for iteration 19: 23.523316779120904\n",
      "Best combined performance on test and validation data for iteration 20: 23.522823298896792\n",
      "Best combined performance on test and validation data for iteration 21: 23.522823298896792\n",
      "Best combined performance on test and validation data for iteration 22: 26.63895256424636\n",
      "Best combined performance on test and validation data for iteration 23: 20.39902036129601\n",
      "Best combined performance on test and validation data for iteration 24: 20.39902036129601\n",
      "Best combined performance on test and validation data for iteration 25: 24.985181875858196\n",
      "Best combined performance on test and validation data for iteration 26: 31.243788469435913\n",
      "Best combined performance on test and validation data for iteration 27: 26.70127656544262\n",
      "Best combined performance on test and validation data for iteration 28: 26.70127656544262\n",
      "Best combined performance on test and validation data for iteration 29: 32.99628695912348\n",
      "Best combined performance on test and validation data for iteration 30: 32.07268323957887\n",
      "Best combined performance on test and validation data for iteration 31: 30.14713900287897\n",
      "Best combined performance on test and validation data for iteration 32: 26.21913387507335\n",
      "Best combined performance on test and validation data for iteration 33: 26.21913387507335\n",
      "Best combined performance on test and validation data for iteration 34: 27.379399658083788\n",
      "Best combined performance on test and validation data for iteration 35: 27.379399658083788\n",
      "Best combined performance on test and validation data for iteration 36: 24.41253276061541\n",
      "Best combined performance on test and validation data for iteration 37: 21.951790611579273\n",
      "Best combined performance on test and validation data for iteration 38: 19.109371454682027\n",
      "Best combined performance on test and validation data for iteration 39: 19.109371454682027\n",
      "Best combined performance on test and validation data for iteration 40: 19.916518734926335\n",
      "Best combined performance on test and validation data for iteration 41: 20.4228450442667\n",
      "Best combined performance on test and validation data for iteration 42: 20.4228450442667\n",
      "Best combined performance on test and validation data for iteration 43: 25.049566091644465\n",
      "Best combined performance on test and validation data for iteration 44: 26.028507214498624\n",
      "Best combined performance on test and validation data for iteration 45: 28.028975898854462\n",
      "Best combined performance on test and validation data for iteration 46: 23.991433989834043\n",
      "Best combined performance on test and validation data for iteration 47: 23.991433989834043\n",
      "Best combined performance on test and validation data for iteration 48: 27.452847821880248\n",
      "Best combined performance on test and validation data for iteration 49: 31.63439669819981\n",
      "================= Best result: 19.109371454682027 =================\n",
      "================= Execution time in minutes: 459.0 =================\n"
     ]
    }
   ],
   "source": [
    "#### Simulated Annealing\n",
    "best_columns_SA_PredictRoomBooking_xgboost = fsObj.SimulatedAnnealing(temperature = 1500,\n",
    "                                                               iterations = 50,\n",
    "                                                               n_perturb = 75,\n",
    "                                                               run_time=1200,\n",
    "                                                               n_features_percent_perturb=1,\n",
    "                                                               alpha=0.9)\n",
    "\n",
    "with open(r\"D:\\\\Results\\\\ResultPickle\\\\best_columns_SA_PredictRoomBooking_xgboost.pickle\", \"wb\") as output_file:\n",
    "    pickle.dump(best_columns_SA_PredictRoomBooking_xgboost, output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combined performance on test and validation data for generation 0 : 10.846244536185685\n",
      "Best combined performance on test and validation data for generation 1 : 14.720267482405589\n",
      "Best combined performance on test and validation data for generation 2 : 13.54480566120316\n",
      "Best combined performance on test and validation data for generation 3 : 13.810323494761793\n",
      "Best combined performance on test and validation data for generation 4 : 12.490870992376736\n",
      "Best combined performance on test and validation data for generation 5 : 12.04747024075549\n",
      "Best combined performance on test and validation data for generation 6 : 11.36160888457563\n",
      "Best combined performance on test and validation data for generation 7 : 12.595967290116311\n",
      "Best combined performance on test and validation data for generation 8 : 13.665149141705879\n",
      "Best combined performance on test and validation data for generation 9 : 12.70160515881298\n",
      "Best combined performance on test and validation data for generation 10 : 12.670447939399494\n",
      "Best combined performance on test and validation data for generation 11 : 13.3945807134705\n",
      "Best combined performance on test and validation data for generation 12 : 11.941070748328453\n",
      "Best combined performance on test and validation data for generation 13 : 11.99962205940307\n",
      "Best combined performance on test and validation data for generation 14 : 12.005492776201141\n",
      "Best combined performance on test and validation data for generation 15 : 11.430298712119143\n",
      "Best combined performance on test and validation data for generation 16 : 12.045092134293018\n",
      "Best combined performance on test and validation data for generation 17 : 12.790546033971625\n",
      "Best combined performance on test and validation data for generation 18 : 12.670680301716951\n",
      "Best combined performance on test and validation data for generation 19 : 11.976418472388033\n",
      "================= Best result: 10.846244536185685 =================\n",
      "================= Execution time in minutes: 764.0 =================\n"
     ]
    }
   ],
   "source": [
    "#### Genetic Algorithm\n",
    "best_columns_GA_PredictRoomBooking_xgboost = fsObj.GeneticAlgorithm(generations=20,\n",
    "                                                             population=75,\n",
    "                                                             run_time=1200)\n",
    "\n",
    "with open(r\"D:\\\\Results\\\\ResultPickle\\\\best_columns_GA_PredictRoomBooking_xgboost.pickle\", \"wb\") as output_file:\n",
    "    pickle.dump(best_columns_GA_PredictRoomBooking_xgboost, output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Test and Validation Cost: 12.8 Average External Test Cost: 11.77\n",
      "Predict Room Booking dataset - Particle Swarm Optimization\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#model_use = XGBClassifier(n_estimators=500,random_state=1,n_jobs=-1,tree_method = 'gpu_hist')\n",
    "test_tree_cost,external_tree_cost = get_metrics(model=model_use,\n",
    "                                                  data_dict=data_dict,\n",
    "                                                  problem_type = 'regression',\n",
    "                                                  dependent_variable=dependent_variable,\n",
    "                                                  features=best_columns_PSO_PredictRoomBooking_xgboost,\n",
    "                                                  cost_funct=cost_funct)\n",
    "print('Average Test and Validation Cost:',round(np.mean(list(test_tree_cost.values())),2),'Average External Test Cost:',round(np.mean(external_tree_cost),2))\n",
    "metric = 'RMSE'\n",
    "title = 'Predict Room Booking dataset - Particle Swarm Optimization'\n",
    "print(title)\n",
    "make_plot(metric=metric,\n",
    "          title=title,\n",
    "          test_tree_cost=test_tree_cost,\n",
    "          external_tree_cost=external_tree_cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Test and Validation Cost: 11.97 Average External Test Cost: 12.33\n",
      "Predict Room Booking dataset - Ant Colony Optimization\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#model_use = XGBClassifier(n_estimators=500,random_state=1,n_jobs=-1)#,tree_method = 'gpu_hist')\n",
    "test_tree_cost,external_tree_cost = get_metrics(model=model_use,\n",
    "                                                  data_dict=data_dict,\n",
    "                                                  problem_type = 'regression',\n",
    "                                                  dependent_variable=dependent_variable,\n",
    "                                                  features=best_columns_ACO_PredictRoomBooking_xgboost,\n",
    "                                                  cost_funct=cost_funct)\n",
    "\n",
    "print('Average Test and Validation Cost:',round(np.mean(list(test_tree_cost.values())),2),'Average External Test Cost:',round(np.mean(external_tree_cost),2))\n",
    "metric = 'RMSE'\n",
    "title = 'Predict Room Booking dataset - Ant Colony Optimization'\n",
    "print(title)\n",
    "make_plot(metric=metric,\n",
    "          title=title,\n",
    "          test_tree_cost=test_tree_cost,\n",
    "          external_tree_cost=external_tree_cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Test and Validation Cost: 19.11 Average External Test Cost: 19.96\n",
      "Predict Room Booking dataset - Simulated Annealing\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#model_use = XGBClassifier(n_estimators=200,random_state=1,n_jobs=-1)\n",
    "test_tree_cost,external_tree_cost = get_metrics(model=model_use,\n",
    "                                                  data_dict=data_dict,\n",
    "                                                  problem_type = 'regression',\n",
    "                                                  dependent_variable=dependent_variable,\n",
    "                                                  features=best_columns_SA_PredictRoomBooking_xgboost,\n",
    "                                                  cost_funct=cost_funct)\n",
    "\n",
    "print('Average Test and Validation Cost:',round(np.mean(list(test_tree_cost.values())),2),'Average External Test Cost:',round(np.mean(external_tree_cost),2))\n",
    "metric = 'RMSE'\n",
    "title = 'Predict Room Booking dataset - Simulated Annealing'\n",
    "print(title)\n",
    "make_plot(metric=metric,\n",
    "          title=title,\n",
    "          test_tree_cost=test_tree_cost,\n",
    "          external_tree_cost=external_tree_cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Test and Validation Cost: 10.85 Average External Test Cost: 11.49\n",
      "Predict Room Booking dataset - Genetic Algorithm\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#model_use = XGBClassifier(n_estimators=500,random_state=1,n_jobs=-1,tree_method = 'gpu_hist')\n",
    "test_tree_cost,external_tree_cost = get_metrics(model=model_use,\n",
    "                                                  data_dict=data_dict,\n",
    "                                                  problem_type = 'regression',\n",
    "                                                  dependent_variable=dependent_variable,\n",
    "                                                  features=best_columns_GA_PredictRoomBooking_xgboost,\n",
    "                                                  cost_funct=cost_funct)\n",
    "\n",
    "print('Average Test and Validation Cost:',round(np.mean(list(test_tree_cost.values())),2),'Average External Test Cost:',round(np.mean(external_tree_cost),2))\n",
    "metric = 'RMSE'\n",
    "title = 'Predict Room Booking dataset - Genetic Algorithm'\n",
    "print(title)\n",
    "make_plot(metric=metric,\n",
    "          title=title,\n",
    "          test_tree_cost=test_tree_cost,\n",
    "          external_tree_cost=external_tree_cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
     ]
    }
   ],
   "source": [
    "patch_sklearn()\n",
    "model_use = LinearRegression(n_jobs=-1)\n",
    "\n",
    "#common_list\n",
    "#linear_features_all\n",
    "\n",
    "fsObj = FeatureSelection(columns_list = linear_features_all,\n",
    "                         data_dict = data_dict_for_linear,\n",
    "                         x_validation_dataframe = x_validation_linear,\n",
    "                         y_validation_dataframe = y_validation_linear,\n",
    "                         model = model_use,\n",
    "                         cost_function = cost_funct,\n",
    "                         cost_function_improvement = 'decrease',\n",
    "                         average = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combined performance on test and validation data for iteration 0: 36.62963765782809\n",
      "Best combined performance on test and validation data for iteration 1: 36.62963765782809\n",
      "Best combined performance on test and validation data for iteration 2: 32.573909737391375\n",
      "Best combined performance on test and validation data for iteration 3: 29.429721151039917\n",
      "Best combined performance on test and validation data for iteration 4: 29.429721151039917\n",
      "Best combined performance on test and validation data for iteration 5: 28.364197232373584\n",
      "Best combined performance on test and validation data for iteration 6: 28.364197232373584\n",
      "Best combined performance on test and validation data for iteration 7: 27.729322439758214\n",
      "Best combined performance on test and validation data for iteration 8: 27.729322439758214\n",
      "Best combined performance on test and validation data for iteration 9: 27.729322439758214\n",
      "Best combined performance on test and validation data for iteration 10: 27.729322439758214\n",
      "Best combined performance on test and validation data for iteration 11: 27.729322439758214\n",
      "Best combined performance on test and validation data for iteration 12: 27.729322439758214\n",
      "Best combined performance on test and validation data for iteration 13: 27.729322439758214\n",
      "Best combined performance on test and validation data for iteration 14: 27.42464367065238\n",
      "Best combined performance on test and validation data for iteration 15: 26.926565344704365\n",
      "Best combined performance on test and validation data for iteration 16: 26.75026683163271\n",
      "Best combined performance on test and validation data for iteration 17: 26.64581809820364\n",
      "Best combined performance on test and validation data for iteration 18: 26.604576523932217\n",
      "Best combined performance on test and validation data for iteration 19: 26.597611378208036\n",
      "Best combined performance on test and validation data for iteration 20: 26.56729788478982\n",
      "Best combined performance on test and validation data for iteration 21: 26.35512967998627\n",
      "Best combined performance on test and validation data for iteration 22: 26.327641834220106\n",
      "Best combined performance on test and validation data for iteration 23: 26.326984281045647\n",
      "Best combined performance on test and validation data for iteration 24: 26.302532781420194\n",
      "================= Best result: 26.302532781420194 =================\n",
      "================= Execution time in minutes: 73.0 =================\n"
     ]
    }
   ],
   "source": [
    "#### Particle Swarm Optimization\n",
    "best_columns_PSO_PredictRoomBooking_linear = fsObj.ParticleSwarmOptimization(iterations = 25,                                                                   \n",
    "                                                                       swarmSize = 100,\n",
    "                                                                       run_time=1200)\n",
    "\n",
    "with open(r\"D:\\\\Results\\\\ResultPickle\\\\best_columns_PSO_PredictRoomBooking_linear.pickle\", \"wb\") as output_file:\n",
    "    pickle.dump(best_columns_PSO_PredictRoomBooking_linear, output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combined performance on test and validation data for iteration 0: 33.9558344474459\n",
      "Best combined performance on test and validation data for iteration 1: 33.9558344474459\n",
      "Best combined performance on test and validation data for iteration 2: 33.9558344474459\n",
      "Best combined performance on test and validation data for iteration 3: 33.9558344474459\n",
      "Best combined performance on test and validation data for iteration 4: 32.94672840285293\n",
      "Best combined performance on test and validation data for iteration 5: 31.643498938939313\n",
      "Best combined performance on test and validation data for iteration 6: 31.643498938939313\n",
      "Best combined performance on test and validation data for iteration 7: 30.81396657118514\n",
      "Best combined performance on test and validation data for iteration 8: 30.49014648854357\n",
      "Best combined performance on test and validation data for iteration 9: 29.78040671707159\n",
      "Best combined performance on test and validation data for iteration 10: 29.749658072218494\n",
      "Best combined performance on test and validation data for iteration 11: 29.633812403976304\n",
      "Best combined performance on test and validation data for iteration 12: 29.56022080858404\n",
      "Best combined performance on test and validation data for iteration 13: 29.48390698859048\n",
      "Best combined performance on test and validation data for iteration 14: 29.414674273047307\n",
      "Best combined performance on test and validation data for iteration 15: 29.34885574290854\n",
      "Best combined performance on test and validation data for iteration 16: 29.30870587600591\n",
      "Best combined performance on test and validation data for iteration 17: 29.193915848899422\n",
      "Best combined performance on test and validation data for iteration 18: 29.191152265708958\n",
      "Best combined performance on test and validation data for iteration 19: 29.191146524061793\n",
      "Best combined performance on test and validation data for iteration 20: 29.18752942640976\n",
      "Best combined performance on test and validation data for iteration 21: 29.187523708945367\n",
      "Best combined performance on test and validation data for iteration 22: 29.186676326247458\n",
      "Best combined performance on test and validation data for iteration 23: 29.17769737552872\n",
      "Best combined performance on test and validation data for iteration 24: 29.17769737552872\n",
      "================= Best result: 29.17769737552872 =================\n",
      "================= Execution time in minutes: 148.0 =================\n"
     ]
    }
   ],
   "source": [
    "#### Ant Colony Optimization\n",
    "best_columns_ACO_PredictRoomBooking_linear = fsObj.AntColonyOptimization(iterations = 25,\n",
    "                                                                N_ants = 100,\n",
    "                                                                run_time=1200,\n",
    "                                                                evaporation_rate=0.8,\n",
    "                                                                Q=0.2)\n",
    "\n",
    "with open(r\"D:\\\\Results\\\\ResultPickle\\\\best_columns_ACO_PredictRoomBooking_linear.pickle\", \"wb\") as output_file:\n",
    "    pickle.dump(best_columns_ACO_PredictRoomBooking_linear, output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combined performance on test and validation data for iteration 0: 57.860966640322275\n",
      "Best combined performance on test and validation data for iteration 1: 39.91800847975971\n",
      "Best combined performance on test and validation data for iteration 2: 39.91800847975971\n",
      "Best combined performance on test and validation data for iteration 3: 46.30299990575589\n",
      "Best combined performance on test and validation data for iteration 4: 50.540956383264515\n",
      "Best combined performance on test and validation data for iteration 5: 44.25132477418461\n",
      "Best combined performance on test and validation data for iteration 6: 49.87552368113889\n",
      "Best combined performance on test and validation data for iteration 7: 50.503729455604514\n",
      "Best combined performance on test and validation data for iteration 8: 43.823573276517905\n",
      "Best combined performance on test and validation data for iteration 9: 45.72521746507481\n",
      "Best combined performance on test and validation data for iteration 10: 52.60654183124974\n",
      "Best combined performance on test and validation data for iteration 11: 52.710052288371344\n",
      "Best combined performance on test and validation data for iteration 12: 55.21689955941055\n",
      "Best combined performance on test and validation data for iteration 13: 51.96127224778088\n",
      "Best combined performance on test and validation data for iteration 14: 41.963074252165995\n",
      "Best combined performance on test and validation data for iteration 15: 41.963074252165995\n",
      "Best combined performance on test and validation data for iteration 16: 40.67894030444383\n",
      "Best combined performance on test and validation data for iteration 17: 38.49723353426767\n",
      "Best combined performance on test and validation data for iteration 18: 38.49723353426767\n",
      "Best combined performance on test and validation data for iteration 19: 44.42172789969946\n",
      "Best combined performance on test and validation data for iteration 20: 45.30135251141694\n",
      "Best combined performance on test and validation data for iteration 21: 36.630534324258704\n",
      "Best combined performance on test and validation data for iteration 22: 34.44248433067706\n",
      "Best combined performance on test and validation data for iteration 23: 34.44248433067706\n",
      "Best combined performance on test and validation data for iteration 24: 38.45223326730494\n",
      "Best combined performance on test and validation data for iteration 25: 40.41417946362917\n",
      "Best combined performance on test and validation data for iteration 26: 40.41417946362917\n",
      "Best combined performance on test and validation data for iteration 27: 33.796975241618455\n",
      "Best combined performance on test and validation data for iteration 28: 33.796975241618455\n",
      "Best combined performance on test and validation data for iteration 29: 38.600513454591656\n",
      "Best combined performance on test and validation data for iteration 30: 39.398967627398164\n",
      "Best combined performance on test and validation data for iteration 31: 39.398967627398164\n",
      "Best combined performance on test and validation data for iteration 32: 41.19320197213749\n",
      "Best combined performance on test and validation data for iteration 33: 41.19320197213749\n",
      "Best combined performance on test and validation data for iteration 34: 45.49007524999622\n",
      "================= Best result: 33.796975241618455 =================\n",
      "================= Execution time in minutes: 106.0 =================\n"
     ]
    }
   ],
   "source": [
    "#### Simulated Annealing\n",
    "best_columns_SA_PredictRoomBooking_linear = fsObj.SimulatedAnnealing(temperature = 1500,\n",
    "                                                               iterations = 35,\n",
    "                                                               n_perturb = 100,\n",
    "                                                               run_time=1200,\n",
    "                                                               n_features_percent_perturb=1,\n",
    "                                                               alpha=0.9)\n",
    "\n",
    "with open(r\"D:\\\\Results\\\\ResultPickle\\\\best_columns_SA_PredictRoomBooking_linear.pickle\", \"wb\") as output_file:\n",
    "    pickle.dump(best_columns_SA_PredictRoomBooking_linear, output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combined performance on test and validation data for generation 0 : 39.84634234137498\n",
      "Best combined performance on test and validation data for generation 1 : 33.81100163507303\n",
      "Best combined performance on test and validation data for generation 2 : 33.74687751445232\n",
      "Best combined performance on test and validation data for generation 3 : 34.63227887803078\n",
      "Best combined performance on test and validation data for generation 4 : 34.370821264349956\n",
      "Best combined performance on test and validation data for generation 5 : 32.99048597236549\n",
      "Best combined performance on test and validation data for generation 6 : 31.46087899449766\n",
      "Best combined performance on test and validation data for generation 7 : 31.9177827862217\n",
      "Best combined performance on test and validation data for generation 8 : 30.95533684373633\n",
      "Best combined performance on test and validation data for generation 9 : 32.58520208698992\n",
      "Best combined performance on test and validation data for generation 10 : 31.411209291815748\n",
      "Best combined performance on test and validation data for generation 11 : 32.983861158773436\n",
      "Best combined performance on test and validation data for generation 12 : 31.328142153899837\n",
      "Best combined performance on test and validation data for generation 13 : 31.8846402540784\n",
      "Best combined performance on test and validation data for generation 14 : 30.744066950436412\n",
      "Best combined performance on test and validation data for generation 15 : 30.93394519078314\n",
      "Best combined performance on test and validation data for generation 16 : 32.558423924723925\n",
      "Best combined performance on test and validation data for generation 17 : 34.84040539673828\n",
      "Best combined performance on test and validation data for generation 18 : 34.55843000212351\n",
      "Best combined performance on test and validation data for generation 19 : 33.7396816919982\n",
      "Best combined performance on test and validation data for generation 20 : 32.37572243518042\n",
      "Best combined performance on test and validation data for generation 21 : 30.701818054039496\n",
      "Best combined performance on test and validation data for generation 22 : 30.30162805806293\n",
      "Best combined performance on test and validation data for generation 23 : 33.49420823518939\n",
      "Best combined performance on test and validation data for generation 24 : 31.449761158907457\n",
      "================= Best result: 30.30162805806293 =================\n",
      "================= Execution time in minutes: 302.0 =================\n"
     ]
    }
   ],
   "source": [
    "#### Genetic Algorithm\n",
    "best_columns_GA_PredictRoomBooking_linear = fsObj.GeneticAlgorithm(generations=25,\n",
    "                                                             population=100,\n",
    "                                                             run_time=1200)\n",
    "\n",
    "with open(r\"D:\\\\Results\\\\ResultPickle\\\\best_columns_GA_PredictRoomBooking_linear.pickle\", \"wb\") as output_file:\n",
    "    pickle.dump(best_columns_GA_PredictRoomBooking_linear, output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Test and Validation Cost: 26.3 Average External Test Cost: 25.48\n",
      "Predict Room Booking dataset - Particle Swarm Optimization\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#model_use = XGBClassifier(n_estimators=500,random_state=1,n_jobs=-1,tree_method = 'gpu_hist')\n",
    "test_tree_cost,external_tree_cost = get_metrics(model=model_use,\n",
    "                                                  data_dict=data_dict,\n",
    "                                                  problem_type = 'regression',\n",
    "                                                  dependent_variable=dependent_variable,\n",
    "                                                  features=best_columns_PSO_PredictRoomBooking_linear,\n",
    "                                                  cost_funct=cost_funct)\n",
    "print('Average Test and Validation Cost:',round(np.mean(list(test_tree_cost.values())),2),'Average External Test Cost:',round(np.mean(external_tree_cost),2))\n",
    "metric = 'RMSE'\n",
    "title = 'Predict Room Booking dataset - Particle Swarm Optimization'\n",
    "print(title)\n",
    "make_plot(metric=metric,\n",
    "          title=title,\n",
    "          test_tree_cost=test_tree_cost,\n",
    "          external_tree_cost=external_tree_cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Test and Validation Cost: 29.18 Average External Test Cost: 27.35\n",
      "Predict Room Booking dataset - Ant Colony Optimization\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_tree_cost,external_tree_cost = get_metrics(model=model_use,\n",
    "                                                  data_dict=data_dict,\n",
    "                                                  problem_type = 'regression',\n",
    "                                                  dependent_variable=dependent_variable,\n",
    "                                                  features=best_columns_ACO_PredictRoomBooking_linear,\n",
    "                                                  cost_funct=cost_funct)\n",
    "\n",
    "print('Average Test and Validation Cost:',round(np.mean(list(test_tree_cost.values())),2),'Average External Test Cost:',round(np.mean(external_tree_cost),2))\n",
    "metric = 'RMSE'\n",
    "title = 'Predict Room Booking dataset - Ant Colony Optimization'\n",
    "print(title)\n",
    "make_plot(metric=metric,\n",
    "          title=title,\n",
    "          test_tree_cost=test_tree_cost,\n",
    "          external_tree_cost=external_tree_cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Test and Validation Cost: 33.8 Average External Test Cost: 33.29\n",
      "Predict Room Booking dataset - Simulated Annealing\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_tree_cost,external_tree_cost = get_metrics(model=model_use,\n",
    "                                                  data_dict=data_dict,\n",
    "                                                  problem_type = 'regression',\n",
    "                                                  dependent_variable=dependent_variable,\n",
    "                                                  features=best_columns_SA_PredictRoomBooking_linear,\n",
    "                                                  cost_funct=cost_funct)\n",
    "\n",
    "print('Average Test and Validation Cost:',round(np.mean(list(test_tree_cost.values())),2),'Average External Test Cost:',round(np.mean(external_tree_cost),2))\n",
    "metric = 'RMSE'\n",
    "title = 'Predict Room Booking dataset - Simulated Annealing'\n",
    "print(title)\n",
    "make_plot(metric=metric,\n",
    "          title=title,\n",
    "          test_tree_cost=test_tree_cost,\n",
    "          external_tree_cost=external_tree_cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Test and Validation Cost: 30.3 Average External Test Cost: 29.17\n",
      "Predict Room Booking dataset - Genetic Algorithm\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_tree_cost,external_tree_cost = get_metrics(model=model_use,\n",
    "                                                  data_dict=data_dict,\n",
    "                                                  problem_type = 'regression',\n",
    "                                                  dependent_variable=dependent_variable,\n",
    "                                                  features=best_columns_GA_PredictRoomBooking_linear,\n",
    "                                                  cost_funct=cost_funct)\n",
    "\n",
    "print('Average Test and Validation Cost:',round(np.mean(list(test_tree_cost.values())),2),'Average External Test Cost:',round(np.mean(external_tree_cost),2))\n",
    "metric = 'RMSE'\n",
    "title = 'Predict Room Booking dataset - Genetic Algorithm'\n",
    "print(title)\n",
    "make_plot(metric=metric,\n",
    "          title=title,\n",
    "          test_tree_cost=test_tree_cost,\n",
    "          external_tree_cost=external_tree_cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "unpatch_sklearn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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