Chapter 8 - Predicting Room Bookings - More Genetic Algorithm Iterations.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": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_metrics_df(model,data_dict,dependent_variable,features):\n",
    "    \n",
    "    prediction_output = {}\n",
    "    \n",
    "    data_external_test = data_dict['data_external_test']\n",
    "    data_validation_test = data_dict['data_validation_test']\n",
    "    data_fold_index = 0\n",
    "\n",
    "    fold_training = data_dict['fold_dict'][data_fold_index]['fold_training']\n",
    "\n",
    "    Y_train = fold_training[dependent_variable]\n",
    "    Y_external = data_external_test[dependent_variable]\n",
    "\n",
    "    model.fit(fold_training[features],Y_train)\n",
    "\n",
    "    Y_external_tree = model.predict(data_external_test[features])\n",
    "    \n",
    "    prediction_output['Y_external_actual'] = Y_external.values.ravel()\n",
    "    prediction_output['Y_external_predicted'] = Y_external_tree\n",
    "    \n",
    "    result_df = pd.DataFrame(prediction_output)\n",
    "    \n",
    "    #result_df['PercentageError'] = np.abs((result_df.Y_external_actual - result_df.Y_external_predicted) / result_df.Y_external_actual * 100)\n",
    "    result_df['External Test Actual'] = result_df['Y_external_actual']\n",
    "    result_df['External Test Predicted'] = result_df['Y_external_predicted']\n",
    "\n",
    "    result_df = result_df[['External Test Actual','External Test Predicted']]\n",
    "    \n",
    "    return result_df"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "## Lightgbm 2nd Optimization - GA 1st Iteration Ouput as input feature list for 2nd iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_use = LGBMRegressor(random_state=1,n_jobs=-1,n_estimators=200)\n",
    "fsObj = FeatureSelection(columns_list = best_columns_GA_PredictRoomBooking_lightgbm,\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": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combined performance on test and validation data for generation 0 : 11.043706496988683\n",
      "Best combined performance on test and validation data for generation 1 : 10.983557588466322\n",
      "Best combined performance on test and validation data for generation 2 : 10.542139731532457\n",
      "Best combined performance on test and validation data for generation 3 : 10.223690915657908\n",
      "Best combined performance on test and validation data for generation 4 : 10.213804046452145\n",
      "Best combined performance on test and validation data for generation 5 : 10.067656670614108\n",
      "Best combined performance on test and validation data for generation 6 : 9.713605238532903\n",
      "Best combined performance on test and validation data for generation 7 : 9.616279982219314\n",
      "Best combined performance on test and validation data for generation 8 : 9.616279982219314\n",
      "Best combined performance on test and validation data for generation 9 : 9.50068793688332\n",
      "Best combined performance on test and validation data for generation 10 : 9.897968230301741\n",
      "Best combined performance on test and validation data for generation 11 : 9.677842145836504\n",
      "Best combined performance on test and validation data for generation 12 : 9.76567968704816\n",
      "Best combined performance on test and validation data for generation 13 : 10.30779436354425\n",
      "Best combined performance on test and validation data for generation 14 : 9.95047775939091\n",
      "Best combined performance on test and validation data for generation 15 : 9.717658196494094\n",
      "Best combined performance on test and validation data for generation 16 : 10.152419554670033\n",
      "Best combined performance on test and validation data for generation 17 : 9.825157834195977\n",
      "Best combined performance on test and validation data for generation 18 : 9.863199160313318\n",
      "Best combined performance on test and validation data for generation 19 : 9.883675374852986\n",
      "================= Best result: 9.50068793688332 =================\n",
      "================= Execution time in minutes: 307.0 =================\n"
     ]
    }
   ],
   "source": [
    "#### Genetic Algorithm\n",
    "best_columns_GA_PredictRoomBooking_lightgbm_v2 = fsObj.GeneticAlgorithm(generations=20,\n",
    "                                                             population=75,\n",
    "                                                             run_time=1200)\n",
    "\n",
    "with open(r\"D:\\\\Results\\\\ResultPickle\\\\best_columns_GA_PredictRoomBooking_lightgbm_v2.pickle\", \"wb\") as output_file:\n",
    "    pickle.dump(best_columns_GA_PredictRoomBooking_lightgbm_v2, output_file)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Test and Validation Cost: 9.51 Average External Test Cost: 9.63\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_v2,\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": "markdown",
   "metadata": {},
   "source": [
    "## Lightgbm 3rd Optimization - GA 2nd Iteration Ouput as input feature list for 3rd iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_use = LGBMRegressor(random_state=1,n_jobs=-1,n_estimators=200)\n",
    "fsObj = FeatureSelection(columns_list = best_columns_GA_PredictRoomBooking_lightgbm_v2,\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": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combined performance on test and validation data for generation 0 : 9.364880809796805\n",
      "Best combined performance on test and validation data for generation 1 : 9.118092608145279\n",
      "Best combined performance on test and validation data for generation 2 : 8.919841833977179\n",
      "Best combined performance on test and validation data for generation 3 : 8.919841833977179\n",
      "Best combined performance on test and validation data for generation 4 : 8.919841833977179\n",
      "Best combined performance on test and validation data for generation 5 : 8.881556855894916\n",
      "Best combined performance on test and validation data for generation 6 : 8.678303846908179\n",
      "Best combined performance on test and validation data for generation 7 : 8.678303846908179\n",
      "Best combined performance on test and validation data for generation 8 : 8.678303846908179\n",
      "Best combined performance on test and validation data for generation 9 : 8.678303846908179\n",
      "Best combined performance on test and validation data for generation 10 : 8.678303846908179\n",
      "Best combined performance on test and validation data for generation 11 : 8.678303846908179\n",
      "Best combined performance on test and validation data for generation 12 : 8.784561054471977\n",
      "Best combined performance on test and validation data for generation 13 : 8.755677705960514\n",
      "Best combined performance on test and validation data for generation 14 : 8.678303846908179\n",
      "Best combined performance on test and validation data for generation 15 : 8.678303846908179\n",
      "Best combined performance on test and validation data for generation 16 : 8.678303846908179\n",
      "Best combined performance on test and validation data for generation 17 : 8.678303846908179\n",
      "Best combined performance on test and validation data for generation 18 : 8.713643032872927\n",
      "Best combined performance on test and validation data for generation 19 : 8.713643032872927\n",
      "================= Best result: 8.678303846908179 =================\n",
      "================= Execution time in minutes: 196.0 =================\n"
     ]
    }
   ],
   "source": [
    "#### Genetic Algorithm\n",
    "best_columns_GA_PredictRoomBooking_lightgbm_v3 = fsObj.GeneticAlgorithm(generations=20,\n",
    "                                                             population=75,\n",
    "                                                             run_time=1200)\n",
    "\n",
    "with open(r\"D:\\\\Results\\\\ResultPickle\\\\best_columns_GA_PredictRoomBooking_lightgbm_v3.pickle\", \"wb\") as output_file:\n",
    "    pickle.dump(best_columns_GA_PredictRoomBooking_lightgbm_v3, output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Test and Validation Cost: 8.68 Average External Test Cost: 8.79\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_v3,\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": "markdown",
   "metadata": {},
   "source": [
    "## Lightgbm 4th Optimization - GA 3rd Iteration Ouput as input feature list for 4th iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_use = LGBMRegressor(random_state=1,n_jobs=-1,n_estimators=200)\n",
    "fsObj = FeatureSelection(columns_list = best_columns_GA_PredictRoomBooking_lightgbm_v3,\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": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combined performance on test and validation data for generation 0 : 9.596298187862573\n",
      "Best combined performance on test and validation data for generation 1 : 8.646556323826047\n",
      "Best combined performance on test and validation data for generation 2 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 3 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 4 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 5 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 6 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 7 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 8 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 9 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 10 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 11 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 12 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 13 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 14 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 15 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 16 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 17 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 18 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 19 : 8.628023485663864\n",
      "================= Best result: 8.628023485663864 =================\n",
      "================= Execution time in minutes: 157.0 =================\n"
     ]
    }
   ],
   "source": [
    "#### Genetic Algorithm\n",
    "best_columns_GA_PredictRoomBooking_lightgbm_v4 = fsObj.GeneticAlgorithm(generations=20,\n",
    "                                                             population=75,\n",
    "                                                             run_time=1200)\n",
    "\n",
    "with open(r\"D:\\\\Results\\\\ResultPickle\\\\best_columns_GA_PredictRoomBooking_lightgbm_v4.pickle\", \"wb\") as output_file:\n",
    "    pickle.dump(best_columns_GA_PredictRoomBooking_lightgbm_v4, output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Test and Validation Cost: 8.63 Average External Test Cost: 8.79\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_v4,\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": "markdown",
   "metadata": {},
   "source": [
    "## Lightgbm 5th Optimization - GA 4th Iteration Ouput as input feature list for 5th iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_use = LGBMRegressor(random_state=1,n_jobs=-1,n_estimators=200)\n",
    "fsObj = FeatureSelection(columns_list = best_columns_GA_PredictRoomBooking_lightgbm_v4,\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": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combined performance on test and validation data for generation 0 : 10.235025921852868\n",
      "Best combined performance on test and validation data for generation 1 : 10.235025921852868\n",
      "Best combined performance on test and validation data for generation 2 : 9.596298187862573\n",
      "Best combined performance on test and validation data for generation 3 : 8.818994062338936\n",
      "Best combined performance on test and validation data for generation 4 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 5 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 6 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 7 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 8 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 9 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 10 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 11 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 12 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 13 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 14 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 15 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 16 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 17 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 18 : 8.628023485663864\n",
      "Best combined performance on test and validation data for generation 19 : 8.628023485663864\n",
      "================= Best result: 8.628023485663864 =================\n",
      "================= Execution time in minutes: 206.0 =================\n"
     ]
    }
   ],
   "source": [
    "#### Genetic Algorithm\n",
    "best_columns_GA_PredictRoomBooking_lightgbm_v5 = fsObj.GeneticAlgorithm(generations=20,\n",
    "                                                             population=75,\n",
    "                                                             run_time=1200)\n",
    "\n",
    "with open(r\"D:\\\\Results\\\\ResultPickle\\\\best_columns_GA_PredictRoomBooking_lightgbm_v5.pickle\", \"wb\") as output_file:\n",
    "    pickle.dump(best_columns_GA_PredictRoomBooking_lightgbm_v5, output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Test and Validation Cost: 8.63 Average External Test Cost: 8.79\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_v5,\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": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of features at each iteration: 44 20 10 9 9\n"
     ]
    }
   ],
   "source": [
    "print('Number of features at each iteration:',len(best_columns_GA_PredictRoomBooking_lightgbm),\n",
    "      len(best_columns_GA_PredictRoomBooking_lightgbm_v2),\n",
    "      len(best_columns_GA_PredictRoomBooking_lightgbm_v3),\n",
    "      len(best_columns_GA_PredictRoomBooking_lightgbm_v4),\n",
    "      len(best_columns_GA_PredictRoomBooking_lightgbm_v5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final list of features:\n",
      "\n",
      " ['AdjustedLeadTimeCumulativeNumberOfRoomsNet_Quartile_Encoded', 'AdjustedLeadTimeCumulativeRevenueNet_Quartile_Encoded', 'AdjustedLeadTimeCumulativeRevenueSubstract_Quartile_Encoded', 'CumulativeNumberOfRoomsNet_Quartile_Encoded', 'CumulativeNumberOfRoomsSubstract_Quartile_Encoded', 'CumulativeRevenueSubstract_Quartile_Encoded', 'DayOfMonth_Encoded', 'DayOfWeek_Encoded', 'MonthOfYear_Encoded']\n"
     ]
    }
   ],
   "source": [
    "print('Final list of features:\\n\\n',best_columns_GA_PredictRoomBooking_lightgbm_v5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicted Vs Actual - External Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e972fbfdc0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PSM601WGvfbP56BS3HJ2VOmPJwsNUXBr5JjeiNLuSZtvJXRaaz3Nb8cDk00r5OOuy78l0NC96wnPQBBvS1fQUGiGRsvfd2bpBZgr5M4P5xi20vrArtWHD3rIY+W6DfK3xOP2tY1bMZVbNTTwdx5OdMxL+zDeajk34M/19qzm5Ruw3mo7VLU0pNoCzgNHvv8k8MDr3hGUqWxSpwReZXc3AZM7cgQqr/KottZEhkD5y0BTl9QbSsisglqxkAiyPdc6J8k5i+yT4Ydhnz1yk0zYkmAvPqhQrDFuKlqvBV6hHO6J979RddFpoypApNfE7IyCyl93jkv5stImVtnQ/RLuh6Rh9oWFgUy80+rPv4aQkHbS4Nrvq4kkUpsFXqL93ztKXGk+MudwHPvvOx4FoQwUrvuDuiK+n+w7mcQzd9nuyCF5i6LF4Jbm/NX7urUiSvcnc1HRU/IUS9K+gHFcnHzhZOVHGblYOQPDSa1q/nNh5ow7+ube4x8o9QBf6VL/D1gjDjBt81ocX9og14+DfO2dGfP1Vd03C20mrLOvRkZ+X3t67GxYl0AvOlaevxhgKGssTHbMkh7Xzcq+/TPX+2E/4XVGuLx3+voZ7/yBZokO+mvwF+sBXntBnIvHIpf9rT/xBxge+cu30DfwQrH97Eg/yBkuk46vVa/WIsnydtK4vhUi8AGhae6PH/X5Z1fyzbL8vUoM6u66HdjtsWfQHKonkM7bbzzvWJvW5Bl+hbmpK/OG136YmWHCv6qm15Tp0aeJBpz0+C2mgUvxtmnJG6ktNJ+s92/Ichp/zoytC61AVCdZ9PTmh9am9Cc7oPNjZdQ2P9+x4t69CTWbi9d143vImmBohC+qeecn2iE6Ujb2K9/pKtMvbdx5/qelk7fWXqSGoDvmXjnlhn0t2RGQ2MDLUK/uDigUJta+HA/YGLLm24dSwYZEDPauf1WGZVm9Fr3urI36DUeXWKjvPR5hZLZb3fCkMB4vypdrMfmVN8CdZMKVKVeVpmpl3oJjSi+6+xpAnyXx3SW3YZl9qSvxJ9LebD9ddLQdGeTfyAfFwx4LutzPb4F81JxDAdecl9jT2hDUTNWF0X6MiUoXb7m82e2Klpo0rj/q+L87t9LUUJhH7sN9Q0MxXt0Nd13hK77+zYcKeHn/vjNyTNGfS0gEuSXzv+0PvDy+6x+uf486zbf3VlX0NxYQbeDa64JjYvXslacq4cs2eWKmPHGS9Z0W6jrqH2hdYXnZ3fnh5dxTOjfkZOyesuPr02D26k91HzQkOlU/FpxvO0KfrTxuw7QV71ZtYna7Hv7qmab8/epArUk/+mDJwCX3VHf+Bmic3PXnug2Uy1c19rSvVXDYt5LXB+UgkM84/OvK1/cuNJ6rLjDxq5aJjZ8dcpyc/PB3Xa55qTZwU+XhtWXaB2v0J9ILOguBlsoLb4A6b0vb8tSP2/arHH9oX60V37OvlvzqnxXx/KKkan3hufatMw9HbEaWqjF6YEsFLWNRm5sdtnKfb54MayDGleDOKl1zfdAWCY/e09Q3V2Zzmi3Qy38iwcDP7xMnzwnKqvemxb0bOgWCaZsgsd6bhHJCehdlSqW0xC/SSx9rQ4P4yXW0rzA9UaJvKgnPXxN+zx6yeqJMPjD2pVDK5Hnvy3XYa4QH9Kz+yIGrPZkkaOSJ2w+5nbQckXJ5osilAKMnWmSIT/W7/6ZqSUIDpa43HKW/NRxMs1cC7r22VDj9irW1tq2gzWNuWN8rC+Ta2qjDiKID7WleGzJSd43Tqyo8siNlDb6C8m8BDx1dKDlDj7L4Zdz9Zf5Y+zIv9Hf4RpXd8mDi790NfiWZOqNBPrjnI2voScH/bqvgLReA1HSredFdCn/HIpS6llioD0Vi/mETK4d5SXNf77x+0bNCNTcdYWteXGk/UDRaX/VFr6sfvLU1H6tbmI+Iul3/gBb3//nbHcXraPTXjD3SzyYiyxDo2zJ0Y+VpZ7y/WtigjMJbPCm9v7Jl8XO+/y0+6Liztldd0qnDSQj3YvkjXN54Q8t6UidV6OUZ9uP8ZkOma1I4oeW+t9LSbMaFvfy+dkVjQ/2et4anPPll/lh7qSCClUYZZqSs2uqyPovGlMOlf5fqz9eu25fpp65q0xgOSzls8xBC8RMKe6JyprzemnpOsyZ/YjdFqT0+rNyOz+9K3y59YLxSzMLxSt7krCxMHB83q2p4T6N3xvhn5Qv5Cd8/FFn++7mxZn9zmbKgGPN01WW8nmMQ+0lb9WRNaDKQL+GLjSZaXD87fmnZZ89Q5td8r0rdIZo3faTlc324+TK1GTy8aQ99sOlLXWczNFCux+oypyeePGgwS74kfWD5uDqQYx+j9bSv1y7aVaus/8ViMouz1l8lwRK4AZs9VQ+rsfiDjzUtfb6cPfGXaHydVQLB0Bc0b/UX6ZXtyAbIe6RoJksh3Ng2HvEV99QkrZXrDW63L689Kqmw9vt18mL5lIViTbFO9xZ+vf3ZGz6MY7YHn295RUc+1Hu256ZvoMFi23OksyYIA2vdaIszMHlSs1zw1et9XYSnVTYO/WLst9jC2MolkPDt9VXrb2x3MifHDB+eu3+0fmONwuOqpk1s5D71BPXwdRRVhn3rHWyXDMPRi7hLllA+uzhbBXnKP049aeoL1od8xeDRZNKaRfAjnGXf4w/9E7qFWl8z0lezFguWWl+0/wklS3DzwPYycfP2za7qec0/Ub9oTy4t9U9NRcXOF9lw/nWOGT2/WWAheImH7fcXa4y/PdDFCmFH+bcXvvet0VwKz/w4WwTe23WXz9JOWdfqzP/xpmyRVFAcqjNs8owek10O0B1w+09DrEXJBxlxXxlolfRv+0CyPutR7vhEJJQpP56z1iUpmxuZ4TGd44ySZn9BMQ3O03czTW97gyrCpd31VaraSmynOgTg5xYkDks17me1cKQx3+k/X1JSCVvEqjJkSnD+vs3SCfhQ1LURyNndO16vuGv20Nbkcf68leI0eCGE9atJ0Y5g5IR3X59SaeG95R6u9fwqZLHB3+4Fxl9lVMj/9BUHCgkezDK7Ib3/WCp81z3GHjNAd+n/tS/Wyu1a7x0UIilv0XFedvtt8qB7vnujqxktW6msXWA9ORZLslbfozNtS2q4kveCeEJ76K60GS8jRGit1P79hfVSQM0Jd1Kv093R8z1cZkis0kmfdE/Wd5kOVt2xgJ7nKVgQvYdne7oTeA3uxTV60HCthyylXL8WZldmqxgR7k1pm+iO+7I71HYOfyhkOveiZoM6ogcnoN4GBrtMletscVZH5nJ2/9tg/VC+y5CsVFSUDlQs0uvtaV8lfnLmcVlZZOearLObGjWRURUHvEHWrIj0VHhIy2MsoXoVxIAWnZXnPFxogeyXJtBDRdJi5urN1Q9L5FluTvM9ZDTQnd0ikq+dl6ptJtldyqn4ZPDN6Bs6zTlm456TQe2jQiLLrp9YyE3a2ydSkHOlkRPjXQOmpS9X7i/Xj1oPU4ezLDRspWBRrLX4ZetM7pneiK4dhhPxeyTzg7V/X+0WrtREAjsLymO8nPkohdF/k58YPmgV3ULE3+D48I/ktCq8Pesz4v8NAnFWmDG33jpHhTCCf6xA2DGoNsMs9rWv127Zlej5Okt5MM7unq4yXuyMdT1pbbU5s/8OW6EGx19xj1TohRs8ZZ3ig0qMc3dsannsvE7eqG5qO0Y+SHKIebEyl/TMWJqpNiTfmV85KbBbmZEyu6Qt4BT+htjoRht1DMZ92R9lu0MmY3U2HvnL2n9U0EdUjitSal11B3G9dtlpzJ8XvVfZg+yL5+3Wd/u4n10RdPpO/Z2Gefbk406XZn69/dE7XVl9qaQUGcpbmZIePpzNXa1asOYWD/eamo/SDlg3JryCC8OKk62zMnsbuExaH+Q2oKLvn4CX2pBL5yiaLvc9i/EyJHhlf2bQ8geATMsmM8K9M++alq/TtT0SrN5gx/4zmc5cdmVKZJOm/7imJTfgTRSoziH/tguUqLohdhr93ztR7ZQuCXhn439YbJ4QUKX3XQE/0m4gXNCPstSet5qTGgCJ4CctazQI91TVDZpYfNqXFgaBd6yDpIRrLFk/0GSD/0TVD5RWhT+7zc/sa6778yE/1n3VPsqdwKdrtq9ArHvvzALqiTExht+CGeDK346LC9D9BGxmUcD342CgtypIJEbp3XFlxXw8dn2NoPlksLwnd5zmu5Cu36XjwUl6cZ6nC/bfOOXq533lblB/9N8tkVXWK7T2b7P82u33l+l378rCKfv9JCrJBvIZHssHJ/p+KODtz9rS702KXb4Re86R/orlkVJbmZ3GTM9QDHUtCXzAj/jMr2LVPB+paURpUZ6mpKkq4M2/PKJl4gZlExCqCYSgjPY7RJ9reryzN752w0S6lhbnd24x+pofVnUKuD/YdK05nouvqK0j1iPhppv6vfalMo6++ZmlrVs8FixfKzVEm+7up6Sj9vm2JWszMj4pLRKRga5esX6uKbD6eEV12R6GAJMyZWKnzj5qp3LhBrCgX8hQiA/G3mRyrJaodGej2/rqnelDW2ewockHuwNxABuHuTdhAzWq9YGpikzRZYWTZcMRlMzOXWP71KDN9DoQNS6I/gIkkmQbEUDsXLzp2tq49a3FK67B65k6qTjwdQfDDBjscuWKCLjgmqMecDTcvu+5/Q+3YSsaRKybEHN1tZYgjBr9Uh7kftXKCzjtyppZMjz5J5vXnL7O8vo+dMFeOGCf6DRettLyubAtsW5XoBJfx2XXFS9+Vs2ak9Rzy8WXnqB8r9aBMt/HcQYG9847s66H4nm+EnuyapU+eMk/9z6xsPs+S3Z09IYOzDw/vuYn0yK7WHQaVVAMb6bqIGZJWz61WgcUhg0UJPgV2ZPFQGUeUMzovJ/BGYV76erUlFnRI3z7MzXGorCj9+R3Tefymf6tmv/9mVqwGSPKS/27RS5N8OZP9jpE+l8iqnA5D3285JKltR5PItT+V4VNWFSTwxDuVp+MDlQ9t+azRvTlqrfYkj39bCv/NRlcWqqTQei/snk3kROhVksq+KSzI0crZdgfY7fqtQveb05lYtTkvTmAvZ4BGCqRyqc/PdWpMjDQZ0X77WD2ye6T7+tATdC2wEGC1uydYXyHSs9qBl9oXyXE5dcC86pjXtJ4H8FYsnj4y5mE9snxw9fxKxm3Nh9u6vpRqhBYOjzxLnQsir6injRE8oshepgrynFG3nw6p9dZLrJx21va3eUMfxh8wL3wSv0TO5ayQYv0ucOzYz5XpKHUWIngJS45eNUHnHjlD6xdlbljTGRumpmW9Hz9xrtbOtz57ak+OxU+dmtgsmQuT6F125qHTEv5Mj+ChdxsW12r9whp94qS5OmZVXYRlQ1330SVR30snO26uh9iURyqegeqVGLpNe2XjDJupzD5tv9CypGOG83iKC3J09Ko6feaMvhy+VmdSNiXVjSlJ80Qp/bdp8++XTKFi7J5rNi7q/ffVpy3ot63EN5WqeJucO6lSGxYn1ns1eL12HrED9uDOlklxkv/mbjP6cLxxCTTI1i+s0QlrYqdpOXX9FK1fmJ3DxVN11emx845L0iFLQ+/XFx47S2cdFnk4YjJmTajU+oU1lnrFXH1a/PIOBZEmsywtHpzpWiJdKs48dJouOna2LevPwipSL59pZFcarwg/RmGBK6TdeMKaiUmsOLFfIZE6SP81f3ZjaiMeEpGX6+qtj8RMfxDl+6fjdhzc9oumzZ+rDjNvWAyTXjt/bNxl0h1brB0VqHNsPCT5eMBQk0VXPWSzE9dO1pp5Y3XWofZVKmNeriNcqw+K0YBL5en96MpCnXNEcFLeOHm9uq9UcyeNSGg7Hz9pXqJF00GLEmy0OgI3E5/pCPka+bkunXXYdFWVF+iEtfFzXk5MYihhJo3uzqVUUphjucftcGYMi0t/YjWKbH64eeLaSZo+PvGZoevGlCTVIy6b90WqaoKCTzPr4k9QlGlOh8N6pdViUHvcqEHWI8KCgeoJn0g2irMOmx43v19pUa6twbr0MJO6KNRUxR/e2f9+vWLWGFuDuTkuQ2cdNr23J3MsYy2UN5rK0vSP9kinimIreTMHx43hoEW1Wj4rc2la0MdhGCHtxkR6+veXlmt8v5XWjioesPrP6jljQuojiUq8bhd7+dLCnO62n7X15g7AqJpMK7EwN0G6H3A4nYZ+cs1BST3EHqqGQwsWiChGSueE1+U0DJ2VQi9Ju+QuOk4vuMfrN+3W8wYFMyRdevyckNcG8slzSWFuRno0JiO4nJuOjj7D6ZxJlTp2dZ3l9Sb67S88dpaqR0Qe0ndUhF62mVQzsshSMvLE2VPbPP+omaqpKlJxb4UlsV8j647djPQkTHQf2LvPUnl4kcmeyIctHT9g25ozqVJVZfn6aJYGzSJO2JPgsWya6Rk2no1qRxbp3CPItxVPOi+HsYayRtvu7InZ//AklktPmBv1veDvfMSK8UmNPBpIlxw3R6PKC7Ro2sik1/FOyeDvuZtSHSZNl8qeYyk/x6mqsnwdn1TvzYBm9at/ZuPwI1n7HYyQfyf2PUZXhrcZNh0TvR2TvPRcdd2mU3t98TvZHHdA8sdKoiLWWyx+8uhVE2wty1BH8BJJs314oE0ycSu66LjZWp9oL8lERPhSkfa/o7BMd7ceqHp/SWKr715VZWmelsyInkw93YoLctIwQ3D6xWqEXHHqAh0fZ+igZRGetK6YNUZfu2BFxMWt9HwZSF85f/nA5XhLwuq51frKpuVR81MmUzn5V+dU3dB0TKpFG3Isx5YSbFyMnZL5h0jJGBOhMZEo02JPjOKCHN10ySrNmFCRYNsi1Xt+OusM6Vm3HWtNd03p+vOXa02E4W0rQnqfZWcjfSiYVVcRNrvwYx3xhy0PnlEigWOn/+VlwZQqS6kkTjlwSlIjjwbSvMkjdMPFK1OaCd1v8ySBr3usp7MaynquXAX5Lt10ySqNH51Y+ybYv3xz+r0yiK+LKXS8/MaF4W2GORMTG00Yd3Np7MJ6dcPGkAmDoknlWBlIJ66dnOkiDCrZ24pE1hoxIMNjwm8oiV4IE31ylYpsDOPaV6aB/XbJVCV2FgaG/b+e279ikk6DuNIzFATt/ojnepaclC+4J2i3L/Eh37FY/moWDtFMNZBS+XmuazhZX248MeJ7jcVTMvbb+2zr7Zccy/HgtJXApjUnk/MybNMDP6Xa0nQ++ON2g2iy5F437Nl8jv5fe/z8g4lJ84GStp6X3cHzKOXPmXGgjPxAkCpeegiPkn9g8ExXOnvxxRoLGO1hevBC3CBSluQ+TPWsGlluJWUHghG8RMJuvHiV7rz6QBvWFP1CESkY0T94Obzqa4lfVM2cQXpBTOL+sTd/oq6o36gduYFJnRqd2T8cq60idMjm297sHlY1UIZaFcwwsvsb3dFysK6o3zjwG46yW6wkoW82C1XvD88V1ebPzWjCzisaztIOT5LDDgd9otH45e/5yXd7Q3vXZ126hSD9yxbtW/7wqnWaNznbruH992v69/O/OtMzseJgM2ls8nnDmyoTG75pRw+nHWVL9b63PM5S2XueZgd794/PTL2J/r63XG+WLrehNBlkhvwnTP7ac1R89nclBXozjyyLHMD84VUHhq0kk0d0rG1bGdkYfN5b+x4DX8fgihHf1yP0gkVsBC+RMIfDkMuZ3kMnk824TMwoHE8yJcrNye7ZI+1uq/vUlzz6hYIVurf1AHs30E+qMakRVaG98Z7umpLaChPRs+/zMjykItI+TOLASN8Zm9mA0l86Ig+1s/f7GiHnzsAzdEPTMXqyc3jn7EvnEKuwbaVlrYkclXEmxUutIBmR4xoMkxeka89m8y+W+bIlcmq/6q7RyFGJDd+0MgFfm5nfW5BIxRk9rlY3Nh+b0HaHA7uOntfc8WctTocbm4/VqNpxGdl28qLdS6z9GtHS/uS4HKrKol5uKSdhCVqB6R/4dquZRE/FbE03J0lVMfIWp1O04xXREbxEWlxef1aKa0j/hTibL6KJ+tAsD3stN4ncggN5+7v14wfoK5vS80TYZ+ToWXfqeSa/1nic7m9daUOJwvWf0d3KbKh2M1yZzbmV3nt2Jp4y27fNW5sP1587FkTbUEbZ3VNut69C+319gfRvXrrK1vUn4puXrtI3Lkr8SfjXGo9LQ2msS7bt4jcyn3cvPffi0HX+qT3JyTQGKOnlty5bra9dkOYeUgNyScyuh7+DrZbnchqaWlue2IcsVPXi/SorZo3WtWctjr2O7PppLcn+pEfpde1Zi7V6XiZSw9i5L7rXZfHHjLXlcf1m907fMZ1omrMkthBUgfZb+iIWt5LkSXPTxSt10yWx20vF+eH1ja9dsFy3XrY6uY2m6B+dfSPgJtck31s+nZyjbJozYQgheIk0sdDlPcYydjSOSwtzU15HtojWy6+sKFfXNHxEf6k6J8KH7Nl2Om7uHxpVKi3MzboJZfrb6y9TZ5Sk0HYHcKwEm+3+LeKtbzAH+Af/w8wMBF+zpO1VWZpY74j8HPt6vVWW5mt0ReKT5+z1B4ZCu5KckKowxck7rAy37xkW6s/ta8CZOfF7GziqAjNhdjpSn1TIkjScvB4zuWOk1R96LDrKEw8EWDmvyovzVD0iu++HQ80v21boe82H2LKuqrLo16zyYusPJgvzEh8xk/wst0HrMAxNqbFvskSXc9DfgCVJTisX1iw2paYsqGdXltzgs0m2VHqSENzGtXfkhrV19d9zVeUFcXsvRhq1WT2iSGURrpFFH7nRUjlSEZyPPtJ1tLQofhwhnYeQ6+jPyTVxafo2MEgRvETGxDrf7QgMJfu0MRtT1EUayj5/8gitX1Sj4w+ao7OPDJ/ZMpurXE84+4Z0f6HhJHX4s3OI+/qFNVEP1CaF59yzR3K/3F0tB9pbDCQsm3P3WeFxBYInkXpy9/jYCXPkypIhsseurtNpG6Zqso2N7lScdeg0ORNMqZKb49TGQ6ZpVp29kzpFcnvLobqzZb08JX1DGPNy4pe38OhrVHj8F9TqiJdmInN3nfAHLfYMY1+5Yp5ubz5En204Vc/Pv0aTZ8TL6Ti4rwGZ8Ps2a40zK72LzznCevqJ97yVesNbrUc75iSZZqavQKeuj35cHLFiQhLrts7hsL8pd/qG4O8T/7xu6Rfkz9QQTGSna89crE9k+Yzz/f3bPydr09mcvmGqVs4Z3fv3QNc9r/uo1cmkzBh/xeYoGx1/IQv2jo7eGzT0wVJ46dYvrLGlDMkyKmoGNKXQYEHwEslL4/mU6IX4XUdt2Gu5cRrY/jR8gXeM8HKky+SaMrmcDh2ydJyKCyIE/7L4gucz+srbZBbpbV+2TXQQsGrumKjvPWyusXVbqd6gWk37h53nJNmLzLI0HqKDKYQQKR9Ws3/gG3/1FbP167bl+pX7oKjLLJ4+SkUp9hK0y/FrJunQpeMs9TocCOsXJX79dzkNbVhca3sFNdL62sx8verpn//MwiiJ3AKLQ5fsmm08S35QSVUVBdrmrVa7ma+1y2cMSEOip6dUyKbs2GyWXhT3++3rcbp2fuK5Bf/UsSilNDOmDBVGGA7ZI8/GnuED5ZClieVJfLJzhn7TtkyvOgMP0h3ZclEeSDHOryw99QbMlNoyLZiaBfX8sOu3GfSv0Pf2mRV60T0+yQ3F/8Uj3Uue7wp/0BGpPXzI0nFypuGhhRWGYYSlvYq6bJrLYkVHQfQg6Izx5VHf61KOrfN79H/4ttVTrftaM5ciaTAjeAnb/adrcsrrSDR4+W9nX64oK124/9i+MJDA3Gb/MFboT+0LtdObWML1uCJ8qXGj7O/5l6kbTTbc4BLVbsPx81bQzMSTqkN7Ne03Ih1D9lSBzdzwY+ddbwZmaE9rjT75o6o+PxCEestRZ1NZotvvK9ZdrQf2/r3DM1I3NR0dcTbttDMc+mfXdLXJxsCpxd/4NU8g6OBacIx927Zd+nMz2qWvcTH4JgLK/P3A7kByYsvXdd8LRsYYijyQxk5Mb84t6+ljUvtdBnKEaEN+aPDv2a46SdJ+X+qB2j3GqJTXYTevnNrcNUNdxtBJ15S47AhRurtTZLSOXZbhkiQn1QdEyX46XrvzWffEJNccWd2Yvjr/e75A/fvfXfF69kcT/9hrKhi4zjVWfLXxeF3bcGrE93IXWq8HpqMtLEmtsjtNTuiR+WD7Ej3tHsCJWocQgpew1Sfrz9T9bdYS78bMQZVgHcBvBK8r9oeb/AV6rHNu1Pd9juSHMPsNpx7tnKt/9B9qkBdaYfWfcGNvBSNZ8YYKpT9ddPZ7rqtOf++cmfTn7cglFcv3Ww7u/Xf/4aYdacwv5y8IH6L6eOds3dJ0pL7bfKht2+kw85S/7nzb1jdQ9hRN11caj9d/nVaHxiRvl69SHvX12rmt5fDeimw0g314eqjAd9nrL1Pxpp9o1PIjQ991ZmdKiWywxxe994P1XG12HUvRt/eeI3ToVdwtJtF4TdsZkfCKw8ueaO7gnh7vmU7PkLf8Iyre9BM589Obi9N6L71kZrdNzG/a7An4fFgUGoT4edsBur7xBO3xpZ7iwmcM9HExMPeb4k0/GZDtDHU+06HiTT9R9dGX9ntnkNbze5862F/+SDNm7/cH7qsdyg3Z4psee4Yx9wiu8/+k/SD9onWVXvcml+4s3oOZD3xlqi/OgkBZ0L39Q39pxE5EO0etVd7SkyyvMpnJabPB+77wdlh/g/SMTbvB+Ysja5kJHFKNZvSnJU84bZh5LMpZH68a5s4p1c9bV+srjcenXgZJPjlUdOo3Ql80DH2m4XS94rb4JCyJxtxguuiZEf4V7ImOWfpOEkG1n7at1f+1x86n9c/O0EbGf7syM7ObOz/9PR+/13xIzODkTl+V3vRGHyqfKI+cypme2PD6RI7bO5oP1neaD4u8nlROAMPQPn+pjIwMfYu/TatNyS5nFkwAEu06HOF1I+iBzFcbj9fPW1fLKEiwsZ/gT/bboGBFJkYrJzsp1q/blsecbMQoCQzRe9YVmEF4V3H0B3ZWJb17EvxgNt27IuWbjimLhrynzAg9J4MFjxoI/cES//5t3blU9+aFD9F8sH1RwutLxeauGdritiHPWb/jwC+H9vvj5YwN94OWDbq1+fDUy2PRXzrmaXf3xF7J8HY/iDNyEk9jE+1YGzRS6Nrb5LfvYbWpgd+XRmH5gG4vlu2F81P6/M+6NuiB9sXaatYl/Nn/dk3SHc0Hx1+wn1azQP91T1Gyd7/OvNjth32+krBr0n5fij0Ws2Sm9lT1DNXvLE7uut9UYq3NOJS6HGTaIL9TYLDa7ok97GWnI7F8O3b7n3uy9vnj5/TY5Q08OTHzot8E9jhGyVHQf12G/HLox63rkx5GZBTHGZpu0/W/00y859NfOkITc6d6k3zdW63tNgbVgj3V1ddL9sr6jfpFUM/hWO1Qu4egtZWn/6noG95qW4OT8cU+CCPvQ+sH7uvesdrutf40/CV3YtcVO37jdFbDHCNiTwTxdtngnaXwQ3+p/udOPQVJPMHnfyZszj8wqc/9s2u6mszI9w63cuWqni5Jets5UZfXn6WmvOR6dEQW+cRIZ+Xck1ce8/2eIPBeXyBA5HfFTn1QdOZtdhQrQjlSr1Y7u2d3d1andmz6Y4wiafVHDi55zb7yO8qjN+YcNs5suM85St9tPlT/rjox7L0GG/NhDkaveWr0tje0vpzsA49g/+mKXN/4c8cC3dB8bNLrfTFnoXKmr1H+uvOSXsdw80jZqVFHW0TKh93jlbD8xZnRueZj4R00kuB2RE6TYYT9I7b9ebW6vP6spMvRbBbpic7ZSV3Lt3tG63Wvtby7jlL70j+YSfTG/krT8fpU/Zn6a8dc/bptefwPDBC/0bff/9i+sPfff+2I/AC2rSSxvKT/6Jwe8vc9bWt1Zf1GeQoST/f2WMdstRdaq1sl05ZGZAQvkZ0G6BFFeUlqk5x8p/lw3dR0tMz8xHoH9dyDTRnqNC3kCOoXRWnIGS1nReQb5JyJgUpQxEl84m6n7587ywOBj44o5WuZeUL09ZRGDyh9vTF2xTiVYfvRfLnxRD0SdON7tGNOxOW8csp6uMneg3TCmP4B7vjrH12Rem7CnoDePl/ivUJ6PNwe+Ul3Inuo6LSbVXTGLZaXrx4RuVHr6+7SFyl4ESlVRSr5aX1G33DvhtzkgsKpDAEvPOYa+Q6+Kur7fkfkSSRiNYiS9Zhjrfb5ivWcP33BwD+1L9RXI/SI93bPkt4zJHP+5ECvw8XTRoYtG8k73ceAmRf5HPige71+l/05CKdNtz9A+0FOApPyJBChT/ZIbTMCD6/cTqu9i/rK23M+dxVV6+amo/REx6yYn/xm01G6oekYtZdP1j87p+mp7obKK57QUQ4Oiz2FTCU4OVWEJ16xzvFI+bpck1eo8LjPK2+Z9aFzwXp6RXbmJz5BxvdaDtEzXRO1fc6Fco6L3lvX7tQVb3rHyOuwf9K5jnxr1wA7PNed1zLZnpv7fcV6tiD+BA7tRuA8avMnn1/SysztyXAbgVQxjpLo+31EijORd/jD64j7fMX6U/sCy+tIuddZpGEB7AAANbVJREFURMnt1JrZ0XsX39V6oG5qOjrs9VuajtTPWg9IanvBmkpDg9hbXX3373fyLd7Li0fKyE29LvpU3cdUePLXYkyqY61ubsZZdot7rHymoTc8YxJ+Qv22t6o3f21P55VkNE45MmQSvPULo4/Cs/KwIpkjz5RDfjn0UMdCvWDM7nvDYhNoTZTJ0WbVBfZLYcSJHaOvvKctHByIDbTJpEZ/gR7qWBiy/PRx5ZKk4gWHqvC4z1srtPr1cjYC/+eVM6lHQj3lk6TqEYH1rg6a6LUnPVyDr1C3t0Qe9cZs4onLjilDMexdVX+Gvll534Bu8/rzl1kO8L3oHq/5uTv1g5YNujro9S7lRHxi+nICT0STaQQ05o5WtGdNnzx1vjq6vMrPden2loNkyNSVCW9B8joDDYkcl0P3ta7UGcX/DnnfUxHe6+trjcep0NGlkxeXSdsir3ePv1x1Mbds/4W83l/ce8Nq8+fqTx2LdEjBK1GX/1LjiTJl6OMx1hnrV/tD+yIdW/hcyGtPd03W8rztUT9TPaJI37l8jd76v5ek1hgrDzJpbOr5s+5tXaMxzka92z3j+xWnztcHj5RpjLMpbNk2f66KHO6w1x/pnK+/dc5WlbNFa/Ne06r8Ny1uvW8vOkoTa2SOiDKZxZcbT9Sc3F1aVBZ/KMebntF63j1B4137oy7z/SvXRX3P6XJJHq/+2TlNHxTN1+T6zfEL3iv543xn2RLNV2AWaI2aqhuajtE1ZX+0/PmOBJ8Af7XxeJU4OtRTPXw3d5LGe0KP5feMan2l6UStL3un97WH2hco1UGfwedZiz9fH0boEd9QMUu/bVumF9wTdLikJTNG6TuXr1FxQY5ano69/h9cuU6fuMWjkc4WnR8hF+wHvjLd0Xyw5uTu0vwUekff3HSU8g2P9vjKlG949PnyByRJq+fb2SMycT37N9CYq9bM3PeTXle0I3pz3jq90Vwg/7gVitQXONaZcH3TiRrlaNJBzlzt8o1QfWeR5oz06lfvT9BlpY/2LnfOEYGGdpdytduXKxkO/bp9hSTpt+3L4mwlwGs65DL8+p9nip5on6Fxrv1alRv+kOR/XZO0NO+tiOswjOh9Avym1D8TxZjK8ICuYRhyjrZ2rD3SMVcn93vt+y0HB8pelHjPrPd9FfpZ2xptKp0Ys1HlSKDe0uArVIWzPeGyRPOz1tV60T3BUp2xsWTg8rz9tG2tHmhfrCazSEck+Nk7mg/W696xmldl/WHaG95qWX1k9ivPQTokSwYujpy9XK3vP6fdJbNUveUXMZdt9+eo0OEJee36phNVYnQouK/f8+46Pdo5T6dbLMMHvjLVKdALy5Sh8PCgfba4a1Rc6JK8kd9fOWeM5k4eIfNn94a955ErYhtjp8+embsbK2arrPlNtZu5KpG0xTVHB7mfkCS9nz9FV9WfIYdM3VR5f/SVxLjmJcLjLJCzskbS67asL5oftvYN6/5mgp+9tTmQk/tASXe0HKI614e6sOSJqMtv84zW1Jw9Ya+7S0OvzadtmKJHn3nXUhme7JwRfrya0gvu8XrXO0LHFD5vaT09vveptUnNqn3Cmolq3Rr++hWnLlCH26u83Mi9Qa9vPEHljraw1zuUp3yFty+kyPMPnHrQFI0sL+hux1ub/VzqDq7uDX892tXRYzqUY/jjrreyNF/fuXyNivJdav1R4LV3vSP0dNcUve6tVqO/SPt8xapyWmzgScquJDrZg56XGDDBsxnv85eE5P/x9IujW6liFeX3NcAnjbV+4Yr0+WBeZ/gTxOe66nR5/Vl6zRP7ifqUmkA5Os1cPdk7aU+ki09iF6REqpwOw+j9bls8tXo16aElfVt92j1Vl9efHfcTe/1lYUOcejzTNVH1Nsy0GUntyMBT9Amjk+89GKzBX6zG7uFq0W7qOTGSRL/jDa9Y3t+2Sjc3HRVzu8UFOQkNCUuqd62kooK+880jV2/gUpKqygt0Y9Mx+nT9acrLCf2O0Z4c9qxnt68iZF3JVIhSNaa2Rv/smq7y0ti95D7XcIq+1xI9Z2CPvByn8nJiD8l5x1uVUq67KTWJBaE7c0KX3x0l8fdYy7P4xvahv1RvBQ3P35bf1yOr4MjQnp+tds5UbtHIiiI91TVDLWbftq2eG7k5TrmjPIS6qv4M3dB0jKrHBY6pin4B86vrw5vK+VEq7Pv9xXrTO0YtZkHEAGw8eStOS/gziTBl6LHOyD3SE1lLJF1Gvh7tnCefw1pPseBcsx3OUr3hHStndw63djNf26eepW3ean2hoS9sF/taY2jmhPi9ZN73Vei6hpP1QOdyfegv1XPuiRE75vyibbU+33BKxHWYMa4DDf4iNfrtPT/cZngfBLdytN07RpX9r4FG8D8TC2QV5YduJ/jz+bmx+0F8palvlIY/UrLbBPnlCKszRjPQvVqipXKIJ5Ff493utErbPNZ7+7/tr+7dit/GBvFffYHhpi+5rQ/dNFy5KjnicrWNWhA3P2Ck+lC7mac9/nJL2youjH3N+V37cv2+3aZZuSNcLL7aeLx+2LpBO12xH6YmW5dLxgdG0MioOIeCRy51KSelXoZWjenuuRZ9XyR/3EbraZwb5X5txejqUXHbV3t8ZZbaTrGuU1av1Xe3HhhzMtpoCvJcMdsz0UQrs8NhRG1fS9J+f0niacC6N9XkL9DTXYHRKiUFOQmfN19vPFaOkHLHP6Y+23Carmn4SNT3g0/74oKckP3ymqdGT7un9LYnH7Xw+7w16qC4ywx39LyErb66abk+/+PYXV06TZd+375ULqdD+72FEXOOWenJX1VeoI7uf5+6frI6dyRR4Agai+okhfaqmDquXC+8Ff8id9qGqfr7CxZ6rkRZ1Y9bDtSnIr3Rb3+0ucrjb8Mm0YNp4a/fePFKtXd61fZaeA+0n7VFn7jlFfc4zczZrT35dRqhloTLuGFxjapHFMZsoG7pDjzHm8AnmGFIoyoLpM7w9/JznYrykDBiVcOUoWabG67JBqeqIgzbOuuw6RpTWagxlYXyy6Eu5aq8OE9mQ+D9Nn+u3o8zC/bhy8arumGv1N2RsShOxSLWed7oL1C5oyP6AkF25EzVOO9OfZA/SR8/ep52fNCsidWxA0St5sAH2SJxOgx98pR5uuN7B6nFHwg2fOSgKfrV36z2Xo3ugLnxe/V989JVamjtirnMTZesVEu7J+J7rtrQgNd20+IkZAnoNF3KN6J0W5E0e2J6JrvqCZB8/KR5emt3syb36+nsVt/xfd1HAzPTjyjNlxnhevGV85epwe3S1+59Nqmy5M47XF3/+WX4ehuP1ydKH9Ev21bqsIIPklr3QLM+u7R0w0UrtLexQ11uX9h7TWbw8K/o67zytAWaVF0q70/jb6/ZLOyeY7bn4hR+kTJlhATKg0Vq0NmRozAZU2rLFL+/SKgcp0OdEU61sVVF8u7r+/vPHfN1QcnfJUkjywrk775Uz5hQIfXraOQLGlr3wYyPKPnxAsG/i3RXy4E6rOBF1boakl5jZNnRQzGaV12z9e8PS7XXX6rzE/ic0X3DtfPbvWpO1ub6oGt+gsHiLsWuIxQV5EhdUSpaFtROn6WOrb+TXHmSN/Z9LhVfazxO60Y4NbH95ZDXTz9kmmomTtTPHqnQX3eP0PUVv5Uk3dZ8uD5Z+peY60w+NUMCv0HvJpK/Rtl1dVvdXV8ZVVEYqcqdkpubj9FXI7xeVD7C6kCnMFecOl9vf9Ai/TW8x2yP0RUFuvKYBdKj0ZexQyavWA2+QiXSbeSbl65SY6tbhiGVxnm40P8c+EJj4KHhp09fqKryxOrwHf6c7oce4b0+Y/HIJU+Eh4RSYP6OBTE+2/+B8PNdE7Qu7zX9sWNgJ6Ibauh5iaQ5I1RSrARTNndOV6eZq04zR3/tnBc1p2Ivh1O5C8IHdfQktZekHFfyT8/CRPheTouNrdw4PbR6BDfeep54PdIxVy974j+9/kXrKm0rsXcijj25ge1+kBv0FDGJu+HI8gJNGBP/NlZeHJrbanPXdF3XcLKaRsxP6gmg0+HQ3EkjYva+afAXq+TCewZkEpBojVUrR1Fu9/eP9F36v2Y44z9/svrEvCjfFRb8De4BtddCb7HRlQUaFVShiNfrJdLbOTmRf8N/d0Yf/vdBxWJ9ruEUNY2Yq8J8l2bXRQ5m9fwu/pIkE6UnMTzqnTg5NYsLclSYn6MtntreXqszxsf+zeIFhXtYCRJVluaHBeX6qyoriBsM7mP05ogcKIZhaESp/fnyehTkRT+mevTsn9yJoZVSR/d9qqK8JGQ/7+vNuZZaNWyfv1RfaDxFWzzxg8YF3TmoIt2nErrcR1rYiDSwK7LcONd4R9CayorzNLW23HLRIpldV9n73SOJdc9JdNIuf3fqgS0W8soWnfntxFaeqhRauK6a0Fyjr3jG6+6WtZKknLoFva/nRrl+39J0pJ7umqzO6oUR30/GS57xurn5GMvLu4usT+6WLk6n9YfBkVxdf7pkGNrrLwv5jFFYLtfU1VE/V1Ga33swZyqYnoz8lWfEfD/6/gxwjZ6sotO/qbxVoet5ojN27txENfoL1TZidtjrNVWFGlleIBlG6MOWCPalJQ9nZjSWBnIN7ymyngPbEaW+6Or+jZNpG0jSVk+13u8enVLSr+5kOF161xW53VWUnxMzgFyYn6NZceoFTqcj4sPVREYlRdsvwYLvUzu9I/R6jF7ZqR5nwcX5bMOpIT3rragszdeksaWaWF0aNf1ThK2G/DUjQkeVvSnk7Q/fQuQYR7DrGk7WG3EmZ+p/re1Srm5sPtZSfU1KaUDXkEbwEkkrK47dWGw+6iYVn3NH1PdjNwr63izZdJfyloVmdWo+5Esy8kIDpW/0XKxd1hqxmbwoFEboUm+1Qvlf9xT5DXs7TW/Ln6evNR6nN/IjT7xy4MLIw+X9lXURX+8sij0k4KNH9K/QGFq/eraOWjlBdWET12RWvSMQfHrDM0b/ihFAC2b1t/RHuASPHx2oWETKhTYyKDDomLZOrsl9MwReeEx4xVmSXMdcpxeXfMlSefrLX39x7793e8stfaZunPVcTJEqcHVRhv7/sj36RAYf2TBVG1bP0tGr6uJus/OYG+Q5zHpy72CGK0c/aVmnrzUeZ2n5/3RN0cmHzdfnzl6c1Pai2bDY/t6N/fX0lrI0oVg/PT1Iu6I8rU6U0T25SlcWDhZ5oH1xUIoQKWfeESo6/ebevwuP+7yKNt4qwxW6H29sOkZfajxRhmPgqmHXfXSJTlo3SZOjpFnZ4R2pLe6x2u8r1m/awodStjoDwdc2Z/jnDUO9Q7vjqYgzUV5eXuIPI1MZHtzzwC1euSzJK9Z1DSfrR63xh345ClMP8i9bYj04EKvO1fOQy6PI+z5n8nIVnR6aIe4FT532rLtOOXMP632tsjpwbfKOnRey7E5fle5rWy05bHzQ3E/xeXfGfN9dOCok1UAk154V/1r9TNfEhMoVrLoytN76qj+wrn1+aw1vd5SeisVn3qaC9RdE/dzHT5yrnnp1NgUvd3pH6GV3rQqOCM/M/oOWDcqZFj0gKyk8NUIEjpKq3ly0fkeOLq8/W9u8gV5+Hz9xbve+Sd1RKyfo351T9Ku2FXGXjZQ+4TvNh+m+1r56ToXND+T83ROheKOc48kL/y5LDztCT027SkuPjX2+RdIzGekeX+A+M7I88BtPTDDH+6Mdc/TPzmn6YcuG3teOWGE9vcHa+WN7e4NK0lEW6pdW1Ywut7ys1YfVPW5pPkp3REnxdG3DqfpGk7X6a3+PdwTaGMFpQj5y+AJdvTF6ZxqzaIReco+T1wycf+m89tzefJjua12Z9OdNBToRvdCdAqNkeeygbHOchxF2sLVj1hCSfS2BNNu8ebNuvfVWvfnmmxoxYoQ2btyo8847j9mekuBwxBnUkFsoIzf6ye2PUZOO9s71jSdokmuvjikKD5Dc0XKI8gyPbnImlzvmxqajlWt4tSSpT4fruVj7bK8oSLYn8e1+kj8+ynkwPsKMqJJUcvSV2v3HO1Ta8FrI611F1fpS44lakrtDR0dIHl1WFB4QOe6AQCW+zWEkPNQtXQwZanRU6gsNJ6nZLJAph/Jmr9eaudXqeigwRLMhvyZsyITVG7RXTt3Tukbveyt6k887HQ75FejtGz5Qsk/O6rNlOPuOrcrSPEUaZD11XKWmjktuWK2zskave6o1PWd372u/bFuh04r+o/90TdaKCBMQ5U5ZLs/Tv5LZ0SxHabwejhF6OReVyaNAriCrw8aLC3J6j59YGvxFUkFZyGZbuofy7/VXqKea2vOkO9Lp8KKnr8d3wYaLtfeRu/SKp1aRmrwN/kKduiBy4N/X3ZBI5EHEt5sPk1N+fdZiD+9EGb3f29DO6oM1cfcjcYf1RfKz1gO0Nn+r/tU1TclXJftUHvdpebb+Qy/+rTyl9bT48xMa3mTFE52BCn3P2ADDMGQEzbRrOHNkFIX3EnArR+4Is+am05jKQh21si7kNSPoPuyTM2Qyg/P6ff75wlXast8l36gVmt3+3whbsNa1r+caZ6dU7ojO7ntO7wiLoJXF+kZdZRP02O4K/b1zpn7QUw7DSE+jJqhes8dXqtHOZknS5BXrLa8iuM7Vf399q/lIFRpu+ZxOKcqv4ygJr3f5CipD6s/OyhoVnvI1C9d++3w4/1yNq3CFPSCIJLj3W6Tcj1NqyuQuzIk6uco1Daepw8xRvL0erU3Rv6fgb30H6VdNTXF75SUrZ9ZBypmxVs7SfO32B37XRAMIPqf1IFpCazYCD3B/3HqQVsWY2T4Wp8X6otF97ObUzpKC0h8snBZ7osBmo0SVau79e6d3hMa79qvRNVLb24q0OO/t3vcK8ly9D1k/UvSfni2HrO/mpqO0JO+tiBPuNJlFeto9RWfoXxa+UeLeKl+hpj3v66WSNZqZ4Gf/0L5Yl5Y+Znn5gjyXjjzQWg7lwhO/JPn7aruO8ureOvddCvSA86mvB6ZVf4owNDdefl6pb8SKy2lo1Zwx6ngn8PrRK+v0f/96L6Ey9HBNXCLvjmd6/3ZWT7P8WSO/WOpILTWGt7ujRJtppadj+H5+yT2uN5e/q7KvThttxvG+VTl0V+t6fbToSS3KeyfmotvM8VpmvKrWgr6A8QvuCTqq4Hk97IveeaFHk1mop91TdYZCJ5ftLAj0uH/JwsjGh7tnNL/rM+tTigt1mS7l9Ut1FJr0BKkYVsHLF154QRdffLGOOOIIXX755Xr22Wd18803y+fz6cILL8x08YadeMOxftZ6gDx5pbo86LX9/hLtd5co0kAhU0ZCvYT6X5Z68vktTnScWBSPdc5RuaNdr5SuVeoDVPrKNG/yCEuBmnTzFVfJkV+iphFzwoKXUmCYdiqJ4d2mU7lGX4WmZqS9k/38X/sSFRjWcikFJ+FvKaiRc1SdvMZ+fb7hFB2wZErYzO+JHELPu+P/lu96KzXOVS+joFR/65il7d5Rusz6JhJ2/lEztWN3c9jrhy0bp0f+K/27a5rqXB9GDF4aDpeKNn5Lnpf/KtfUyBWOwhO+JN++tyMOe3dNWqaWvfv106cMfb3i16l/mSB/65yl/plXX/KM1y/bVmiHc5J6+h0fvny83t/fpo8cFLunrWv8At1Xcp7a9odWLN8ds17tZr4qR4c+Crmp6SiVOTp0laQXiteosalN28vXKrSPUnTBk+b093jHbG0oeNXSevZ1pwDo33N61ZwxeuWt/Tp+zSQ1/S/6zJfxju8ms0h/7Ijdg8ntDDwQabSQAc9ROkp5y06W/29/i7tsfwVHX6OuVx7Vs3vzNXZx9JnjIzl06biIqVD+2L5QrX6rw53SY+HUKh22bLxu+MVztqzPSkDDa+Tqf10ztMxIvDduYpK5byT+mcJjPyd/8x756nclsT3pg6Wf0F9//WJoKSIUwww6Yf7eOUvHFyaR9zS/VPe0rtFOb5X2+0v07cp7o28wglEVhdoblLa2xcxX8NXk2DVT5HI69MBma8nDrz5tgf723HuaUhs4fwsO/5S6nn1AzrGzZOREDnYtnFplOT/tIx1zdVjBy9oZYfK7/jpGzVVOAnlvv918mJblbo84SVfcbQXVMfOXniT97yeavPLAsOUOXTZOeqPv72s2LtIj/92pkvyckGCbKUdaApeFp3xdvl0vKzeoV2yyw8anreoO1eYEJst41V0bUi+XAvmu39/fptM2TLW83jkTR/Rex4L9rWOWDirYonHTAr2Kv9p4vD5f/kBCZe7PNXamCo68Ws5Rk6QX/h3/A90qDr9Uz/79T1rc+R9Jpp7qnp28ZOoi/W1vU0jw0opdvhHa1W59RnkrcuYeJs/Lj8RdzuMs0L1tazWxtK/nvL97ErXWoOtBmz9Xrn6zK7/uHasfthyki0rC78EVPb1fk6zuO6vqwl6LPPFVejoZdRmB8nscgevWp06drz9s3qEVs8ZIHzYltK684lIdN2ti2ITp+Rsu1fu7duu7v/yvGvxF+mGE6/Y5R8zQrg9DM3C+4q7V7CRnc/9n5zR96C/ROOd+/aFjsa5fMk5/+OfbcT/nKRunF9zjtSB3Z+9rpgy95BmvvAPOlmti4l18XvKM16K8d/Ru3hRFe0zxV3Ol/tY0SauL+1J7NfiLdVXDmd2jIpLLWdtWMl43NB2jPQmkM0q1Q9sXG09WgdGvvElFLwl3RjKsgpff/e53NXPmTN18c2BI19q1a+X1evWDH/xAZ599tvLzM9sYGexedMzWAXatzJSecU/SsUvr7Fpjr0c7rD/h/WP7Qh1T+Lzqc8cq0SS/7Wa+7m1bqynlqQ8NM4MuYJ88JfLQ7oHwvrdcY12NurJ+oz6TGz9vyivuWh1b+Jwe6bAanpGMUYHK7+Ods3VEwUu9rx++zPpwDyv+3p3zKFeBFl3PkIhEtJgFMo1I+eOSv/E5R02S57Un5KyeId/7gaDwHS2HqMzRrq8VVejBDrv6Bke3em51yHAZKZBvaNLYMknRg1o9DIdLufOPjPq+c2SdnCPronzWoY66A9T2j/AG/u/alspp+HVW3BJE5jUj/1b/7pqm0sK+nk2lRbkpnWc+R56WHB0+O+F7vhF6rzse3+ks1r1ta7XQaU9fwD90LNaB+VvkNOJXdv7aMVf7fCU66aTQMhbkuXR59/d+snSqJu5+RE/5Fyh8Tu2Adn9O0j0Zd1cs1Jbte/Vu0Syls4LmGjtDrrEztDaJz0ZriCczo6fdPn6S9WvqUFVeHBRETWASoB7OMVPlHDNV2vYveSS1lYbPCGwm+DAzcvCy+z0FeuomFbyUtQdd0TgcRsh3ubPlIN0U9P4xqwPrthq8nFlXqZlB+d5c4+fLNT72NTORY/bhjoX6a8e8NAxxDTwEivUgKJrft4UOjxyzcK3MBWsiNnRLC3NDph6cNq5c08aVq+u5t+V+75WEty1JHyaQ081ZMbZ3+G0PMyh4GdIDyxn7YUR+XuDeaBhGYOh/BKVFuTo1zsO+/nJcjojHxIMdS/RQx0JdtiFwPn5oId+2Fa7axOt4teNrVHv2RWq56xnJ55EpQ/9zT9YaZ6GkxAJbkRgFqX83V83MkODlQx0LwnrNR9NQOUdvbXlVz2uWvtz92ucbT5UkLes3399Wz1g90zVR/+icoSvK/tz7es/hb7jy9I/O6drqGavwJAApMP2hG7LZv/IP1LstTjWNXa3FCuSv7qkH+XLjTwrz/ZYNmuTaqxZ/viavX6O1C2q1tV/w0nA4ZOaXdeeqjWxtcC9GG77rr9v70hfMnzzC+szcDqfubj2w7wFZN1OGcmclNxP28+6J2tpQo9kjI49ECqzfod2+Ctt+Z5+r77fb7Yuf+9/Oo6vDzI0/n4cVca7Nw9WwyXnpdrv19NNP65BDDgl5/bDDDlNbW5uefTa5yiT61NYGKoNHrwoMq+w/o3G8JxlfazxOtzYfISl9TdlP1p+pzT2zm/crz+kHBxqqwZMDjDrgRH2y/kwdsDJQ6TllvX2TvdSOLFLdmBK1KPCEscPfd5H6RemFutV9asrbmDCmRLUj4wcZ13fntFwflNvyw+LA/njRmK053b0abm4+Wo9PulJeOcNyNwXrmXDEUzxan6w/S0+7o1dqV88ZEzIhkjF6qj7XcIr+0hHaEIo1k3gsM8aXa3RFgabWhlYaev52K0efrD9Lf4jQU2xURUFvr4CDlwRyeS3qHmbUM/xy+azQRlCjvzDq8euzcHt0TVutwuO/oNyFfZNUdZkurTrA3ryJklRSGL8y0zNDem5Radg+7HHC2kAjI96kJlaNHRF6bPXkgfpH18zeYbqZ0JMwPloO2Id8q+U2ndpfnngZ83P7GumjKwP7/NjVdQmto6dhud0zKmTCkA2LQnNkeuQKnJMxhlnOmDtLn6w/U+MPCJ8sTQpcr7/SdGLY61bP08UzxujJrpk65IBZGlmerxnjyy19TgoEAkb1m2nyxLWBa/OCKdZzrqbT294q/bMz+tCwAxcEfp9EZt6OpGcimjMOjhxo7cnxtXJO5DzE1RYm2bNqe274YMT/dE3W4csDZVg9N3YuZKvmTQn0XDpp3WQ92L5IPtOQvzDwux+6NDDpXKQ8Ye9HydvrmrJChcd+TrvH9/VS61lPz8QAJ6yxFjiMNG2RP0s6T5hBBdlnU0DIDj332APm9l2zjl1dJ6+cIQ8tO8zAb+ozQ5suViYJjKS4IEdlxbkqyHNqyfTg4cOB/fSyu1Y/bOlrrD/ZFX58J9pDJ3fBUSF/Wx1RcnX96fp603EaP6o4pJ6WCNPsGTYulS4/vvf1whO/FHH5973leqKz7zv33KMOWtS3/ZPWBe79C6bad909df0UeeVUXXXf7/p81wRt82RuwqWeHPs9w28XTw9NizBzQnnI3//XvkTN/nyZhYHrR8/1JNjew25S0RnfirrNgxf3febvnZEHeve/pn6y/qy+Nk4/K2cHlj1qZV/qm9mTR+kPHUu04cC+IdZ+OeSXI6ye45dDP2tbo5XrIieDyXE59Lv25Ro5J37Oz3hGludr+rhySZKRHzgOjGJrx9i6BeFDmXvqqBGXXz5dD7Qv1YoF4dd4R4Reof0VTVyghzsW6qmumZo1MXBv8nSn2wlOC9STs95KbvZE2XmL6V//7ll/8HGTjA4zN+a1q+c3WhjlWjKt+3hYOiNySpJTDgzUAXuuWe/OC+Ts79/b3+/o+02CO5/YObpvdEXgtz7/qEBZTlo3KaRucMbBU7VmXujTgb8q0H7YW973cJyUhpENm56X7777rjwej+rq6kJenzAhcDLu2LFDq1fHTgwdyXA5riJ9z/49oKtHFMowAg2KE9dOCjvpxo8uln/f/rD1nHzgZB25YrzOu+EJyR+6LcMwIu9jo/9yoeWK9rvcdc2GwHa6lwle7tCl43TIklrtaejLs3fI0nE6eEmtDMPQT66JkQPDCN/mwYtr9dizu8LK1/P39ecHJkS44cctmtz2gjZ3TddHDpoiw5AuOX2VTNPs+1xe4Oa90ztCcxI45r54zpKI23eUj5G/8YPe12fWVYR9v+bCcbqq/gyNGVWukyoK9JNr1nevy9DxB5t9+fH6fS8pkH/xJ9es18P/2anf/n27qsryddMlK0P2fY9Nx8zS+UfP7Ht6a0itZmhg4lOnzFOO5Vnp+gq1YvZoXXTs7N4eByGz5J25SF+467/a9WFb1J6ShfkuLZha1btvzjh4au/3XjJjZNg+u6J+o/wyVNg9HP3ZrjqtD1p1q1mg37ct0Zve0fpqhGMi8F+HHKNDK1p3XLFWuXl5YctHOwdivRb8ev/ZGyMt/+qIg7X/w/+oq+YArQma0OLMQ6ZJTweevq9bMFbHrJoQ+0ZrOCxfLwvzXfrJNevVcmfgye9FV1ygi2KUM5HrcP/9lsh6nM7Y14E3zAm6umGjTssri7kuw+g7b3rKc8cVa3vPj4I8V8ztRPvdXzan6vL6vmNnRfd7Zx42Tb+8+3A5m94L5P3st+1Ixowo1F3XbIhcBkPa6y/TilmjQ85bSaosyddPrlnf+12ilb92VHHvd1w+a1TM79r/rWs2Lgz77qvmjtHKOaNTquzFunfE/Wy/z93aHOh5fESU9X30iBk6+/DpEctrpQw9y3zvU2u6/zb0xJvVquvYor051b33iUXTwq9TwcomTFPb01LR/EN0dfUC3Xz/C1HL0ftbB7323eZDNTtnlw4sLNOOvGla3Bmar+3+ttW6e0pVSBlypixT1//6hmpHupaFFTfo7zGVhb3ru+uhOfpb5xxdn5snwwg8hDxtwxS9+V5TyPok6cbmY3t7k4RuzyFH9VTpw77cvj3riXnvj3AeBs9Z1PNaz/0nx+UIua4let0K9p63QjWuBhkOZ/TrWYzPx9t+vHMhmfMk2mfmTR4Rto9PWDtJx6+ZGMgh+mbgtdtbD9Oa3K162RMI6gTXSaJvNPjeGvrWp06dr4ndAbJI62jx52uLp1a3Nh+uGmeDPn/2Yn313mdjfpfg73pdw8nyyKU7go+ToDzVhiEV5YU3wyKt+wfX9E3A0djqjrlsNPvLZ6tm11/1kHe1Ll45Rc3dA1tcleHBHsMInC+SdFz3NiLd/1bPrdaqOWNsbWQfsWK8Dl8+LmSd97QFUn7c3f+cM+LXYaJJZNnCoz8t96uP68WnAgGHmqrANcj7dpm8O1/S5WuXhazv752z9PfOWbrR6ZJhSHMnjeg9Xvu2H1qAnvd7rg8LplbpJ0sDgYxNN/iVI69W52/rXd5hGNp09Cz5m/ZKkupHzJdZb6ggzxXxu9VVl4T9fiO76/b9y3LQohpNH18edi8/csV4HbZ8vJpfCP0ehhG/fpSIGy9e2bueggPPk/u1Scqdd5il32zauNByx6tLzY1w/el735BjxHj59+8MO3d7XHbS3KA2RuCNf+St1wcNT2l/7Sot6l42P8+Z3P7pNxFemz+vd/tbPdWakbNbnTmlktpDlvv4SXP1zNa9+vere2K3mfsVp6ggUP9sf/Bf8u0JXHxXHLxBuTMT77wTvO54333VnDFaOTt6Ha6v3hf5/SNXTtARK8brvBukh9oX6sKSahlGIGj8lU3LdN2PA3m6d1culmPnc/LKobaqeZIC6a+CH3hauV9WlOSpoaUrYn3lhotXdrfhDa2eG7g+PvDUDsmULjhmllZ1P0w+98gZvZ/drvG6umGjTsytiFkODKPgZUtLYABHcXFoL7SiokBDrrW1Newz8VRU2JuDLxvlHnWp3HvfUVVVhCfbUxao+dm/KG/sVHW9v02VMxaqMMJyPZnzXC6n3JKqygukDumwFRP0yH/e0aKZYzRyZKlcTkNen6mqqhKtWVijXz36hhbNHBOy7XlTqvTSm/s0aXylyorzetddVVWiSWPL9Nb7TaquLlNejlO+6rKwgd4jR5ZqSm2Z3tzVpJoxZcqPUHEsKAoEyWZPGhH5eweZP7VKL27rK48U6M3W0u7R0jnVeuzZXVo1f2zvetpKq+Rr3qeq0ZVydA9HWLRwun75aPf6ZoyOuE1n7hRd+9ujVDNpkg6MUyYrRlzyHZketxx50YdELJ5VrT/96x0dsKAm5n6oXXGgdmx9Qh+MP0wb+y23aOYY/fbv27V2Ya1GjixVVXmB9jV2xFyfxxvoHTBhTIkKauapY8dLGjF+vHIT+N4r543VC9v2afmcsWHbGl1ZqD317Ro5slRrF9bqvr+Gju+YNbFSW3bUS1Lc37+/nsmZ2sx8fabhNK1aNKl3HesW1urJ53fpya5ZmjquXFVVJSrIc6mjy6txo4sjbut7zpOU3/yurhpTKWe/mblHjyoNec2sWKA9ry1R6ZIj9MH9X4lZ/uDXNywdp8f/965mTx0VcfnFC6foxw926sp5E1VVVaIVc8boP698oPFjy3onCIp2LvWouOKnMpwuOXITS83RM+xu5MjYvYTi/U7B2TvH11SEVAhyc5xye3xat3hcwr+3JK2aX6Mtbzdo6Zyxqiwv1ANPbteCGWMirqsgz6mOLp+qqkq0fO5YPf3aXq2YG3qMFuW74pYj+P3iksA5PKW2TFPHVejP/3474nJjFh+onz60RVKgN9nWdxo0vqZChfmJTxrTe37Nre7dxgELanT/X1/XolmB63lRvkttnV6NX7pKbzz/sB7pnKeLSx4PK1c0M+sq9drb9RpfU9FbqczLdarL7Yt7PCRq0fRReu71vZo8YURf/q4ERfpOuS5H2Os9x1uk5X/ctlwdRWP15Sj7p+c4rtl0i/IiLFO5/Ejd+rscHb52vfVjuWqGfFfeK0deofKCHtxFOg5XzB2r597Yp2Vzx0p7Aq85xs7QgzvH6Mzqci2YPlrqN49PpPWYh5wh34rDtfO7gVzjtWPKVFVV0vv9RlSVhl0rZnYHKVbNq4743YLvwZJk5ASOmSUzQ++pT3jm6eSjl6kswjrmTvNJD72m9XGuBavnj9U/X3xfMyaNlMvpkNfn713elRfozTx/alXva+PGlEoNgeN35MhSfbbxWBn5xfpRAtebwuLA/pg+vkKv72zQT3SCfvzJA+QsCu8NP3FsqXa836zqMWX6i7tWc3J3qWrcOOV0SY2SmvwFmj6+IuJ3PGjJOD3yn3c0Z1rkukjlp++TDEMOC5Pj9CgvzlNja1dS11dJ6hg5TnnVkzWrbb5+9b++hl2060Dw9T74Wjy7X+fkKXUjIs5WXV6SLwWlMX7bO0pve0fpgrpAr6rFMyLfK3vqDnXVpYHj2SyUy2mELRtcb+25D/Rc7/rfDyJ91+LSwEQQPfUIq2bNm66rnjpTx6+bHHK+Ba+jp37U81p5cV7Sv1s6VFWVaMyIQn2wP1DG0pM+pfrH79W/XpjW+37cdVioh4Z/aLo0dbqmv/tU4L5U233vHHmgtPTAkEXLinNVO6pEr761X+NrK60P1+1WdNrn1fLSExo1Zap6Z0mXQ79uX6l9M07S8WvGS9/+jw5a0n2dqipR5Sd+JF+DKX17s9Yvrk36NztgQY227WrS0jl919m5k6v08vbALEeLZwde3zNylgo+DNQnqsaOidmWSF2JVHNa0p+OVV+wsp9GXHCT/J3tvdfa/Fxn3M8tWDRDd/3Bp6vnTUrqt+hpJ0vSqGMuU9tT9+nZ4nV6+9+P6YnOWbqse513tRyoMc4mXbZ0sfT0U5o3pUoVJfl68vldmjl5pJw5Lv371T1aGXRdqSrLDylTtPKZ531dpqdL/q4OOUsqkwpK53e3p2dNrEyp3rZuUa3lz69fXKsnnt2lWVNG9n63nPzA/Wru5CotmDNen3sqMMrzlhk1mrJlv958t1E1Y/rupZH2SfA184ctB+nA1eP0f39/UwuitNv7O2jJOD36352aOy3y/WPdolr95vFtWjhztPRK9HJAMsxEE/oMUs8995xOP/103X333Vq1qm8SCa/Xq9mzZ+vKK69MeNKehoY2+XzZMi/ywDN9Xvn2vS3nqMkyO5rkKCyPuFzzD8+RJDmqJsi/7x255h2hjtnHqaIkT/ubOnu70nd0eeXx+lValCu/aYa818Pr86upza0R3RXOrhf/LH/zhypYc7Y8Xr9aOzzdiX0l7wdvqv3Br0qScucfIc+OZ1Ry+s3yeP1qaXdHrLT22N/cqbKiXLn6BYz6618eSepy+9Tp9qqsOE8fNnaoqiy/96JvdrXJ31ov54i+4SB+09S+xg45DCNww4pWpqZOlRXHL5Od+pc/mn2NHaoszY84BPLDxg6NKMuXwzDU5fGpo8ur8uLICf17NLR0qbjAJZfplb/xfTlHTkyo3KZpal9TZ8Syuz0+tXeXoWc5j9evgjyX/KapsqJcdbp9chhKOLDT0u6W02GordMrQ1JFaZ6c3U9NfX6/Glq65DAMlRTmKsflUJfbp9317aqpKlSOKzy3l8frU2uHt/eYlgL70zRNjaqInuTf/drf5X1/qwo3XBzyemuHJ+x7+f2m6ls6w9I89Oi/L31+vxpb3Crr2NV7fpVedI/VXZSQnmtHtPW3tnvkcMT/nVof+Jr8e7bJ+5HvqbI88NCpvrlTXp9flaX5amzpUmX3MZqo4P1jSqpv6ox6Hne5fer0+FRWlBvxGG1qcys/x6m83Mh53to7PfKbCmsM1Td3qqQwV06HofrmThXmu+T3S8VBaQFM09T7+9tVlO9ScUGOmttiXwOtfufea1v3az3X7ODvuq+pQwW5TvnuDdxjrRwvXp8/rIydbq+6PH6VFdmbByjSdTyWlna3utw+Fea7ZBiGPD6/SgtDy9Tc5lZujiNsdtNOt1duT+A+119DS5eK8l3KjTKbvPuVx+Tbv1MF6yJnNIt13bNqf1OnHA5DBXnOsLIHr7/lznMlSQXn/6T3fur3+dT00K3Krxglz5bAxA55594V/Xj++13yvv6Uijd+S47iSvn2vSPfvreVOyPyxEr7mzpVXpLbe02VAteA/c2dEYcO979nxju/pNj3sh491/KqsoKIv2f/+kPXi39W139+JaN4hEo23qKm1i7l57mUF+V3jqbnPG/r9KggxueD60KNzR0qUKfySgNBv/3vvCl/QbnKKyvDet5L3feD5ujXsGR0eXzq7PKGBJeT0XOvKsrP6a0rRtJz3/AuP1eVC0KPpX1NHSorylVzm0cjoozmaHvoZvl2vSrn9LXqXHiG3F6f8nKcqizNj1kP85um3ni3UdNqy+VwGGpucyvH5ehN7RBSPleuSs+/M+ScinTutrS75XKGr6PnWIj0G8YSXB/zbPuXjJJRco3pS+sTXD9K9ji1m8fr15a36zVpbKlKCnPl9vjU1hlaL0rk3mC1HhpJvHtFzz5zdv/+yd5j+2to6VKn26vRFYVyOAzta+pQZUn4dWpfY0fSdRkp8j3E6/NrT0OHutw+TRrbPdmfp1P739muyrG1chamntc/HaLVD5va3HIYks9vJnwMNLW5lRfhvt5fqvfi9sd/KO+b/5Z/7FyVH3Nl7zp3fdimsqLc3mtfc5tbTW1ujRtV3HvfMQz13p/6l6Oxtav33tHR5ZXX51dJYXrzKtY3d6rUQns6ksbWLrV3ejVmRKHlYzpamyb4vry3oV2GYWhkeUFITKC1wyNDob0wO7q8vXW8nnuL66M/Vn6eM2Z9P2K5Ytxbg+Me8do+Q5XT6bDUMXDYBC+3bdumo48+WrfffntI3svGxkYtX75cX/ziF3XGGWcktM6GhjZ5vcM3eGlVy53nSOoLXubMO0L5K8Ins7Cb6elS6y8+qbxlpySdZBhAZL49fQ8HSi68Jy3b6Pznz+XbvVVFJ381pfWYfr/k7ZJhIfk60sM0TbX98tPKmbxcectOznRxkCLTGxi6akTogWe6O9T6iyuUt+Ijyp15YPR1mKbkbu/NJzdUuV/qDl4WVap4Y/Qcd7BH13N/kGfL31R0xi0yHIkH3rwfvKGOP92kwhO+IOcIeycKlCTT2xVIo+JMvNc7gKHPt+9ttT/wVRUe/ZnApHL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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#train on training data\n",
    "#predict on test data\n",
    "result_df = get_metrics_df(model=model_use,\n",
    "                           data_dict=data_dict,\n",
    "                           dependent_variable=dependent_variable,\n",
    "                           features=best_columns_GA_PredictRoomBooking_lightgbm_v5)\n",
    "\n",
    "#plot actual vs predicted\n",
    "result_df.plot()\n",
    "plt.xticks([])\n",
    "plt.yticks(fontsize=12)\n",
    "plt.ylabel(dependent_variable[0], fontsize=18)\n",
    "plt.legend(bbox_to_anchor=(0.5, 1.1),loc=\"upper center\", fontsize=18,ncol=2)\n",
    "# plt.title('Actual Vs. Predicted for external test data')"
   ]
  },
  {
   "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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