Chapter 8 - Coupon Recommendation.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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_metrics(model,data_dict,problem_type,dependent_variable,features,cost_funct):\n",
    "    '''\n",
    "    problem_type = 'regression' or classification\n",
    "    '''\n",
    "    \n",
    "    test_tree_cost = {}\n",
    "    \n",
    "    external_tree_cost = []\n",
    "    external_tree_precision = []\n",
    "    external_tree_recall = []\n",
    "    \n",
    "    data_external_test = data_dict['data_external_test']\n",
    "    data_validation_test = data_dict['data_validation_test']\n",
    "    \n",
    "    #for each training data\n",
    "    for data_fold_index in data_dict['fold_dict'].keys():\n",
    "        fold_training = data_dict['fold_dict'][data_fold_index]['fold_training']\n",
    "        fold_test = data_dict['fold_dict'][data_fold_index]['fold_test']\n",
    "        \n",
    "        #based on regression/classification\n",
    "        if problem_type == 'regression':\n",
    "            cost_function = cost_funct\n",
    "        else:\n",
    "            cost_function = f1_score\n",
    "        \n",
    "        Y_train = fold_training[dependent_variable]\n",
    "        Y_test = fold_test[dependent_variable]\n",
    "        Y_validation = data_validation_test[dependent_variable]\n",
    "        Y_external = data_external_test[dependent_variable]\n",
    "        \n",
    "        model.fit(fold_training[features],Y_train)\n",
    "                \n",
    "        Y_test_tree = model.predict(fold_test[features])\n",
    "        Y_validation_tree = model.predict(data_validation_test[features])\n",
    "        Y_external_tree = model.predict(data_external_test[features])\n",
    "        \n",
    "        place_holder = np.mean([cost_funct(Y_test,Y_test_tree),cost_funct(Y_validation,Y_validation_tree)])\n",
    "        \n",
    "        #predict for test and calculate cost function\n",
    "        test_tree_cost['CV Sample-'+str(data_fold_index)] = place_holder\n",
    "\n",
    "        #predict for external test and calculate cost function\n",
    "        external_tree_cost.append(cost_funct(Y_external,Y_external_tree))\n",
    "        \n",
    "        if problem_type != 'regression':\n",
    "            external_tree_precision.append(precision_score(Y_external,Y_external_tree))\n",
    "            external_tree_recall.append(recall_score(Y_external,Y_external_tree))\n",
    "            \n",
    "    #return cost\n",
    "    if problem_type != 'regression':\n",
    "        #print('External Test precision: ' + str(round(np.mean(external_tree_precision),2)) + ', recall: ' + str(round(np.mean(external_tree_recall),2)))\n",
    "        print('External Test recall: ' + str(round(np.mean(external_tree_recall),2)))\n",
    "    return test_tree_cost,external_tree_cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_plot(metric,title,test_tree_cost,external_tree_cost):\n",
    "    sns.set(rc={'figure.figsize':(16,8)})\n",
    "    sns.set_context('paper', font_scale = 1.3)\n",
    "    if metric=='RMSE':\n",
    "        fig, ax = plt.subplots(1, 2)\n",
    "    else:\n",
    "        fig, ax = plt.subplots(1, 2, sharey=True)\n",
    "        \n",
    "    validation_name = [' External_Test','External_Test ',' External_Test ',' External_Test  ','  External_Test']\n",
    "\n",
    "    ax[0].bar(list(test_tree_cost.keys()),list(test_tree_cost.values()))\n",
    "    ax[0].set_title(metric + ' for test data and validation data')\n",
    "\n",
    "    ax[1].bar(validation_name,external_tree_cost)\n",
    "    ax[1].set_title(metric + ' for external test data')\n",
    "\n",
    "    #plt.title('')\n",
    "    \n",
    "def get_data_for_FS(data_dict,dependent_variable,features_all):\n",
    "    \n",
    "    data_dict_for_SA = {}\n",
    "\n",
    "#     x_validation = data_dict['data_external_test'][features_all]\n",
    "#     y_validation = data_dict['data_external_test'][dependent_variable]\n",
    "    \n",
    "    x_validation = data_dict['data_validation_test'][features_all]\n",
    "    y_validation = data_dict['data_validation_test'][dependent_variable]\n",
    "\n",
    "    #for each training data\n",
    "    for data_fold_index in data_dict['fold_dict'].keys():\n",
    "        \n",
    "        for feature in features_all:\n",
    "            if feature not in data_dict['fold_dict'][data_fold_index]['fold_training'].columns:\n",
    "                print(feature,'not present')\n",
    "\n",
    "        data_dict_for_SA[data_fold_index]={'x_train':data_dict['fold_dict'][data_fold_index]['fold_training'][features_all],\n",
    "                                           'y_train':data_dict['fold_dict'][data_fold_index]['fold_training'][dependent_variable],\n",
    "                                           'x_test':data_dict['fold_dict'][data_fold_index]['fold_test'][features_all],\n",
    "                                           'y_test':data_dict['fold_dict'][data_fold_index]['fold_test'][dependent_variable]}\n",
    "    \n",
    "#     for feature in features_all:\n",
    "#         if '_Encoded' not in feature:\n",
    "            \n",
    "    \n",
    "    return data_dict_for_SA,x_validation,y_validation,features_all\n",
    "\n",
    "def last_preprocessing(data_dict):\n",
    "    \n",
    "    columns = ['LeadTime_Quartile_countEncoded','LeadTime_Quartile_percentEncoded','LeadTime_Quartile_countRankEncoded','LeadTime_Quartile_MeanEncoded','ADR_Quartile_countEncoded','ADR_Quartile_percentEncoded','ADR_Quartile_countRankEncoded','ADR_Quartile_MeanEncoded']\n",
    "\n",
    "    for feature in 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",
    "\n",
    "    return data_dict\n",
    "\n",
    "def last_preprocessing_linear(data_dict):\n",
    "    \n",
    "    columns = ['LeadTime_Quartile_countEncoded','LeadTime_Quartile_percentEncoded','LeadTime_Quartile_countRankEncoded','LeadTime_Quartile_MeanEncoded','ADR_Quartile_countEncoded','ADR_Quartile_percentEncoded','ADR_Quartile_countRankEncoded','ADR_Quartile_MeanEncoded']\n",
    "    for feature in 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",
    "\n",
    "    return data_dict\n",
    "\n",
    "\n",
    "def cost_funct(y_true,y_pred):\n",
    "    return precision_score(y_true,y_pred)\n",
    "#     return f1_score(y_true,y_pred)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": [
    "## Coupon Recommendation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#'HotelCancellations', 'CouponRecommendation', 'PredictRoomBooking','CarSales'\n",
    "problem = 'CouponRecommendation'\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)\n",
    "\n",
    "#common_list = anova_fs(linear_features_all,data_dict,dependent_variable)"
   ]
  },
  {
   "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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_use = LGBMClassifier(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 = 'increase',\n",
    "                         average = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combined performance on test and validation data for iteration 0: 0.7006358118686336\n",
      "Best combined performance on test and validation data for iteration 1: 0.7022145974428009\n",
      "Best combined performance on test and validation data for iteration 2: 0.7044411791055148\n",
      "Best combined performance on test and validation data for iteration 3: 0.7114434079058697\n",
      "Best combined performance on test and validation data for iteration 4: 0.7114434079058697\n",
      "Best combined performance on test and validation data for iteration 5: 0.7114434079058697\n",
      "Best combined performance on test and validation data for iteration 6: 0.7114436671529354\n",
      "Best combined performance on test and validation data for iteration 7: 0.7114436671529354\n",
      "Best combined performance on test and validation data for iteration 8: 0.7114436671529354\n",
      "Best combined performance on test and validation data for iteration 9: 0.7114436671529354\n",
      "Best combined performance on test and validation data for iteration 10: 0.7114436671529354\n",
      "Best combined performance on test and validation data for iteration 11: 0.7114436671529354\n",
      "Best combined performance on test and validation data for iteration 12: 0.7114436671529354\n",
      "Best combined performance on test and validation data for iteration 13: 0.7122159927153519\n",
      "Best combined performance on test and validation data for iteration 14: 0.7344549065914497\n",
      "Best combined performance on test and validation data for iteration 15: 0.7386665752697248\n",
      "Best combined performance on test and validation data for iteration 16: 0.7424530699181349\n",
      "Best combined performance on test and validation data for iteration 17: 0.7428962344680649\n",
      "Best combined performance on test and validation data for iteration 18: 0.7433264495185069\n",
      "Best combined performance on test and validation data for iteration 19: 0.7433264495185069\n",
      "Best combined performance on test and validation data for iteration 20: 0.7433264495185069\n",
      "Best combined performance on test and validation data for iteration 21: 0.7433264495185069\n",
      "Best combined performance on test and validation data for iteration 22: 0.7433264495185069\n",
      "Best combined performance on test and validation data for iteration 23: 0.7433264495185069\n",
      "Best combined performance on test and validation data for iteration 24: 0.7433264495185069\n",
      "================= Best result: 0.7433264495185069 =================\n",
      "================= Execution time in minutes: 48.0 =================\n"
     ]
    }
   ],
   "source": [
    "#### Particle Swarm Optimization\n",
    "best_columns_PSO_CouponRecommendation_lightgbm = fsObj.ParticleSwarmOptimization(iterations = 25,                                                                   \n",
    "                                                                       swarmSize = 75,\n",
    "                                                                       run_time=1200)\n",
    "\n",
    "with open(r\"D:\\\\Results\\\\ResultPickle\\\\best_columns_PSO_CouponRecommendation_lightgbm.pickle\", \"wb\") as output_file:\n",
    "    pickle.dump(best_columns_PSO_CouponRecommendation_lightgbm, output_file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combined performance on test and validation data for iteration 0: 0.6982250525745932\n",
      "Best combined performance on test and validation data for iteration 1: 0.7056923971597648\n",
      "Best combined performance on test and validation data for iteration 2: 0.7072372356055165\n",
      "Best combined performance on test and validation data for iteration 3: 0.7076795449119786\n",
      "Best combined performance on test and validation data for iteration 4: 0.7076795449119786\n",
      "Best combined performance on test and validation data for iteration 5: 0.7076795449119786\n",
      "Best combined performance on test and validation data for iteration 6: 0.7076795449119786\n",
      "Best combined performance on test and validation data for iteration 7: 0.7076795449119786\n",
      "Best combined performance on test and validation data for iteration 8: 0.7076795449119786\n",
      "Best combined performance on test and validation data for iteration 9: 0.7076795449119786\n",
      "Best combined performance on test and validation data for iteration 10: 0.7076795449119786\n",
      "Best combined performance on test and validation data for iteration 11: 0.7076795449119786\n",
      "Best combined performance on test and validation data for iteration 12: 0.7076795449119786\n",
      "Best combined performance on test and validation data for iteration 13: 0.7076795449119786\n",
      "Best combined performance on test and validation data for iteration 14: 0.7076795449119786\n",
      "Best combined performance on test and validation data for iteration 15: 0.7076795449119786\n",
      "Best combined performance on test and validation data for iteration 16: 0.7076795449119786\n",
      "Best combined performance on test and validation data for iteration 17: 0.7076795449119786\n",
      "Best combined performance on test and validation data for iteration 18: 0.7076795449119786\n",
      "Best combined performance on test and validation data for iteration 19: 0.7076795449119786\n",
      "Best combined performance on test and validation data for iteration 20: 0.7076795449119786\n",
      "Best combined performance on test and validation data for iteration 21: 0.7076795449119786\n",
      "Best combined performance on test and validation data for iteration 22: 0.7076795449119786\n",
      "Best combined performance on test and validation data for iteration 23: 0.7076795449119786\n",
      "Best combined performance on test and validation data for iteration 24: 0.7076795449119786\n",
      "================= Best result: 0.7076795449119786 =================\n",
      "================= Execution time in minutes: 55.0 =================\n"
     ]
    }
   ],
   "source": [
    "#### Ant Colony Optimization\n",
    "best_columns_ACO_CouponRecommendation_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_CouponRecommendation_lightgbm.pickle\", \"wb\") as output_file:\n",
    "    pickle.dump(best_columns_ACO_CouponRecommendation_lightgbm, output_file)\n"
   ]
  },
  {
   "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: 0.6850294320610384\n",
      "Best combined performance on test and validation data for iteration 1: 0.6865482095527399\n",
      "Best combined performance on test and validation data for iteration 2: 0.6865482095527399\n",
      "Best combined performance on test and validation data for iteration 3: 0.6886874200676658\n",
      "Best combined performance on test and validation data for iteration 4: 0.6932167806715575\n",
      "Best combined performance on test and validation data for iteration 5: 0.6932167806715575\n",
      "Best combined performance on test and validation data for iteration 6: 0.697172464606141\n",
      "Best combined performance on test and validation data for iteration 7: 0.6990318412550924\n",
      "Best combined performance on test and validation data for iteration 8: 0.6990318412550924\n",
      "Best combined performance on test and validation data for iteration 9: 0.718802433829079\n",
      "Best combined performance on test and validation data for iteration 10: 0.7254792202137356\n",
      "Best combined performance on test and validation data for iteration 11: 0.729665731648333\n",
      "Best combined performance on test and validation data for iteration 12: 0.729665731648333\n",
      "Best combined performance on test and validation data for iteration 13: 0.7312365372233796\n",
      "Best combined performance on test and validation data for iteration 14: 0.7321730243357193\n",
      "Best combined performance on test and validation data for iteration 15: 0.7445156331160874\n",
      "Best combined performance on test and validation data for iteration 16: 0.750586628220499\n",
      "Best combined performance on test and validation data for iteration 17: 0.750586628220499\n",
      "Best combined performance on test and validation data for iteration 18: 0.750586628220499\n",
      "Best combined performance on test and validation data for iteration 19: 0.7509812961421859\n",
      "Best combined performance on test and validation data for iteration 20: 0.7519183737374933\n",
      "Best combined performance on test and validation data for iteration 21: 0.7519183737374933\n",
      "Best combined performance on test and validation data for iteration 22: 0.7543173496395246\n",
      "Best combined performance on test and validation data for iteration 23: 0.7543779567659099\n",
      "Best combined performance on test and validation data for iteration 24: 0.7544389926770595\n",
      "Best combined performance on test and validation data for iteration 25: 0.7549691956701212\n",
      "Best combined performance on test and validation data for iteration 26: 0.7551446643780805\n",
      "Best combined performance on test and validation data for iteration 27: 0.7551446643780805\n",
      "Best combined performance on test and validation data for iteration 28: 0.7551446643780805\n",
      "Best combined performance on test and validation data for iteration 29: 0.7557799809499527\n",
      "Best combined performance on test and validation data for iteration 30: 0.7558484233788583\n",
      "Best combined performance on test and validation data for iteration 31: 0.7558501180788137\n",
      "Best combined performance on test and validation data for iteration 32: 0.755944011665644\n",
      "Best combined performance on test and validation data for iteration 33: 0.755944011665644\n",
      "Best combined performance on test and validation data for iteration 34: 0.7596251387650703\n",
      "================= Best result: 0.7596251387650703 =================\n",
      "================= Execution time in minutes: 44.0 =================\n"
     ]
    }
   ],
   "source": [
    "#### Simulated Annealing\n",
    "best_columns_SA_CouponRecommendation_lightgbm = fsObj.SimulatedAnnealing(temperature = 1500,\n",
    "                                                               iterations = 35,\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_CouponRecommendation_lightgbm.pickle\", \"wb\") as output_file:\n",
    "    pickle.dump(best_columns_SA_CouponRecommendation_lightgbm, output_file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combined performance on test and validation data for generation 0 : 0.731354061565839\n",
      "Best combined performance on test and validation data for generation 1 : 0.7323325525794722\n",
      "Best combined performance on test and validation data for generation 2 : 0.7293010011266717\n",
      "Best combined performance on test and validation data for generation 3 : 0.7243331464880364\n",
      "Best combined performance on test and validation data for generation 4 : 0.7265422968036674\n",
      "Best combined performance on test and validation data for generation 5 : 0.7319741445147908\n",
      "Best combined performance on test and validation data for generation 6 : 0.732876370323985\n",
      "Best combined performance on test and validation data for generation 7 : 0.7356335668826178\n",
      "Best combined performance on test and validation data for generation 8 : 0.7361468090631462\n",
      "Best combined performance on test and validation data for generation 9 : 0.7329073770798741\n",
      "Best combined performance on test and validation data for generation 10 : 0.7348031922725193\n",
      "Best combined performance on test and validation data for generation 11 : 0.732240672747152\n",
      "Best combined performance on test and validation data for generation 12 : 0.735556682803475\n",
      "Best combined performance on test and validation data for generation 13 : 0.7359499762325928\n",
      "Best combined performance on test and validation data for generation 14 : 0.7370840991385287\n",
      "Best combined performance on test and validation data for generation 15 : 0.7340246104858974\n",
      "Best combined performance on test and validation data for generation 16 : 0.7395648625774094\n",
      "Best combined performance on test and validation data for generation 17 : 0.734177119798076\n",
      "Best combined performance on test and validation data for generation 18 : 0.7346843705941861\n",
      "Best combined performance on test and validation data for generation 19 : 0.7350603431612109\n",
      "================= Best result: 0.7395648625774094 =================\n",
      "================= Execution time in minutes: 110.0 =================\n"
     ]
    }
   ],
   "source": [
    "#### Genetic Algorithm\n",
    "best_columns_GA_CouponRecommendation_lightgbm = fsObj.GeneticAlgorithm(generations=20,\n",
    "                                                             population=75,\n",
    "                                                             run_time=1200)\n",
    "\n",
    "with open(r\"D:\\\\Results\\\\ResultPickle\\\\best_columns_GA_CouponRecommendation_lightgbm.pickle\", \"wb\") as output_file:\n",
    "    pickle.dump(best_columns_GA_CouponRecommendation_lightgbm, output_file)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "External Test recall: 0.81\n",
      "Average Test and Validation Cost: 0.74 Average External Test Cost: 0.75\n",
      "Coupon Recommendation 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 = 'classification',\n",
    "                                                  dependent_variable=dependent_variable,\n",
    "                                                  features=best_columns_PSO_CouponRecommendation_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 = 'Precision'\n",
    "title = 'Coupon Recommendation 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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "External Test recall: 0.81\n",
      "Average Test and Validation Cost: 0.71 Average External Test Cost: 0.75\n",
      "Coupon Recommendation 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 = 'classification',\n",
    "                                                  dependent_variable=dependent_variable,\n",
    "                                                  features=best_columns_ACO_CouponRecommendation_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 = 'Precision'\n",
    "title = 'Coupon Recommendation 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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "External Test recall: 0.82\n",
      "Average Test and Validation Cost: 0.76 Average External Test Cost: 0.75\n",
      "Coupon Recommendation 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 = 'classification',\n",
    "                                                  dependent_variable=dependent_variable,\n",
    "                                                  features=best_columns_SA_CouponRecommendation_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 = 'Precision'\n",
    "title = 'Coupon Recommendation 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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "External Test recall: 0.81\n",
      "Average Test and Validation Cost: 0.74 Average External Test Cost: 0.75\n",
      "Coupon Recommendation 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 = 'classification',\n",
    "                                                  dependent_variable=dependent_variable,\n",
    "                                                  features=best_columns_GA_CouponRecommendation_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 = 'Precision'\n",
    "title = 'Coupon Recommendation dataset - Genetic Algorithm'\n",
    "print(title)\n",
    "make_plot(metric=metric,\n",
    "          title=title,\n",
    "          test_tree_cost=test_tree_cost,\n",
    "          external_tree_cost=external_tree_cost)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model_use = LGBMRegressor(random_state=1,n_jobs=-1,n_estimators=100)\n",
    "model_use = XGBClassifier(n_estimators=200,random_state=1,n_jobs=-1,tree_method = 'gpu_hist')\n",
    "# model_use = RandomForestRegressor(random_state=1,n_jobs=-1,n_estimators=200)\n",
    "\n",
    "fsObj = FeatureSelection(columns_list = tree_features_all,\n",
    "                         data_dict = data_dict_for_tree,\n",
    "                         x_validation_dataframe = x_validation_tree,\n",
    "                         y_validation_dataframe = y_validation_tree,\n",
    "                         model = model_use,\n",
    "                         cost_function = cost_funct,\n",
    "                         cost_function_improvement = 'increase',\n",
    "                         average = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combined performance on test and validation data for iteration 0: 0.7362948958627057\n",
      "Best combined performance on test and validation data for iteration 1: 0.7362948958627057\n",
      "Best combined performance on test and validation data for iteration 2: 0.7497382092203332\n",
      "Best combined performance on test and validation data for iteration 3: 0.7532911174575516\n",
      "Best combined performance on test and validation data for iteration 4: 0.7550580307908683\n",
      "Best combined performance on test and validation data for iteration 5: 0.756768221282444\n",
      "Best combined performance on test and validation data for iteration 6: 0.756768221282444\n",
      "Best combined performance on test and validation data for iteration 7: 0.756768221282444\n",
      "Best combined performance on test and validation data for iteration 8: 0.756768221282444\n",
      "Best combined performance on test and validation data for iteration 9: 0.756768221282444\n",
      "Best combined performance on test and validation data for iteration 10: 0.756768221282444\n",
      "Best combined performance on test and validation data for iteration 11: 0.756768221282444\n",
      "Best combined performance on test and validation data for iteration 12: 0.756768221282444\n",
      "Best combined performance on test and validation data for iteration 13: 0.756768221282444\n",
      "Best combined performance on test and validation data for iteration 14: 0.7576614669281122\n",
      "Best combined performance on test and validation data for iteration 15: 0.76075608476314\n",
      "Best combined performance on test and validation data for iteration 16: 0.76075608476314\n",
      "Best combined performance on test and validation data for iteration 17: 0.76075608476314\n",
      "Best combined performance on test and validation data for iteration 18: 0.76075608476314\n",
      "Best combined performance on test and validation data for iteration 19: 0.76075608476314\n",
      "Best combined performance on test and validation data for iteration 20: 0.76075608476314\n",
      "Best combined performance on test and validation data for iteration 21: 0.76075608476314\n",
      "Best combined performance on test and validation data for iteration 22: 0.76075608476314\n",
      "Best combined performance on test and validation data for iteration 23: 0.76075608476314\n",
      "Best combined performance on test and validation data for iteration 24: 0.76075608476314\n",
      "================= Best result: 0.76075608476314 =================\n",
      "================= Execution time in minutes: 218.0 =================\n"
     ]
    }
   ],
   "source": [
    "#### Particle Swarm Optimization\n",
    "best_columns_PSO_CouponRecommendation_xgboost = fsObj.ParticleSwarmOptimization(iterations = 25,                                                                   \n",
    "                                                                       swarmSize = 75,\n",
    "                                                                       run_time=1200)\n",
    "\n",
    "with open(r\"D:\\\\Results\\\\ResultPickle\\\\best_columns_PSO_CouponRecommendation_xgboost.pickle\", \"wb\") as output_file:\n",
    "    pickle.dump(best_columns_PSO_CouponRecommendation_xgboost, output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combined performance on test and validation data for iteration 0: 0.7187748946637977\n",
      "Best combined performance on test and validation data for iteration 1: 0.725937221053848\n",
      "Best combined performance on test and validation data for iteration 2: 0.7268075746093646\n",
      "Best combined performance on test and validation data for iteration 3: 0.7314115965945571\n",
      "Best combined performance on test and validation data for iteration 4: 0.7347900766853397\n",
      "Best combined performance on test and validation data for iteration 5: 0.7352973786014767\n",
      "Best combined performance on test and validation data for iteration 6: 0.7352973786014767\n",
      "Best combined performance on test and validation data for iteration 7: 0.7352973786014767\n",
      "Best combined performance on test and validation data for iteration 8: 0.7352973786014767\n",
      "Best combined performance on test and validation data for iteration 9: 0.7352973786014767\n",
      "Best combined performance on test and validation data for iteration 10: 0.7352973786014767\n",
      "Best combined performance on test and validation data for iteration 11: 0.7352973786014767\n",
      "Best combined performance on test and validation data for iteration 12: 0.7352973786014767\n",
      "Best combined performance on test and validation data for iteration 13: 0.7352973786014767\n",
      "Best combined performance on test and validation data for iteration 14: 0.7352973786014767\n",
      "Best combined performance on test and validation data for iteration 15: 0.7352973786014767\n",
      "Best combined performance on test and validation data for iteration 16: 0.7352973786014767\n",
      "Best combined performance on test and validation data for iteration 17: 0.7352973786014767\n",
      "Best combined performance on test and validation data for iteration 18: 0.7352973786014767\n",
      "Best combined performance on test and validation data for iteration 19: 0.7352973786014767\n",
      "Best combined performance on test and validation data for iteration 20: 0.7352973786014767\n",
      "Best combined performance on test and validation data for iteration 21: 0.7352973786014767\n",
      "Best combined performance on test and validation data for iteration 22: 0.7352973786014767\n",
      "Best combined performance on test and validation data for iteration 23: 0.7352973786014767\n",
      "Best combined performance on test and validation data for iteration 24: 0.7352973786014767\n",
      "================= Best result: 0.7352973786014767 =================\n",
      "================= Execution time in minutes: 236.0 =================\n"
     ]
    }
   ],
   "source": [
    "#### Ant Colony Optimization\n",
    "best_columns_ACO_CouponRecommendation_xgboost = fsObj.AntColonyOptimization(iterations = 25,\n",
    "                                                                N_ants = 75,\n",
    "                                                                run_time=1200,\n",
    "                                                                evaporation_rate=0.8,\n",
    "                                                                Q=0.2)\n",
    "\n",
    "with open(r\"D:\\\\Results\\\\ResultPickle\\\\best_columns_ACO_CouponRecommendation_xgboost.pickle\", \"wb\") as output_file:\n",
    "    pickle.dump(best_columns_ACO_CouponRecommendation_xgboost, output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combined performance on test and validation data for iteration 0: 0.6946076018290951\n",
      "Best combined performance on test and validation data for iteration 1: 0.6953180489805726\n",
      "Best combined performance on test and validation data for iteration 2: 0.6999428259374281\n",
      "Best combined performance on test and validation data for iteration 3: 0.7117363007111861\n",
      "Best combined performance on test and validation data for iteration 4: 0.7127818814377189\n",
      "Best combined performance on test and validation data for iteration 5: 0.7127818814377189\n",
      "Best combined performance on test and validation data for iteration 6: 0.7200488169525305\n",
      "Best combined performance on test and validation data for iteration 7: 0.7314599793262843\n",
      "Best combined performance on test and validation data for iteration 8: 0.7314599793262843\n",
      "Best combined performance on test and validation data for iteration 9: 0.7327115533982257\n",
      "Best combined performance on test and validation data for iteration 10: 0.7331605376763315\n",
      "Best combined performance on test and validation data for iteration 11: 0.7370142133006071\n",
      "Best combined performance on test and validation data for iteration 12: 0.7392535742862941\n",
      "Best combined performance on test and validation data for iteration 13: 0.7455366672172246\n",
      "Best combined performance on test and validation data for iteration 14: 0.7455366672172246\n",
      "Best combined performance on test and validation data for iteration 15: 0.7461985801128779\n",
      "Best combined performance on test and validation data for iteration 16: 0.7461985801128779\n",
      "Best combined performance on test and validation data for iteration 17: 0.7476342301143963\n",
      "Best combined performance on test and validation data for iteration 18: 0.751649109663558\n",
      "Best combined performance on test and validation data for iteration 19: 0.7523733625303806\n",
      "Best combined performance on test and validation data for iteration 20: 0.7538159587850493\n",
      "Best combined performance on test and validation data for iteration 21: 0.7559572553609847\n",
      "Best combined performance on test and validation data for iteration 22: 0.7632584304966052\n",
      "Best combined performance on test and validation data for iteration 23: 0.7712936970635665\n",
      "Best combined performance on test and validation data for iteration 24: 0.7712936970635665\n",
      "Best combined performance on test and validation data for iteration 25: 0.7727400380859611\n",
      "Best combined performance on test and validation data for iteration 26: 0.7727400380859611\n",
      "Best combined performance on test and validation data for iteration 27: 0.7727400380859611\n",
      "Best combined performance on test and validation data for iteration 28: 0.7741132871682302\n",
      "Best combined performance on test and validation data for iteration 29: 0.7745030495216507\n",
      "Best combined performance on test and validation data for iteration 30: 0.7745030495216507\n",
      "Best combined performance on test and validation data for iteration 31: 0.7753927907984237\n",
      "Best combined performance on test and validation data for iteration 32: 0.7767624132298371\n",
      "Best combined performance on test and validation data for iteration 33: 0.7767624132298371\n",
      "Best combined performance on test and validation data for iteration 34: 0.7768568826965697\n",
      "================= Best result: 0.7768568826965697 =================\n",
      "================= Execution time in minutes: 284.0 =================\n"
     ]
    }
   ],
   "source": [
    "#### Simulated Annealing\n",
    "best_columns_SA_CouponRecommendation_xgboost = fsObj.SimulatedAnnealing(temperature = 1500,\n",
    "                                                               iterations = 35,\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_CouponRecommendation_xgboost.pickle\", \"wb\") as output_file:\n",
    "    pickle.dump(best_columns_SA_CouponRecommendation_xgboost, output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combined performance on test and validation data for generation 0 : 0.7419680655431494\n",
      "Best combined performance on test and validation data for generation 1 : 0.7311723690460725\n",
      "Best combined performance on test and validation data for generation 2 : 0.7270934491767339\n",
      "Best combined performance on test and validation data for generation 3 : 0.7272898788569537\n",
      "Best combined performance on test and validation data for generation 4 : 0.7276694674015769\n",
      "Best combined performance on test and validation data for generation 5 : 0.7259376879835002\n",
      "Best combined performance on test and validation data for generation 6 : 0.7455016159876806\n",
      "Best combined performance on test and validation data for generation 7 : 0.7456633505349142\n",
      "Best combined performance on test and validation data for generation 8 : 0.7423828911484722\n",
      "Best combined performance on test and validation data for generation 9 : 0.7412492979643244\n",
      "Best combined performance on test and validation data for generation 10 : 0.7426349013155201\n",
      "Best combined performance on test and validation data for generation 11 : 0.7391731049769976\n",
      "Best combined performance on test and validation data for generation 12 : 0.740102890697432\n",
      "Best combined performance on test and validation data for generation 13 : 0.7498834153800745\n",
      "Best combined performance on test and validation data for generation 14 : 0.7426783213371764\n",
      "Best combined performance on test and validation data for generation 15 : 0.7410865045301324\n",
      "Best combined performance on test and validation data for generation 16 : 0.7455401390653684\n",
      "Best combined performance on test and validation data for generation 17 : 0.7444659876007776\n",
      "Best combined performance on test and validation data for generation 18 : 0.7442839544013907\n",
      "Best combined performance on test and validation data for generation 19 : 0.7427246496494575\n",
      "================= Best result: 0.7498834153800745 =================\n",
      "================= Execution time in minutes: 627.0 =================\n"
     ]
    }
   ],
   "source": [
    "#### Genetic Algorithm\n",
    "best_columns_GA_CouponRecommendation_xgboost = fsObj.GeneticAlgorithm(generations=20,\n",
    "                                                             population=75,\n",
    "                                                             run_time=1200)\n",
    "\n",
    "with open(r\"D:\\\\Results\\\\ResultPickle\\\\best_columns_GA_CouponRecommendation_xgboost.pickle\", \"wb\") as output_file:\n",
    "    pickle.dump(best_columns_GA_CouponRecommendation_xgboost, output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "External Test recall: 0.75\n",
      "Average Test and Validation Cost: 0.76 Average External Test Cost: 0.76\n",
      "Coupon Recommendation 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 = 'classification',\n",
    "                                                  dependent_variable=dependent_variable,\n",
    "                                                  features=best_columns_PSO_CouponRecommendation_xgboost,\n",
    "                                                  cost_funct=cost_funct)\n",
    "print('Average Test and Validation Cost:',round(np.mean(list(test_tree_cost.values())),2),'Average External Test Cost:',round(np.mean(external_tree_cost),2))\n",
    "metric = 'Precision'\n",
    "title = 'Coupon Recommendation dataset - Particle Swarm Optimization'\n",
    "print(title)\n",
    "make_plot(metric=metric,\n",
    "          title=title,\n",
    "          test_tree_cost=test_tree_cost,\n",
    "          external_tree_cost=external_tree_cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "External Test recall: 0.72\n",
      "Average Test and Validation Cost: 0.74 Average External Test Cost: 0.77\n",
      "Coupon Recommendation 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 = 'classification',\n",
    "                                                  dependent_variable=dependent_variable,\n",
    "                                                  features=best_columns_ACO_CouponRecommendation_xgboost,\n",
    "                                                  cost_funct=cost_funct)\n",
    "\n",
    "print('Average Test and Validation Cost:',round(np.mean(list(test_tree_cost.values())),2),'Average External Test Cost:',round(np.mean(external_tree_cost),2))\n",
    "metric = 'Precision'\n",
    "title = 'Coupon Recommendation dataset - Ant Colony Optimization'\n",
    "print(title)\n",
    "make_plot(metric=metric,\n",
    "          title=title,\n",
    "          test_tree_cost=test_tree_cost,\n",
    "          external_tree_cost=external_tree_cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "External Test recall: 0.59\n",
      "Average Test and Validation Cost: 0.78 Average External Test Cost: 0.79\n",
      "Coupon Recommendation dataset - Simulated Annealing\n"
     ]
    },
    {
     "data": {
      "image/png": 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444/jyy+/jKOOOiqqVPnvVQFq1KgRe++9d1x//fXx2Wef5S4W3bJly8x0G2+8cUREuU5h/PHHH+P111+Pww8/PNZZZ53MYwcccED06dMn3nrrrdw1XAoLC6NatfK/JZRlG3fu3DkKCwtj0KBB8fbbb0e7du1il112Wak70r700kvRunXrWHfddXNtm266aRQXF2dO6bjqqqsiImLGjBkxceLEmDJlSrz11lsREbnTQUqz3377xX777Rdz586Njz76KCZPnhwffvhhfPTRR8udr0aNGnHzzTdHxKLTehavc/GpEz+fd/vtt8/8vfHGG0dKKX744YdYuHBhvPHGG3HkkUdmarLddtut8ILiNWrUiH322ScefPDBuOiii6JGjRrx4IMP5l5rERHt27eP9u3bx4IFC2LixIkxefLk+Pjjj3On8cydOzdq1aq13PW89tprMW/evOjatWumfa+99srcMXjbbbeNkSNHRkopPv3005g8eXJMnDgxnnrqqVK3y/LWN2fOnNh3330z7fXq1YtOnTrFI488kjvVPaL07Rux7H1ozpw5Zdrm5a3zknr27Bk9e/aMOXPm5OZ7//33Y9q0aVG/fv2ybAYAWC5j50XWlLHzs88+GxERu+22W2Z7duzYMapUqRJPP/10tG/fPiIiTjrppHjmmWfijDPOiJo1a8b9998fNWvWXOayn3vuuVi4cOFSy27WrFlsuOGG8fTTT8ehhx6aa2/UqFGpyyltzPT9999HxKqNe1Zk4sSJMXXq1DjwwANj4cKFsXDhwohYdDOftm3bxvjx4yMiok2bNlG1atU4+OCDo2vXrtGuXbto27ZtnHvuueVa3y8xdl3Z7x+wJhMaskZba621YuTIkbm/q1evHvXq1YuNNtqo1Ol//gE/ffr0mDx5cqmDp4hFd02rV69eRES5vrTvscceMWjQoLj99ttjxIgRceutt0atWrXiwAMPjHPOOSdq1KiRmX5xmLXhhhsutazFbTNnzsy1rb322plpFg+CFn+YlsX3338fKaUyr/Pn266syrKNCwoKYvjw4XHjjTfGE088kbsuTJMmTaJPnz7RqlWrMq/vu+++i/XXX3+p9g033DATGr733nvxpz/9KV5++eWoXr16NGjQILbbbruIiGVecyZi0Qf6X/7yl7j//vvjxx9/jI022igaN25cpu3z4osvxuWXXx7vvvtu1KxZM7beeusoLCwsdZ3Lq/GMGTMipbTM57kiBx10UIwYMSKeeuqp2H333ePvf/977LbbblG3bt1cXwYNGhQjRoyIGTNmxPrrrx+NGjXK9Wl522exxdt68f6z2G9+85ulpr399tvjhhtuiK+++irq1q0bRUVF5VpXxKLBV8Sy96F58+bFjz/+mGsr7z5Unm1enjov6fvvv49LL700Hn/88Zg/f3789re/jWbNmsVaa61V5u0AAMtj7LzImjJ2nj59ekQsugZiab744ovc/1etWjUOOOCAeO2112LrrbeOhg0blmnZy7qG35LLXl5/S9t2S45LVnbcsyKL+z9w4MDMNRqX9OOPP0aLFi1i+PDhMWzYsBg1alTcfvvtUbNmzdhjjz3ioosuijp16pRpfb/E2HVlv3/AmkxoyBqtSpUq0axZs5Wef/Gb/GWXXVbq47/97W/j22+/jYj/fnAtady4cbkPxZ/r0qVLdOnSJebMmRP//ve/Y9SoUTFixIjYZJNNoqSkJDPteuutFxERX3/99VLLWXzzi59/gK2qOnXqRJUqVSp9nWXZxhGLBpZ9+vSJPn36xOTJk2P8+PFx4403xsknnxzjx49farC4LPXq1YtvvvlmqfbFdYyImDVrVvTs2TM23njjuP/++6OoqCiqV68eP/zwQ9x9993LXf5ll10Wo0ePjr59+8auu+6aG4iceuqpuYuHl2bKlClxwgknRKtWreKRRx6JrbbaKqpUqRIffPBBqRcPX9FzrFKlyjKf54rq1qhRo2jUqFE8/PDDsdFGG8WkSZPi4osvzj1+8803x6BBg6JPnz6xzz775IKyv/zlL/Hqq6+WqY+L5/l5H5esQ0TEP/7xj+jbt2/06tUrjjzyyNyXlttvvz2efvrpMq0rInJHli7r9VyjRo2oXbt2Jjguj7Ju81Wp85lnnhlvvfVW7sLjiwfvBx10UPz0008r1W8AWJKx88qrjLFz3bp1o6CgIO64446oXr36Uo8veebMl19+GX/961+jcePG8fbbb8ctt9wSxx9//HKXHRExaNCg3JGVS/p5GLgyKnJ8+3OLn/uJJ54Yu+22W6nTLP5+0Lp162jdunXMmzcv3njjjXjsscdi5MiRUbt27cwYd3kqe+y6Kt8/YE3m7sn8qrVp0yamTJkSv/nNb6JZs2a5f5MmTYoBAwbEjBkzYquttooNNtggnnjiicy87777bpxwwgm5w++X1KdPnzjkkEMipRRrrbVWtGvXLvr37x9Vq1aNzz77bKnpGzZsGBtttFE8/PDDmV88582bF4888kg0bNiw1F+5VsU666wTLVq0iCeeeCJ++OGHzGMPPvhg1KlTZ5m/Ii/Pkqd+RJRtG7/33nux8847xz//+c+IiNhiiy2iR48ecfDBB8d3332XO3Xk58suTfv27ePFF1/MfOBPmzYtc+ezjz/+OKZNmxaHH354NG3aNDdIW3xawZI1+Pk6X3755WjatGnsv//+ucBw5syZ8eqrry731+r//Oc/8eOPP0ZJSUlss802ueWWts4VqVGjRrRq1SrGjBmTCZMmTJiw3LtKL+mggw6K8ePHx6hRo2LjjTeOdu3aZZ7jxhtvHEcffXRuADVv3rx45plnIqJsv4S2bNky1l577XjkkUcy7WPHjs38/dJLL0WVKlXi1FNPzRzlsHi7LF5X1apVV7i+tdZaK/7+979n2r/77rsYN25ctG7deoV9Xp6ybvPy1Lm019bOO+8cnTp1ygWGU6dOjQkTJpTr9QEAlcXYuWLHzm3atImUUnz33XeZ7VmrVq246qqrcnePTinFeeedFxGLftw97LDDYsCAAfH222/nllXaGDxi0RGFSy57s802i7/+9a/x8ssvl3sb/FxFjm9/PtbbaqutYsMNN4yPP/440/9mzZrF7bffHvfff39UrVo1Bg4cGJ07d465c+dG9erVo1WrVnHhhRfGJptsknvtrGgcGVH5Y9fyfP+AfOJIQ37VevbsGQ899FAcffTRcfzxx8emm24ab775ZgwaNCgaN24cDRo0iCpVqsSZZ54Zffr0iTPPPDP222+/mDVrVlx33XXxu9/9Lnbfffelfkndaaed4v7774+zzz479ttvvygoKIj77rsvIiJ+//vfL9WPKlWqxLnnnhtnnXVW9OrVKw4//PBIKcXw4cPj008/jcGDB1fK8//jH/8YxxxzTBx55JFRUlIS66yzTtx7773x8ssvx8UXX1zmo/uWVLdu3fjoo4/ixRdfjObNm5dpG6eUYt11142+ffvGd999Fw0aNIiJEyfGHXfcETvttFPuV9ufL7u0X0hPPvnkGDNmTBxzzDHRu3fvKCgoiCFDhmQ+iLfaaquoW7duDB8+PNZbb72oXbt2vPzyyzF06NAoKCjInMb683UWFxfHgw8+GMOGDYsmTZrE1KlT49Zbb41p06ZFRMSCBQtKHZgsHhwMGDAg5s6dG9WqVYtx48bFHXfcERGRWWdZnHXWWXHEEUdEz549o6SkJGbNmhXXXnttmWu2zz77xBVXXBH33Xdf9OrVKzPQLC4ujqeffjquueaaaN++fXzzzTcxYsSI+OCDDyJi0fWQVnTK0TrrrBOnn356XH755XHhhRfGHnvsERMmTMhd92bJdd15553Rt2/f2HvvvWPWrFlx33335QLKxYPyxb+WP/fcc7Hlllsudd2dunXrxsknnxxXX311nHXWWbHvvvvGzJkz44YbboiffvopzjjjjDJtl+UpyzYvT53r1q0bb7/9drz88sux/fbbR3FxcYwdOzbuvffe2HLLLeOjjz6Km266KebPn1/u1wcAVAZj54odO++yyy6x0047xbnnnhvHH398NGvWLD7//PMYOHBgzJkzJ3dU6G233RbPPfdcXHnllbHBBhvE2WefHU8//XT88Y9/jNGjR8daa62VOzLviSeeiHbt2sU222wThx56aFx55ZXxzTffRJs2beK7776LG2+8MSZPnhx//OMfV3l7VOT4trSx3jnnnBNnn312nHXWWbH33ntH1apV4957741//vOfcemll0ZERLt27WLIkCFx0kknxRFHHBFrrbVWPPHEE/HZZ5/lrom5onFkROWPXcvz/QPyidCQX7X69evHPffcE9ddd11cffXVMWPGjNhoo42iR48ecfLJJ+eClIMOOihq164dN910U/Tu3TvWXXfd2HnnnePMM8+MddZZZ6mBz5577hlz586Nv/3tb3HaaafFwoULo1GjRnHDDTcs8/p8e+21V9SuXTtuvPHGOOuss6JatWpRXFwcI0aMKNc1/cqjVatWcfvtt8eAAQPiwgsvjJRSbLvttnH99dcv8zSAFTnuuOPisssui+OOOy6GDRsWrVq1KtM2vuWWW+Laa6+NQYMGxfTp02P99dePvffeO0477bTlLvvnNt9887jzzjvjiiuuiPPPPz/WWmutOPTQQ2PrrbeOCRMmRERE7dq144Ybbogrr7wyd52cBg0axGWXXRYPP/xwvPLKK5FSioKCgqXWed5550VKKW6++eaYPXt2bLzxxtGpU6c45phjok+fPvHKK6/kftld0hZbbBHXXXddDBgwIE499dSoVatWbLPNNnHTTTdF//79y/1rb3FxcQwfPjyuueaaOOOMM2K99daLkpKSePTRR8s0/3rrrRe77rprPProo3HggQdmHuvVq1fMnDkzRo8eHcOGDYsNN9ww2rZtGyeccEKccMIJ8fLLL8fmm2++wnUcc8wxUatWrRg2bFg8+OCD0aBBg7j66qujZ8+euWn233//+PLLL+Puu++OBx98MOrXrx/FxcVxxx13RPfu3ePll1+OHXfcMbbZZpvYe++944EHHohXXnkl/vGPfyy1vuOPPz422mijuO2226J3796xzjrrxA477BBXX311FBUVlWm7LE9Ztnl56nzSSSfF4MGDo1evXvHwww/HX/7yl7jsssviqquuirlz58Zvf/vbOOSQQ6JKlSpx7bXXxpQpU8q03QGgshg7V+zYefGP20OGDMlt1/XWWy/atGkTp556amyyySYxYcKEuPrqq6Nz5865G77VqlUr+vXrFz179oy//OUvcckll0TXrl3jgQceiH79+sWBBx4Yffv2jUsvvTS23nrruPfee2Po0KFRu3btKC4ujssuu2ylzij6uYoc35Y21tt3331j3XXXjZtuuinOOOOMqFKlSmyzzTZx7bXX5sLk7bffPoYMGRI33nhjnHPOOfHTTz/F1ltvHVdccUXuJn9lGUdGVP7YtazfPyCfFCRX5AQAAAAAluCahgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyqq3uDqyM77//MRYsWLi6u7FGqFevVnz77ezV3Q3KQK3yh1rlB3XKH2r1X1WrVom6ddde3d2gAhiP/pd9PH+oVf5Qq/yhVvlBnbLKOibNy9BwwYKFMX++QVpBwaL/LliwMFJavX1h+dQqf6hVflCn/KFW/FoZjy5iH88fapU/1Cp/qFV+UKeV5/RkAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAxkqFhnfeeWd06dIlmjdvHt26dYvXX399udO/+uqrcdhhh0XLli2jS5cucdNNN8WCBQtWZtUAAAAAQCUrd2g4atSo6Nu3b+y3334xcODAqFOnTpSUlMRnn31W6vSTJ0+O4447LurUqRMDBw6M7t27x4ABA+Lmm29e5c4DAAAAABWvXKFhSikGDRoU3bt3j969e0eHDh1iyJAhsd5668Vtt91W6jyPPfZYLFy4MAYMGBA77bRTHHvssXHIIYfE3XffXSFPAAAAAACoWOUKDSdNmhRTp06Nzp0759qqV68eHTt2jPHjx5c6z9y5c6Nq1apRs2bNXNu6664bM2bMWMkuAwAAAACVqVyh4cSJEyMiYosttsi0b7755jFp0qRIKS01z7777htVq1aNv/71rzFjxox47bXX4p577om999575XsNAACwBikoWPP/5Us/F/cVgNWrWnkmnjVrVkRE1KpVK9Neq1atmD9/fsyZMyfWXnvtzGNbbLFFnHPOOXHxxRfnrmO4/fbbxwUXXLAq/fZBEtkPftZsapU/1Co/qFP+UCt+zbyu7eOL1V13nahRverq7kaZ1K9fZ3V3oUzmzlsQ38/4YXV3Y7WwX+UPtcoP6rTyyhUaLj6SsGAZW7pKlaUPXLznnnvioosuisMPPzx23333+OKLL+Laa6+N3r17x4033ljqPCtSr16tFU/0PyRfPvhRq3yiVvlBnfKHWvFrYzyaZR+POKbv4/HDnPmruxu/CuusVS2GX7x7bLDB//bryn6VP9QqP6hT+ZUrNKxTZ9EGnj17dtSrVy/XPnv27KhevXrmuoWL3XzzzdG5c+f4v//7v1zb1ltvHYccckg89dRTseuuu5a7099+OzsWLFhY7vl+bQoKFr3op02bGaWcGc4aRK3yh1rlB3XKH2qVVbVqFWHTr4Tx6CL28f9ugx/mzI8ffxIaVqT/1deV/Sp/qFV+UKellXVMWq7QcMstt4yIiClTpsRmm22Wa58yZUo0bNiw1Hk+//zzOPjggzNtzZo1i+rVq8dHH320UqFhRCj0ElKyPfKFWuUPtcoP6pQ/1IpfI6/p/7KPUxn+119X/+vPP5+oVX5Qp/Ir17nBDRo0iE022STGjh2ba5s3b16MGzcu2rZtW+o8DRs2jH//+9+ZtrfffjvmzZsXm2666Up0GQAAAACoTOU60rCgoCB69eoV/fr1i9q1a0fLli1j5MiRMWPGjDj66KMjImLy5Mkxffr0KC4ujoiIP/zhD3H66afHxRdfHL///e/jiy++iAEDBsTvfve76NKlS4U/IQAAAABg1ZQrNIyI6NGjR8yZMydGjBgRw4YNi0aNGsXQoUNzRw0OHjw4Ro8eHRMmTIiIiN///vdRpUqVGDJkSIwaNSo23HDD2GWXXeLMM8+MGjVqVOyzAQAAAABWWblDw4iIkpKSKCkpKfWx/v37R//+/TNtu+++e+y+++4rsyrgVyQfbnG/uI/50FfX4wAAAKCyrFRoCFBe6667TtSoUXV1d6PM6tevs7q7sEJz5y6IGTN+WN3dAACACpcPP+I74IBfO6EhUOkKCiJq1Kgax/R9PH6YM391d+dXYZ21qsXwi3ePggIDAAAAfl0ccFDxKvOAgzU9NM2ncDdizfp+JzQEfjE/zJkfP/4kNAQAAErngIOKV5kHHORTwJsP4W7EmnVGmdAQAACA/wn5cKSRo6IWccDBmk/AW/HWtDPKhIbkvXz4MPXBDwAAq1c+HREV4ago8oeA99dLaEhe88FfOXzws6YH3IL4RfLh+asVAGsCR0RVjjXtqCigYgkNyVs++CuHD37yKYz/Xw7i86lOEf/btQJgzeGIKICyExqS93zwQ8URxle8ygji1aly+NEEAAD+S2gIwFKE8flBnQAAgMpSZXV3AAAAAABYswgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACAjGqruwMAAEDpCgpWdw+Wb3H/1vR+LpbS6u4BAOQPoSEAAKyB1l13nahRo+rq7kaZ1K9fZ3V3oUzmzl0QM2b8sLq7AQB5QWgIAABrmIKCiBo1qsYxfR+PH+bMX93d+VVYZ61qMfzi3aOgwBGHAFAWQkMAAFhD/TBnfvz4k9AQAPjluREKAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkVFvdHVhTFRSs7h6s2OI+5kNfIyJSWt09AAAAAKAshIalWHfddaJGjaqruxtlVr9+ndXdhTKZO3dBzJjxw+ruBgAAAAArIDT8mYKCiBo1qsYxfR+PH+bMX93d+dVYZ61qMfzi3aOgwBGHAAAAAGs6oeEy/DBnfvz4k9AQAAAAgP89boQCAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACAjGorM9Odd94Zt956a3z55ZfRqFGjOP/886O4uLjUaTt37hxTp04t9bFTTjklevfuvTJdAAAAAAAqSblDw1GjRkXfvn3j5JNPjmbNmsWIESOipKQkHnroofjtb3+71PSDBg2KuXPnZtpuvfXWGD9+fOy5554r33MAAAAAoFKUKzRMKcWgQYOie/fuuSME27VrF3vssUfcdtttcf755y81T+PGjTN/v/nmmzFmzJjo169fbLXVVqvQdQAAAACgMpTrmoaTJk2KqVOnRufOnXNt1atXj44dO8b48ePLtIw///nP0bx58zjwwAPL11MAAAAA4BdRrtBw4sSJERGxxRZbZNo333zzmDRpUqSUljv/mDFj4rXXXotzzz03CgoKytdTAAAAAOAXUa7Tk2fNmhUREbVq1cq016pVK+bPnx9z5syJtddee5nz33bbbbHDDjtEy5YtV6KrWZWVOcoyK1dFbl+1qlxqlR8qetuqVeWxT+UP25eyqszXitdh5fHZmT98duYPtcoP3v/yx5qybct9TcOIWOZRglWqLPvAxY8//jheeumlGDRoUHlWWap69WqteCLWSPXr11ndXaCM1Co/qFP+UKv8oVaUhfFo/rKP5w+1yh9qlR/UKX+sKbUqV2hYp86iTs+ePTvq1auXa589e3ZUr149atasucx5x44dG7Vr144OHTqsZFf/69tvZ8eCBQtXeTmlKShYc4rzazRt2sxYwVnsZaZWlUut8kNF1ilCrSqTfSp/VPR+taSqVasIm34lKnM8GmE/r0w+O/OHz878oVb5wftf/qjM8WhE2cek5QoNt9xyy4iImDJlSmy22Wa59ilTpkTDhg2XO+/48eOjY8eOUaNGjfKscpkqc+NReVJSu3yhVvlBnfKHWuUPtaKsvE7yk308f6hV/lCr/KBO+WNNqVW5boTSoEGD2GSTTWLs2LG5tnnz5sW4ceOibdu2y5wvpRRvvfVWtGjRYuV7CgAAAAD8Isp1pGFBQUH06tUr+vXrF7Vr146WLVvGyJEjY8aMGXH00UdHRMTkyZNj+vTpUVxcnJtv6tSpMXv27BUejQgAAAAArH7lCg0jInr06BFz5syJESNGxLBhw6JRo0YxdOjQ2HTTTSMiYvDgwTF69OiYMGFCbp7p06dHRETdunUrqNsAAAAAQGUpd2gYEVFSUhIlJSWlPta/f//o379/pq158+aZEBEAAAAAWHOV65qGAAAAAMCvn9AQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgY6VCwzvvvDO6dOkSzZs3j27dusXrr7++3Om/+eab+OMf/xg77LBDtG7dOk499dT44osvVmbVAAAAAEAlK3doOGrUqOjbt2/st99+MXDgwKhTp06UlJTEZ599Vur0c+fOjWOPPTbeeeed+POf/xx9+/aN9957L0466aRIKa3yEwAAAAAAKla18kycUopBgwZF9+7do3fv3hER0a5du9hjjz3itttui/PPP3+peR544IH49NNP49FHH42NN944IiLq168fZ511VkycODEaNmxYAU8DAAAAAKgo5TrScNKkSTF16tTo3Llzrq169erRsWPHGD9+fKnzjBkzJnbZZZdcYBgRscMOO8TTTz8tMAQAAACANVC5QsOJEydGRMQWW2yRad98881j0qRJpZ5u/P7770fDhg3juuuui3bt2kWzZs3i5JNPjm+++Wblew0AAAAAVJpynZ48a9asiIioVatWpr1WrVoxf/78mDNnTqy99tqZx6ZPnx733XdfbL755nH55ZfH999/H1dccUX07t077rzzzigoKFipjq/kbKttuSxSkdtXrSqXWuWHit62alV57FP5w/alrCrzteJ1WHl8duYPn535Q63yg/e//LGmbNtyX9MwIpYZ9FWpsvSBi/Pnz4/58+fHzTffHLVr146IRdc0PPbYY+OFF16Itm3blrfPUa9erRVPxBqpfv06q7sLlJFa5Qd1yh9qlT/UirIwHs1f9vH8oVb5Q63ygzrljzWlVuUKDevUWdTp2bNnR7169XLts2fPjurVq0fNmjWXmmedddaJ4uLiXGAYEdG6deuoVq1avP/++ysVGn777exYsGBhuecri4KCNac4v0bTps2MirpptlpVLrXKDxVZpwi1qkz2qfxR0fvVkqpWrSJs+pWozPFohP28MvnszB8+O/OHWuUH73/5ozLHoxFlH5OWKzTccsstIyJiypQpsdlmm+Xap0yZssybmmyxxRYxb968TNvChQtj4cKFK31qckRU6saj8qSkdvlCrfKDOuUPtcofakVZeZ3kJ/t4/lCr/KFW+UGd8seaUqty3QilQYMGsckmm8TYsWNzbfPmzYtx48Yt84jB9u3bx6uvvpq58cmzzz4bCxcujJYtW65ktwEAAACAylKuIw0LCgqiV69e0a9fv6hdu3a0bNkyRo4cGTNmzIijjz46IiImT54c06dPj+Li4oiIOProo+P++++P4447Lk455ZSYOXNmXHHFFdGhQ4do1qxZhT8hAAAAAGDVlCs0jIjo0aNHzJkzJ0aMGBHDhg2LRo0axdChQ2PTTTeNiIjBgwfH6NGjY8KECRERscEGG8Qdd9wR/fv3jz/+8Y9Ro0aN2GOPPeLcc8+t2GcCAAAAAFSIcoeGERElJSVRUlJS6mP9+/eP/v37Z9oaNGgQN9xww8qsCgAAAAD4hZXrmoYAAAAAwK+f0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBjpULDO++8M7p06RLNmzePbt26xeuvv77c6U899dQoKipa6t/s2bNXZvUAAAAAQCWqVt4ZRo0aFX379o2TTz45mjVrFiNGjIiSkpJ46KGH4re//W2p80yYMCF69OgR++67b6Z97bXXXrleAwAAAACVplyhYUopBg0aFN27d4/evXtHRES7du1ijz32iNtuuy3OP//8peb58ccfY/LkyXHuuedGcXFxhXQaAAAAAKg85To9edKkSTF16tTo3Llzrq169erRsWPHGD9+fKnzfPDBB7Fw4cIoLCxctZ4CAAAAAL+IcoWGEydOjIiILbbYItO++eabx6RJkyKltNQ8EyZMiGrVqsWQIUOiTZs20aJFizj11FPj66+/XvleAwAAAACVplynJ8+aNSsiImrVqpVpr1WrVsyfPz/mzJmz1HUKJ0yYEPPnz4/q1avHoEGDYsqUKXHttdfGMcccE6NHj44aNWqsVMcLClZqttW2XBapyO2rVpVLrfJDRW9btao89qn8YftSVpX5WvE6rDw+O/OHz878oVb5wftf/lhTtm25r2kYEVGwjN5XqbL0gYs9evSIXXfdNdq2bRsRETvssENsvfXWceihh8bjjz8e++yzT3n7HPXq1VrxRKyR6tevs7q7QBmpVX5Qp/yhVvlDrSgL49H8ZR/PH2qVP9QqP6hT/lhTalWu0LBOnUWdnj17dtSrVy/XPnv27KhevXrUrFlzqXkaNmwYDRs2zLS1aNEi6tatGxMmTFip0PDbb2fHggULyz1fWRQUrDnF+TWaNm1mlHIW+0pRq8qlVvmhIusUoVaVyT6VPyp6v1pS1apVhE2/EpU5Ho2wn1cmn535w2dn/lCr/OD9L39U5ng0ouxj0nKFhltuuWVEREyZMiU222yzXPuUKVOWCgYXe/zxx2PdddeNHXfcMdeWUoq5c+fGeuutV57VZ1TmxqPypKR2+UKt8oM65Q+1yh9qRVl5neQn+3j+UKv8oVb5QZ3yx5pSq3LdCKVBgwaxySabxNixY3Nt8+bNi3HjxuVOP/65++67L/r16xcLFizItf3rX/+KOXPmxA477LCS3QYAAAAAKku5jjQsKCiIXr16Rb9+/aJ27drRsmXLGDlyZMyYMSOOPvroiIiYPHlyTJ8+PYqLiyMiolevXnH00UfHGWecEd26dYuJEyfGddddF7vvvnu0aNGiwp8QAAAAALBqyhUaRiy6scmcOXNixIgRMWzYsGjUqFEMHTo0Nt1004iIGDx4cIwePTomTJgQERGtW7eOm266KQYOHBi9e/eOWrVqxUEHHRSnnXZaxT4TAAAAAKBClDs0jIgoKSmJkpKSUh/r379/9O/fP9O28847x84777wyqwIAAAAAfmHluqYhAAAAAPDrJzQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABkrFRreeeed0aVLl2jevHl069YtXn/99TLPO2jQoCgqKlqZ1QIAAAAAv4Byh4ajRo2Kvn37xn777RcDBw6MOnXqRElJSXz22WcrnPf999+PG264YaU6CgAAAAD8MsoVGqaUYtCgQdG9e/fo3bt3dOjQIYYMGRLrrbde3Hbbbcudd8GCBdGnT59Yf/31V6nDAAAAAEDlKldoOGnSpJg6dWp07tw511a9evXo2LFjjB8/frnzDh8+PGbPnh1HHHHEyvUUAAAAAPhFVCvPxBMnToyIiC222CLTvvnmm8ddd90VKaUoKChYar5JkybFwIED45Zbbom33npr5Xu7hFJWs0Yvl0UqcvuqVeVSq/xQ0dtWrSqPfSp/2L6UVWW+VrwOK4/PzvzhszN/qFV+8P6XP9aUbVuu0HDWrFkREVGrVq1Me61atWL+/PkxZ86cWHvttTOPpZTiwgsvjP322y9atWpVIaFhvXq1VjwRa6T69eus7i5QRmqVH9Qpf6hV/lArysJ4NH/Zx/OHWuUPtcoP6pQ/1pRalSs0TClFRJR6NGFERJUqS5/tfNddd8WkSZNiyJAhK9G90n377exYsGBhhS1vSQUFa05xfo2mTZsZ//9ltMrUqnKpVX6oyDpFqFVlsk/lj4rer5ZUtWoVYdOvRGWORyPs55XJZ2f+8NmZP9QqP3j/yx+VOR6NKPuYtFyhYZ06i14Ms2fPjnr16uXaZ8+eHdWrV4+aNWtmpv/888/jyiuvjMsvvzzWWmutmD9/fi54nD9/flSpUqXUoLEsKnPjUXlSUrt8oVb5QZ3yh1rlD7WirLxO8pN9PH+oVf5Qq/ygTvljTalVuULDLbfcMiIipkyZEptttlmufcqUKdGwYcOlpn/++edj9uzZceqppy71WJMmTaJ3795xyimnlLfPAAAAAEAlKldo2KBBg9hkk01i7Nix0bZt24iImDdvXowbNy523XXXpabv1KlT3HfffZm2Rx55JIYNGxb33Xdf/OY3v1mFrgMAAAAAlaFcoWFBQUH06tUr+vXrF7Vr146WLVvGyJEjY8aMGXH00UdHRMTkyZNj+vTpUVxcHPXq1cucxhwR8eqrr0ZERLNmzSroKQAAAAAAFalcoWFERI8ePWLOnDkxYsSIGDZsWDRq1CiGDh0am266aUREDB48OEaPHh0TJkyo8M4CAAAAAJWv3KFhRERJSUmUlJSU+lj//v2jf//+y5z3mGOOiWOOOWZlVgsAAAAA/AJW7tbFAAAAAMCvltAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgY6VCwzvvvDO6dOkSzZs3j27dusXrr7++3OlfeOGFOOSQQ6K4uDi6dOkSI0aMWJnVAgAAAAC/gHKHhqNGjYq+ffvGfvvtFwMHDow6depESUlJfPbZZ6VO/84770SvXr2iYcOGMXjw4DjssMOif//+8be//W2VOw8AAAAAVLxyhYYppRg0aFB07949evfuHR06dIghQ4bEeuutF7fddlup89x2222x+eabR//+/aNdu3ZRUlIS++67b9xxxx0V8gQAAAAAgIpVrtBw0qRJMXXq1OjcuXOurXr16tGxY8cYP358qfOceeaZMWDAgKhSpUpmnrlz565klwEAAACAylSu0HDixIkREbHFFltk2jfffPOYNGlSpJSWmmejjTaKbbbZJiIiZs6cGQ888EA88MADcdhhh61klwEAAACAylStPBPPmjUrIiJq1aqVaa9Vq1bMnz8/5syZE2uvvXap83755Zexyy67REREs2bN4tBDD12Z/uYUFKzS7L/4clmkIrevWlUutcoPFb1t1ary2Kfyh+1LWVXma8XrsPL47MwfPjvzh1rlB+9/+WNN2bblCg0XH0lYsIzeL3kK8s+tvfbacdttt8W0adPi2muvjW7dusUDDzywzJBxeerVq7XiiVgj1a9fZ3V3gTJSq/ygTvlDrfKHWlEWxqP5yz6eP9Qqf6hVflCn/LGm1KpcoWGdOos6PXv27KhXr16uffbs2VG9evWoWbPmMuetW7du7LjjjhERsc0228S+++4bjz/+eOy///7l7vS3386OBQsWlnu+sigoWHOK82s0bdrMKOUs9pWiVpVLrfJDRdYpQq0qk30qf1T0frWkqlWrCJt+JSpzPBphP69MPjvzh8/O/KFW+cH7X/6ozPFoRNnHpOUKDbfccsuIiJgyZUpsttlmufYpU6ZEw4YNS53nySefjHr16kXLli1zbYWFhVG9evX46quvyrP6jMrceFSelNQuX6hVflCn/KFW+UOtKCuvk/xkH88fapU/1Co/qFP+WFNqVa4boTRo0CA22WSTGDt2bK5t3rx5MW7cuGjbtm2p8wwbNiwuv/zyzE1SXnrppZg3b14UFhauZLcBAAAAgMpSriMNCwoKolevXtGvX7+oXbt2tGzZMkaOHBkzZsyIo48+OiIiJk+eHNOnT4/i4uKIiDjhhBPiuOOOiz59+sQ+++wTkydPjgEDBkTr1q2jQ4cOFf6EAAAAAIBVU67QMCKiR48eMWfOnBgxYkQMGzYsGjVqFEOHDo1NN900IiIGDx4co0ePjgkTJkRExE477RQ33nhjDBo0KP7whz9E7dq1Y6+99oozzjhjmTdUAQAAAABWn3KHhhERJSUlUVJSUupj/fv3j/79+2faOnTo4KhCAAAAAMgT5bqmIQAAAADw6yc0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyFip0PDOO++MLl26RPPmzaNbt27x+uuvL3f6p556Kg455JBo2bJldO7cOf70pz/FrFmzVmbVAAAAAEAlK3doOGrUqOjbt2/st99+MXDgwKhTp06UlJTEZ599Vur0L774Ypx00kmxzTbbxMCBA+Okk06KRx55JM4666xV7jwAAAAAUPGqlWfilFIMGjQounfvHr17946IiHbt2sUee+wRt912W5x//vlLzXPrrbfGdtttF5dffnmurXbt2nH66afHxx9/HFtttdUqPgUAAAAAoCKV60jDSZMmxdSpU6Nz5865turVq0fHjh1j/Pjxpc7TvHnz6NGjR6atYcOGERExderU8vYXAAAAAKhk5TrScOLEiRERscUWW2TaN99887jrrrsipRQFBQWZx04++eSllvPUU09FxH/DQwAAAABgzVGu0HDxzUtq1aqVaa9Vq1bMnz8/5syZE2uvvfZyl/Hee+/FTTfdFL///e9js802K2d3/+tn2WSFqazlskhFbl+1qlxqlR8qetuqVeWxT+UP25eyqszXitdh5fHZmT98duYPtcoP3v/yx5qybct9TcOIWOpowsWqVFn+2c4ffPBBlJSUxMYbbxyXXnppeVadUa9erRVPxBqpfv06q7sLlJFa5Qd1yh9qlT/UirIwHs1f9vH8oVb5Q63ygzrljzWlVuUKDevUWdTp2bNnR7169XLts2fPjurVq0fNmjWXOe8rr7wSf/jDH2L99dePYcOGxbrrrruSXY749tvZsWDBwpWef3kKCtac4vwaTZs2M/5/9rzK1KpyqVV+qMg6RahVZbJP5Y+K3q+WVLVqFWHTr0Rljkcj7OeVyWdn/vDZmT/UKj94/8sflTkejSj7mLRcoeGWW24ZERFTpkzJnFo8ZcqU5V6f8Mknn4zTTz89ttpqqxg6dGjUr1+/PKstVWVuPCpPSmqXL9QqP6hT/lCr/KFWlJXXSX6yj+cPtcofapUf1Cl/rCm1Ktfdkxs0aBCbbLJJjB07Ntc2b968GDduXLRt27bUed5888047bTTomnTpjFy5MgKCQwBAAAAgMpTriMNCwoKolevXtGvX7+oXbt2tGzZMkaOHBkzZsyIo48+OiIiJk+eHNOnT4/i4uKIiLjkkkuiWrVqccIJJ8SHH36YWV7Dhg1X6TRlAAAAAKDilSs0jIjo0aNHzJkzJ0aMGBHDhg2LRo0axdChQ2PTTTeNiIjBgwfH6NGjY8KECfHZZ5/FO++8ExERxx9//FLLuv7662O33XZbxacAAAAAAFSkcoeGERElJSVRUlJS6mP9+/eP/v37R0TEb3/725gwYcLK9w4AAAAA+MWV65qGAAAAAMCvn9AQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgY6VCwzvvvDO6dOkSzZs3j27dusXrr79epvlmzZoVnTp1ijFjxqzMagEAAACAX0C5Q8NRo0ZF3759Y7/99ouBAwdGnTp1oqSkJD777LPlzjdr1qz4wx/+sMLpAAAAAIDVq1yhYUopBg0aFN27d4/evXtHhw4dYsiQIbHeeuvFbbfdtsz5XnrppTjkkEPivffeW+UOAwAAAACVq1yh4aRJk2Lq1KnRuXPnXFv16tWjY8eOMX78+GXOd/LJJ0dhYWHccsstK99TAAAAAOAXUa08E0+cODEiIrbYYotM++abbx533XVXpJSioKBgqfluv/32KCwsjE8//XTlewoAAAAA/CLKFRrOmjUrIiJq1aqVaa9Vq1bMnz8/5syZE2uvvfZS8xUWFq5CF0tXSja5Ri+XRSpy+6pV5VKr/FDR21atKo99Kn/YvpRVZb5WvA4rj8/O/OGzM3+oVX7w/pc/1pRtW67QMKUUEVHq0YQREVWqrNTNmMutXr1aK56INVL9+nVWdxcoI7XKD+qUP9Qqf6gVZWE8mr/s4/lDrfKHWuUHdcofa0qtyhUa1qmzqNOzZ8+OevXq5dpnz54d1atXj5o1a1Zs75bh229nx4IFCytl2QUFa05xfo2mTZsZ/z97XmVqVbnUKj9UZJ0i1Koy2afyR0XvV0uqWrWKsOlXojLHoxH288rkszN/+OzMH2qVH7z/5Y/KHI9GlH1MWq7QcMstt4yIiClTpsRmm22Wa58yZUo0bNiwnF1cNZW58ag8KaldvlCr/KBO+UOt8odaUVZeJ/nJPp4/1Cp/qFV+UKf8sabUqlznEzdo0CA22WSTGDt2bK5t3rx5MW7cuGjbtm2Fdw4AAAAA+OWV60jDgoKC6NWrV/Tr1y9q164dLVu2jJEjR8aMGTPi6KOPjoiIyZMnx/Tp06O4uLgy+gsAAAAAVLJyhYYRET169Ig5c+bEiBEjYtiwYdGoUaMYOnRobLrpphERMXjw4Bg9enRMmDChwjsLAAAAAFS+coeGERElJSVRUlJS6mP9+/eP/v37l/rYZpttJkwEAAAAgDVcua5pCAAAAAD8+gkNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMlYqNLzzzjujS5cu0bx58+jWrVu8/vrry53+vffei6OOOipatmwZHTt2jJtvvnllVgsAAAAA/ALKHRqOGjUq+vbtG/vtt18MHDgw6tSpEyUlJfHZZ5+VOv20adOiZ8+eUa1atbj22mvj0EMPjWuuuSaGDx++qn0HAAAAACpBtfJMnFKKQYMGRffu3aN3794REdGuXbvYY4894rbbbovzzz9/qXlGjhwZKaUYPHhwrLXWWtGhQ4eYO3du3HjjjXHkkUdG1apVK+aZAAAAAAAVolxHGk6aNCmmTp0anTt3zrVVr149OnbsGOPHjy91nueffz7atWsXa621Vq5tt912i+nTp8fbb7+9kt0GAAAAACpLuULDiRMnRkTEFltskWnffPPNY9KkSZFSKnWeLbfccqnpIyI++eST8qweAAAAAPgFlOv05FmzZkVERK1atTLttWrVivnz58ecOXNi7bXXXmqe0qZfcnnlVbVq5d30uaBg0X8bNagXc+YuqLT1/K9Zq8ai09CrVasSpWTLK0WtKoda5YfKqFOEWlUG+1T+qKz9akmVOYbhl1XZtbSfVzyfnfnDZ2f+UKv84P0vf/wS49GIso9jClJphwcuw0MPPRR//OMf44UXXoh69erl2u+999648MIL480334yaNWtm5mnatGmceeaZ0bNnz1zb/Pnzo0mTJvF///d/cfjhh5d19QAAAADAL6BcP5HWqVMnIiJmz56daZ89e3ZUr159qcAwIqJ27dqlTr/k8gAAAACANUe5QsPF1yacMmVKpn3KlCnRsGHDUudp0KBBqdNHxDLnAQAAAABWn3KFhg0aNIhNNtkkxo4dm2ubN29ejBs3Ltq2bVvqPDvuuGM8++yz8eOPP+baxowZE+uvv34UFRWtZLcBAAAAgMpSrhuhFBQURK9evaJfv35Ru3btaNmyZYwcOTJmzJgRRx99dERETJ48OaZPnx7FxcUREdG9e/cYMWJEHH/88dGzZ89477334qabbopzzjknqlUr1+oBAAAAgF9AuW6EstjQoUNjxIgR8e2330ajRo3i/PPPjxYtWkRExHnnnRejR4+OCRMm5KZ/8803489//nO8/fbbscEGG8QRRxwRJSUlFfcsAAAAAIAKs1KhIQAAAADw61WuaxoCAAAAAL9+QkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAA/Eq55+GaS21WbE3aRmtSXyidGuU/NSybX3I7CQ1/5h//+EccddRR0aZNm2jVqlV069YtHnnkkdzjDzzwQBQVFcVLL720zGV06dIlzjjjjGU+/vXXX8cll1wSHTt2jKZNm0b79u3jjDPOiA8//LBCn8vK+vTTT6OoqCjGjBmzysuaOnVq/OEPf4jtt98+2rVrF1dccUXMmzevAnqpVhEVW6vF5s+fHwcffHAMHz68QpanThVbp3//+99x5JFHRqtWrWKnnXaKc845J7755psK6KVaRVRsrd5999046qijomXLltGhQ4cYOHCg978KVBnvfxERo0ePjqKiovj0008rdLlQVqNGjYqioqJl/jv88MPLvKy77747Bg4cWIm9LZ/OnTvHZZddVqZpF+/jy/vXuXPnVe7T559/Hj179ozvv/9+hdMeeeSRy+3PvffeW6Z1fv/993HWWWfFhAkTVrX7FeLFF1+MoqKiePfdd8s0/cCBA1dYm4p43Y0ZMyb+7//+b6Xnty8tsibuS4up0SJrco3KYkV9nzRpUpmW88EHH0TPnj0rtG+rYuDAgdGqVasyT7+iz4iioqJ48cUXV7lfgwYNirvuumuVl1NW1X6xNeWBPn36xMMPPxzdu3ePnj17RkFBQYwdOzbOPPPMeP/99+OMM86Irl27xqWXXhqPPvpotG7deqllvP766zF58uS48MILS13H7Nmzo3v37lGrVq0444wzYpNNNomvvvoqhg8fHt26dYt77rkntt5668p+qr+IuXPnRs+ePaNWrVpxxRVXxOeffx5XXXVVzJs3Ly644IJVWrZaVY558+bFeeedF//5z39i7733XuXlqVPF+uijj+KYY46Jdu3axdVXXx3ff/99XHfddXHcccfFfffdF9WqrfxbulpVrC+++CKOOeaYaNasWQwcODCmTp0aV1xxRcyaNSvOP//8VVq2WlWer7/+Oi6//PLV3Q2IiIhhw4bFOuuss1R77dq1y7yMG2+8MXbdddeK7NYv5je/+U3cfffdub/HjRsXQ4YMyWyXGjVqrPJ6nn/++Xj22WfLPP0OO+wQf/zjH0t9bIsttijTMt599914+OGH47jjjivzetckhxxySOy88865vy+99NKoVatWZrtsvPHGq7ye2267LerUqbPKy7EvrZn70pLUaM2v0YocffTRseeee5b62CabbFKmZTz22GPxn//8pyK79Yu65JJLYtasWbm/u3XrttR22WabbVZ5PQMHDlzl7xPlITT8/+69994YNWpUDB06NNq3b59r79ChQ9SrVy9uuOGG2HPPPaOoqCi6dOkSjz/+eFx44YVRtWrVzHIefvjh2HDDDWOnnXYqdT3//Oc/49NPP41nn3021l9//Vx7x44do0uXLjF8+PDo169f5TzJX9jf//73mDp1ajz55JPxm9/8JiIiatasGZdeemmcdNJJmedfHmpVOd5777245JJL4pNPPqmQ5alTxbv99ttjww03jIEDB0b16tUjImLLLbeMQw45JJ5//vnMAL481Kri3XPPPZFSioEDB8baa68dEYsCqZtvvjnOPffcqFJl5Q70V6vK1bdv31hrrbVixowZq7srEE2bNo26deuu7m6sNjVq1Iji4uLc3x9//HFErP7tUrdu3Uy//hdtvPHGmVCwdu3aUadOnTV2u6zu18zqtqbuS0tak/qyOuRDjVbkt7/97Rr7HvBLKS0Q/DVsF6cn/3/Dhw+PTp06Zb6ELXbcccdFjx49YsGCBRERsf/++8e0adOWOu1rwYIF8Y9//CP22Wefpb6gLTZt2rSIiFi4cGGmvXbt2nHBBRdkvvTPnDkz+vbtmzs1rF27dnHRRRfFDz/8EBH/PYx57NixcdRRR0Xz5s1j9913j6effjreeuutOPjgg6NFixZxyCGHxPvvv59bbufOneOGG26Ic889N4qLi6NDhw5x4403Lnf7vPnmm3HkkUdG8+bNo127dnH55ZfH3LlzlzvP888/H02bNs0FhhERu+22W8ybNy9eeOGF5c67PGpV8bWKiDj33HNj7bXXjvvuu2+F05aFOlV8nbbZZpvo2bNnLjCMiNhqq60iYtGlAFaWWlV8rY444ogYPnx4LjCMiKhevXosWLBgqedfHmpVOe9/EYt+3X755ZfjlFNOKdP0sLrdfPPNUVRUFM8//3yu7ZprrommTZvG+++/H507d46pU6fG3/72tygqKspNs6L95Mgjj4wLL7wwjj322GjevHn85S9/iYEDB8aBBx4Yf//736Nr167RrFmz6NatW7z99tu5+VJKMWzYsNhrr72iWbNmsd1220WvXr1i4sSJlbodPvnkkzjhhBOiZcuWscMOO8T555+fCf5/+OGHuOCCC2KnnXaK5s2bxwEHHBD//Oc/I2LRqZGLj9bYYYcdKuTUxvnz58e+++4bXbp0yW3XOXPmxO677x7du3eP559/Po466qiIWPQ+fd555+XmHTZsWOy2227RtGnT2GeffTKXX1j8Xrr4c6BVq1bx5ptvRufOnePmm2+Oiy66KHbYYYdo1apVXHjhhTFnzpzcvF9++WWce+650b59+2jSpEnssssucdVVV8X8+fNX+fkuzwMPPBB77bVXNG3aNLp27Zo5kioi4o033ogePXpEy5Yto3Xr1nHqqafmxjNHHnlkvPTSSzF27NhKv2SEfWmRNW1fWpIaLbIm12hFpkyZEsXFxXH22Wfn2j788MNo2rRp/PWvf42BAwfGoEGDYubMmVFUVBSjRo2KiIhZs2bFJZdcEm3bto3mzZvHMccckxkvjho1Ktq0aRM33XRTtG7dOjp37hwff/xxbuzZs2fPaNGiRXTo0CGGDRuW6dPrr78eJSUlsf3220fTpk1jzz33jAceeKBSt8O8efPir3/9a+yyyy7RrFmzOOSQQ+KVV17JTDN69Ojca2uXXXaJP//5z/HTTz9FRORe35dffnmFnLJeJon0xRdfpMLCwnTnnXeWafoFCxaknXfeOV100UWZ9qeffjoVFhamCRMmLHPet99+O2277bZpt912S0OHDk3vvfdeWrhwYanTHn/88alz587p4YcfTs8//3waNGhQ2nbbbdOgQYNSSilNmTIlFRYWptatW6dhw4alZ555Jh1wwAGpdevWqUuXLumee+5JTz31VNp5553TwQcfnFtup06d0vbbb59KSkrSuHHj0jXXXJOKiorSTTfdlFnuE088kVJKacKECal58+appKQkPfXUU+muu+5KO+ywQzr99NOXu50OPPDAdM455yzVvt1226Xrr79+ufMui1pVTq0Wz7tYYWFhGjZs2ArnWRZ1qrw6/dyDDz6YCgsL04svvljueVNSq1+iVrNmzUpPPvlkat269VLbrTzUqvJq9e2336Z27dqlBx98MD3xxBOpsLAwTZkyZYXzQWW4//77U2FhYZo+fXqaN2/eUv8W74vz589PBxxwQNpzzz3TvHnz0jvvvJMaN26chgwZklJatB+3b98+nX766em1115LKZVtPzniiCNS48aN05///Of09NNPp7feeisNGDAgtWzZMu2+++7pkUceSWPHjk1du3ZNXbt2zc13yy23pGbNmqW//e1v6cUXX0z33HNP2nHHHdNRRx2Vm6ZTp07pT3/60yptlxkzZuTavvrqq7Tjjjumgw46KD3xxBPpwQcfTB07dkzdunVLCxYsSCml1KdPn9S5c+f0j3/8Iz3//PPprLPOSttuu2368MMP07Rp09I111yTCgsL07PPPps+//zz5fbhiCOOSCeeeGKpdZk/f35uujfffDM1atQoDR48OKWUUv/+/VOLFi3SxIkT08yZM9PIkSNTYWFhGj16dJo0aVJKKaVrr702NWnSJA0cODA9/fTT6eKLL07bbrttGjduXErpv+95bdq0Sf/4xz/S6NGj07x581KnTp3Sdtttl84+++z0zDPPpFtvvTVtu+226YYbbkgpLfos2GeffdL++++fnnjiifTcc8+lfv365dafUkovvPBCKiwsTO+8885K1eaII45IJ510Uqbt3nvvTUVFRelPf/pTGj9+fPrrX/+att1223THHXeklFL6/vvvU+vWrdMZZ5yRnnvuufTYY4+l3XbbLR166KEppZQ++OCDtP/++6cePXqk1157Lf3000/l7pd9afnbZXXuS2qUPzUqi8LCwnTrrbeWWsPFfUsppWHDhqXCwsL0yiuvpPnz56dDDjkk7b333umnn35Kn3/+eerTp08qLi5Or732Wpo2bVpasGBBOuKII1K7du3SPffck8aOHZuOOOKI1Lp16/TFF1/ktlWjRo3S/vvvn8aPH58ef/zxzPvloEGD0nPPPZfOO++8VFhYmP7973+nlFKaOnVqatGiRTr99NPTs88+m5566ql07LHHpkaNGuXGgAMGDEjbb7/9Km2Xn3+nPvvss1NxcXEaPnx4GjduXDrllFNSs2bNcu+/L730Utp2223T4MGD04svvphuv/321KxZs3TVVVellFJ67bXXUmFhYbrsssvS22+/vdJ9Kw+hYUrpjTfeSIWFhelf//pXmee54oorUps2bdK8efNybeecc0464IADVjjv3//+99S6detUWFiY+yJ19tlnp3fffTc3zZw5c9Kxxx6bnnnmmcy8PXr0SD179kwp/Xfw0Ldv39zjjzzySCosLEy33XZbrm3o0KGpUaNGub526tQpderUKc2dOzc3zQUXXJDatGmTFixYsNQXsdNOOy117do181zHjRuXCgsLM33+ua5du6ZLL710qfadd945/eUvf1nhdiqNWlVOrX5uVUNDdfpl6vT555+n9u3bp0MPPXSZgc6KqFXl1mrhwoWpSZMmqbCwMHXu3Dl9+umnK5xnWdSq8mp19tlnp5KSkpRSEhqy2i3+srisfw8//HBu2rfffjs1btw4DR06NB1wwAHpoIMOyoRXP//SWpb95IgjjsjtZ4sNGDBgqVDpgQceSIWFhbnQq1+/funGG2/MPJfrrrsuNWnSZJn9WZntsuSX6CuuuCK1bt060/b++++noqKi3HvD7rvvnvnxZO7cuenyyy9P77333jKXuyxHHHHEMuvStGnTzLSXX355atGiRRozZkxq1KhR5v3u5yHdd999l5o1a5YGDhyYWcZpp52W9ttvv5TSf99LL7/88sw0nTp1Svvuu2+m7eijj06HH354Simlzz77LB1xxBHp/fffz0zTsWPH3Hap6NBwwYIFqX379um8887LTHfFFVekHXfcMc2dOzf3xXfxF/iUUnr55ZfTddddl3vtlRZGlod9afnbZXXuSz/vixqVvl3WhBqVxfJquHg8mNKi94aDDjoo7b///unWW29NjRs3Tm+99Vbu8Z+HdIvr9dJLL+Xafvrpp9ShQ4d02WWXZZ7T2LFjc9Msfr9ccvvPnTs3NW/ePF1zzTW5ZR977LGZ18fkyZNTYWFheuihh0rtz8pslyW/U3/44YeZH2xSWvRd4ZBDDkl/+MMfUkop3Xjjjally5aZH0ruu+++NGrUqGUut7K5pmFE7vSsVI7bVu+///5xyy235K4jNmfOnHjiiSfi9NNPX+G8++yzT3Tt2jXGjx8f48ePj+effz4efPDBePjhh+PKK6+MvfbaK2rWrBm33nprpJRiypQp8cknn8T7778fn376aWy++eaZ5TVr1iz3//Xr14+IRdc/WGy99daLBQsWxA8//JC7JsLuu++eOcVx1113jXvvvTc++eSTqFmzZmb5L7/8cuyxxx4REbnTGHbccceoXr16vPDCC1FUVJQ7HW6xxTdkKCgoKHUbrOz1vNSq8mpVkdSp8uv05ZdfxjHHHBMREVddddUy97UVUavKrdX8+fPjxhtvjB9//DEGDx4chx56aIwaNSo22mijFW6rn1OryqnV008/HU888UQ8/PDDK9wm8Ev629/+VuqNAZa82Ubjxo3j2GOPjSuvvDKqVasWDzzwwDIvOxCx4v1k2223jYiIBg0aLDVWq1GjRu7xiMhdfmbxpQgW31hp2rRp8dFHH8XHH38czz33XIXdNX5Zz6dly5axzjrr5J5Pw4YNY9NNN40XXnghdtttt2jVqlXcc8898dVXX0WnTp2iY8eOmVOCy6t169ZxzjnnLNX+8+112mmnxRNPPBG9e/eOVq1axZFHHrnMZb7xxhvx008/xS677JI5ZXiXXXaJRx99NL777rtcW8OGDZeav0WLFpm/f/Ob38QHH3wQEYtuQDBixIhYsGBBfPLJJ/HJJ5/Eu+++G7Nmzaq02nzyySfx9ddfR4cOHZZ6Prfcckt88MEHsc0228R6660XJ554Yuy1117RoUOHaNu2bbnuVFpW9qUVWx370pLUaMVWd41W5Nhjj4299tprqfYlb2ZUpUqVuOyyy+Kggw6KK664Ik488cRo0qTJMpf58ssvR61ataJly5a551ylSpVo27btUpc7W9F7Y/Xq1aNevXrx448/RsSi64F36NAh5syZEx988EF88skn8e9//zsiotLquPiSQaW91y8+dXq77baLH374Ifbdd9/4/e9/H506dYoDDzxwpb/rVQShYfz3bj6fffbZMqf58ssvM1/yfve730Xjxo3j0UcfjZ133jmefPLJmDt3bpnvOFuzZs3YbbfdYrfddouIiLfffjvOOOOM+NOf/hS///3vo0qVKjFmzJj485//HFOnTo31118/WrRoEWuttdZSyyrtrlJLXkerNBtuuGHm78UXup8xY0bmGoQREd99912MHDkyRo4cudRyvvrqq3jppZdy12ZZbMKECVG7du2YPXv2UvPMnj17pe+EplaVU6uKpk6VW6cPP/wwjjvuuJg3b14MGzZsqXCmPNSqcmtVvXr13PUHt9tuu+jYsWPce++90bt37+X2sTRqVfG1evXVV+OSSy6JU089NTbaaKOYP39+7jqOCxcujIULF670j1ywqho1alSmC+DvvffecfPNN8cWW2wRDRo0WO60K9pPFivtZnU1a9bMfGlZvG8s/iHjww8/jAsvvDBee+21WGeddaJRo0YVcrfP5fnuu+/ijTfeKPVL59dffx0Ri77cb7TRRvHggw/GU089FVWqVIkOHTrEn//855W6KV+dOnUyP4Isy9prrx277bZbDB8+PHbeeeflfuFbHAoecsghpT7+zTff5N5XF//osqSf/4hSpUqVzDVp77777rj22mtj+vTpsdFGG0VxcXHUrFmzXD9Clcfi53PaaaeV+vjXX38djRs3jpEjR8b1118fo0ePjttvvz3q1q0bxx9/fPTq1atC+2NfWrHVsS8tSY1WbHXXaEU23njjMr03FhUVRWFhYbz99tvRoUOH5U773XffxezZs0t9zj9/PqW9N/58PLrke+P8+fPj8ssvj3vuuSfmz58fW265Ze6GJZX93ti2bdtSH//xxx+jVatWMXjw4Bg2bFjcdNNNMXjw4Nhss83i//7v/1b6pperSmgYi15w2267bYwfPz4OP/zwpR6fPXt2dO3aNQ477LDMra3322+/GDx4cFx66aXx8MMPxy677LLCnfHQQw+N4uLi6NOnT6a9SZMmcdJJJ8V5550X3377bcycOTNOP/30OPjgg+Okk07KfQksKSkp80Xdl2fJXywj/nvR+9J2ttq1a8fuu+9e6kBmgw02iDp16pR684wGDRrElClTMm3ffvttzJo1q9RfAspCrSqnVhVNnSqvTm+++WYcd9xxUbt27bj99ttXOGBaEbWqnFq98MILsWDBgswNS9Zff/3YaKONMgPV8lCriq/V22+/HZ999ln0798/+vfvn3msS5cuccABByzVDmuSlFJcdtllsdVWW8WkSZPitttui549ey5z+hXtJytr4cKFcdJJJ8UGG2wQjzzySGy99dZRUFAQN954Y7z44osrvdwVqV27dnTq1ClOPvnkpR5bd911I2LRl8ZTTjklTjnllPj444/jn//8Z1x//fVx3XXXxaWXXlppffvoo4/ijjvuiKKiohgyZEjstddesemmm5Y67eIf04cMGbLUjyUREZtttll88803K9WPl156KffjyOGHHx716tWLiEVHcleWxc+nb9++0bhx46Ue33LLLSNi0Q9b1157bcydOzdeffXVGDFiRFx11VXRunXrpY6erGz2pTV3X1pMjdb8GpXF/fffH++++2787ne/i0svvTTuvffeZZ75VqdOndhwww1jyJAhSz22qmfL3XDDDTFq1Kj461//GjvttFOsvfba8dVXX8Xo0aNXabnLU6dOnahatWrcddddpf6QtDh47ty5c3Tu3DlmzpwZ48ePjyFDhsQZZ5wRzz33XKWH06Xx8/n/d+SRR8aTTz6ZuSPTYjfccEPMmTNnqcNt99lnn5g9e3Y8/fTT8cwzz8QBBxywwvVsuumm8fe//73UD/6JEyfGBhtsEPXq1Yt33nkn5s2bF8cff3zuS9j3338f//nPfyok+R43blxmOWPGjImNN9449yG+pO222y4++eSTaNasWe7f+uuvH1dffXVMnDgxateunXls8S8MO+64Y7z55pvx5ZdfZtZTvXr1VTr1QK0qvlaVQZ0qvk6ffvpp9OrVK+rXrx933nnnKgeGi6lVxdfqgQceiAsuuCATnE2aNCmmTp2auatfealVxdaqSZMmcd9992X+nXvuuRGx6Mv7yhwRCr+ke++9N1555ZX4y1/+Ej179owBAwZkfrD9+ZGyK9pPVtb06dNj8uTJ0a1bt9hmm21yX4aeffbZiKi8ozYWP59tt90293waNmwY1157bbz11luxcOHC2GeffWL48OEREbHVVlvFiSeeGMXFxfH5559HxMpfMmd5Ukpx0UUXxVZbbRV33nln1K9fPy655JLc4z8/pbJ58+ZRrVq1+O677zK1+c9//hM33XTTKvXx9ddfj6pVq8YJJ5yQCwynTJkSU6ZMqbS6bLXVVrHeeuvFV199lXk+X375ZQwcODDmzp0bzz77bLRt2zamT58eNWrUiLZt28YFF1wQEf89ov6XPNLbvrRm7ktLUqM1v0Yr8s0338QVV1wRhx9+eFxzzTXx/vvvx6233pp7vLQaTp8+PdZdd91MHe+999547LHHVqkvr7/+ejRr1iy6dOmSO/Plueeei4jKreGCBQvixx9/zDyfp556Ku66666oWrVq7nJGEYtCxj333DN69uwZM2fOjFmzZkXEL19HRxr+fwceeGCMGzcuTjjhhDjyyCOjbdu2MWfOnHjsscfioYceipNPPjmaN2+emad+/frRvn37uOKKK2Lttdde4eG1EYsO03/uuefioIMOimOPPTYaNWoUc+fOjX/9619xxx13xJ///OeoUqVKbLvttlG1atW48soro1u3bjFt2rS46aab4vvvv8+dh78q3n///fjjH/8Y+++/f7zyyisxevTo6Nu3b6nTnnTSSdG9e/c466yzYr/99osffvghBg0aFDNmzMhc6+Hn9t577xgyZEgcd9xxcdppp8WXX34ZV155ZRx++OGrdHi0WlV8rSqDOlV8nfr37x8zZ86Miy66KD7//PPcACBiUchT2tEJZaFWFV+rY489Nv7xj3/EKaecEkcccURMnz49Bg4cGFtuuWUceOCBK913tarYWi0OEpe0+IeuwsLC2GyzzVb5OcDKeuutt0q9xlfEous0ff3113HFFVfEIYccEs2bN4/f/e538dBDD8XFF1+cuzZS3bp14z//+U+89NJLscMOO1TaOKF+/fqxySabxLBhw2K99daLgoKCuPfee3PXb/rxxx+X+VxWxbHHHhsPPPBAnHjiidGjR48oKCiIm266KSZMmBAXXnhhVKlSJYqLi+P666+PmjVrxtZbbx2vv/56vPrqq7mjbhafEvnYY49Fu3btVrjff//99/H666+X+li9evViyy23jDvuuCP+/e9/xx133BG1atWKCy64IE488cR48MEHY7/99ssdiTd27NioUaNGbL311tG9e/e47LLL4ttvv40mTZrEf/7zn7juuuviwAMPXKUjS5o1a5Y7Da9Lly7x6aefxvXXXx8LFy6skPfp0lSrVi1OOumkuOqqq2LhwoXRunXrmDhxYlx99dXRokWL2GCDDXLPqXfv3tGrV6+oXr16/O1vf4u6detGmzZtImJRbd5777148cUXl3npi7KwL63Y6tiXlqRGK7a6a7Qin3322TLfGzfffPOoX79+9OvXL6pVqxann3561K1bN4466qgYNGhQdO3aNRo0aBB169aN2bNnx9ixY6NZs2bRqVOnaNSoUfTq1St3RsvDDz8c9913X1x99dWr1N+mTZvGLbfcErfffntss8028frrr8eQIUOioKAg5syZs0rLXpbGjRvHrrvuGmeddVacfPLJ0bBhw3jmmWfilltuiTPPPDMiFl03d+DAgXHhhRfGXnvtFTNmzIgbbrghtt9++1yGUrdu3XjllVeiZcuWv8hR2ULD/69KlSpx3XXXxZ133hmjRo2Ku+++OwoKCmKbbbaJAQMGLPMQ/v333z/OOOOMOOKII8r0gd6gQYMYNWpU3HDDDTFixIj/1979hDT9x3Ecf5l/xkIPkmFglx3Cw2CQ9IeChCZEdOiQYuDAP6CL4AsjVrNNinDhCuYWO8jUNDexi+hJwW6dOuihqC6evHRJEIUiD3PYIX5f2u+nfqnUbf2ej+vnzXfffd989uX73vfzeWt1dVUVFRVyOp1KJpNqbGyU9OOfgYGBAQ0NDcnr9er48eNqamrS9evXlUgkzCrz72ppadG3b99kGIZqa2sVDofV0tKyY6zL5dLExITi8bgMw5DdbtfZs2d19+5d8x/Lndjtdo2PjyscDsvv96uqqkoej0d37tz5o3MnV/ufq4NAnvY3T1tbW3r9+rWy2az8fv9/xoPBoNkY5VeRq/2fU/X19Uqn0xocHJTP51N5ebncbrfu3btnuY/fXshVcfz+Afuhq6tr17H379+rv79fZWVl5oOG3W5XKBSSYRiamZlRc3OzOjo6FIlE1NPTo1evXh3YPCkpKVEikdDjx4/l8/lUVVWl06dPa3h4WF6vV+/evdPFixd/+/i7qaur09TUlKLRqPx+v0pLS+VyuZRKpcytcILBoGw2m5LJpNbW1lRXV6fe3l5zyeL58+d17tw59ff3q7W1VQ8fPtzzM5eWlnTz5s0dx65du6b79+8rFovpxo0bamhokCRdvnxZbrdbAwMDunTpkk6dOqWrV69qeHhYHz9+VDKZVDAYVE1NjV6+fGnuT3vr1i3dvn37j67RhQsXFAgENDk5qenpaZ04cULNzc368uWLFhYWDuyNms7OTh09elQTExMaHR3VsWPHzHuR9OOh9/nz54pGowoEAspkMnK5XHrx4oX5YNzW1qbe3l51d3crlUqZ1/NXMZes5WMu/YwcWct3jqykUimlUqkdxx49eqTa2lotLCzo6dOnZvHSMAzNz8/rwYMHSqfTunLlimZnZ+Xz+eTz+dTT06OxsTFFo1E9efJEm5ubcjgcisViOzZd+RVer1efP39WIpFQJpORw+FQJBLR+Pi43r59K4/H80fH3008HtezZ880NDSkjY0NnTx5Un19fWazrDNnzmhwcFAjIyOam5uTzWZTY2NjTkOb7u5uJZNJLS4u6s2bNwfS2PRnJdsHdadAwXK73WpqajKXAKBwkaviQJ6KB7kqHuQKAAAAyC/eNAQAAABwaP7pTr6XI0eO5H3/rf+jra0ty5jS0tI9u0Hj8DCXCh85Kn7b29vKZrOWcQf9xl++/J3fCgAAAEBBCoVClh0q6V6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      "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 = 'classification',\n",
    "                                                  dependent_variable=dependent_variable,\n",
    "                                                  features=best_columns_SA_CouponRecommendation_xgboost,\n",
    "                                                  cost_funct=cost_funct)\n",
    "\n",
    "print('Average Test and Validation Cost:',round(np.mean(list(test_tree_cost.values())),2),'Average External Test Cost:',round(np.mean(external_tree_cost),2))\n",
    "metric = 'Precision'\n",
    "title = 'Coupon Recommendation dataset - Simulated Annealing'\n",
    "print(title)\n",
    "make_plot(metric=metric,\n",
    "          title=title,\n",
    "          test_tree_cost=test_tree_cost,\n",
    "          external_tree_cost=external_tree_cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "External Test recall: 0.73\n",
      "Average Test and Validation Cost: 0.75 Average External Test Cost: 0.76\n",
      "Coupon Recommendation 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 = 'classification',\n",
    "                                                  dependent_variable=dependent_variable,\n",
    "                                                  features=best_columns_GA_CouponRecommendation_xgboost,\n",
    "                                                  cost_funct=cost_funct)\n",
    "\n",
    "print('Average Test and Validation Cost:',round(np.mean(list(test_tree_cost.values())),2),'Average External Test Cost:',round(np.mean(external_tree_cost),2))\n",
    "metric = 'Precision'\n",
    "title = 'Coupon Recommendation dataset - Genetic Algorithm'\n",
    "print(title)\n",
    "make_plot(metric=metric,\n",
    "          title=title,\n",
    "          test_tree_cost=test_tree_cost,\n",
    "          external_tree_cost=external_tree_cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
     ]
    }
   ],
   "source": [
    "patch_sklearn()\n",
    "model_use = LogisticRegression(random_state=1,n_jobs=-1)\n",
    "\n",
    "fsObj = FeatureSelection(columns_list = linear_features_all,\n",
    "                         data_dict = data_dict_for_linear,\n",
    "                         x_validation_dataframe = x_validation_linear,\n",
    "                         y_validation_dataframe = y_validation_linear,\n",
    "                         model = model_use,\n",
    "                         cost_function = cost_funct,\n",
    "                         cost_function_improvement = 'increase',\n",
    "                         average = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combined performance on test and validation data for iteration 0: 0.6033032676937446\n",
      "Best combined performance on test and validation data for iteration 1: 0.6038369351972633\n",
      "Best combined performance on test and validation data for iteration 2: 0.6044649489450067\n",
      "Best combined performance on test and validation data for iteration 3: 0.6044649489450067\n",
      "Best combined performance on test and validation data for iteration 4: 0.6044649489450067\n",
      "Best combined performance on test and validation data for iteration 5: 0.6044649489450067\n",
      "Best combined performance on test and validation data for iteration 6: 0.6044649489450067\n",
      "Best combined performance on test and validation data for iteration 7: 0.6044649489450067\n",
      "Best combined performance on test and validation data for iteration 8: 0.6044649489450067\n",
      "Best combined performance on test and validation data for iteration 9: 0.6044649489450067\n",
      "Best combined performance on test and validation data for iteration 10: 0.6044649489450067\n",
      "Best combined performance on test and validation data for iteration 11: 0.6044649489450067\n",
      "Best combined performance on test and validation data for iteration 12: 0.6047911089843686\n",
      "Best combined performance on test and validation data for iteration 13: 0.604900321860675\n",
      "Best combined performance on test and validation data for iteration 14: 0.604900321860675\n",
      "Best combined performance on test and validation data for iteration 15: 0.6058576881080644\n",
      "Best combined performance on test and validation data for iteration 16: 0.6058576881080644\n",
      "Best combined performance on test and validation data for iteration 17: 0.6058576881080644\n",
      "Best combined performance on test and validation data for iteration 18: 0.6058576881080644\n",
      "Best combined performance on test and validation data for iteration 19: 0.6058576881080644\n",
      "Best combined performance on test and validation data for iteration 20: 0.6058597529725833\n",
      "Best combined performance on test and validation data for iteration 21: 0.6058597529725833\n",
      "Best combined performance on test and validation data for iteration 22: 0.6061869072407837\n",
      "Best combined performance on test and validation data for iteration 23: 0.6061869072407837\n",
      "Best combined performance on test and validation data for iteration 24: 0.6061869072407837\n",
      "================= Best result: 0.6061869072407837 =================\n",
      "================= Execution time in minutes: 187.0 =================\n"
     ]
    }
   ],
   "source": [
    "#### Particle Swarm Optimization\n",
    "best_columns_PSO_CouponRecommendation_logistic = fsObj.ParticleSwarmOptimization(iterations = 25,                                                                   \n",
    "                                                                       swarmSize = 75,\n",
    "                                                                       run_time=1200)\n",
    "\n",
    "with open(r\"D:\\\\Results\\\\ResultPickle\\\\best_columns_PSO_CouponRecommendation_logistic.pickle\", \"wb\") as output_file:\n",
    "    pickle.dump(best_columns_PSO_CouponRecommendation_logistic, output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combined performance on test and validation data for iteration 0: 0.6052643319838451\n",
      "Best combined performance on test and validation data for iteration 1: 0.6055425649227003\n",
      "Best combined performance on test and validation data for iteration 2: 0.6055425649227003\n",
      "Best combined performance on test and validation data for iteration 3: 0.6074935646163113\n",
      "Best combined performance on test and validation data for iteration 4: 0.6074935646163113\n",
      "Best combined performance on test and validation data for iteration 5: 0.6077576839151769\n",
      "Best combined performance on test and validation data for iteration 6: 0.6086847776280335\n",
      "Best combined performance on test and validation data for iteration 7: 0.6087579314916947\n",
      "Best combined performance on test and validation data for iteration 8: 0.6087579314916947\n",
      "Best combined performance on test and validation data for iteration 9: 0.6087579314916947\n",
      "Best combined performance on test and validation data for iteration 10: 0.6087579314916947\n",
      "Best combined performance on test and validation data for iteration 11: 0.6087579314916947\n",
      "Best combined performance on test and validation data for iteration 12: 0.6087579314916947\n",
      "Best combined performance on test and validation data for iteration 13: 0.6087579314916947\n",
      "Best combined performance on test and validation data for iteration 14: 0.6087579314916947\n",
      "Best combined performance on test and validation data for iteration 15: 0.6087579314916947\n",
      "Best combined performance on test and validation data for iteration 16: 0.6087579314916947\n",
      "Best combined performance on test and validation data for iteration 17: 0.6087579314916947\n",
      "Best combined performance on test and validation data for iteration 18: 0.6087579314916947\n",
      "Best combined performance on test and validation data for iteration 19: 0.6087579314916947\n",
      "Best combined performance on test and validation data for iteration 20: 0.6087579314916947\n",
      "Best combined performance on test and validation data for iteration 21: 0.6087579314916947\n",
      "Best combined performance on test and validation data for iteration 22: 0.6087579314916947\n",
      "Best combined performance on test and validation data for iteration 23: 0.6087579314916947\n",
      "Best combined performance on test and validation data for iteration 24: 0.6087579314916947\n",
      "================= Best result: 0.6087579314916947 =================\n",
      "================= Execution time in minutes: 187.0 =================\n"
     ]
    }
   ],
   "source": [
    "#### Ant Colony Optimization\n",
    "best_columns_ACO_CouponRecommendation_logistic = 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_CouponRecommendation_logistic.pickle\", \"wb\") as output_file:\n",
    "    pickle.dump(best_columns_ACO_CouponRecommendation_logistic, output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combined performance on test and validation data for iteration 0: 0.5990315054653397\n",
      "Best combined performance on test and validation data for iteration 1: 0.6002150332085237\n",
      "Best combined performance on test and validation data for iteration 2: 0.6026037806626952\n",
      "Best combined performance on test and validation data for iteration 3: 0.6042208075852844\n",
      "Best combined performance on test and validation data for iteration 4: 0.6044607019125834\n",
      "Best combined performance on test and validation data for iteration 5: 0.6031436360880409\n",
      "Best combined performance on test and validation data for iteration 6: 0.6045601245567804\n",
      "Best combined performance on test and validation data for iteration 7: 0.6047024551369433\n",
      "Best combined performance on test and validation data for iteration 8: 0.6047337454632997\n",
      "Best combined performance on test and validation data for iteration 9: 0.6149329540487187\n",
      "Best combined performance on test and validation data for iteration 10: 0.6145738529151674\n",
      "Best combined performance on test and validation data for iteration 11: 0.6147570386110944\n",
      "Best combined performance on test and validation data for iteration 12: 0.6154117113438473\n",
      "Best combined performance on test and validation data for iteration 13: 0.6203748851017338\n",
      "Best combined performance on test and validation data for iteration 14: 0.6292544503576715\n",
      "Best combined performance on test and validation data for iteration 15: 0.6287621619544492\n",
      "Best combined performance on test and validation data for iteration 16: 0.6450526484599626\n",
      "Best combined performance on test and validation data for iteration 17: 0.6446326044847576\n",
      "Best combined performance on test and validation data for iteration 18: 0.6516580398453397\n",
      "Best combined performance on test and validation data for iteration 19: 0.6597340932208509\n",
      "Best combined performance on test and validation data for iteration 20: 0.6597340932208509\n",
      "Best combined performance on test and validation data for iteration 21: 0.6623304343601047\n",
      "Best combined performance on test and validation data for iteration 22: 0.6638958890068515\n",
      "Best combined performance on test and validation data for iteration 23: 0.6647723847219886\n",
      "Best combined performance on test and validation data for iteration 24: 0.6684281355867127\n",
      "Best combined performance on test and validation data for iteration 25: 0.6769452082395307\n",
      "Best combined performance on test and validation data for iteration 26: 0.6684218314118723\n",
      "Best combined performance on test and validation data for iteration 27: 0.6691199184496561\n",
      "Best combined performance on test and validation data for iteration 28: 0.6714273784579443\n",
      "Best combined performance on test and validation data for iteration 29: 0.6714273784579443\n",
      "Best combined performance on test and validation data for iteration 30: 0.6700346080209598\n",
      "Best combined performance on test and validation data for iteration 31: 0.6713469719138561\n",
      "Best combined performance on test and validation data for iteration 32: 0.6781054474055168\n",
      "Best combined performance on test and validation data for iteration 33: 0.6780525179139387\n",
      "Best combined performance on test and validation data for iteration 34: 0.6783006071114972\n",
      "================= Best result: 0.6783006071114972 =================\n",
      "================= Execution time in minutes: 256.0 =================\n"
     ]
    }
   ],
   "source": [
    "#### Simulated Annealing\n",
    "best_columns_SA_CouponRecommendation_logistic = fsObj.SimulatedAnnealing(temperature = 1500,\n",
    "                                                               iterations = 35,\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_CouponRecommendation_logistic.pickle\", \"wb\") as output_file:\n",
    "    pickle.dump(best_columns_SA_CouponRecommendation_logistic, output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best combined performance on test and validation data for generation 0 : 0.6035517537273216\n",
      "Best combined performance on test and validation data for generation 1 : 0.6057853483835236\n",
      "Best combined performance on test and validation data for generation 2 : 0.605750007232913\n",
      "Best combined performance on test and validation data for generation 3 : 0.6062625712625472\n",
      "Best combined performance on test and validation data for generation 4 : 0.6063969344229204\n",
      "Best combined performance on test and validation data for generation 5 : 0.6080259972816114\n",
      "Best combined performance on test and validation data for generation 6 : 0.6064274918425209\n",
      "Best combined performance on test and validation data for generation 7 : 0.6078519727601703\n",
      "Best combined performance on test and validation data for generation 8 : 0.6077901563582361\n",
      "Best combined performance on test and validation data for generation 9 : 0.6064154329404412\n",
      "Best combined performance on test and validation data for generation 10 : 0.6074507123924718\n",
      "Best combined performance on test and validation data for generation 11 : 0.6080048299103806\n",
      "Best combined performance on test and validation data for generation 12 : 0.6068531104888036\n",
      "Best combined performance on test and validation data for generation 13 : 0.6057037869863868\n",
      "Best combined performance on test and validation data for generation 14 : 0.6073988271603772\n",
      "Best combined performance on test and validation data for generation 15 : 0.606695021551537\n",
      "Best combined performance on test and validation data for generation 16 : 0.6058605459503911\n",
      "Best combined performance on test and validation data for generation 17 : 0.6092141140472719\n",
      "Best combined performance on test and validation data for generation 18 : 0.6055142069940054\n",
      "Best combined performance on test and validation data for generation 19 : 0.6088074207543702\n",
      "================= Best result: 0.6092141140472719 =================\n",
      "================= Execution time in minutes: 576.0 =================\n"
     ]
    }
   ],
   "source": [
    "#### Genetic Algorithm\n",
    "best_columns_GA_CouponRecommendation_logistic = fsObj.GeneticAlgorithm(generations=20,\n",
    "                                                             population=75,\n",
    "                                                             run_time=1200)\n",
    "\n",
    "with open(r\"D:\\\\Results\\\\ResultPickle\\\\best_columns_GA_CouponRecommendation_logistic.pickle\", \"wb\") as output_file:\n",
    "    pickle.dump(best_columns_GA_CouponRecommendation_logistic, output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "External Test recall: 0.8\n",
      "Average Test and Validation Cost: 0.61 Average External Test Cost: 0.63\n",
      "Coupon Recommendation dataset - Particle Swarm Optimization\n"
     ]
    },
    {
     "data": {
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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 = 'classification',\n",
    "                                                  dependent_variable=dependent_variable,\n",
    "                                                  features=best_columns_PSO_CouponRecommendation_logistic,\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 = 'Precision'\n",
    "title = 'Coupon Recommendation dataset - Particle Swarm Optimization'\n",
    "print(title)\n",
    "make_plot(metric=metric,\n",
    "          title=title,\n",
    "          test_tree_cost=test_tree_cost,\n",
    "          external_tree_cost=external_tree_cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "External Test recall: 0.8\n",
      "Average Test and Validation Cost: 0.61 Average External Test Cost: 0.64\n",
      "Coupon Recommendation dataset - Ant Colony Optimization\n"
     ]
    },
    {
     "data": {
      "image/png": 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z+Oabb+Loo4+OSpX+e1WAatWqxd577x033XRTfPXVV7mLRbdo0SIz30YbbRQRUaZTGH/++ed466234vDDD4+1114789gBBxwQvXv3jvfeey93DZfCwsKoUqXsbwml6eOOHTtGYWFhDBw4MN5///1o27Zt7Lzzzit0R9pXX301WrVqFeuss05u2iabbBLFxcWZUzquueaaiIiYPn16TJgwISZPnhzvvfdeRETudJCS7LfffrHffvvFnDlz4rPPPotJkybFp59+Gp999tkyl6tWrVrccsstEbHwtJ5F21x06sSvl91uu+0yf2+00UaRUoqffvopFixYEG+//XYcddRRmZpsu+22y72geLVq1WKfffaJhx56KC6++OKoVq1aPPTQQ7nXWkREu3btol27djF//vyYMGFCTJo0KT7//PPcaTxz5syJmjVrLnM7b775ZsydOzc6d+6cmb7XXntl7hjcsGHDGDVqVKSU4ssvv4xJkybFhAkT4tlnny2xX5a1vdmzZ8e+++6bmV63bt3o0KFDPProo7lT3SNK7t+Ipe9Ds2fPLlWfl7XOi+vWrVt069YtZs+enVvu448/jqlTp0a9evVK0w0AsEzGzgutKWPnF154ISIidtttt0x/7rLLLlGpUqV47rnnol27dhER0bNnz3j++efjjDPOiOrVq8cDDzwQ1atXX+q6X3zxxViwYMES627WrFlssMEG8dxzz8Whhx6am96oUaMS11PSmOnHH3+MiJUb9yzPhAkTYsqUKXHggQfGggULYsGCBRGx8GY+bdq0iXHjxkVEROvWraNy5cpx8MEHR+fOnaNt27bRpk2bOO+888q0vVUxdl3R7x+wJhMaskZba621YtSoUbm/q1atGnXr1o0NN9ywxPl//QE/bdq0mDRpUomDp4iFd02rW7duRESZvrTvscceMXDgwLjzzjtj5MiRcfvtt0fNmjXjwAMPjHPPPTeqVauWmX9RmLXBBhsssa5F02bMmJGbVqNGjcw8iwZBiz5MS+PHH3+MlFKpt/nrviut0vRxQUFBDB8+PIYOHRpPPvlk7rowTZo0id69e0fLli1Lvb0ffvgh1ltvvSWmb7DBBpnQ8KOPPoq//OUv8dprr0XVqlWjfv36se2220ZELPWaMxELP9CvvPLKeOCBB+Lnn3+ODTfcMBo3blyq/nnllVfiiiuuiA8//DCqV68eW221VRQWFpa4zWXVePr06ZFSWurzXJ6DDjooRo4cGc8++2zsvvvu8be//S122223qFOnTq4tAwcOjJEjR8b06dNjvfXWi0aNGuXatKz+WWRRXy/afxb53e9+t8S8d955ZwwZMiS+/fbbqFOnThQVFZVpWxELB18RS9+H5s6dGz///HNuWln3obL0eVnqvLgff/wxLr300njiiSdi3rx58fvf/z6aNWsWa621Vqn7AQCWxdh5oTVl7Dxt2rSIWHgNxJL8+9//zv1/5cqV44ADDog333wzttpqq2jQoEGp1r20a/gtvu5ltbekvlt8XLKi457lWdT+AQMGZK7RuLiff/45ttlmmxg+fHgMGzYsRo8eHXfeeWdUr1499thjj7j44oujdu3apdreqhi7ruj3D1iTCQ1Zo1WqVCmaNWu2wssvepO/7LLLSnz897//fXz//fcR8d8PrsWNHTs296H4a506dYpOnTrF7Nmz41//+leMHj06Ro4cGRtvvHF07949M++6664bERH/+c9/lljPoptf/PoDbGXVrl07KlWqVOHbLE0fRywcWPbu3Tt69+4dkyZNinHjxsXQoUPj5JNPjnHjxi0xWFyaunXrxnfffbfE9EV1jIiYOXNmdOvWLTbaaKN44IEHoqioKKpWrRo//fRT3Hvvvctc/2WXXRZjxoyJPn36xK677pobiJx66qm5i4eXZPLkyXHiiSdGy5Yt49FHH40tt9wyKlWqFJ988kmJFw9f3nOsVKnSUp/n8urWqFGjaNSoUTzyyCOx4YYbxsSJE+PPf/5z7vFbbrklBg4cGL1794599tknF5RdeeWV8cYbb5SqjYuW+XUbF69DRMTf//736NOnT/To0SOOOuqo3JeWO++8M5577rlSbSsickeWLu31XK1atahVq1YmOC6L0vb5ytT5zDPPjPfeey934fFFg/eDDjoofvnllxVqNwAszth5xVXE2LlOnTpRUFAQd911V1StWnWJxxc/c+abb76J6667Lho3bhzvv/9+3HrrrXHCCScsc90REQMHDswdWbm4X4eBK6I8x7e/tui5n3TSSbHbbruVOM+i7wetWrWKVq1axdy5c+Ptt9+Oxx9/PEaNGhW1atXKjHGXpaLHrivz/QPWZO6ezG9a69atY/LkyfG73/0umjVrlvs3ceLE6N+/f0yfPj223HLLWH/99ePJJ5/MLPvhhx/GiSeemDv8fnG9e/eOQw45JFJKsdZaa0Xbtm2jX79+Ubly5fjqq6+WmL9Bgwax4YYbxiOPPJL5xXPu3Lnx6KOPRoMGDUr8lWtlrL322rHNNtvEk08+GT/99FPmsYceeihq16691F+Rl2XxUz8iStfHH330Uey0007xj3/8IyIiNt988+jatWscfPDB8cMPP+ROHfn1ukvSrl27eOWVVzIf+FOnTs3c+ezzzz+PqVOnxuGHHx5NmzbNDdIWnVaweA1+vc3XXnstmjZtGvvvv38uMJwxY0a88cYby/y1+t13342ff/45unfvHltvvXVuvSVtc3mqVasWLVu2jKeeeioTJo0fP36Zd5Ve3EEHHRTjxo2L0aNHx0YbbRRt27bNPMeNNtoojjnmmNwAau7cufH8889HROl+CW3RokXUqFEjHn300cz0p59+OvP3q6++GpUqVYpTTz01c5TDon5ZtK3KlSsvd3trrbVW/O1vf8tM/+GHH2Ls2LHRqlWr5bZ5WUrb52Wpc0mvrZ122ik6dOiQCwynTJkS48ePL9PrAwAqirFz+Y6dW7duHSml+OGHHzL9WbNmzbjmmmtyd49OKcX5558fEQt/3D3ssMOif//+8f777+fWVdIYPGLhEYWLr3vTTTeN6667Ll577bUy98Gvlef49tdjvS233DI22GCD+PzzzzPtb9asWdx5553xwAMPROXKlWPAgAHRsWPHmDNnTlStWjVatmwZF110UWy88ca5187yxpERFT92Lcv3D8gnjjTkN61bt27x8MMPxzHHHBMnnHBCbLLJJvHOO+/EwIEDo3HjxlG/fv2oVKlSnHnmmdG7d+8488wzY7/99ouZM2fGjTfeGH/4wx9i9913X+KX1B133DEeeOCBOOecc2K//faLgoKCuP/++yMi4o9//OMS7ahUqVKcd955cdZZZ0WPHj3i8MMPj5RSDB8+PL788ssYNGhQhTz/s88+O4499tg46qijonv37rH22mvHfffdF6+99lr8+c9/LvXRfYurU6dOfPbZZ/HKK69E8+bNS9XHKaVYZ511ok+fPvHDDz9E/fr1Y8KECXHXXXfFjjvumPvV9tfrLukX0pNPPjmeeuqpOPbYY6NXr15RUFAQgwcPznwQb7nlllGnTp0YPnx4rLvuulGrVq147bXX4rbbbouCgoLMaay/3mZxcXE89NBDMWzYsGjSpElMmTIlbr/99pg6dWpERMyfP7/EgcmiwUH//v1jzpw5UaVKlRg7dmzcddddERGZbZbGWWedFUceeWR069YtunfvHjNnzowbbrih1DXbZ5994qqrror7778/evTokRloFhcXx3PPPRfXX399tGvXLr777rsYOXJkfPLJJxGx8HpIyzvlaO21147TTz89rrjiirjoootijz32iPHjx+eue7P4tu6+++7o06dP7L333jFz5sy4//77cwHlokH5ol/LX3zxxdhiiy2WuO5OnTp14uSTT45rr702zjrrrNh3331jxowZMWTIkPjll1/ijDPOKFW/LEtp+rwsda5Tp068//778dprr8V2220XxcXF8fTTT8d9990XW2yxRXz22Wdx8803x7x588r8+gCAimDsXL5j55133jl23HHHOO+88+KEE06IZs2axddffx0DBgyI2bNn544KHTFiRLz44otx9dVXx/rrrx/nnHNOPPfcc3H22WfHmDFjYq211sodmffkk09G27ZtY+utt45DDz00rr766vjuu++idevW8cMPP8TQoUNj0qRJcfbZZ690f5Tn+Laksd65554b55xzTpx11lmx9957R+XKleO+++6Lf/zjH3HppZdGRETbtm1j8ODB0bNnzzjyyCNjrbXWiieffDK++uqr3DUxlzeOjKj4sWtZvn9APhEa8ptWr169+Otf/xo33nhjXHvttTF9+vTYcMMNo2vXrnHyySfngpSDDjooatWqFTfffHP06tUr1llnndhpp53izDPPjLXXXnuJgc+ee+4Zc+bMiTvuuCNOO+20WLBgQTRq1CiGDBmy1Ovz7bXXXlGrVq0YOnRonHXWWVGlSpUoLi6OkSNHlumafmXRsmXLuPPOO6N///5x0UUXRUopGjZsGDfddNNSTwNYnuOPPz4uu+yyOP7442PYsGHRsmXLUvXxrbfeGjfccEMMHDgwpk2bFuutt17svffecdpppy1z3b+22Wabxd133x1XXXVVXHDBBbHWWmvFoYceGltttVWMHz8+IiJq1aoVQ4YMiauvvjp3nZz69evHZZddFo888ki8/vrrkVKKgoKCJbZ5/vnnR0opbrnllpg1a1ZstNFG0aFDhzj22GOjd+/e8frrr+d+2V3c5ptvHjfeeGP0798/Tj311KhZs2ZsvfXWcfPNN0e/fv3K/GtvcXFxDB8+PK6//vo444wzYt11143u3bvHY489Vqrl11133dh1113jscceiwMPPDDzWI8ePWLGjBkxZsyYGDZsWGywwQbRpk2bOPHEE+PEE0+M1157LTbbbLPlbuPYY4+NmjVrxrBhw+Khhx6K+vXrx7XXXhvdunXLzbP//vvHN998E/fee2889NBDUa9evSguLo677rorjjjiiHjttddihx12iK233jr23nvvePDBB+P111+Pv//970ts74QTTogNN9wwRowYEb169Yq11147tt9++7j22mujqKioVP2yLKXp87LUuWfPnjFo0KDo0aNHPPLII3HllVfGZZddFtdcc03MmTMnfv/738chhxwSlSpVihtuuCEmT55cqn4HgIpi7Fy+Y+dFP24PHjw416/rrrtutG7dOk499dTYeOONY/z48XHttddGx44dczd8q1mzZvTt2ze6desWV155ZVxyySXRuXPnePDBB6Nv375x4IEHRp8+feLSSy+NrbbaKu6777647bbbolatWlFcXByXXXbZCp1R9GvlOb4taay37777xjrrrBM333xznHHGGVGpUqXYeuut44YbbsiFydttt10MHjw4hg4dGueee2788ssvsdVWW8VVV12Vu8lfacaRERU/di3t9w/IJwXJFTkBAAAAgMW4piEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACAjCqruwEr4scff4758xes7masEerWrRnffz9rdTeDUlCr/KFW+UGd8oda/VflypWiTp0aq7sZlAPj0f+yj+cPtcofapU/1Co/qFNWacekeRkazp+/IObNM0grKFj43/nzF0RKq7ctLJta5Q+1yg/qlD/Uit8q49GF7OP5Q63yh1rlD7XKD+q04pyeDAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgIwqq7sBAAAAsCoUFKzuFizfojbmQ1sjIlKqmPXmw/PPp1pVVJ34bRMaAgAA8Ju3zjprR7VqlVd3M0qtXr3aq7sJpTJnzvyYPv2ncl2nWpW/iqgTv31CQwAAgJW0ph9plE9HREWU/1FRBQUR1apVjmP7PBE/zZ5Xviv/H7b2WlVi+J93j4KC8quZWpW/iqgT/xuEhgAAACshn46KyocjoiIq7qion2bPi59/EUTlA7WC1U9oCAAAsIIcFVX+HBUF+WVNP4L5f/1I65UhNAQAAFhJjooC/hc50rr8rUnXnxQaAgAAAFAmjrQuf2vakdZCQwAAAABWiCOtf7sqre4GAAAAAABrFkcaAgDAGmpNv2i7i8sDwG+X0BAAANZALi5f/taki8sDwJpOaAgAAGsYF5cvf2vaxeUBYE0nNAQAgDWUi8sDAKuLG6EAAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMqqs7gYA/zsKClZ3C5ZvURvzoa0pre4WAAAA8FslNARWiXXWWTuqVau8uptRavXq1V7dTViuOXPmx/TpP63uZgAAAPAbJDQEKlxBQUS1apXj2D5PxE+z563u5vwmrL1WlRj+592joMARhwAAAJQ/oSGwyvw0e178/IvQEAAAANZ0boQCAAAAAGQ40hCAJazpN4LJpxvWRDiFHAAAyD9CQwAy8ummNflww5oIN60BAADyj9BwKfLh6BVH2gDlzU1ryp+b1gAAAPlIaFiCfDrKJsKRNvkQmgp4yTduWgMAAPC/TWj4K46yqRgVdaSNgLdiOJUSAAAA/rcJDZfCUTZrPgFvxXAqJQAAACA0JO8JeAEAAADKV6UVWejuu++OTp06RfPmzaNLly7x1ltvLXP+7777Ls4+++zYfvvto1WrVnHqqafGv//97xXZNAAAAABQwcocGo4ePTr69OkT++23XwwYMCBq164d3bt3j6+++qrE+efMmRPHHXdcfPDBB3H55ZdHnz594qOPPoqePXtGcu4jAAAAAKxxynR6ckopBg4cGEcccUT06tUrIiLatm0be+yxR4wYMSIuuOCCJZZ58MEH48svv4zHHnssNtpoo4iIqFevXpx11lkxYcKEaNCgQTk8DQAAAACgvJTpSMOJEyfGlClTomPHjrlpVatWjV122SXGjRtX4jJPPfVU7LzzzrnAMCJi++23j+eee05gCAAAAABroDKFhhMmTIiIiM033zwzfbPNNouJEyeWeLrxxx9/HA0aNIgbb7wx2rZtG82aNYuTTz45vvvuuxVvNQAAAABQYcp0evLMmTMjIqJmzZqZ6TVr1ox58+bF7Nmzo0aNGpnHpk2bFvfff39sttlmccUVV8SPP/4YV111VfTq1SvuvvvuKCgoWKGGr+Biq229LFSe/atWFUut8kN5961aVZz/1b5d9Lz/V58/v20V+bq2z1Qcn535w3g0f6hVfvD+lz/WlL4t8zUNI2KpQV+lSkseuDhv3ryYN29e3HLLLVGrVq2IWHhNw+OOOy5efvnlaNOmTVnbHHXr1lz+TKyR6tWrvbqbQCmpVX5Qp/zxv16r//Xnz2+P8Wj+8n6UP9Qqf6hVflCn/LGm1KpMoWHt2gsbPWvWrKhbt25u+qxZs6Jq1apRvXr1JZZZe+21o7i4OBcYRkS0atUqqlSpEh9//PEKhYbffz8r5s9fUOblSqOgYM0pzm/R1Kkzorxumq1WFUut8kN51ilCrSpSedcqXyx6Tf2vPv9fq1y5krDpN6Iix6MR3o8rks/O/GE8mj/UKj94/8sfFT12Lu2YtEyh4RZbbBEREZMnT45NN900N33y5MlLvanJ5ptvHnPnzs1MW7BgQSxYsGCFT02OCF888lRKapcv1Co/qFP++F+v1f/68+e3yWs6P3k/yh9qlT/UKj+oU/5YU2pVphuh1K9fPzbeeON4+umnc9Pmzp0bY8eOXeoRg+3atYs33ngjc+OTF154IRYsWBAtWrRYwWYDAAAAABWlTEcaFhQURI8ePaJv375Rq1ataNGiRYwaNSqmT58exxxzTERETJo0KaZNmxbFxcUREXHMMcfEAw88EMcff3yccsopMWPGjLjqqquiffv20axZs3J/QgAAAADAyilTaBgR0bVr15g9e3aMHDkyhg0bFo0aNYrbbrstNtlkk4iIGDRoUIwZMybGjx8fERHrr79+3HXXXdGvX784++yzo1q1arHHHnvEeeedV77PBAAAAAAoF2UODSMiunfvHt27dy/xsX79+kW/fv0y0+rXrx9DhgxZkU0BAEuxEpcGXmUWtTEf2hqxZlw7BgAA1gQrFBoCAKvXOuusHdWqVV7dzSi1fLmz3pw582P69J9WdzMAAGC1ExoCQJ4pKIioVq1yHNvnifhp9rzV3ZzfjLXXqhLD/7x7FBQ44hAAAISGAJCnfpo9L37+RWgIAACUv0qruwEAAAAAwJpFaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAICMFQoN77777ujUqVM0b948unTpEm+99dYy5z/11FOjqKhoiX+zZs1akc0DAAAAABWoSlkXGD16dPTp0ydOPvnkaNasWYwcOTK6d+8eDz/8cPz+978vcZnx48dH165dY999981Mr1Gjxoq1GgAAAACoMGUKDVNKMXDgwDjiiCOiV69eERHRtm3b2GOPPWLEiBFxwQUXLLHMzz//HJMmTYrzzjsviouLy6XRAAAAAEDFKdPpyRMnTowpU6ZEx44dc9OqVq0au+yyS4wbN67EZT755JNYsGBBFBYWrlxLAQAAAIBVokyh4YQJEyIiYvPNN89M32yzzWLixImRUlpimfHjx0eVKlVi8ODB0bp169hmm23i1FNPjf/85z8r3moAAAAAoMKU6fTkmTNnRkREzZo1M9Nr1qwZ8+bNi9mzZy9xncLx48fHvHnzomrVqjFw4MCYPHly3HDDDXHsscfGmDFjolq1aivU8IKCFVpsta2Xhcqzf9WqYqlVfijvvlWrimOfyh/6l9KqyNeK12HF8dmZP3x25g+1yg/e//LHmtK3Zb6mYUREwVJaX6nSkgcudu3aNXbddddo06ZNRERsv/32sdVWW8Whhx4aTzzxROyzzz5lbXPUrVtz+TOxRqpXr/bqbgKlpFb5QZ3yh1rlD7WiNIxH85d9PH+oVf5Qq/ygTvljTalVmULD2rUXNnrWrFlRt27d3PRZs2ZF1apVo3r16kss06BBg2jQoEFm2jbbbBN16tSJ8ePHr1Bo+P33s2L+/AVlXq40CgrWnOL8Fk2dOiNKOIt9hahVxVKr/FCedYpQq4pkn8of5b1fLa5y5UrCpt+IihyPRtjPK5LPzvzhszN/qFV+8P6XPypyPBpR+jFpmULDLbbYIiIiJk+eHJtuumlu+uTJk5cIBhd54oknYp111okddtghNy2lFHPmzIl11123LJvPqMjOo+KkpHb5Qq3ygzrlD7XKH2pFaXmd5Cf7eP5Qq/yhVvlBnfLHmlKrMt0IpX79+rHxxhvH008/nZs2d+7cGDt2bO7041+7//77o2/fvjF//vzctH/+858xe/bs2H777Vew2QAAAABARSnTkYYFBQXRo0eP6Nu3b9SqVStatGgRo0aNiunTp8cxxxwTERGTJk2KadOmRXFxcURE9OjRI4455pg444wzokuXLjFhwoS48cYbY/fdd49tttmm3J8QAAAAALByyhQaRiy8scns2bNj5MiRMWzYsGjUqFHcdtttsckmm0RExKBBg2LMmDExfvz4iIho1apV3HzzzTFgwIDo1atX1KxZMw466KA47bTTyveZAAAAAADlosyhYURE9+7do3v37iU+1q9fv+jXr19m2k477RQ77bTTimwKAAAAAFjFynRNQwAAAADgt09oCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMhYodDw7rvvjk6dOkXz5s2jS5cu8dZbb5V62YEDB0ZRUdGKbBYAAAAAWAXKHBqOHj06+vTpE/vtt18MGDAgateuHd27d4+vvvpquct+/PHHMWTIkBVqKAAAAACwapQpNEwpxcCBA+OII46IXr16Rfv27WPw4MGx7rrrxogRI5a57Pz586N3796x3nrrrVSDAQAAAICKVabQcOLEiTFlypTo2LFjblrVqlVjl112iXHjxi1z2eHDh8esWbPiyCOPXLGWAgAAAACrRJlCwwkTJkRExOabb56Zvtlmm8XEiRMjpVTichMnTowBAwZE3759o1q1aivWUgAAAABglahSlplnzpwZERE1a9bMTK9Zs2bMmzcvZs+eHTVq1Mg8llKKiy66KPbbb79o2bJlvPfeeyvZ5IUKCsplNatsvSxUnv2rVhVLrfJDefetWlUc+1T+0L+UVkW+VrwOK47PzvzhszN/qFV+8P6XP9aUvi1TaLjoSMKCpbS+UqUlD1y85557YuLEiTF48OAVaF7J6tatufyZWCPVq1d7dTeBUlKr/KBO+UOt8odaURrGo/nLPp4/1Cp/qFV+UKf8sabUqkyhYe3aCxs9a9asqFu3bm76rFmzomrVqlG9evXM/F9//XVcffXVccUVV8Raa60V8+bNywWP8+bNi0qVKpUYNC7P99/PivnzF5R5udIoKFhzivNbNHXqjFjKWexlplYVS63yQ3nWKUKtKpJ9Kn+U9361uMqVKwmbfiMqcjwaYT+vSD4784fPzvyhVvnB+1/+qMjxaETpx6RlCg232GKLiIiYPHlybLrpprnpkydPjgYNGiwx/0svvRSzZs2KU089dYnHmjRpEr169YpTTjmlLE3IqcjOo+KkpHb5Qq3ygzrlD7XKH2pFaXmd5Cf7eP5Qq/yhVvlBnfLHmlKrMoWG9evXj4033jiefvrpaNOmTUREzJ07N8aOHRu77rrrEvN36NAh7r///sy0Rx99NIYNGxb3339//O53v1uJpgMAAAAAFaFMoWFBQUH06NEj+vbtG7Vq1YoWLVrEqFGjYvr06XHMMcdERMSkSZNi2rRpUVxcHHXr1s2cxhwR8cYbb0RERLNmzcrpKQAAAAAA5alMoWFERNeuXWP27NkxcuTIGDZsWDRq1Chuu+222GSTTSIiYtCgQTFmzJgYP358uTcWAAAAAKh4ZQ4NIyK6d+8e3bt3L/Gxfv36Rb9+/Za67LHHHhvHHnvsimwWAAAAAFgFyn7rYgAAAADgN01oCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMhYodDw7rvvjk6dOkXz5s2jS5cu8dZbby1z/pdffjkOOeSQKC4ujk6dOsXIkSNXZLMAAAAAwCpQ5tBw9OjR0adPn9hvv/1iwIABUbt27ejevXt89dVXJc7/wQcfRI8ePaJBgwYxaNCgOOyww6Jfv35xxx13rHTjAQAAAIDyV6bQMKUUAwcOjCOOOCJ69eoV7du3j8GDB8e6664bI0aMKHGZESNGxGabbRb9+vWLtm3bRvfu3WPfffeNu+66q1yeAAAAAABQvsoUGk6cODGmTJkSHTt2zE2rWrVq7LLLLjFu3LgSlznzzDOjf//+UalSpcwyc+bMWcEmAwAAAAAVqUyh4YQJEyIiYvPNN89M32yzzWLixImRUlpimQ033DC23nrriIiYMWNGPPjgg/Hggw/GYYcdtoJNBgAAAAAqUpWyzDxz5syIiKhZs2Zmes2aNWPevHkxe/bsqFGjRonLfvPNN7HzzjtHRESzZs3i0EMPXZH25hQUrNTiq3y9LFSe/atWFUut8kN5961aVRz7VP7Qv5RWRb5WvA4rjs/O/OGzM3+oVX7w/pc/1pS+LVNouOhIwoKltH7xU5B/rUaNGjFixIiYOnVq3HDDDdGlS5d48MEHlxoyLkvdujWXPxNrpHr1aq/uJlBKapUf1Cl/qFX+UCtKw3g0f9nH84da5Q+1yg/qlD/WlFqVKTSsXXtho2fNmhV169bNTZ81a1ZUrVo1qlevvtRl69SpEzvssENERGy99dax7777xhNPPBH7779/mRv9/fezYv78BWVerjQKCtac4vwWTZ06I0o4i32FqFXFUqv8UJ51ilCrimSfyh/lvV8trnLlSsKm34iKHI9G2M8rks/O/OGzM3+oVX7w/pc/KnI8GlH6MWmZQsMtttgiIiImT54cm266aW765MmTo0GDBiUu88wzz0TdunWjRYsWuWmFhYVRtWrV+Pbbb8uy+YyK7DwqTkpqly/UKj+oU/5Qq/yhVpSW10l+so/nD7XKH2qVH9Qpf6wptSrTjVDq168fG2+8cTz99NO5aXPnzo2xY8dGmzZtSlxm2LBhccUVV2RukvLqq6/G3Llzo7CwcAWbDQAAAABUlDIdaVhQUBA9evSIvn37Rq1ataJFixYxatSomD59ehxzzDERETFp0qSYNm1aFBcXR0TEiSeeGMcff3z07t079tlnn5g0aVL0798/WrVqFe3bty/3JwQAAAAArJwyhYYREV27do3Zs2fHyJEjY9iwYdGoUaO47bbbYpNNNomIiEGDBsWYMWNi/PjxERGx4447xtChQ2PgwIHxpz/9KWrVqhV77bVXnHHGGUu9oQoAAAAAsPqUOTSMiOjevXt07969xMf69esX/fr1y0xr3769owoBAAAAIE+U6ZqGAAAAAMBvn9AQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkLFCoeHdd98dnTp1iubNm0eXLl3irbfeWub8zz77bBxyyCHRokWL6NixY/zlL3+JmTNnrsimAQAAAIAKVubQcPTo0dGnT5/Yb7/9YsCAAVG7du3o3r17fPXVVyXO/8orr0TPnj1j6623jgEDBkTPnj3j0UcfjbPOOmulGw8AAAAAlL8qZZk5pRQDBw6MI444Inr16hUREW3bto099tgjRowYERdccMESy9x+++2x7bbbxhVXXJGbVqtWrTj99NPj888/jy233HIlnwIAAAAAUJ7KdKThxIkTY8qUKdGxY8fctKpVq8Yuu+wS48aNK3GZ5s2bR9euXTPTGjRoEBERU6ZMKWt7AQAAAIAKVqYjDSdMmBAREZtvvnlm+mabbRb33HNPpJSioKAg89jJJ5+8xHqeffbZiPhveAgAAAAArDnKFBouunlJzZo1M9Nr1qwZ8+bNi9mzZ0eNGjWWuY6PPvoobr755vjjH/8Ym266aRmb+1+/yibLTUWtl4XKs3/VqmKpVX4o775Vq4pjn8of+pfSqsjXitdhxfHZmT98duYPtcoP3v/yx5rSt2W+pmFELHE04SKVKi37bOdPPvkkunfvHhtttFFceumlZdl0Rt26NZc/E2ukevVqr+4mUEpqlR/UKX+oVf5QK0rDeDR/2cfzh1rlD7XKD+qUP9aUWpUpNKxde2GjZ82aFXXr1s1NnzVrVlStWjWqV6++1GVff/31+NOf/hTrrbdeDBs2LNZZZ50VbHLE99/PivnzF6zw8stSULDmFOe3aOrUGfH/s+eVplYVS63yQ3nWKUKtKpJ9Kn+U9361uMqVKwmbfiMqcjwaYT+vSD4784fPzvyhVvnB+1/+qMjxaETpx6RlCg232GKLiIiYPHly5tTiyZMnL/P6hM8880ycfvrpseWWW8Ztt90W9erVK8tmS1SRnUfFSUnt8oVa5Qd1yh9qlT/UitLyOslP9vH8oVb5Q63ygzrljzWlVmW6e3L9+vVj4403jqeffjo3be7cuTF27Nho06ZNicu88847cdppp0XTpk1j1KhR5RIYAgAAAAAVp0xHGhYUFESPHj2ib9++UatWrWjRokWMGjUqpk+fHsccc0xEREyaNCmmTZsWxcXFERFxySWXRJUqVeLEE0+MTz/9NLO+Bg0arNRpygAAAABA+StTaBgR0bVr15g9e3aMHDkyhg0bFo0aNYrbbrstNtlkk4iIGDRoUIwZMybGjx8fX331VXzwwQcREXHCCScssa6bbropdtttt5V8CgAAAABAeSpzaBgR0b179+jevXuJj/Xr1y/69esXERG///3vY/z48SveOgAAAABglSvTNQ0BAAAAgN8+oSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgY4VCw7vvvjs6deoUzZs3jy5dusRbb71VquVmzpwZHTp0iKeeempFNgsAAAAArAJlDg1Hjx4dffr0if322y8GDBgQtWvXju7du8dXX321zOVmzpwZf/rTn5Y7HwAAAACwepUpNEwpxcCBA+OII46IXr16Rfv27WPw4MGx7rrrxogRI5a63KuvvhqHHHJIfPTRRyvdYAAAAACgYpUpNJw4cWJMmTIlOnbsmJtWtWrV2GWXXWLcuHFLXe7kk0+OwsLCuPXWW1e8pQAAAADAKlGlLDNPmDAhIiI233zzzPTNNtss7rnnnkgpRUFBwRLL3XnnnVFYWBhffvnlirf0V0rYzBq9XhYqz/5Vq4qlVvmhvPtWrSqOfSp/6F9KqyJfK16HFcdnZ/7w2Zk/1Co/eP/LH2tK35YpNJw5c2ZERNSsWTMzvWbNmjFv3ryYPXt21KhRY4nlCgsLV6KJS6pbt+byZ2KNVK9e7dXdBEpJrfKDOuUPtcofakVpGI/mL/t4/lCr/KFW+UGd8seaUqsyhYYppYiIEo8mjIioVGmFbsZcZt9/Pyvmz19QIesuKFhzivNbNHXqjPj/L6OVplYVS63yQ3nWKUKtKpJ9Kn+U9361uMqVKwmbfiMqcjwaYT+vSD4784fPzvyhVvnB+1/+qMjxaETpx6RlCg1r1174Ypg1a1bUrVs3N33WrFlRtWrVqF69ehmbueIqsvOoOCmpXb5Qq/ygTvlDrfKHWlFaXif5yT6eP9Qqf6hVflCn/LGm1KpMhwZuscUWERExefLkzPTJkydHgwYNyq9VAAAAAMBqU6bQsH79+rHxxhvH008/nZs2d+7cGDt2bLRp06bcGwcAAAAArHplOj25oKAgevToEX379o1atWpFixYtYtSoUTF9+vQ45phjIiJi0qRJMW3atCguLq6I9gIAAAAAFaxMoWFERNeuXWP27NkxcuTIGDZsWDRq1Chuu+222GSTTSIiYtCgQTFmzJgYP358uTcWAAAAAKh4ZQ4NIyK6d+8e3bt3L/Gxfv36Rb9+/Up8bNNNNxUmAgAAAMAarkzXNAQAAAAAfvuEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkCE0BAAAAAAyhIYAAAAAQIbQEAAAAADIEBoCAAAAABlCQwAAAAAgQ2gIAAAAAGQIDQEAAACADKEhAAAAAJAhNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgAAAAAZQkMAAAAAIENoCAAAAABkCA0BAAAAgAyhIQAAAACQITQEAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZKxQaHj33XdHp06donnz5tGlS5d46623ljn/Rx99FEcffXS0aNEidtlll7jllltWZLMAAAAAwCpQ5tBw9OjR0adPn9hvv/1iwIABUbt27ejevXt89dVXJc4/derU6NatW1SpUiVuuOGGOPTQQ+P666+P4cOHr2zbAQAAAIAKUKUsM6eUYuDAgXHEEUdEr169IiKibdu2sccee8SIESPiggsuWGKZUaNGRUopBg0aFGuttVa0b98+5syZE0OHDo2jjjoqKleuXD7PBAAAAAAoF2U60nDixIkxZcqU6NixY25a1apVY5dddolx48aVuMxLL70Ubdu2jbXWWis3bbfddotp06bF+++/v4LNBgAAAAAqSplCwwkTJkRExOabb56Zvtlmm8XEiRMjpVTiMltsscUS80dEfPHFF2XZPAAAAACwCpTp9OSZM2dGRETNmjUz02vWrBnz5s2L2bNnR40aNZZYpqT5F19fWVWuXHE3fS4oWPjfRvXrxuw58ytsO/9r1qq28DT0KlUqRQnZ8gpRq4qhVvmhIuoUoVYVwT6VPypqv1pcRY5hWLUqupb28/LnszN/+OzMH2qVH7z/5Y9VMR6NKP04piCVdHjgUjz88MNx9tlnx8svvxx169bNTb/vvvvioosuinfeeSeqV6+eWaZp06Zx5plnRrdu3XLT5s2bF02aNIn/+7//i8MPP7y0mwcAAAAAVoEy/URau3btiIiYNWtWZvqsWbOiatWqSwSGERG1atUqcf7F1wcAAAAArDnKFBouujbh5MmTM9MnT54cDRo0KHGZ+vXrlzh/RCx1GQAAAABg9SlTaFi/fv3YeOON4+mnn85Nmzt3bowdOzbatGlT4jI77LBDvPDCC/Hzzz/npj311FOx3nrrRVFR0Qo2GwAAAACoKGW6EUpBQUH06NEj+vbtG7Vq1YoWLVrEqFGjYvr06XHMMcdERMSkSZNi2rRpUVxcHBERRxxxRIwcOTJOOOGE6NatW3z00Udx8803x7nnnhtVqpRp8wAAAADAKlCmG6Esctttt8XIkSPj+++/j0aNGsUFF1wQ22yzTUREnH/++TFmzJgYP358bv533nknLr/88nj//fdj/fXXjyOPPDK6d+9efs8CAAAAACg3KxQaAgAAAAC/XWW6piEAAAAA8NsnNAQAAAAAMoSGAAAAAECG0BAAAAAAyBAaAgDAb5R7Hq651Gb51qQ+WpPaQsnUKP+pYemsyn4SGv7K3//+9zj66KOjdevW0bJly+jSpUs8+uijuccffPDBKCoqildffXWp6+jUqVOcccYZS338P//5T1xyySWxyy67RNOmTaNdu3ZxxhlnxKefflquz2VFffnll1FUVBRPPfXUSq9rypQp8ac//Sm22267aNu2bVx11VUxd+7ccmilWkWUb60WmTdvXhx88MExfPjwclmfOpVvnf71r3/FUUcdFS1btowdd9wxzj333Pjuu+/KoZVqFVG+tfrwww/j6KOPjhYtWkT79u1jwIAB3v/KUUW8/0VEjBkzJoqKiuLLL78s1/VCaY0ePTqKioqW+u/www8v9bruvffeGDBgQAW2tmw6duwYl112WanmXbSPL+tfx44dV7pNX3/9dXTr1i1+/PHH5c571FFHLbM99913X6m2+eOPP8ZZZ50V48ePX9nml4tXXnklioqK4sMPPyzV/AMGDFhubcrjdffUU0/F//3f/63w8valhdbEfWkRNVpoTa5RaSyv7RMnTizVej755JPo1q1bubZtZQwYMCBatmxZ6vmX9xlRVFQUr7zyykq3a+DAgXHPPfes9HpKq8oq21Ie6N27dzzyyCNxxBFHRLdu3aKgoCCefvrpOPPMM+Pjjz+OM844Izp37hyXXnppPPbYY9GqVasl1vHWW2/FpEmT4qKLLipxG7NmzYojjjgiatasGWeccUZsvPHG8e2338bw4cOjS5cu8de//jW22mqrin6qq8ScOXOiW7duUbNmzbjqqqvi66+/jmuuuSbmzp0bF1544UqtW60qxty5c+P888+Pd999N/bee++VXp86la/PPvssjj322Gjbtm1ce+218eOPP8aNN94Yxx9/fNx///1RpcqKv6WrVfn697//Hccee2w0a9YsBgwYEFOmTImrrroqZs6cGRdccMFKrVutKs5//vOfuOKKK1Z3MyAiIoYNGxZrr732EtNr1apV6nUMHTo0dt111/Js1irzu9/9Lu69997c32PHjo3Bgwdn+qVatWorvZ2XXnopXnjhhVLPv/3228fZZ59d4mObb755qdbx4YcfxiOPPBLHH398qbe7JjnkkENip512yv196aWXRs2aNTP9stFGG630dkaMGBG1a9de6fXYl9bMfWlxarTm12h5jjnmmNhzzz1LfGzjjTcu1Toef/zxePfdd8uzWavUJZdcEjNnzsz93aVLlyX6Zeutt17p7QwYMGClv0+UhdDw/7vvvvti9OjRcdttt0W7du1y09u3bx9169aNIUOGxJ577hlFRUXRqVOneOKJJ+Kiiy6KypUrZ9bzyCOPxAYbbBA77rhjidv5xz/+EV9++WW88MILsd566+Wm77LLLtGpU6cYPnx49O3bt2Ke5Cr2t7/9LaZMmRLPPPNM/O53v4uIiOrVq8ell14aPXv2zDz/slCrivHRRx/FJZdcEl988UW5rE+dyt+dd94ZG2ywQQwYMCCqVq0aERFbbLFFHHLIIfHSSy9lBvBloVbl769//WuklGLAgAFRo0aNiFgYSN1yyy1x3nnnRaVKK3agv1pVrD59+sRaa60V06dPX91NgWjatGnUqVNndTdjtalWrVoUFxfn/v78888jYvX3S506dTLt+l+00UYbZULBWrVqRe3atdfYflndr5nVbU3dlxa3JrVldciHGi3P73//+zX2PWBVKSkQ/C30i9OT/7/hw4dHhw4dMl/CFjn++OOja9euMX/+/IiI2H///WPq1KlLnPY1f/78+Pvf/x777LPPEl/QFpk6dWpERCxYsCAzvVatWnHhhRdmvvTPmDEj+vTpkzs1rG3btnHxxRfHTz/9FBH/PYz56aefjqOPPjqaN28eu+++ezz33HPx3nvvxcEHHxzbbLNNHHLIIfHxxx/n1tuxY8cYMmRInHfeeVFcXBzt27ePoUOHLrN/3nnnnTjqqKOiefPm0bZt27jiiitizpw5y1zmpZdeiqZNm+YCw4iI3XbbLebOnRsvv/zyMpddFrUq/1pFRJx33nlRo0aNuP/++5c7b2moU/nXaeutt45u3brlAsOIiC233DIiFl4KYEWpVfnX6sgjj4zhw4fnAsOIiKpVq8b8+fOXeP5loVYV8/4XsfDX7ddeey1OOeWUUs0Pq9stt9wSRUVF8dJLL+WmXX/99dG0adP4+OOPo2PHjjFlypS44447oqioKDfP8vaTo446Ki666KI47rjjonnz5nHllVfGgAED4sADD4y//e1v0blz52jWrFl06dIl3n///dxyKaUYNmxY7LXXXtGsWbPYdttto0ePHjFhwoQK7YcvvvgiTjzxxGjRokVsv/32ccEFF2SC/59++ikuvPDC2HHHHaN58+ZxwAEHxD/+8Y+IWHhq5KKjNbbffvtyObVx3rx5se+++0anTp1y/Tp79uzYfffd44gjjoiXXnopjj766IhY+D59/vnn55YdNmxY7LbbbtG0adPYZ599MpdfWPReuuhzoGXLlvHOO+9Ex44d45ZbbomLL744tt9++2jZsmVcdNFFMXv27Nyy33zzTZx33nnRrl27aNKkSey8885xzTXXxLx581b6+S7Lgw8+GHvttVc0bdo0OnfunDmSKiLi7bffjq5du0aLFi2iVatWceqpp+bGM0cddVS8+uqr8fTTT1f4JSPsSwutafvS4tRooTW5RsszefLkKC4ujnPOOSc37dNPP42mTZvGddddFwMGDIiBAwfGjBkzoqioKEaPHh0RETNnzoxLLrkk2rRpE82bN49jjz02M14cPXp0tG7dOm6++eZo1apVdOzYMT7//PPc2LNbt26xzTbbRPv27WPYsGGZNr311lvRvXv32G677aJp06ax5557xoMPPlih/TB37ty47rrrYuedd45mzZrFIYccEq+//npmnjFjxuReWzvvvHNcfvnl8csvv0RE5F7fV1xxRbmcsl4qifTvf/87FRYWprvvvrtU88+fPz/ttNNO6eKLL85Mf+6551JhYWEaP378Upd9//33U8OGDdNuu+2WbrvttvTRRx+lBQsWlDjvCSeckDp27JgeeeSR9NJLL6WBAwemhg0bpoEDB6aUUpo8eXIqLCxMrVq1SsOGDUvPP/98OuCAA1KrVq1Sp06d0l//+tf07LPPpp122ikdfPDBufV26NAhbbfddql79+5p7Nix6frrr09FRUXp5ptvzqz3ySefTCmlNH78+NS8efPUvXv39Oyzz6Z77rknbb/99un0009fZj8deOCB6dxzz11i+rbbbptuuummZS67NGpVMbVatOwihYWFadiwYctdZmnUqeLq9GsPPfRQKiwsTK+88kqZl01JrVZFrWbOnJmeeeaZ1KpVqyX6rSzUquJq9f3336e2bdumhx56KD355JOpsLAwTZ48ebnLQUV44IEHUmFhYZo2bVqaO3fuEv8W7Yvz5s1LBxxwQNpzzz3T3Llz0wcffJAaN26cBg8enFJauB+3a9cunX766enNN99MKZVuPznyyCNT48aN0+WXX56ee+659N5776X+/funFi1apN133z09+uij6emnn06dO3dOnTt3zi136623pmbNmqU77rgjvfLKK+mvf/1r2mGHHdLRRx+dm6dDhw7pL3/5y0r1y/Tp03PTvv3227TDDjukgw46KD355JPpoYceSrvsskvq0qVLmj9/fkoppd69e6eOHTumv//97+mll15KZ511VmrYsGH69NNP09SpU9P111+fCgsL0wsvvJC+/vrrZbbhyCOPTCeddFKJdZk3b15uvnfeeSc1atQoDRo0KKWUUr9+/dI222yTJkyYkGbMmJFGjRqVCgsL05gxY9LEiRNTSindcMMNqUmTJmnAgAHpueeeS3/+859Tw4YN09ixY1NK/33Pa926dfr73/+exowZk+bOnZs6dOiQtt1223TOOeek559/Pt1+++2pYcOGaciQISmlhZ8F++yzT9p///3Tk08+mV588cXUt2/f3PZTSunll19OhYWF6YMPPlih2hx55JGpZ8+emWn33XdfKioqSn/5y1/SuHHj0nXXXZcaNmyY7rrrrpRSSj/++GNq1apVOuOMM9KLL76YHn/88bTbbrulQw89NKWU0ieffJL233//1LVr1/Tmm2+mX375pcztsi8tu19W576kRvlTo9IoLCxMt99+e4k1XNS2lFIaNmxYKiwsTK+//nqaN29eOuSQQ9Lee++dfvnll/T111+n3r17p+Li4vTmm2+mqVOnpvnz56cjjzwytW3bNv31r39NTz/9dDryyCNTq1at0r///e9cXzVq1Cjtv//+ady4cemJJ57IvF8OHDgwvfjii+n8889PhYWF6V//+ldKKaUpU6akbbbZJp1++unphRdeSM8++2w67rjjUqNGjXJjwP79+6fttttupfrl19+pzznnnFRcXJyGDx+exo4dm0455ZTUrFmz3Pvvq6++mho2bJgGDRqUXnnllXTnnXemZs2apWuuuSallNKbb76ZCgsL02WXXZbef//9FW5bWQgNU0pvv/12KiwsTP/85z9LvcxVV12VWrdunebOnZubdu6556YDDjhgucv+7W9/S61atUqFhYW5L1LnnHNO+vDDD3PzzJ49Ox133HHp+eefzyzbtWvX1K1bt5TSfwcPffr0yT3+6KOPpsLCwjRixIjctNtuuy01atQo19YOHTqkDh06pDlz5uTmufDCC1Pr1q3T/Pnzl/gidtppp6XOnTtnnuvYsWNTYWFhps2/1rlz53TppZcuMX2nnXZKV1555XL7qSRqVTG1+rWVDQ3VadXU6euvv07t2rVLhx566FIDneVRq4qt1YIFC1KTJk1SYWFh6tixY/ryyy+Xu8zSqFXF1eqcc85J3bt3TykloSGr3aIvi0v798gjj+Tmff/991Pjxo3Tbbfdlg444IB00EEHZcKrX39pLc1+cuSRR+b2s0X69++/RKj04IMPpsLCwlzo1bdv3zR06NDMc7nxxhtTkyZNltqeFemXxb9EX3XVValVq1aZaR9//HEqKirKvTfsvvvumR9P5syZk6644or00UcfLXW9S3PkkUcutS5NmzbNzHvFFVekbbbZJj311FOpUaNGmfe7X4d0P/zwQ2rWrFkaMGBAZh2nnXZa2m+//VJK/30vveKKKzLzdOjQIe27776Zacccc0w6/PDDU0opffXVV+nII49MH3/8cWaeXXbZJdcv5R0azp8/P7Vr1y6df/75mfmuuuqqtMMOO6Q5c+bkvvgu+gKfUkqvvfZauvHGG3OvvZLCyLKwLy27X1bnvvTrtqhRyf2yJtSoNJZVw0XjwZQWvjccdNBBaf/990+33357aty4cXrvvfdyj/86pFtUr1dffTU37Zdffknt27dPl112WeY5Pf3007l5Fr1fLt7/c+bMSc2bN0/XX399bt3HHXdc5vUxadKkVFhYmB5++OES27Mi/bL4d+pPP/0084NNSgu/KxxyyCHpT3/6U0oppaFDh6YWLVpkfii5//770+jRo5e63ormmoYRudOzUhluW73//vvHrbfemruO2OzZs+PJJ5+M008/fbnL7rPPPtG5c+cYN25cjBs3Ll566aV46KGH4pFHHomrr7469tprr6hevXrcfvvtkVKKyZMnxxdffBEff/xxfPnll7HZZptl1tesWbPc/9erVy8iFl7/YJF111035s+fHz/99FPumgi777575hTHXXfdNe6777744osvonr16pn1v/baa7HHHntEROROY9hhhx2iatWq8fLLL0dRUVHudLhFFt2QoaCgoMQ+WNHrealVxdWqPKlTxdfpm2++iWOPPTYiIq655pql7mvLo1YVW6t58+bF0KFD4+eff45BgwbFoYceGqNHj44NN9xwuX31a2pVMbV67rnn4sknn4xHHnlkuX0Cq9Idd9xR4o0BFr/ZRuPGjeO4446Lq6++OqpUqRIPPvjgUi87ELH8/aRhw4YREVG/fv0lxmrVqlXLPR4RucvPLLoUwaIbK02dOjU+++yz+Pzzz+PFF18st7vGL+35tGjRItZee+3c82nQoEFssskm8fLLL8duu+0WLVu2jL/+9a/x7bffRocOHWKXXXbJnBJcVq1atYpzzz13iem/7q/TTjstnnzyyejVq1e0bNkyjjrqqKWu8+23345ffvkldt5558wpwzvvvHM89thj8cMPP+SmNWjQYInlt9lmm8zfv/vd7+KTTz6JiIU3IBg5cmTMnz8/vvjii/jiiy/iww8/jJkzZ1ZYbb744ov4z3/+E+3bt1/i+dx6663xySefxNZbbx3rrrtunHTSSbHXXntF+/bto02bNmW6U2lp2ZeWb3XsS4tTo+Vb3TVanuOOOy722muvJaYvfjOjSpUqxWWXXRYHHXRQXHXVVXHSSSdFkyZNlrrO1157LWrWrBktWrTIPedKlSpFmzZtlrjc2fLeG6tWrRp169aNn3/+OSIWXg+8ffv2MXv27Pjkk0/iiy++iH/9618RERVWx0WXDCrpvX7RqdPbbrtt/PTTT7HvvvvGH//4x+jQoUMceOCBK/xdrzwIDeO/d/P56quvljrPN998k/mS94c//CEaN24cjz32WOy0007xzDPPxJw5c0p9x9nq1avHbrvtFrvttltERLz//vtxxhlnxF/+8pf44x//GJUqVYqnnnoqLr/88pgyZUqst956sc0228Raa621xLpKuqvU4tfRKskGG2yQ+XvRhe6nT5+euQZhRMQPP/wQo0aNilGjRi2xnm+//TZeffXV3LVZFhk/fnzUqlUrZs2atcQys2bNWuE7oalVxdSqvKlTxdbp008/jeOPPz7mzp0bw4YNWyKcKQu1qthaVa1aNXf9wW233TZ22WWXuO+++6JXr17LbGNJ1Kr8a/XGG2/EJZdcEqeeempsuOGGMW/evNx1HBcsWBALFixY4R+5YGU1atSoVBfA33vvveOWW26JzTffPOrXr7/MeZe3nyxS0s3qqlevnvnSsmjfWPRDxqeffhoXXXRRvPnmm7H22mtHo0aNyuVun8vyww8/xNtvv13il87//Oc/EbHwy/2GG24YDz30UDz77LNRqVKlaN++fVx++eUrdFO+2rVrZ34EWZoaNWrEbrvtFsOHD4+ddtppmV/4FoWChxxySImPf/fdd7n31UU/uizu1z+iVKpUKXNN2nvvvTduuOGGmDZtWmy44YZRXFwc1atXL9OPUGWx6PmcdtppJT7+n//8Jxo3bhyjRo2Km266KcaMGRN33nln1KlTJ0444YTo0aNHubbHvrR8q2NfWpwaLd/qrtHybLTRRqV6bywqKorCwsJ4//33o3379suc94cffohZs2aV+Jx//XxKem/89Xh08ffGefPmxRVXXBF//etfY968ebHFFlvkblhS0e+Nbdq0KfHxn3/+OVq2bBmDBg2KYcOGxc033xyDBg2KTTfdNP7v//5vhW96ubKEhrHwBdewYcMYN25cHH744Us8PmvWrOjcuXMcdthhmVtb77fffjFo0KC49NJL45FHHomdd955uTvjoYceGsXFxdG7d+/M9CZNmkTPnj3j/PPPj++//z5mzJgRp59+ehx88MHRs2fP3JfA7t27l/qi7suy+C+WEf+96H1JO1utWrVi9913L3Egs/7660ft2rVLvHlG/fr1Y/LkyZlp33//fcycObPEXwJKQ60qplblTZ0qrk7vvPNOHH/88VGrVq248847lztgWh61qphavfzyyzF//vzMDUvWW2+92HDDDTMD1bJQq/Kv1fvvvx9fffVV9OvXL/r165d5rFOnTnHAAQcsMR3WJCmluOyyy2LLLbeMiRMnxogRI6Jbt25LnX95+8mKWrBgQfTs2TPWX3/9ePTRR2OrrbaKgoKCGDp0aLzyyisrvN7lqVWrVnTo0CFOPvnkJR5bZ511ImLhl8ZTTjklTjnllPj888/jH//4R9x0001x4403xqWXXlphbfvss8/irrvuiqKiohg8eHDstddesckmm5Q476If0wcPHrzEjyUREZtuuml89913K9SOV199NffjyOGHHx5169aNiIVHcleURc+nT58+0bhx4yUe32KLLSJi4Q9bN9xwQ8yZMyfeeOONGDlyZFxzzTXRqlWrJY6erGj2pTV3X1pEjdb8GpXGAw88EB9++GH84Q9/iEsvvTTuu+++pZ75Vrt27dhggw1i8ODBSzy2smfLDRkyJEaPHh3XXXdd7LjjjlGjRo349ttvY8yYMSu13mWpXbt2VK5cOe65554Sf0haFDx37NgxOnbsGDNmzIhx48bF4MGD44wzzogXX3yxwsPpkvj5/P876qij4plnnsnckWmRIUOGxOzZs5c43HafffaJWbNmxXPPPRfPP/98HHDAAcvdziabbBJ/+9vfSvzgnzBhQqy//vpRt27d+OCDD2Lu3Llxwgkn5L6E/fjjj/Huu++WS/I9duzYzHqeeuqp2GijjXIf4ovbdttt44svvohmzZrl/q233npx7bXXxoQJE6JWrVqZxxb9wrDDDjvEO++8E998801mO1WrVl2pUw/UqvxrVRHUqfzr9OWXX0aPHj2iXr16cffdd690YLiIWpV/rR588MG48MILM8HZxIkTY8qUKZm7+pWVWpVvrZo0aRL3339/5t95550XEQu/vK/IEaGwKt13333x+uuvx5VXXhndunWL/v37Z36w/fWRssvbT1bUtGnTYtKkSdGlS5fYeuutc1+GXnjhhYiouKM2Fj2fhg0b5p5PgwYN4oYbboj33nsvFixYEPvss08MHz48IiK23HLLOOmkk6K4uDi+/vrriFjxS+YsS0opLr744thyyy3j7rvvjnr16sUll1ySe/zXp1Q2b948qlSpEj/88EOmNu+++27cfPPNK9XGt956KypXrhwnnnhiLjCcPHlyTJ48ucLqsuWWW8a6664b3377beb5fPPNNzFgwICYM2dOvPDCC9GmTZuYNm1aVKtWLdq0aRMXXnhhRPz3iPpVeaS3fWnN3JcWp0Zrfo2W57vvvourrroqDj/88Lj++uvj448/jttvvz33eEk1nDZtWqyzzjqZOt53333x+OOPr1Rb3nrrrWjWrFl06tQpd+bLiy++GBEVW8P58+fHzz//nHk+zz77bNxzzz1RuXLl3OWMIhaGjHvuuWd069YtZsyYETNnzoyIVV9HRxr+fwceeGCMHTs2TjzxxDjqqKOiTZs2MXv27Hj88cfj4YcfjpNPPjmaN2+eWaZevXrRrl27uOqqq6JGjRrLPbw2YuFh+i+++GIcdNBBcdxxx0WjRo1izpw58c9//jPuuuuuuPzyy6NSpUrRsGHDqFy5clx99dXRpUuXmDp1atx8883x448/5s7DXxkff/xxnH322bH//vvH66+/HmPGjIk+ffqUOG/Pnj3jiCOOiLPOOiv222+/+Omnn2LgwIExffr0zLUefm3vvfeOwYMHx/HHHx+nnXZafPPNN3H11VfH4YcfvlKHR6tV+deqIqhT+depX79+MWPGjLj44ovj66+/zg0AIhaGPCUdnVAaalX+tTruuOPi73//e5xyyilx5JFHxrRp02LAgAGxxRZbxIEHHrjCbVer8q3VoiBxcYt+6CosLIxNN910pZ8DrKj33nuvxGt8RSy8TtN//vOfuOqqq+KQQw6J5s2bxx/+8Id4+OGH489//nPu2kh16tSJd999N1599dXYfvvtK2ycUK9evdh4441j2LBhse6660ZBQUHcd999ues3/fzzz0t9LivjuOOOiwcffDBOOumk6Nq1axQUFMTNN98c48ePj4suuigqVaoUxcXFcdNNN0X16tVjq622irfeeiveeOON3FE3i06JfPzxx6Nt27bL3e9//PHHeOutt0p8rG7durHFFlvEXXfdFf/617/irrvuipo1a8aFF14YJ510Ujz00EOx33775Y7Ee/rpp6NatWqx1VZbxRFHHBGXXXZZfP/999GkSZN4991348Ybb4wDDzxwpY4sadasWe40vE6dOsWXX34ZN910UyxYsKBc3qdLUqVKlejZs2dcc801sWDBgmjVqlVMmDAhrr322thmm21i/fXXzz2nXr16RY8ePaJq1apxxx13RJ06daJ169YRsbA2H330UbzyyitLvfRFadiXlm917EuLU6PlW901Wp6vvvpqqe+Nm222WdSrVy/69u0bVapUidNPPz3q1KkTRx99dAwcODA6d+4c9evXjzp16sSsWbPi6aefjmbNmkWHDh2iUaNG0aNHj9wZLY888kjcf//9ce21165Ue5s2bRq33npr3HnnnbH11lvHW2+9FYMHD46CgoKYPXv2Sq17aRo3bhy77rprnHXWWXHyySdHgwYN4vnnn49bb701zjzzzIhYeN3cAQMGxEUXXRR77bVXTJ8+PYYMGRLbbbddLkOpU6dOvP7669GiRYtVclS20PD/q1SpUtx4441x9913x+jRo+Pee++NgoKC2HrrraN///5LPYR///33jzPOOCOOPPLIUn2g169fP0aPHh1DhgyJkSNHxrfffhvVqlWLJk2axJAhQ2LnnXeOiIW/DFx++eUxaNCgOOGEE2KDDTaIXXfdNfbdd9/o379/LmVeUQcffHD89NNP0atXr9hwww2jb9++cfDBB5c4b/PmzWP48OFx/fXXR69evaJGjRqx/fbbx9lnn537xbIkNWrUiNtvvz369u0bZ511VtSuXTu6du0aZ5xxxkq1Xa3Kv1YVQZ3Kt07z5s2LsWPHxvz58+Oss85a4vELLrggd2OUslKr8t+nioqK4o477ohrr702TjvttKhatWp07NgxzjnnnOVex29Z1Co/3v+gPBx33HFLfeydd96JPn36RJUqVXJfNGrUqBG9e/eOXr16xQMPPBAHHXRQHHPMMXHFFVdEjx494oknnqiw/aSgoCD69+8ff/nLX+K0006L2rVrR4sWLWLo0KFxwgknxFtvvRVt27Zd4fUvzSabbBJ33nlnXHPNNXHWWWdF5cqVo3nz5jFixIjcpXAuuOCCqF69egwZMiSmTp0am2yySZx33nm5UxZbt24drVq1ij59+sShhx4af/7zn5e5zddeey26dOlS4mN77rlnnH/++XHdddfFgQceGNtuu21ERHTo0CE6duwYl19+eey0007xhz/8IfbYY48YOnRovPfeezFkyJC44IILYv3114+77rord33aE088MXr27LlSfdSmTZs499xzY+TIkXHffffFRhttFAcddFDMmDEjHn/88Qo7oubYY4+NtddeO4YPHx633HJL1KtXL/dZFLHwS++tt94a11xzTZx77rkxd+7caN68eQwbNiz3xfiII46I8847L44//vgYMWJErj/Lyr60fKtjX1qcGi3f6q7R8owYMSJGjBhR4mP/93//FxtuuGE8/vjjceWVV+bCy169esWjjz4aF198cdxxxx3RuXPnGD16dJx22mlx2mmnRY8ePeK2226La665Jvr16xc///xzNGjQIK677roSb7pSFieccEJ888030b9//5g7d240aNAgrrjiirj99tvjzTffjK5du67U+pfm+uuvjxtuuCEGDRoUP/zwQ2y66aZx4YUX5m6W1bJly7j22mvj5ptvjkceeSSqV68eO++8c+aGNscff3wMGTIkXn311XjxxRcr5MamiytIFfVJwRqrY8eOseuuu+ZOAWDNpVb5QZ3yh1rlD7UCAIDVy5GGAADAKrPo7uTLUqlSpdV+/a3/RfPmzVvuPJUrV17m3aBZdexLaz41yn8ppZg/f/5y56voI/5Wl9/mswIAANZIvXv3Xu4dKt29fPVo0qTJcue54447ctcdZPWyL6351Cj/vfrqq3H00Ucvd77x48evgtasek5PBgAAVpkvv/wyvv/++2XOU7duXTcjWg3efffd5c7ToEGDqFWr1ipoDctjX1rzqVH+mzlzZnzxxRfLne/XN9f7rRAaAgAAAAAZTpwHAAAAADKEhgAAAABAhtAQAAAAAMgQGgIAAAAAGUJDAAAAACBDaAgAAAAAZAgNAQAAAIAMoSEAAAAAkPH/AD5/fU2Q4xK1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_tree_cost,external_tree_cost = get_metrics(model=model_use,\n",
    "                                                  data_dict=data_dict,\n",
    "                                                  problem_type = 'classification',\n",
    "                                                  dependent_variable=dependent_variable,\n",
    "                                                  features=best_columns_ACO_CouponRecommendation_logistic,\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 = 'Precision'\n",
    "title = 'Coupon Recommendation dataset - Ant Colony Optimization'\n",
    "print(title)\n",
    "make_plot(metric=metric,\n",
    "          title=title,\n",
    "          test_tree_cost=test_tree_cost,\n",
    "          external_tree_cost=external_tree_cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "External Test recall: 0.77\n",
      "Average Test and Validation Cost: 0.68 Average External Test Cost: 0.67\n",
      "Coupon Recommendation dataset - Simulated Annealing\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_tree_cost,external_tree_cost = get_metrics(model=model_use,\n",
    "                                                  data_dict=data_dict,\n",
    "                                                  problem_type = 'classification',\n",
    "                                                  dependent_variable=dependent_variable,\n",
    "                                                  features=best_columns_SA_CouponRecommendation_logistic,\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 = 'Precision'\n",
    "title = 'Coupon Recommendation dataset - Simulated Annealing'\n",
    "print(title)\n",
    "make_plot(metric=metric,\n",
    "          title=title,\n",
    "          test_tree_cost=test_tree_cost,\n",
    "          external_tree_cost=external_tree_cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "External Test recall: 0.79\n",
      "Average Test and Validation Cost: 0.61 Average External Test Cost: 0.63\n",
      "Coupon Recommendation dataset - Genetic Algorithm\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_tree_cost,external_tree_cost = get_metrics(model=model_use,\n",
    "                                                  data_dict=data_dict,\n",
    "                                                  problem_type = 'classification',\n",
    "                                                  dependent_variable=dependent_variable,\n",
    "                                                  features=best_columns_GA_CouponRecommendation_logistic,\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 = 'Precision'\n",
    "title = 'Coupon Recommendation dataset - Genetic Algorithm'\n",
    "print(title)\n",
    "make_plot(metric=metric,\n",
    "          title=title,\n",
    "          test_tree_cost=test_tree_cost,\n",
    "          external_tree_cost=external_tree_cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "unpatch_sklearn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}