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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# **1. IMPORT MODULES**"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.pipeline import Pipeline, FeatureUnion\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.impute import SimpleImputer\n",
"from sklearn.svm import SVC\n",
"from sklearn.naive_bayes import MultinomialNB, BernoulliNB, ComplementNB\n",
"from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"\n",
"from yellowbrick.classifier import DiscriminationThreshold\n",
"\n",
"from Modules.Evaluation import *\n",
"from Modules.Transformers import *\n",
"from Modules.Hyperparameter import *\n",
"from Modules.Calibrate import *\n",
"from Modules.Threshold import *\n",
"from Modules.LearningCurve import *\n",
"from Modules.Builder import *\n",
"from Modules.Importance import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# **2. DATA OVERVIEW**"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1000 entries, 0 to 999\n",
"Data columns (total 26 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 BIO_AUT 1000 non-null bool \n",
" 1 BIO_FOL 1000 non-null int64 \n",
" 2 BIO_PER 1000 non-null bool \n",
" 3 BIO_PIC 1000 non-null int64 \n",
" 4 BIO_POL 1000 non-null bool \n",
" 5 COMMENT 1000 non-null object \n",
" 6 INTERACTION_COM 1000 non-null int64 \n",
" 7 INTERACTION_NEM 1000 non-null int64 \n",
" 8 INTERACTION_OEM 1000 non-null float64\n",
" 9 INTERACTION_PEM 1000 non-null int64 \n",
" 10 STATUS_EXT 1000 non-null float64\n",
" 11 STATUS_AVG 1000 non-null float64\n",
" 12 STATUS_COM 1000 non-null float64\n",
" 13 STATUS_EXT_PIC 1000 non-null float64\n",
" 14 STATUS_EXT_VID 1000 non-null float64\n",
" 15 STATUS_INT_PIC 1000 non-null float64\n",
" 16 STATUS_INT_VID 1000 non-null float64\n",
" 17 STATUS_NEM 1000 non-null float64\n",
" 18 STATUS_OEM 1000 non-null float64\n",
" 19 STATUS_OFF 1000 non-null float64\n",
" 20 STATUS_PEM 1000 non-null float64\n",
" 21 STATUS_POL 1000 non-null float64\n",
" 22 STATUS_REM 1000 non-null float64\n",
" 23 STATUS_SHA 1000 non-null float64\n",
" 24 STATUS_STD 1000 non-null float64\n",
" 25 TROLL 1000 non-null bool \n",
"dtypes: bool(4), float64(16), int64(5), object(1)\n",
"memory usage: 175.9+ KB\n"
]
}
],
"source": [
"df = pd.read_csv(\"./Datasets/Data.csv\", delimiter=\";\") # load data\n",
"df.info() "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"BIO_AUT 0\n",
"BIO_FOL 0\n",
"BIO_PER 0\n",
"BIO_PIC 0\n",
"BIO_POL 0\n",
"COMMENT 0\n",
"INTERACTION_COM 0\n",
"INTERACTION_NEM 0\n",
"INTERACTION_OEM 0\n",
"INTERACTION_PEM 0\n",
"STATUS_EXT 0\n",
"STATUS_AVG 0\n",
"STATUS_COM 0\n",
"STATUS_EXT_PIC 0\n",
"STATUS_EXT_VID 0\n",
"STATUS_INT_PIC 0\n",
"STATUS_INT_VID 0\n",
"STATUS_NEM 0\n",
"STATUS_OEM 0\n",
"STATUS_OFF 0\n",
"STATUS_PEM 0\n",
"STATUS_POL 0\n",
"STATUS_REM 0\n",
"STATUS_SHA 0\n",
"STATUS_STD 0\n",
"TROLL 0\n",
"dtype: int64"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.isnull().sum() # Check null values"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.duplicated(keep=False).sum() # Count duplicates"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False 833\n",
"True 167\n",
"Name: TROLL, dtype: int64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[\"TROLL\"].value_counts() # Check troll label distribution"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"X = df.iloc[:,:-1] # Features\n",
"y = df.iloc[:,-1:] # Troll label"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# **3. PREPROCESSING PIPELINE**"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# load data\n",
"dfFreqWords = pd.read_csv(\"./Datasets/FreqWords.csv\")\n",
"dfBadPhrases = pd.read_csv(\"./Datasets/BadPhrases.csv\") \n",
"dfBadWords = pd.read_csv(\"./Datasets/BadWords.csv\") \n",
"\n",
"#Filter columns\n",
"numFilter = [\"int16\", \"int32\", \"int64\", \"float16\", \"float32\", \"float64\"]\n",
"\n",
"textCol = \"COMMENT\" # Comment column\n",
"numericalCol = df.select_dtypes(include=numFilter).columns.to_list() # Numeric columns"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"transformPipeline = Pipeline([ \n",
" (\"sentimental\", SentimentalTransformer(textCol)), \n",
" (\"text\", TextTransformer(textCol,dfFreqWords[\"word\"].to_list())),\n",
" (\"toxic\", ToxicTransformer(dfBadWords, dfBadPhrases, textCol)) \n",
"])\n",
"\n",
"textPreprocessing = Pipeline([ # Comment preprocessing pipeline\n",
"(\"remFreq\",FrequentWords(10,200)), # Remove frequent words\n",
" (\"tfidf\",TfidfVectorizer(ngram_range = (2,3),max_features = 5000)) # Equivalent to:\n",
"\n",
" # (\"bow\", CountVectorizer(ngram_range = (2,3),max_features = 5000)), # strings to token integer counts CountVectorizer(min_df=2,max_df=3)\n",
" # (\"tfidf\", TfidfTransformer()), # integer counts to weighted TF-IDF scores\n",
"])\n",
"\n",
"numericalPreprocessing = Pipeline([ # Numeric preprocessing pipeline\n",
" (\"NANs\", SimpleImputer(missing_values=np.nan, strategy=\"mean\")) # Deal with nan values\n",
"]) \n",
"\n",
"preprocessPipeline = FeatureUnion([ # Put preprocessors in pipeline\n",
" (\"text\", Pipeline([\n",
" (\"selectorcomment\", ItemSelector(key=textCol)), # Select comment columns\n",
" (\"textPreprocessing\", textPreprocessing) # Text preprocessing pipeline\n",
" ])),\n",
" (\"num\", Pipeline([ \n",
" (\"selectorNum\", ItemSelector(key=numericalCol)), # Select numeric columns\n",
" (\"numPreprocessing\", numericalPreprocessing) # Preprocess numerics\n",
" ])\n",
" )\n",
"]) \n",
"\n",
"basePipeline = Pipeline([ # Create base pipeline\n",
" (\"transform\", transformPipeline), # Expand and clean features\n",
" (\"preprocess\", preprocessPipeline) # Fill Nans, remove frequent words, apply Tfid\n",
"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# **4. 3-WAY SPLIT**"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"X_train size:\t 600 \ty_train size:\t 600\n",
"X_test size:\t 200 \ty_test size:\t 200\n",
"X_val size:\t 200 \ty_val size:\t 200\n",
"X size:\t\t 1000 \ty size:\t\t 1000\n"
]
}
],
"source": [
"# Split data into:\n",
"# 60% - train set - used to train data\n",
"# 20% - validation set - validate models\n",
"# 20% - test set - final test\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)\n",
"X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=1) # 0.25 x 0.8 = 0.2\n",
"\n",
"y_train = y_train[\"TROLL\"]\n",
"y_test = y_test[\"TROLL\"]\n",
"y_val = y_val[\"TROLL\"]\n",
"y = y[\"TROLL\"]\n",
"\n",
"print(\"X_train size:\\t\",len(X_train),\"\\ty_train size:\\t\",len(y_train))\n",
"print(\"X_test size:\\t\",len(X_test),\"\\ty_test size:\\t\",len(y_test))\n",
"print(\"X_val size:\\t\",len(X_val),\"\\ty_val size:\\t\",len(y_val))\n",
"print(\"X size:\\t\\t\",len(X),\"\\ty size:\\t\\t\",len(y))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# **5. MODEL EVALUATION**"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# Models to train\n",
"models = {\n",
" \"DecisionTree\": DecisionTreeClassifier(),\n",
" \"RandomForest\": RandomForestClassifier(),\n",
" \"SVC\": SVC(probability=True),\n",
" \"KNeighborsClassifier\" : KNeighborsClassifier(),\n",
" \"GradientBoosting\" : GradientBoostingClassifier(),\n",
" \"ComplementNB\" : ComplementNB(),\n",
" \"BernoulliNB\" : BernoulliNB(),\n",
" \"MultinomialNB\" : MultinomialNB(),\n",
"\n",
" \"AdaBoost-DecisionTree\": AdaBoostClassifier(base_estimator=DecisionTreeClassifier()),\n",
" \"AdaBoost-RandomForest\": AdaBoostClassifier(base_estimator=RandomForestClassifier()),\n",
" \"AdaBoost-SVC\": AdaBoostClassifier(base_estimator=SVC(probability=True)),\n",
" \"AdaBoost-GradientBoosting\" : AdaBoostClassifier(base_estimator=GradientBoostingClassifier()),\n",
" \"AdaBoost-ComplementNB\" : AdaBoostClassifier(base_estimator=ComplementNB()),\n",
" \"AdaBoost-BernoulliNB\" : AdaBoostClassifier(base_estimator=BernoulliNB()),\n",
" \"AdaBoost-MultinomialNB\" : AdaBoostClassifier(base_estimator=MultinomialNB())\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preprocessing comments...\n",
"Preprocessing finished...\n",
"Training DecisionTree model...\n",
"Training finished.\n",
"Training RandomForest model...\n",
"Training finished.\n",
"Training SVC model...\n",
"Training finished.\n",
"Training KNeighborsClassifier model...\n",
"Training finished.\n",
"Training GradientBoosting model...\n",
"Training finished.\n",
"Training ComplementNB model...\n",
"Training finished.\n",
"Training BernoulliNB model...\n",
"Training finished.\n",
"Training MultinomialNB model...\n",
"Training finished.\n",
"Training AdaBoost-DecisionTree model...\n",
"Training finished.\n",
"Training AdaBoost-RandomForest model...\n",
"Training finished.\n",
"Training AdaBoost-SVC model...\n",
"Training finished.\n",
"Training AdaBoost-GradientBoosting model...\n",
"Training finished.\n",
"Training AdaBoost-ComplementNB model...\n",
"Training finished.\n",
"Training AdaBoost-BernoulliNB model...\n",
"Training finished.\n",
"Training AdaBoost-MultinomialNB model...\n",
"Training finished.\n",
"Returning trained models...\n",
"\n",
"Preprocessing comments...\n",
"Preprocessing finished...\n",
"Validating DecisionTree model...\n",
"Validation done.\n",
"Validating RandomForest model...\n",
"Validation done.\n",
"Validating SVC model...\n",
"Validation done.\n",
"Validating KNeighborsClassifier model...\n",
"Validation done.\n",
"Validating GradientBoosting model...\n",
"Validation done.\n",
"Validating ComplementNB model...\n",
"Validation done.\n",
"Validating BernoulliNB model...\n",
"Validation done.\n",
"Validating MultinomialNB model...\n",
"Validation done.\n",
"Validating AdaBoost-DecisionTree model...\n",
"Validation done.\n",
"Validating AdaBoost-RandomForest model...\n",
"Validation done.\n",
"Validating AdaBoost-SVC model...\n",
"Validation done.\n",
"Validating AdaBoost-GradientBoosting model...\n",
"Validation done.\n",
"Validating AdaBoost-ComplementNB model...\n",
"Validation done.\n",
"Validating AdaBoost-BernoulliNB model...\n",
"Validation done.\n",
"Validating AdaBoost-MultinomialNB model...\n",
"Validation done.\n",
"Returning results...\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Model</th>\n",
" <th>Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>AdaBoost-GradientBoosting</td>\n",
" <td>-0.048282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>GradientBoosting</td>\n",
" <td>-0.048304</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>AdaBoost-DecisionTree</td>\n",
" <td>-0.048500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>DecisionTree</td>\n",
" <td>-0.052000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>AdaBoost-RandomForest</td>\n",
" <td>-0.064354</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>RandomForest</td>\n",
" <td>-0.064703</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>AdaBoost-SVC</td>\n",
" <td>-0.123891</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>SVC</td>\n",
" <td>-0.124579</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>KNeighborsClassifier</td>\n",
" <td>-0.130120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>BernoulliNB</td>\n",
" <td>-0.174884</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>AdaBoost-BernoulliNB</td>\n",
" <td>-0.250000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>MultinomialNB</td>\n",
" <td>-0.534934</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>ComplementNB</td>\n",
" <td>-0.547931</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>AdaBoost-ComplementNB</td>\n",
" <td>-0.645212</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>AdaBoost-MultinomialNB</td>\n",
" <td>-0.672938</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Model Score\n",
"0 AdaBoost-GradientBoosting -0.048282\n",
"1 GradientBoosting -0.048304\n",
"2 AdaBoost-DecisionTree -0.048500\n",
"3 DecisionTree -0.052000\n",
"4 AdaBoost-RandomForest -0.064354\n",
"5 RandomForest -0.064703\n",
"6 AdaBoost-SVC -0.123891\n",
"7 SVC -0.124579\n",
"8 KNeighborsClassifier -0.130120\n",
"9 BernoulliNB -0.174884\n",
"10 AdaBoost-BernoulliNB -0.250000\n",
"11 MultinomialNB -0.534934\n",
"12 ComplementNB -0.547931\n",
"13 AdaBoost-ComplementNB -0.645212\n",
"14 AdaBoost-MultinomialNB -0.672938"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Train models\n",
"modelsTrained = fitModels(X_train, y_train,basePipeline,models) \n",
"\n",
"# Evaluate models using brier loss\n",
"modelsEval = evaluateModels(X_val,y_val,modelsTrained) \n",
"\n",
"modelsEval"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# **6. CHOOSE MODEL**"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"models = {\n",
" \"AdaBoost-GradientBoosting\" : buildPipeline(basePipeline,AdaBoostClassifier(base_estimator=GradientBoostingClassifier())),\n",
" \"AdaBoost-RandomForest\" : buildPipeline(basePipeline,AdaBoostClassifier(base_estimator=RandomForestClassifier())),\n",
" \"AdaBoost-DecisionTree\": buildPipeline(basePipeline,AdaBoostClassifier(base_estimator=DecisionTreeClassifier()))\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# **7. HYPERPARAMETER TUNNING**"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"gridAdaGradient = {\n",
" \"base_estimator__loss\":[\"deviance\",\"exponential\"],\n",
" \"base_estimator__learning_rate\": [0.01, 0.025, 0.05, 0.075, 0.1, 0.15, 0.2],\n",
" \"base_estimator__min_samples_split\": np.linspace(0.1, 0.5, 12),\n",
" \"base_estimator__min_samples_leaf\": np.linspace(0.1, 0.5, 12),\n",
" \"base_estimator__max_depth\":[3,5,8,11,15,18,20],\n",
" \"base_estimator__max_features\":[\"auto\", \"sqrt\", \"log2\"],\n",
" \"base_estimator__criterion\": [\"friedman_mse\", \"squared_error\"],\n",
" \"base_estimator__subsample\":[0.5, 0.618, 0.8, 0.85, 0.9, 0.95, 1.0],\n",
" \"base_estimator__n_estimators\":[1,10,50,100,150,200],\n",
" \"n_estimators\":[1,10,50,100,150,200],\n",
" \"learning_rate\": [0.01, 0.025, 0.05, 0.075, 0.1, 0.15, 0.2],\n",
" \"algorithm\": [\"SAMME\",\"SAMME.R\"]\n",
"} \n",
"gridAdaRndForest = {\n",
" \"base_estimator__n_estimators\": [int(trees) for trees in np.linspace(start = 100, stop = 2000, num = 10)],\n",
" \"base_estimator__max_features\": [\"auto\", \"sqrt\", \"log2\"],\n",
" \"base_estimator__max_depth\": [int(levels) for levels in np.linspace(10, 100, num = 10)] + [None],\n",
" \"base_estimator__min_samples_split\": [2, 5, 8, 10, 12],\n",
" \"base_estimator__min_samples_leaf\": [1, 2, 4, 6, 8, 10],\n",
" \"base_estimator__bootstrap\":[True, False],\n",
" \"n_estimators\":[1,10,50,100,150,200],\n",
" \"learning_rate\": [0.01, 0.025, 0.05, 0.075, 0.1, 0.15, 0.2],\n",
" \"algorithm\": [\"SAMME\",\"SAMME.R\"]\n",
"} \n",
"gridAdaDecTree = {\n",
" \"base_estimator__max_features\": [\"auto\", \"sqrt\", \"log2\"],\n",
" \"base_estimator__max_depth\": [int(levels) for levels in np.linspace(10, 100, num = 10)] + [None],\n",
" \"base_estimator__min_samples_split\": [2, 5, 8, 10, 12],\n",
" \"base_estimator__min_samples_leaf\": [1, 2, 4, 6, 8, 10],\n",
" \"base_estimator__criterion\" :[\"gini\", \"entropy\"],\n",
" \"base_estimator__splitter\" :[\"best\", \"random\"],\n",
" \"base_estimator__ccp_alpha\": [0.1, .01, .001, .0001, .00001],\n",
" \"n_estimators\":[1,10,50,100,150,200],\n",
" \"learning_rate\": [0.01, 0.025, 0.05, 0.075, 0.1, 0.15, 0.2],\n",
" \"algorithm\": [\"SAMME\",\"SAMME.R\"]\n",
"}\n",
"grids = {\n",
" \"AdaBoost-GradientBoosting\" : gridAdaGradient,\n",
" \"AdaBoost-RandomForest\" : gridAdaRndForest,\n",
" \"AdaBoost-DecisionTree\" : gridAdaDecTree\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preprocessing comments...\n",
"Preprocessing finished...\n",
"\n",
"Tunning model: AdaBoost-GradientBoosting\n",
"Starting Bayes Search...\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"\n",
"Best F1 Score: 0.8491219727610896\n",
"Best Hyperparameters:\n",
"OrderedDict([('algorithm', 'SAMME'), ('base_estimator__criterion', 'friedman_mse'), ('base_estimator__learning_rate', 0.15), ('base_estimator__loss', 'exponential'), ('base_estimator__max_depth', 5), ('base_estimator__max_features', 'auto'), ('base_estimator__min_samples_leaf', 0.17272727272727273), ('base_estimator__min_samples_split', 0.42727272727272736), ('base_estimator__n_estimators', 10), ('base_estimator__subsample', 1.0), ('learning_rate', 0.1), ('n_estimators', 50)])\n",
"Returning model...\n",
"\n",
"Tunning model: AdaBoost-RandomForest\n",
"Starting Bayes Search...\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"\n",
"Best F1 Score: 0.8480819693062254\n",
"Best Hyperparameters:\n",
"OrderedDict([('algorithm', 'SAMME'), ('base_estimator__bootstrap', False), ('base_estimator__max_depth', 50), ('base_estimator__max_features', 'auto'), ('base_estimator__min_samples_leaf', 4), ('base_estimator__min_samples_split', 8), ('base_estimator__n_estimators', 311), ('learning_rate', 0.2), ('n_estimators', 200)])\n",
"Returning model...\n",
"\n",
"Tunning model: AdaBoost-DecisionTree\n",
"Starting Bayes Search...\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"Fitting 30 folds for each of 1 candidates, totalling 30 fits\n",
"\n",
"Best F1 Score: 0.8357928082596272\n",
"Best Hyperparameters:\n",
"OrderedDict([('algorithm', 'SAMME.R'), ('base_estimator__ccp_alpha', 0.001), ('base_estimator__criterion', 'entropy'), ('base_estimator__max_depth', 40), ('base_estimator__max_features', 'sqrt'), ('base_estimator__min_samples_leaf', 1), ('base_estimator__min_samples_split', 10), ('base_estimator__splitter', 'best'), ('learning_rate', 0.1), ('n_estimators', 150)])\n",
"Returning model...\n"
]
}
],
"source": [
"# Find best hyperparameters\n",
"modelsTunned = tuneModels(X_train, y_train, models, grids)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# **8. MODEL CALIBRATION**"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training AdaBoost-GradientBoosting model ...\n",
"Training done.\n",
"Predicting AdaBoost-GradientBoosting model probabilities.\n",
"Predicting done.\n",
"Calibrating AdaBoost-GradientBoosting model using sigmoid...\n",
"Model calibrated.\n",
"Predicting AdaBoost-GradientBoosting model probabilities.\n",
"Predicting done.\n",
"Calibrating AdaBoost-GradientBoosting model using isotonic...\n",
"Model calibrated.\n",
"Predicting AdaBoost-GradientBoosting model probabilities.\n",
"Predicting done.\n"
]
},
{
"data": {
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",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Brier Loss:\n",
"No-Calibrated:\t0.09283227968039988\n",
"Sigmoid:\t0.02547743051294379\n",
"Isotonic:\t0.024905666712887357\n",
"\n",
"Training AdaBoost-RandomForest model ...\n",
"Training done.\n",
"Predicting AdaBoost-RandomForest model probabilities.\n",
"Predicting done.\n",
"Calibrating AdaBoost-RandomForest model using sigmoid...\n",
"Model calibrated.\n",
"Predicting AdaBoost-RandomForest model probabilities.\n",
"Predicting done.\n",
"Calibrating AdaBoost-RandomForest model using isotonic...\n",
"Model calibrated.\n",
"Predicting AdaBoost-RandomForest model probabilities.\n",
"Predicting done.\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Brier Loss:\n",
"No-Calibrated:\t0.12062487623481838\n",
"Sigmoid:\t0.03261702147549427\n",
"Isotonic:\t0.03330223526674724\n",
"\n",
"Training AdaBoost-DecisionTree model ...\n",
"Training done.\n",
"Predicting AdaBoost-DecisionTree model probabilities.\n",
"Predicting done.\n",
"Calibrating AdaBoost-DecisionTree model using sigmoid...\n",
"Model calibrated.\n",
"Predicting AdaBoost-DecisionTree model probabilities.\n",
"Predicting done.\n",
"Calibrating AdaBoost-DecisionTree model using isotonic...\n",
"Model calibrated.\n",
"Predicting AdaBoost-DecisionTree model probabilities.\n",
"Predicting done.\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Brier Loss:\n",
"No-Calibrated:\t0.044191862652738674\n",
"Sigmoid:\t0.03966930797068198\n",
"Isotonic:\t0.038737748130962875\n",
"\n"
]
}
],
"source": [
"modelsCalibrated = calibrateModels(X_train,y_train,X_val,y_val,modelsTunned)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"modelsSelectedCalibrated = {\n",
" \"AdaBoost-GradientBoosting\":modelsCalibrated[\"AdaBoost-GradientBoosting\"][0],\n",
" \"AdaBoost-RandomForest\":modelsCalibrated[\"AdaBoost-RandomForest\"][1],\n",
" \"AdaBoost-DecisionTree\":modelsCalibrated[\"AdaBoost-DecisionTree\"][1]\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# **9. THRESHOLD MOVING**"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicting AdaBoost-GradientBoosting model...\n",
"Prediction finished.\n",
"Predicting AdaBoost-RandomForest model...\n",
"Prediction finished.\n",
"Predicting AdaBoost-DecisionTree model...\n",
"Prediction finished.\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best threshold for model AdaBoost-GradientBoosting is: 0.5074971568832457\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best threshold for model AdaBoost-RandomForest is: 0.5375368855012448\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best threshold for model AdaBoost-DecisionTree is: 0.334345621815116\n"
]
}
],
"source": [
"# Find optimal threshold using precision-recall curve. Focusing on positive labels as precision is more important to us.\n",
"modelsThreshold = handleThresholds(X_val,y_val,modelsSelectedCalibrated)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# **10. MODEL SCORE**"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training AdaBoost-GradientBoosting model...\n",
"Training finished.\n",
"Training AdaBoost-RandomForest model...\n",
"Training finished.\n",
"Training AdaBoost-DecisionTree model...\n",
"Training finished.\n",
"Returning trained models...\n",
"\n",
"Predicting AdaBoost-GradientBoosting model...\n",
"Prediction finished.\n",
"Predicting AdaBoost-RandomForest model...\n",
"Prediction finished.\n",
"Predicting AdaBoost-DecisionTree model...\n",
"Prediction finished.\n"
]
}
],
"source": [
"# Train models\n",
"modelsFinal = trainModels(X_train,y_train,modelsThreshold)\n",
"\n",
"# Predict probabilities for labels\n",
"probasFinal = predictModels(X_test,modelsFinal,proba=False)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"AdaBoost-GradientBoosting\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 576x396 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t\t\tClassification Report\n",
" precision recall f1-score support\n",
"\n",
" False 0.96 0.99 0.97 168\n",
" True 0.93 0.78 0.85 32\n",
"\n",
" accuracy 0.95 200\n",
" macro avg 0.94 0.88 0.91 200\n",
"weighted avg 0.95 0.95 0.95 200\n",
"\n",
"AdaBoost-RandomForest\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 576x396 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t\t\tClassification Report\n",
" precision recall f1-score support\n",
"\n",
" False 0.98 0.99 0.98 168\n",
" True 0.93 0.88 0.90 32\n",
"\n",
" accuracy 0.97 200\n",
" macro avg 0.95 0.93 0.94 200\n",
"weighted avg 0.97 0.97 0.97 200\n",
"\n",
"AdaBoost-DecisionTree\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 576x396 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t\t\tClassification Report\n",
" precision recall f1-score support\n",
"\n",
" False 0.98 0.95 0.96 168\n",
" True 0.78 0.88 0.82 32\n",
"\n",
" accuracy 0.94 200\n",
" macro avg 0.88 0.91 0.89 200\n",
"weighted avg 0.94 0.94 0.94 200\n",
"\n"
]
}
],
"source": [
"plotModelResults(y_test, probasFinal)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cross-validating model AdaBoost-GradientBoosting\n",
"Using accuracy\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 3.1min\n",
"[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 7.5min finished\n",
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Validation Done: 0.941\n",
"Using neg_brier_score\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 2.9min\n",
"[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 7.4min finished\n",
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Validation Done: -0.09848349301734935\n",
"Using roc_auc\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 2.9min\n",
"[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 7.4min finished\n",
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Validation Done: 0.9443183210784314\n",
"Using f1\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 2.9min\n",
"[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 7.4min finished\n",
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Validation Done: 0.7827222222222222\n",
"Cross-validating model AdaBoost-RandomForest\n",
"Using accuracy\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 36.9min\n",
"[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 101.0min finished\n",
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Validation Done: 0.9385000000000001\n",
"Using neg_brier_score\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 39.9min\n",
"[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 104.2min finished\n",
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Validation Done: -0.05462528241618188\n",
"Using roc_auc\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 39.9min\n",
"[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 106.0min finished\n",
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Validation Done: 0.967875306372549\n",
"Using f1\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 37.9min\n",
"[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 105.3min finished\n",
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Validation Done: 0.7742380952380954\n",
"Cross-validating model AdaBoost-DecisionTree\n",
"Using accuracy\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 4.3min\n",
"[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 11.0min finished\n",
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Validation Done: 0.9340000000000002\n",
"Using neg_brier_score\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 4.2min\n",
"[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 10.5min finished\n",
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Validation Done: -0.055580050534411084\n",
"Using roc_auc\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 4.2min\n",
"[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 10.6min finished\n",
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Validation Done: 0.9716299019607842\n",
"Using f1\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 4.0min\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Validation Done: 0.8040952380952381\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 10.5min finished\n"
]
},
{
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Scorer</th>\n",
" <th>AdaBoost-GradientBoosting</th>\n",
" <th>AdaBoost-RandomForest</th>\n",
" <th>AdaBoost-DecisionTree</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Accuracy</td>\n",
" <td>0.941000</td>\n",
" <td>0.938500</td>\n",
" <td>0.934000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Neg Brier Score</td>\n",
" <td>-0.098483</td>\n",
" <td>-0.054625</td>\n",
" <td>-0.055580</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Roc Auc</td>\n",
" <td>0.944318</td>\n",
" <td>0.967875</td>\n",
" <td>0.971630</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>F1</td>\n",
" <td>0.782722</td>\n",
" <td>0.774238</td>\n",
" <td>0.804095</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Scorer AdaBoost-GradientBoosting AdaBoost-RandomForest \\\n",
"0 Accuracy 0.941000 0.938500 \n",
"1 Neg Brier Score -0.098483 -0.054625 \n",
"2 Roc Auc 0.944318 0.967875 \n",
"3 F1 0.782722 0.774238 \n",
"\n",
" AdaBoost-DecisionTree \n",
"0 0.934000 \n",
"1 -0.055580 \n",
"2 0.971630 \n",
"3 0.804095 "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results = getResults(X_test, y_test, modelsFinal)\n",
"results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# **11. LEARNING CURVE**"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1440x360 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1440x360 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1440x360 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Using F1 score for evaluation\n",
"plotModelsLearningCurve(X, y, modelsFinal)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# **12. FEATURE IMPORTANCE**"
]
},
{
"cell_type": "code",
"execution_count": 186,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Preparing data...\n",
"Preparation done.\n",
"Training model AdaBoost-GradientBoosting\n",
"Training done.\n",
"Training model AdaBoost-RandomForest\n",
"Training done.\n",
"Training model AdaBoost-DecisionTree\n",
"Training done.\n"
]
},
{
"data": {
"image/png": 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yMjJo164dK1euRFVVTCYTW7ZsoV27dqiqSkpKCn369OGxxx6jT58+KIpCWVlZs4qsqqpa5bYpyZWK7elXW1sb6BCEEEKIoCcbxU6T3Nxc8vLysFqtVFdXExcXx4033siLL77I2WefzZEjRygvL2fAgAGcccYZzJw5kzZt2lBdXU2bNm0488wzWbduHVVVVVRVVdGhQweioqIoKCigqqqKlJQUqqursVqtdOnSBZfLhV6vR1VV8vPzsVgsXHfddfz8889s3boVs9mM3W7H5/M1+3o7KiqKmpoa7XeDwYDb7Q7AJyaarFmzpsX1xLJRTAghxF+RbBQLAna7nffee485c+awfft2rTo7YMAAEhISiI6O5siRI5hMJhRFYfbs2cycOZOvv/6aDh06UFlZyY033shjjz3GsWPH2LBhAz6fj4yMDCorKwkPD6d3797odDqOHj3KgQMHKC0tRVVV/H4/n3/+OUVFRSiKQt++ffF6vVxwwQWkpKQAaO8Lv1RkmxJavV4vVdoAMZvNgQ5BCCGECHqS1J5G2dnZqKpKXFwcSUlJFBYWUl5ezrx58xg7diwRERF4vV6ysrIICwujf//+DB06FFVVGTJkCGFhYSxdupTZs2dTX1/P1VdfzUMPPcSBAwcwGAyoqkp5eTkRERFab6zdbsfv9+NwOGjbti11dXWoqsrmzZsByMvLo7i4GACv10t1dTVw4uEkvV4vB5YCpGldsRBCCCFOTpLa0yg/Px9oXGNrt9t5/fXXiY+PZ/78+Zx55plkZGRwySWXMGfOHK677jo2bNjAq6++qm34Cg8PZ8aMGYwfP57s7GwsFgvR0dH06NGDgQMH8vrrrxMVFUVZWRmRkZF4vV7atWtHhw4dMJvNxMTEEB4ejsViQa/XExMTQ1paGmFhYWRkZNCmTRsURWnxYJLD4UCvl3OFgWAwGAIdghBCCBH0JEs5jSoqKhgzZgz19fU89dRT6HS6Fh83aNAgnn76aT755BOioqLQ6XTaNq8mTa0AL7/88glfTzudTjwej/a/o0ePEhMTw759+8jMzOT777/XlivYbDZ0Oh0FBQXamtuGhoYW42qq/orTq7CwkPT09ECHIYQQQgQ1SWpPo+zs7BMWK6xdu1b7+fj7Vq1adcLzFyxYAMDWrVvJyMhgwoQJfPXVV8TFxTFlyhR27dpFeHg4Q4cOZdq0aZjNZoqLi2nfvj233HILEydO5IorruD777/nmmuu4bvvvqOkpKTZhIP4+HjttL1er5dENgjExsYGOgQhhBAi6ElS20q43W4mTZrE4cOH8Xq9jBs3DmicVlBaWso555yDz+cjLi6Obdu2oaoqCQkJHD16lIKCAt588028Xi8ffvghOp2ODz/8EL/f36wyq6oqVVVVGI1GFEWRhDZIyJxaIYQQ4tSkp/Y0ycnJ+T+tv126dCnR0dEsWbKEnJwcHn/8ccaOHcuOHTswm80oioKqqiQlJbFq1SpMJhM2m424uDhUVSUyMhKAI0eO4PF48Pl8DBkypNlIDFVV8Xq9NDQ0yKEwIYQQQrQqktS2Evv37yc7OxuATz/9lIaGBrZu3Yrdbmf79u2MGjWKHj168O2332Kz2SgrK8NgMFBZWYnX66WgoABoPEnfNOJr9erVOJ1OoDGhlcpscPJ6vYEOQQghhAh6ktS2Eunp6fz4449AY5Lj9/tZtWoVaWlp2O12tm3bxvfff098fDyhoaEAHD58WJszazQaAaisrGxWhT1+Lm2/fv1O5yWJ/5AktUIIIcSpSVLbSlx//fXU1NQwYsQI9uzZQ1RUFImJiVr7wA8//IDb7aayspLMzEytHUFRFBRFISQkpMXlCaqqYjAY8Hq9fP/998DJV+GGhIT8cRcoTqrpLyRCCCGEODlJalsJo9HI1KlTWbx4MWeccYbWNnDjjTcCMGzYMAD69u3LokWLiImJwWw2az21xydGYWFhWjXX4/Fo1dysrCzgxMULTRwOxx9zceI3NU2jEEIIIcTJyfSDVqh9+/bs3LmT7OxsXC4XF154ISaTiaysLLZt20Z2djYmk4mRI0cSExPDa6+9hs/nIyoqitraWpxOJ6GhodrUg6bRXT/99JP2Hqqq4vP5AnWJ4jhNfwERQgghxMlJUttKde7cmWXLlp3ycbm5uURHR1NfX098fDyKolBTU4PP50On02E2m3G5XHg8Hvx+v9auIAlt8KitrSU+Pj7QYQghhBBBTZLaP6nc3FzWrVvHwYMHqaiooFOnTqiqysGDBwEwmUxYrVbcbjculwudTqcdQAsPD0dRFOrq6gJ7EQKAQ4cOSVIrhBBCnIL01LZCL7zwwn9UpbXZbIwbN46OHTty8OBBPB4POp0Og8GgbalKSEjAZDJplVsAq9WKzWb7Q69B/Od69OgR6BCEEEKIoCdJ7Z9Y165dgcae2dTUVB566CGio6MxGAz84x//ICYmhqKiIpxOJ36/n8jISCwWi7ZpzGQynXQSgjh96uvrAx2CEEIIEfQkqf0Taykh1ev1tG3blgMHDmCz2QgLC6NHjx5ar62qqtroLqfTiU6nQ6/Xn/T1xB9v9+7dgQ5BCCGECHqS1P4JrF+/nqVLl7Z4X05ODmeeeSbHjh1j5syZ9OzZkz179jBo0CCSk5Ox2+3U19ejKAoGg4GRI0ficDjQ6XSYTCa8Xi/JyckoiiKrcwPE5XIFOgQhhBAi6Cl+yVT+9KZNm8ZHH33E119/zbJly3jyyScBMJvN+Hw+wsPDqaiowO/3NxvlZTAYtBm2InD27NnT4u1Op5MdO3ac5miEEEKIwOrevbu2fOp4Mv0giDRNLHA4HJSXlzN69Gjy8vLYu3cvjzzyCEOGDGHhwoWsXr0aj8dDREQEM2bM4NNPP6WwsJAbbriBhx56iKSkJA4fPsyZZ57JM888w/vvv4/L5WLp0qXk5+fj9/vJyMigqKgIh8OhjfOKiIho1r8pCW1wcDgcss1NCCGEOAVpPwgyNpuNd955h1tvvZXFixfzxhtv8Oyzz5Kbm4vP56Ompoa5c+dy3XXXsW3bNm644QamT5/OgQMHANi2bRtOp5O0tDS+/PJLxo4dq63BbdeuHV999RUAVVVVtGvXDvhlg1jTMgZFUbRJCAAWi+V0fgTiV2STmxBCCHFqUqkNMk0TCyIiIkhPT0dRFCIjI3E6naiqisFg4MEHH6S8vJyGhgYeeeQR9uzZw+uvv47X60Wn03HvvfdyxhlncN5559G7d2/OOOMM5s+fz2OPPYbL5cJoNFJdXU11dTUAMTExlJWVERISgtVqBRq3WNlsNvx+v8yrDTCpmAshhBCnJpXaIPNbEwZ2797NF198wWuvvcaVV16pfSUdERGB0WikpqYGgA4dOgCNFb6ePXsyaNAg0tLSqK6upra2FlVViYiIICkpCYCysjIA7Ha7FoPdbpeDYUEiJiYm0CEIIYQQQU8qta1IWloaoaGh5OTkaBXVsrIy6uvrcbvdREdHA6CqKrm5uTidTp599llMJhMlJSXa19herxeHw3FCBVav1+NyuWRFbpBZt24dQ4YMCXQYQgghRFCT6QetVG5uLkuXLiUkJIT6+nruv/9+Bg4cyODBg7nttttYsmQJhw8fxufzERcXx5EjRzCZTNjtdvR6PR6Pp8XXbVqXC8gYryCxfft2jEbjCbfL9AMhhBB/RSebfiDtB63U5s2bqampwWg04vP5qKio4K677kKv1zN//nz27NlDQ0MDNpuNw4cPYzAYtPaCpqS1JccntLJsITiUlJQEOgQhhBAi6ElS24q53e4WJyVUVFRoyxTee+89fD5fs5aCsLAwVPW3/+ibVuWKwGuaUiGEEEKIk5Oe2lYqKyuLqKgo4MRJCT6fD51Oh8Fg4PXXXwcgNTVVG/vV1I97qvYCVVVPqOoaDAZATuQLIYQQIrhIpbYV+632AI/Hg81mo76+HlVVtdYDs9lMSEiIVsmF5nNoj6/gttSm4Ha7JaEVQgghRNCRpPZPSqfTERERwZEjR1BVFb2+sSjv9Xq1DWJNSbHb7daSWZl8EHyqqqoCHYIQQggR9GT6wZ/Q448/zt69e1m2bJl227Jly5g2bRpWqxW9Xs+QIUNYsWKF1n7w6xW5InisWLGC9PT0E26X6QdCCCH+ik42/UB6av+EsrKytJm1TfR6PfHx8YwdO5a0tDSmTZsGQGRkJC6XS2spMBgM0l4QZNq2bRvoEIQQQoigJ0ntn1BOTk6Lt6ekpJCfn0/37t3R6/UoikJKSgr79u0jOjoah8NBcnIyR44ckTaEINLSjFohhBBCNCdJ7V9Abm4uCxYsoLi4GIfDwa5du6iqqsJgMHD++edz6NAhbVXu4cOHCQsL0yYkiMCzWq2Eh4cHOgwhhBAiqElS24rl5uby1Vdf4XA4KCoq4tZbbyUnJ4c9e/bw/PPPAxAVFUW/fv04dOgQGRkZdOvWjffff58+ffqQn5/Pv/71L9xuN8nJyRw7dgy/3y8JbZBpmlIhhBBCiJOT6QetnNVqZdasWcycOZPZs2cD8MQTT/DUU08xdOhQjhw5wowZM1AUBafTybp16/D5fLhcLqKioggNDcXv93Ps2LEAX4k4maZxbEIIIYQ4OUlqf0dLly79jw9ZDR48GKfT+X9+z8zMTACSk5NxuVwA7N+/n2eeeYbZs2dz+PBh4uLiUBSFQ4cOccMNN6DT6di3bx9lZWVYrVYURUGn02mvKV91B5emJRtCCCGEODlpP/gdzZo1i2uuueYPee3c3Fzy8vKwWq1UV1dz1113AbB8+XIOHTqETqfD5/Nx22234fP52L17N1deeSVr165l37592sGvV155pdkyhibHL1qQ9oPgUlFRQXx8fKDDEEIIIYKaJLW/4WQ9qxs3buSNN94AwOFwMHXqVH788UfKy8t54IEHeOutt5gyZQqbN28G4IorrmDMmDHaa+bl5VFRUcF5551HfHw8nTt3Zu/evbRr145du3aRlpZGfn4+MTExxMbG0tDQQEhICNu3b6dr16688MIL3HDDDUDj5rC1a9ei1+u1LWLDhw9nyZIlLFq0qMXrkskGrcvxG9+EEEII0TJpPziFlnpW9+7dy8svv8z8+fMZPHgwq1atYtiwYcTHx/Pqq6+ybt06jhw5wrJly3j//ff59NNP2bNnj/aadrud2NhYXn75ZRoaGkhOTqaiooLLL7+cxx9/nG7dujFu3DgURWH69OkUFhaSkZFBfHw8PXv2ZM2aNQDaVrBevXrhcrlQFAWHw0F9fT1ut7vFwcS/Xq37W6t2RXBo6c9RCCGEEM1JUnsKLfWsJiYm8sILLzBx4kQ2bNiAx+Np9pz9+/fTt29fFEXBYDDQs2dP9u/fr92fnZ2N0Wjk/PPPx2Kx0LlzZzweDz169OCLL77g008/paCgAI/HQ2JiIjqdjqioKKKjoykuLmbz5s0YDAaio6O19wgJCaFPnz64XC7Ky8tRVRWv13vCyflfL5CThXLBr6KiItAhCCGEEEFPktpTaKmSOXnyZF588UWmTJlCQkKClhgqioLP5yM9PV1rPXC73WzZsoW0tDTt+fn5+bjdbr7//nusVit79uxBr9czf/58evXqxRVXXEFcXBx+v5/S0lK8Xi+hoaHo9Xq+++47fv75Zy666CJcLhc+n4+QkBB8Ph+pqakAbNq0CZfLRVxcnJZwywD/1uv4Q3xCCCGEaJn01P4Xrr76aq6//nosFgtxcXHa4oK+ffsyfvx45s+fz8aNGxk+fDhut5tLLrmEbt26ac+vqKigsrKS+++/n/j4eKqrqwkLC+Nvf/sbL774IhERERQXF1NfX88dd9xB+/btUVWVc845B4PBQENDAxkZGaxZs4aQkBDWr19PYmIiq1atQqfToaqq1ooAoKqqVGRbMZlGIYQQQpya4pds57TJzc1l1apVZGRkcM8993DppZeydu1aRo0aRXR0NHV1dfzrX//SKnMFBQVMmTIFn8/HoUOH6Nu3Ly+//DKLFi3inXfeQa/Xa1VcgPLycjweD0lJSVRVVdG7d2++++47wsPDiYyMpLCwkKysLHw+H1u3btUOjDWtyz169CjQmATLYbLgsX379hYr7U6nkx07dgQgIiGEECJwunfv3uJ5E2k/CBJXXnklc+fObfZV8759+3j00UeZO3cuDz74ICaTifr6eubPn8+dd97JRRddhNfrJTMzk1tuuYUzzzyT7t27M2zYMNq1a8fMmTNp164dOp2OW2+9FaPRSGFhIXv37kWn05GSkgI0JrXFxcXa+0pCG1yaermFEEIIcXLSfnCaZWRkMGHCBK01oEmHDh1OeGxCQgJvvfUWISEh2Gw2wsPDKSwspFOnTgwdOpRJkyZhtVr55ptvsFgsdO3alW3btvHFF19QXFzMrbfeSmJiIkajkeXLl+N2u0lJSeHw4cOoqkpdXR3QmMTKFITgVVFRIS0IQgghxClIpfY0MplMlJeXA42HxY7XUlL5wgsvcO+99zJ16lQyMjLw+/20bduWffv2sXDhQiIjI2nfvj3/+Mc/WL16tbahrE+fPgwYMIBBgwYRHR2N2WymXbt2KIpCUVERdXV1xMTENNtUJQfJgtfxhwyFEEII0TJJak+jAQMGcPToUUaMGMHKlSsJCwv7zcdfddVV3Hnnndx4440cPHiQsrIyYmJiuO+++5g1axabNm0iNDSU0NBQEhIStMprRkYG0JjcFhcXo9fr2blzJ36/H4vFgqIoXHDBBYSHh9OxY0d0Op2WVP86uZaT94EnI72EEEKIU5ODYq3UwoULOXz4MI899hhWq5Wrr76aZcuWcf/99zNmzBiGDBnCggULKCkpITExkXfffZf4+Hi6du3KBx98wAUXXEBxcTElJSVUV1cTEhKC0+nE7/ej0+lOmL0rAmfFihWkp6efcLscFBNCCPFXdLKDYpLUtiJNK3atVitVVVVERUXh8XjIz8+nU6dOtG/fnmPHjnH06FFsNhuqqvLSSy/hcrmYMGECqqoSGhqKzWbD5/Ph9/sxGAzodLoTenxF8Dh+G93xJKkVQgjxV3SypFYOirUydrud9957j6qqKoYNG8aaNWu46KKLeP755znjjDOYOnUql1xyCWPGjKG0tJTBgwdz9tlnExYWht1ux+124/V66dmzJzt27MDtdmvVWa/Xq/2/EEIIIURrIj21rUx2djaqqhIXF4fFYqGqqgr4ZXrC/v37yc7OBhrX+TZNOYiOjkan0+FyuVAUpdmcWo/HoyWyXq8XvV5/Qm+tTEcInLy8vECHIIQQQgQ9aT8IoNzcXAoLC5kwYQJOp5OBAwfSt29f9u3bR0lJCUlJSQwePJivv/6apKQkfv75Z1RVRafTkZiYyL59+2jTpg3Hjh0jPj4ep9NJfX09Xq+XM888k1GjRnH//fej0+m0dgNoTFDlj731kOULQgghxC9k+UIrYbfbCQsLY/r06bhcLlJTU7HZbHi9Xq699lqMRiMGg4Hi4mIeeughDh06RHR0NG63m5qaGj766COio6PZsmUL06dPR6fTkZGRoc051el0GAwGAG2d7q/p9Y1dKTExMafvwsVJNY2BE0IIIcTJ/WZSm5uby7Rp005XLKe0fv16li5detL76+rqGD58OH//+99PY1TNPfDAA7hcLg4fPszVV1/No48+yp49e9i0adNvPs/v9+Pz+SgsLGTKlCmsWrWK6upqCgsLATh27Bjr1q2jrq6O2NhYunXrxoUXXojRaGTevHnY7XYAJkyYwIABAzAajVqrQUFBgXYQ7PiK7a+XLjT93DT5oLq6+vf9cMR/JTIyMtAhCCGEEEGvVVVqBw4cyPDhw096f0FBAQkJCcyZM+c0RtXcq6++itFo5KeffqJfv35MnTqV1atXs2/fvhMe29IyBrvdzrJly7j33nuJjY3l4MGDuFwuOnTowKWXXkpGRgYDBgzg4osvpry8HK/Xy5dffonP5yMmJob33nuPlStX4vV6aWhowGQyERcXR2xsLHq9nuzsbAwGAykpKaiq2mwl7vEJrsynDR6yGEMIIYQ4tVNOP/j5558ZM2YMVquVe+65h/PPP59Vq1axaNEi7THTp09n7969vPPOOxgMBo4cOcJll13GHXfcwcSJE6mpqaGmpoabb76ZFStW8OqrrwLQv39/vv32W1avXs0777yDXq8nNTWVf/zjH2zZsoWpU6ei1+uxWCxMmzaN1atXaz2or7zyCjt27MBms5Gens4zzzzDc889R1lZGa+//jrFxcXa+86aNYuZM2eyefNmAK644grGjBnDxIkT0ev1FBcX43K5uOyyy1i3bh3Hjh3jrbfeIjw8nPvvvx+/34/b7eaZZ57hgw8+ICsri0suuYSbb76ZAQMGMHbsWB5//HGuvfZaJkyYwMKFC5k5cyYOh4PIyEg+/PBDDAYD3bp1o0ePHtrnVlBQwOrVq/n888/p0aMHRqMRu93Ot99+y5IlS1BVlYaGBmw2G7fddhv//Oc/OXDgADt37sRoNJKQkECbNm34/PPPCQ0NpbS0lGuvvRaDwYDdbqeurg6j0YjNZsPhcKAoCj/99BOKolBRUaG1HzQdEjs+wT3ZBIRfJ8Lij6eqrervnkIIIURAnPK/lqGhocydO5fZs2fz7LPP4vP5OHjwILNnz2bBggV06NCBb775BoDi4mJmzJjB0qVLeffdd7XXOOecc1iyZAkWi6XF9/j0008ZO3Ysixcv5rzzzsNqtfLFF19w4YUXsnDhQq677jptWxaA1WrFYrHw3nvvsWTJEn7++Weqq6uZNGkS55xzDvfee2+z9/3pp584cuQIy5Yt4/333+fTTz/VZn+mpqYyZ84cOnbsyJEjR3jnnXe46KKLWLt2Ldu2bSMiIoJ33nmHyZMnY7Vaueiii1i/fj0Oh4O6ujq+++47/H4/O3fupHfv3gDExsYyfvx4rrjiCu644w6GDh3K2LFjmyW0O3fuZMOGDfz44498//33ZGRkMG7cOMLCwlixYgUJCQmsWbOG7777jgcffJDPP/+cv//975xzzjn89NNPLFy4kJdeeok33niD0tJSzGYzQ4YMobq6mtraWnr27El8fDzTpk1DURSioqJo27YtV111FYmJibRv3x5FUTCZTIwYMeI//gdGEtrTr6amJtAhCCGEEEHvlEltVlYWiqIQGxtLREQENTU1xMbG8uijj/LYY4+xZ88erQczIyMDvV6P2WwmJCREe42mcVO/1tTb+dhjj7Fp0yZuuukmfvrpJ1RV5fbbb6eqqooxY8awatUq7fASNH5tX1VVxYMPPsiTTz6pzV/9tePHXPXt2xdFUTAYDPTs2ZP9+/cDcMYZZwBgsVjo1KmT9rPL5WLgwIFkZ2dz55138vrrr6OqKllZWVpCetFFF1FVVcWPP/5Ir169/ldjr959911SUlLQ6XSEhoYyefJk7b7q6mrCw8NJTEwEGsd47d27l44dO7Jz507uvPNObr/9drxeL99//z3Hjh3j8OHDrFmzhtraWlJTU9m+fTsej4dnn30WvV7P8OHDcTgcbNu2DbPZzIEDB/B6vYSEhLB8+fJmn21UVNR/fB3ij+d0OgMdghBCCBH0TpnUbt++HWg8gW232zEYDLz++uu8+uqrPP/885hMpmajolrSdPvxPaRHjx6ltrYWgKVLl3LPPfewcOFCANasWcMnn3zC0KFDWbBgAZ07d2bZsmXa661fv55jx47xz3/+kwcffBCHw9HiiKqm901PT9daD9xuN1u2bCEtLe03YwbYsGGD1qN7xx138M9//hNVVenevTvvvvsu5513HllZWbz88stcdNFFJ30dRVFOqHDeeuutlJWV4fP5cLvdjBs3jsGDB5OSkkJ0dDRWq5WysjIANm7cSPv27dm6dSt6vZ45c+YwY8YMZsyYgdVqJSkpieeff56LL76YSy65hH/961/4fD6t6l1dXc369eu11wsJCcFisZCSksLDDz9MVlYWYWFhQGMCdXxVXASe9NQKIYQQp3bKnlqHw8Ho0aOx2+08++yzhIeH06dPH4YOHYrZbMZisVBWVkabNm1O+Wbdu3cnIiKCYcOGkZ6erj2nR48ejBs3jqioKMLCwjj//PMpKipi4sSJmM1mDAYDzz77rDZBoEePHrz11ltcf/31GI1G2rZtqyVsLRk0aBAbN25k+PDhuN1uLrnkErp163bKeDMzM7n//vt59tlncblcJCQksGLFCi688ELuvPNOli9fzo8//khBQQGpqanALxMYqqurtcpvWVkZs2fP5v3332f48OGMHj2aefPm0aZNGy666CLq6upISkpi/PjxQGMSfNNNN3HppZdqv7/77rt88803HD16lL59+2K1Whk2bBizZ8/GbrfzxBNPoCgKHo+HPXv24Pf7WbduHTabDYB9+/bh9XopLCwkISGByspKDAYDjz32GIqiaCO/QFoMgs2RI0eIj48PdBhCCCFEUJPlC6ewcOFCioqKmDRpElarlZycHJYsWcJ9993H9ddfz5VXXsmrr75KWFgY5513Hs8++yyLFi3C5XLxyiuvMHz4cJ588kkWLlyIoiiMHTuW/v3788knn/DII49QUlLC888/z7Zt2xg2bBgHDx6kW7duHDp0iNdee43MzEyuvvpqXC4XXq+X2tpannzySR577DHMZjMRERHU19fj8/lwOp34/X70ej0ejwedTkdUVBSVlZXabaqqoqqqtjmsqW2jV69ebNu2TRLaINTU//1rsnxBCCHEX5EsX/gvHb92Njw8nPT0dA4fPgz80o+blJSE0+nkwIED9OjRo1mf7N69eykuLmbs2LGMGTOGmpoaKisrm71H0zIEo9GITqdj7ty5nHnmmTz++OM88MADlJeXc/nll3PllVc2+yr6ggsu4Oabb6ahoQGDwdBs/iygJcFNmtogvF6vNtGhyc8//ywJbZCSg2JCCCHEqUlSewrp6en8+OOPQOPUhYKCgpO2WjQd5Dq+T7Zjx4506tSJ+fPns2DBAnJyckhKStKe8+tCeVOC+/XXXzNo0CDeeustMjIy+Pzzz1m9enWzxyqKQkREBIqi4HK5gOYbw0wmE2azGWhcqND0Xicb5v+/OegmTp+IiIhAhyCEEEIEPUlqT+H666+npqaGESNGMHr0aO6++25iY2NbfGzXrl0ZMGAAI0aM4MYbb+TKK68kMzOTfv36MWLECHJycjh48CDx8fHahq+jR4+2+Fo9evTg3//+N1dffTU2m42zzjqLyy67jPr6eu0xHo+HmpoafD6fth3M6/VqSW1cXBz19fVastp0e0tJUlRUVLMJEyJ4bNmyJdAhCCGEEEFPempPo9zcXJYvX47L5cLpdOJyuaivr8dqtTJq1Ci2bt3KsGHDWLhwIdXV1aiqyvvvv8/48eMpKirC7/cTFhbGBRdcQF5eHtXV1VrvbFhYGHa7naysLI4ePUpRURGKomgtB36/n4iICKxWa4uTIkAWKwQr6akVQgghfiE9tUHCYrHw73//m/nz56OqKl988QVbt26ltLSU22+/nV27dnHhhRfy+eef8/DDD+N0Ohk5ciTdu3dn48aNLF++nM8++4zLL7+c5ORkvv76a5555hkiIiI466yziI6OJioqipdffpmOHTsCMHjwYNLT0+ncubMWh16vx2g0alVco9F40mRXBJb01AohhBCnJt83n2ZNCyGKioqoqqrSxnjZbDYOHz7M7bffzttvv82YMWNITEzUtpBlZ2ejqipxcXGEhIRgs9koKyvj/vvvBxoPhx09epR9+/ah0+lwOBxYrVb0ej27d+9Gr9czYsQIduzYQUREBKqqUl5ejtlspqGhQevJFcFHlmEIIYQQpyaV2tNMVRs/8jZt2pCcnMycOXNYsGABN910Ez179jzp0on8/HwAKioq0Ov1PPnkkyQlJfHWW2+xYMECBg0aRGhoKOPGjSMzM5O33nqLc889l969e9OmTRuKi4t57bXXSE5OpkOHDlr1z+/3azGd7ACZTqf7gz8V8VuaFpYIIYQQ4uSkUhsgMTExjB07llGjRuH1eklNTeXSSy/F5XK1uHSioqKCMWPGUF9fz1NPPYVOp+Pxxx9n/Pjx+P1+iouL8fl8rFixgpKSEkpLS/nwww/R6/WEhYXh9XopLi7GaDRy7NgxbZxXaGgoDQ0NAM0OoR3P6/Wets9FnEgWLwghhBCnJgfFWoHc3FwKCwuZMGHCSR/z+OOPs3HjRtasWcPTTz/NkiVLGDlyJImJibz22muEhIRgt9vx+/28+OKLTJo0CYC0tDQOHTqEyWTijDPOkJP2Qeh//ud/6Nq16wm3y0ExIYQQf0UnOygmldo/kabey6aK68qVK7FarXi9XuLi4iguLsbtdvP0009rz6murgYaE6SmhFZRFDk0JoQQQohWRZLaAGoa8eXz+bj33ntZsWIFe/bswW634/V6WblyJQA5OTlA42GxphFd//znP3n33Xe1Sl16ejpnn30255xzDtDYK+t0Ohk2bBgffPCBNhIsNjaWhIQEdu3aBdBsq1gTv98viW0QaZovLIQQQoiTk4NiAWaxWFi8eDFdu3YlNzeX2bNn88QTT+BwOPj222+bPTY2NpZ27drx448/snfvXvbs2cO3335LXl4eBQUFHDlyBI/HQ0JCAueffz5+v5+lS5cSEhJCeHg4er2empoaLaHV6/UnTD1oGvEl28WCR7t27QIdghBCCBH0pFL7B8rNzeWrr77C4XBQVFTErbfeSk5ODjt37uS5556juroal8tFcXExS5cuxePxcPHFF5OUlERVVRUvvvgiJpOJQYMGcc8993Ds2DEGDBjAjBkzyM3Npaamht69exMSEoLP58NsNmOz2XA4HBgMBtq3b8+hQ4cYOXIkubm51NfXYzQatYNfHo/nhJibqrMnW8IgFdzTTz5vIYQQ4tSkUvsHs1qtzJo1i5kzZzJ79mwAJk+ezJNPPsn48eM544wzmDJlCmPGjMFgMJCXl8ekSZOwWCy89NJLLFq0iIULF2qv1zR+KyYmhoSEBGbNmkVKSgoWi4WPP/4Yn8+HzWbjq6++Ij8/H71ez7Jlyzh27BjwSyIbGhr6X43qkgTr9GtaqSyEEEKIk5Ok9g+WmZkJQHJysvZVf1lZmXaaPTk5mb179xITE0N4eDh///vfefrpp1FVlc6dOxMaGopef2JB/eqrr6ZNmzbcd999FBYWYrfb6dChA3q9nqqqKhITEzGbzdTV1WG1WrXnNVVgGxoafnNUV0xMzO/2GYj/G5lwIIQQQpyaJLV/sJZ6UxMSEti9ezc5OTmcffbZtG/fHoCwsDDmzZvH008/TZ8+fQgNDW32vB49enDfffdpvyclJZGWlobBYGDw4MHayC6DwYDVaiUtLY2wsDBcLheKomhV3v9EVVXVf3fB4nfXrVu3QIcghBBCBD3pqQ2A559/nueeew6/349Op+PFF18EoG/fvowfP5677rpLe6zb7aa+vp4bbriBgoIC1q9fD8Bbb71FSkoKISEh+P1+jh49yoABA1i8eDFerxePx6NtDWtasHCyg2HSUhDcKisrZVWuEEIIcQqyfCHILVy4kKKiIiZNmoTVaiUnJ4clS5Zw1VVXcfnll/PYY49x2223YbPZqKuro7i4mNDQUHw+H7W1teh0OpxOJ5dccok2Iky0LvPnz+fss88+4XZZviCEEOKv6GTLF6T9IMjt37+fTz75BKfTSXh4OOnp6Rw+fLjZetsePXpQVFRESUkJ9fX1lJeXU1tbi9/vp1+/fvj9fr766isAdDodZrNZe/2EhIQW3zcsLOyPvzjxH+ndu3egQxBCCCGCnrQfBLn09HScTicHDx5k8uTJ7Nmzh9raWqKioigpKaF///5YrVYURSEiIoL4+HiqqqrweDxcdtllbNiwAWicdbp7926gscIHjXNqy8rKWnxfm812ei5QnFJFRQUpKSmBDkMIIYQIatJ+EERyc3PJy8vDarVSXV3NXXfdxaBBgzjrrLOIjY2lvr6euLg4TCYThYWFPPTQQ4SGhrJx40bWrl2LzWajX79+bN++nfr6evR6PR6PB1VVSUpKQlEUiouLtR7ak/XTSp9tcNm9e3eLBw6l/UAIIcRfkbQftBJ2u5333nuPOXPmMGXKFFRVJSYmhg8//JBzzjmHsLAwUlNTuffee8nPz2f58uV8+umn+P1+VFXFYDBof9CXXHIJOp2OqKgoqqqqKC0txe/306lTJ+DkM2cloQ0ust1NCCGEODVJaoNMdnY2qqoSFxeHxWLRRmutW7eOrKwsUlJSqK6uZuHChRQWFuL3+zGbzSQnJ+P1evnuu+/o0aMHgJbsVlVV4Xa7MZvNKIrCoUOHmiVK/5tRX+L0a5piIYQQQoiTk57aIJOfnw809lFarVZiY2OBxlml1113HdHR0djtdgwGA/v370en02G329m/fz+KouB2u7VDYfDLsoWQkBDq6uowGAy43W7tflVVT7oSVwSH4/+8hBBCCNEyKdEFmYqKCsaMGcP48eN56qmntFW2mzdvJjY2lrKyMlwuF23atAEaD3RFRkYCjYmrwWDA5/NplViLxQKAwWBAp9PRpk0bkpOTtfdrSmhb6k0RwaHpz1cIIYQQJydJbZDJzs5m3rx55ObmMnDgQADWrl2LwWAgIyOD5557jlGjRlFXV8e3335LfHw8Op0ORVG0/lm/36/1xdbV1QGNX2F7vV4OHjxIenr6Ce/bNBFBBJ/q6upAhyCEEEIEPUlqW5EOHToA8NNPP1FVVcW4ceOorq7GaDTi9/ux2WxYLBatZcFoNBIfHw/AsGHDtKkGmzdvPuG1pa82eCUmJgY6BCGEECLoSSYTRHJycpgwYcJJ729KPBsaGnA6nTidTt544w1SUlLw+/3o9XqsVivZ2dkAuFwuysvLAfjkk0+aPf/XpK82eB07dizQIQghhBBBT5LaVqhfv348/fTTmEwmZsyYQUlJCZGRkYwYMYK4uDjWrl2LqqqYzWZCQ0PR6/W4XC46duyozaxVVRVFUZpVaI/fNCaCR1PlXQghhBAnJ0ltK3F8FTc3N5fzzjuPBQsWsH//foYOHUrPnj357LPPuPvuu4mKisLn8+FyufD5fHg8Hnw+H3v37sXn81FSUoLP58Pv9zer0EpfbXCSkV5CCCHEqclIr1YmJyeH4uJivv76a5KSkmjTpg179+4lISGB8PBwPB4P8fHxjBw5kgULFlBbW0tmZiZ79uwhOjpaS5AMBgN+vx+LxUJ1dTVer1db4CCtCMFFKrVCCCHEqUmlthW66KKLWLRoEa+88goPPPAA+/bt49ChQ7Rt25YffvgBp9NJXl4e4eHh+P1+MjMzURSF2tpa9Ho9JpMJp9OJ2+2moqICRVFQFEWr3orgUlRUFOgQhBBCiKAnSW0rkpuby7Rp08jIyKC6upry8nIURdES1Ntuu43+/fvTuXNn/v3vf7N48WKMRiMmk4lBgwaRmJhIWloaS5YsQVVVIiMjURQFr9cLoE1HMBqNAISGhgbycsX/13TYTwghhBAnJ+0HAZabm8u6detwOByUl5czevRo8vLy2Lt3L4888ghDhgxh4cKFrF69mtLSUrxeL+3atcNut2O325k2bRq1tbXU1dVx4YUX0q1bN0pLS+nevTt+vx+v18vHH3/MOeecg8/n4+jRo9x22234fD5qamq0RPZ4LpcLaHlKgjj9QkJCAh2CEEIIEfQkqQ2A4xPZvXv3EhYWRvv27SkvL+ftt9/m888/54UXXmDy5Mn861//oqqqio8++ogVK1bwxhtvsHfvXmw2G3379mXXrl2oqsoHH3zAiBEj+PHHH0lJScHn82mtCAMGDCA/Px+r1YrT6Ww2zN9gMGhJbJO4uDgqKipO98ciTiIzMzPQIQghhBBBT9oPAsRms/HOO+/wt7/9jdraWt544w3GjBmDy+XC7/fjdrvp1q0bixcvxu/3c9ttt7F8+XLq6upYuXIlXbt2Zc+ePfj9fiIiIlAUhbq6OqKiopg5cyZ+v59HH32UsWPHotPpePTRR4mOjtYOgZnNZlRV1baLHT/gXxLa4CIVcyGEEOLUJKkNkK5duwKNXy1HRUWhKAphYWF4vV5UVUWv17N7927uuusuSkpKuPPOO7nyyiux2+34fD4qKioICwtDr9dz5MgRPvnkE/x+P6WlpXz44YcAdOvWjbKyMrxeL++88w7t2rVDp9ORnJyMy+VCVVWtnzYuLk6LLSoq6rR/HuLkdDpdoEMQQgghgp4ktQGiKMpJ79u9ezcbNmwgMzOT5557DkVRePLJJ5kzZw4JCQlkZ2dTWlqKy+XisssuIywsjPfee4/o6GhiYmK0Nbjvv/8+ISEhGAwGnE4nFosFr9dLaWmpNr+2pKQEgKefflp7//r6+j/02sX/jsypFUIIIU5NktoA69+/P927dwegT58+ZGRkkJaWRkxMDNXV1dx555307NmTu+66i9tvv50rrriC6dOnM2jQICIiIujSpQvnnXceZ599NklJSbzzzjsYDAbOPPNMrrnmGs4880yio6MpKSnh0KFDWCwWUlNTSU5OJjw8nMWLFwMwZswYoDHZNhgMvxnzbyXk4vdXUFAQ6BCEEEKIoKf4ZTBpq5Sbm0thYSETJkxg/fr1rFixgilTprBr1y6mTZvGww8/zBNPPMHdd9+N2WzmySefZNCgQSxcuBBVVYmLiyMkJISPP/6Ynj170qNHD/Lz8/F4PNrUBL/fT48ePdi2bVugL/cvbfv27dqYteM5nU527NgRgIiEEEKIwOnevTsmk+mE22X6wZ9UWloaoaGh/OMf/6C4uBidTscXX3zBLbfcwpYtW0hOTmbfvn0sXboUr9fLli1b8Pl8hIeHU19fj8lkwuPxsH379kBfyl+e3W5vMakVQgghxC+kUvsX5HK5eOKJJygqKqKkpASv18uVV17Jxo0b2bVrF263G2gc99X0swic3bt3t9jyIZVaIYQQf0VSqRUao9HI1KlTgcY2hpUrV6IoCgsXLqR3794AtG/fXlvPqqqqNgpMnH7SwyyEEEKcmiS1AXB8P2yguN1uJk2axM8//0x5eTmlpaWcf/752oivpKQkDh48CNAsoZUE9/QrKiqiXbt2gQ5DCCGECGoy/aCVW79+PUuXLv1fP2/p0qVER0dzxx13cP311+NwOEhNTSUkJITk5GTtxP3f/vY3bRpCQkKCtlL3VNVDqS7+ftq2bRvoEIQQQoigJ5XaAPn5558ZM2YMVquVe+65h/PPP59Vq1axaNEi7THTp09n79692piuI0eOcNlll3HHHXcwceJEampqqKmp4eabb+aBBx7g1VdfBRrHhH377besXr2ad955B71eT2pqKv/4xz/YsmULU6dOpbi4mMTERFwul1YFLCsrw2KxUFFRgcFgQK/Xs3nzZq2v1mazaUmtTqfD4/Gc9PqkVfv343a75aCYEEIIcQqS1AZIaGgos2fPpqqqimHDhjFw4EAOHjzI7NmzCQ0N5cknn+Sbb74hMTGR4uJiPv74Y1wuFwMGDOCOO+4A4JxzzsFisbBy5UrsdjvDhw8nKSmJmpoannrqKaqrq7n22mvJy8tj69atDBs2jMzMTAYOHMjKlSs5fPgwfr8fvV5PVVUVDz/8MA6Hg7CwMBISEti7dy8Oh0OL2WazaT//VkIrfl+bN2+mX79+gQ5DCCGECGqS1AZIVlYWiqIQGxtLREQENTU1xMbG8uijjxIWFkZhYSG9evUCICMjA71ej17f+Md13333sWXLFr7//nvOO+88AKxWK9u3b0dVVRRFYcWKFURFRfH5558Dje0AkZGR7Ny5E7/fr1VSa2tryc/PR1GUZpvEmrZYSfIaeNHR0YEOQQghhAh60lMbIE3zX8vLy7Hb7RgMBl5//XVeffVVnn/+eUwm00n7V61WK+eeey533303X375JTqdjvz8fDp27Mi0adPwer243W6qq6sBuPvuuzGZTNTU1BAREYHf78doNNKrVy9UtfEfgab30uv10g8bZJKTkwMdghBCCBH0pFIbIA6Hg9GjR2O323n22WcJDw+nT58+DB06FLPZjMVioaysjDZt2pzw3MzMTCorK4mJicFms7Ft2zYcDgf79u1jwoQJpKamUlxcrE0yeOutt7REtba2FmicVbt169YTel+lMht8HA4HkZGRgQ5DCCGECGqyfKGVOX4cmNPp5OyzzyY2NpbQ0FDcbjeKojBhwgTuvvtu4uPjqaysxO/3NxvDpdPptIQXZExXsNuwYQNRUVEn3C7LF4QQQvwVnWz5grQftHImk4lLLrmEf/zjH8TExHD06FHmzZuH2WwmPDwcgPPPP1/rx4UT2xkkoQ1uLSW0QgghhGhOktpWJicnR1vaYDKZePTRR1EUhTPOOIO5c+cSHx/PggULaN++PVdccQX9+/dHp9MRFxenJbNNSayqqrRv3/6E9zj+sREREdrtaWlpf/DVCSGEEEL8dySp/ZN6/vnn+fjjj/n555/ZsWMHN9xwA6qqoqoq8fHxGAwGfD4fBw8e1GbSNqmoqNB6bRsaGrTbDx06dNqvQ9CsVUQIIYQQLZOktpX7deV27dq15ObmMnPmTIxGIwaDgXvvvZdVq1bh8/mYMWMGOTk5uN3uZmPFbr/9dnQ6HdDYcxsWFgac/OCYTEg4feSzFkIIIU5Nkto/KZvNxieffEJKSgqLFy/G6XQycOBApk2bxnfffQfAwYMH8fv9lJWVMWvWLLxeLxaLBZ/Ph9vtbjYf9deJlZwvPH1kIoUQQghxajLSK4jk5uaybt06HA4H5eXljB49mry8PPbu3csjjzzCkCFDGDRoEB07dqRjx47U19fj9/s5duwYdrudqVOnYjKZeO2111BVlXfeeYecnBzefPNNrFYrVqsVk8mkJUkWi4XKykp8Ph8Wi4WamhptgoLL5Wp2gEyS2MBZtGgR48aNC3QYQgghRFCTpDbI2Gw25syZw2effcbcuXNZtmwZGzZsYP78+QwZMoRjx46Rm5tLdHQ0I0aMoLa2VptL+9BDDxETE0NZWRmDBw/m1ltvpWvXrgwaNIj6+no6derEp59+itFoBCAsLAyTyYTL5dLm1x7fQysVwuCQk5MT6BCEEEKIoCftB0Gma9euQOPUgfT0dG29rdPpBBpXph7fFmA2m3nnnXe4/fbb2b9/P8888wyxsbHs27eP3NxcfD4f69ev58cff2T16tXYbDZKSkpQVZWDBw/icrnw+/0YDAagsc2gqbdWBIfy8vJAhyCEEEIEPUlqg8ypDgU1rbVt0nSgq6SkhNjYWBRFISYmBofDwYsvvqi9ZlhYGFVVVXi9Xm2Grd1u19oKmqq3IHNrg40cFBNCCCFOTdoPWrkjR44wevRo7dDXY489xqFDhzCZTNhsNgBCQ0Opra3FbDYDjSO7mkZ6NbFarYD0zgajlmYJCyGEEKI5WZPbio0YMQK3201ycjL5+fmUlpby3HPP8e6777J//35iY2OxWq243W5tRi2A2+0GJIFtLbZv396skt5E1uQKIYT4K5I1uX9CaWlpZGVlYbPZuPvuuxkwYACLFy9m4sSJJCYmUltbS2xsLD6fD5/Ph8fj0Xpoj/9KOzQ0FGje2iB9tcGjpYRWCCGEEM1JUtuKTZkyhS5dumiHy8rLy6moqGD69OlUVlYCv7QVmM1mdDod4eHhKIqCXq/XEtemiQdN/bkgSa0QQgghWhfpqW2lmmbabt26lerqaqKjoykvL8fv92MymVAUBZ/Pp1VkbTYbfr8ft9uN0WjE5XJp9+l0OrxeL+Hh4dTX1wO/tCiIwHM6nS1+zSKEEEKIX0ilthWz2WwMGDCAsLAwEhMTOe+889Dr9XTs2JGhQ4fi9Xq59NJLteqs0WjUJiGEh4drPbVNB8guuugi7bV/PWVBBM7xs4OFEEII0TKp1LZiXbt2JT09nfr6esrLyzl06BAAX375JREREQAsXboUVVXx+Xy4XC5UVaW6uhoAk8mE0+nUkqZ58+Zpr+31ek/z1YiTiYqKCnQIQgghRNCTclwr1tQ+0PT/brebiIgITCaTNq7L7/fj9Xq1r6+PbzloWujQ0uYwmY0aPFwuV6BDEEIIIYKeJLV/ImeeeSYJCQmEhoaSkpKCXq/HaDRiMBi0xKhr167NWgsURSEiIgJFUTCbzdp9Ta0JktwGXmFhYaBDEEIIIYKeJLWtQG5uLvfccw+33nor11xzDbm5ueTl5bF69WosFgvTp0+nU6dObN68mYaGBkJCQggNDSU0NLTZzwBnnXUWPp+vWXtBfX09Xbt2xel0nrBNTGbZBl5mZmagQxBCCCGCnvTUthI2m405c+bw2WefMXfuXJYtW8aGDRuYP38+WVlZfPHFF1x55ZVkZmby9ttvc/DgQTweDw6HA/jlMNjcuXO16QfwS9K6c+fOwFyYOKVfzxUWQgghxImkUttKNM2ijYiIID09HUVR+PHHH9m6dSt33nknDoeDzMxMcnJyuOaaa2hoaMDr9aLT6UhPT6ehoYG0tDR0Oh0ul4uQkBAADAaD9h4DBgwIyLWJ31ZRURHoEIQQQoigJ0ltK3GySp3H4+HNN98kKiqKt99+G4AffviB/v37M3r0aNLS0igpKaF9+/ZUVlbStm1boqOjtQpu05QEnU7Ht99+KxXBILR79+5AhyCEEEIEPWk/aOXCw8OJiYnhnnvu4cknn+T666+nrq6On3/+mby8PO1xVqsVRVEoKSnBbrdrt9fU1AAywiuYxcTEBDoEIYQQIuhJpbYVyMnJYcKECQAMHDiQKVOmAJCSksKll14KwOWXX05CQgLLli2jrKwMi8WircaNiIjQ2gyaVuQCdOnSpdkkhFMtXJAqbmDs3bs30CEIIYQQQU+S2j+hXr16YbPZ8Pl8mEwmoqOjtYNiDodDm0u7f/9+bdpB01rdXzs+kZVJCIFx2WWXBToEIYQQIuhJ+0ErlpOTo/1sMplYu3YtAFdccQV6vZ6SkhJcLhdnnnkmeXl5WoLaVJHNyMhg165dqKpKdHQ0tbW1WhtCUwIriWzglZSU0K5du0CHIYQQQgQ1SWr/JHJzc1m3bh0Oh4O9e/disVg4evQoDoeDuLg4oDFBNZvNWrV2165d+P1+/H4/lZWVJ7ymoiioqir9tgEmCa0QQghxatJ+8Cdis9l45513+Nvf/kZJSQlZWVnEx8djs9l4/PHHyczMpGvXrnTs2BGTycTYsWNp27Yter0ei8WCXq/X+m3hlxW7TZoqvKfqvRVCCCGEON0kOwkiM2bMYPHixf/187t27coLL7yA1+vFZDIxe/ZsnnjiCQ4cOICiKBQVFRESEkJ8fDyKorBixQqqq6vx+/0kJCTg8/ma9dVeffXVtGnTRvu9qRWhpd5b8cdpGr8mhBBCiJOT9oM/ka+//prq6mpqa2txuVw8/vjjfPHFFzQ0NPDBBx9gs9nYunUrdXV1QPNkad++fSe83kcffYROp9N+l/7awGg62CeEEEKIk5Ok9v/o+F7W8vJyRo8eTV5eHnv37uWRRx5hyJAhrFy5krlz56KqKllZWUyYMIHKykomTpxIfX09fr+fqVOnApCXl8eqVauoqanhvvvuY/DgwSxcuJDVq1fj8XiwWq20bduWoqIijh07RkpKCocPH0ZRFCIjI6msrKRPnz5s3ryZDz74gKioKBRFobCwEEVRtIRWVdUTKq4t3Sb9tIEXHh4e6BCEEEKIoCdJ7e/AZrMxZ84cPvvsM+bOncuyZcvYsGED8+fPp2/fvsyYMYPly5cTGhrKww8/zNSpU/niiy8wGo0YDAb69+/Pgw8+yJEjR+jWrRvz5s3j9ddf55FHHqFTp04oisKiRYt48803mTdvHkePHiUqKoqwsDCKi4uJiorC6XTi9/tRVZWffvoJVVUxGAyce+65rF69mpqaGnQ6nVb1a6mFQNoKgpPVapXEVgghhDgF6an9HXTt2hVoXDmbnp6uVU2dTidFRUVUVVUxfvx4Ro0axf79+6mqqqKuro5XXnmFW2+9lY0bN7J8+XIGDRpEZWUlNTU1fPzxx2RkZLBkyRIaGhq0CrDb7WbgwIF069YNv9/PkCFDuO2227Db7TidTjweD2FhYcTHx+N0Olm9ejXQ2DrQdAisT58+ZGRkBOzzEv87LbWGCCGEEKI5qdT+Dn5r01abNm1ITk5mzpw5GAwGcnNzKS4upk2bNmzfvp3ExEQiIyOZNm0aJpMJj8dDUVERtbW11NTUcO2113Lw4EEmTpxIcXGx1moQEhJCREQEAJGRkfj9fjweDzqdjvj4eFJTU7FaraSlpbF37168Xi8GgwGHw8FPP/3ULEaj0YjL5fpDPyPx3wsNDQ10CEIIIUTQk0rtHywmJoaxY8cyatQohg0bxvr164mLi6NXr17k5eXx8ssvs2vXLm644QbtOW3atCE+Pp4uXbqwcOFCkpOTmT9/Ph999BEhISHY7XbtsQcOHMDj8eD1elEUBb/fT11dHXv27KG+vp4dO3agKAoDBw7EZrMBjb2zx4/ukoQ2uFVXVwc6BCGEECLoSaX2/+j4rV4DBw5k4MCBQGNLwr/+9S+gcTTW1VdfrT0uNzeX0NBQ3n77bdavX8+KFSto27YtI0aM4OjRo8TExHDbbbexePFiRo8eTZcuXXjppZd49913iYuLY8SIESxevJi2bdvy4Ycfotfr6d27NwaDgQEDBvD+++9TXl7OmDFj0Ov1fPDBB1q1z2QyER4e3uKyBRGcjEZjoEMQQgghgp5UagMgJyeHCRMmAI2J8JQpU4DGRPjyyy/nvvvuY8WKFdTW1jJixAhee+01tm/fzqZNm1i5ciXXX38955xzDklJSZSXl/PJJ58wdOhQamtrWbFiBTqdjk6dOjFp0iTMZjMRERGEh4dryxQGDx4cyMsX/0udO3cOdAhCCCFE0JOkNghZrVZmzZrFzJkzmT17NgB79+7l5ZdfZv78+QwePJhVq1YxbNgw4uPjefXVV9m1axdWq5Vly5bx6KOPcuTIEfbs2cMFF1xA+/btyczMRK/X8/rrrxMWFgaAxWLBYDCgqqrWF/xb/cEiMJp6p4UQQghxctJ+EIQyMzMBSE5O1vpdExMTeeGFFzCbzZSWltKnT59mzykvLycpKQlFUdDpdERHR7N//346dOgAwKhRo1ixYgUvv/wyHo8HVVXp3Lkze/fu1WbXHq+pP1cEXnV1NdHR0YEOQwghhAhqktQGoZaqpZMnT+aLL74gPDycm2++mfLycnJzc7WFD+effz5r165l+PDhAJSVlZGWlsbKlSvZtWsXWVlZOJ1Oxo8fz+bNmzl8+DA///wzXq+32dKFpkRWEtrgUV9fL0mtEEIIcQqS1LYSV199Nddffz0Wi4WSkhIURcHr9eL3+9m3bx8333yz1qrgdDrxer3ceOONZGZmUltbqy1dePPNN7XXbNoWJksXgpu0HwghhBCnJkltC3Jzc1m+fDk+n497772XmpqaE9bczpgxg8LCQiorK6mrq2Py5Mn07duXQYMG0bFjRzp27Mjf//53nnjiCZxOJyaTieeee46YmBjuu+8+rFYrDoeDhx9+mLPPPvuEVboAs2fPJjs7m1tuuYXi4mIGDhzIDz/8QHl5OTqdDqvVqiWkTzzxBHa7ncjIyGYjv8rKyrSE1mAw4Ha7m12rtBkIIYQQ4s9ADoqdhMViYfHixcydO5cZM2Ywd+5cFi9eTGlpKd9++y3QWOF0OBy8/PLLPPvsswAcO3aMCRMm0K9fP6ZOncqoUaNYsGABN998M9OmTaOoqIiKigrefvttXnnlFe6++25qampO+h6bNm3isssuQ1VV5s2bh9FoJDU1lfr6erZu3YrJZGLTpk3a4a/a2lrtGlwuF8XFxdrvv05oQdoMWoMjR44EOgQhhBAi6Eml9iSaDljdddddjB8/nvHjxwNgs9k4fPgwAL169eLYsWN07tyZiooKAKKjo9m1axeFhYUUFBQwa9Ys3n33Xfx+PwaDgc6dOzNy5EgefPBBPB4PLpeLMWPGcOjQIYYPH47H46G4uJgtW7YQHx/PLbfcwhdffEFlZSVGo5GioiKsVivZ2dl89913NDQ00Lt3bxwOB0Cz/lhpK/hzaNeuXaBDEEIIIYKeJLW/kpuby6xZs2hoaGDAgAE8+OCDJCcn88ADD/DCCy/Q0NDAmjVrqKiooFu3blRVVTF69GhsNhuTJ09GURRmz56Nw+EgISGBCRMm0KdPH/bv38/bb7/NZZddhtVqZdiwYQwfPpyBAwdSWVmJwWAgLi6OgoICYmNjqaqqorS0lC1bttCrVy9txW1ISAhut5sNGzYAjZXWQYMGsWbNGjweT7NEVq/Xa60HovWSNblCCCHEqUlS24LQ0FAuvPBC+vXrh6qqjB07lr///e+0bduWLl26kJKSQkVFBfv27ePYsWNERkaycOFC7r//fvx+P+PHj6ewsJDhw4fz9NNP43Q6sVqtVFdX8/HHHzN58mQWL17MN998A8CgQYPo27cvTz31FA6HA0VRSElJwel0UlNTw86dO/H5fOh0Oi699FIWL16M2WzG4/Hg8XhYvXo1Xq8XnU5HeHi41oLQlNCaTCacTmfAPk/xf3P8SmMhhBBCtEx6altw7rnnahu/oHHyQFhYGJ988gmvvfYa55xzDtCYjJ5xxhksX76cM888k9jYWP79739rz2vbti3/+te/WLhwIU8//TTdu3cnMjKSGTNm8N1337F06VLMZjORkZFcffXVnHvuucTExJCcnMyMGTN48MEHCQsLQ1VV3G43SUlJXHfddaiqSmhoKC6Xi/PPP59JkyZp79k0czY8PFw7cCYJbevWUi+0EEIIIZqTElALVPXEXD8pKYl9+/bRqVMntm7dqt3e0kzZ4/tam7Rr146tW7cyYsQI7TFXXHEFDQ0NfPzxx0BjRbXpccOHDycqKgq/368lNceOHeOOO+5Ap9PhdrsZMWIES5cuZdu2baiqil6vJysrix07dlBXV8fmzZsxGo3cfvvtvP7660DzCQgtTUMQwcftdmMymQIdhhBCCBHUpFL7H3rqqaeYNGkSY8eOZdu2bZx55plceeWVLT42IyODvLw8PvvsM+22mJgY/va3v7Fv3z58Ph/Z2dksWLCA6Oho1q9fT2lpKRUVFXg8Hnr16sWAAQOIiIjAarXyt7/9jcTERHQ6HQaDgdjYWMLCwti+fTshISF8/PHH3HDDDbjdbrZu3Yrdbueqq67illtuwe/3M2vWLK0v0+/3a4n4pZdeisFgaBb7r/s3dTrd7/kxiv9CU/+0EEIIIU5OktpfycnJadZ60DRaa/v27bz99tvMnTuXM844g6SkJNq0acOyZcu0xy5btow2bdpwxhln8Pnnn3P55Zc3e+2srCyGDRvG0qVLGTx4MFVVVaSnpzNq1Cj279/P5Zdfzr/+9S969epFfX09Z5xxBl9++SWpqano9Xp27NjB0KFDGT9+PHl5efzzn/+kY8eOREVF8dRTT9G9e3fuuusuzGYzBQUF+P1+unTpQteuXTGZTKiqyhNPPIGqqpjNZjIyMvB4PJjNZmJiYgBoaGhoFnPTggYROOeee26gQxBCCCGCniS1/6HY2Fj+/ve/c+ONN7J7925Gjhz5X71OU2tDmzZtSE5OZs6cOSxYsICbbrqJnj178sknnzB06FAWLFhA586dWbZsGePGjdOSzuNfw2QyUVlZidfrpa6uTptnGhkZyU033URKSgoHDx6kV69ePPDAAwDMnDkTaKzANm2qcjqdVFVVabeL4FJSUhLoEIQQQoigJz21/6FLLrmESy655Hd7vZiYGMaOHcuoUaPwer2kpqZy6aWX4nK5mDhxImazGYPBwLPPPktsbCxut5uXX36ZkJAQ7TXi4+Pp378/1113He3atSMtLQ2AAQMGkJeXx549e7DZbNqcW5/PR0hICD169GDLli1MmTIFv99PRkYGu3fvxu/3S2U2CB3/FxohhBBCtEzxy0qpP5Xc3FwKCwuZMGECCxYsYObMmTzwwAMsWrSI8vJyamtr8Xq9hISE4HA48Pl8GI1GXC7Xf/T6slb39Nu+fTtGo/GE251OJzt27AhAREIIIUTgdO/evcUD1NJ+8CdUUFDAtGnTGDRoEGFhYcyePZsDBw5QW1tLTEwMOp2OhoYGbUKDqqotTnxoiSS0p19LCa0QQgghmpOk9k8mJyeHQYMG8fnnn3Pfffdhs9lwOp2kpaWhqirR0dEYDAZtCkKHDh1O2ET2W321LY0wE3+s/fv3BzoEIYQQIuhJT20rk5uby/Lly/H5fNx7773s37+f1atX4/F4iIiIYMaMGfzwww/ExMTwyiuvcNttt1FaWkpNTQ16vZ76+nptwoHf7+fgwYMnVF9/q6/WYDD8x60K4vfRsWPHQIcghBBCBD1Jalshi8XCzJkz8fl8bN68mblz56KqKgMHDuSss87C6XSSnp7OW2+9RWFhIX6/X2sx+PVSiPDwcLxeL3a7/T96b0loTz+32y0tCEIIIcQpSFLbChxfne3duzcAw4cPR1VVdDod+/bt4+DBg9qEA0VROHjwIHv37kWn06GqKl6vF4fDQWlpabPKbH19faAuS/yHpI9ZCCGEODXpqW0FNm/ezL59+4DGE+8//PADPp8Pu93Otm3bGDZsGKGhoXi9Xq0ftikR8nq9eDwe9Ho9fr//hEqtCH6yIlcIIYQ4NUlqWwmLxcLixYsxGo3Y7XYMBgNhYWG43W7Gjx/Pzz//jKqqWgLUVLFtIsls61VbWxvoEIQQQoigJ0ltKxEVFQU0rkxVVZXdu3c3m1Ha1HNptVrR6/XExsY2q/C1dPhLJhm0DtJ+IIQQQpyaJLWtRFMC2q1bN21xgt/vR6fTodPpUBRFq876/X4qKyvxeDy/OZ5LkqXW4eDBg4EOQQghhAh6ktS2AllZWZx11llA48rUbt26odPpSEhIIDQ0FKfTidlsBhoTVZPJhF6vJzw8nPbt26OqKoqiaOtWVVXVHt/kt5JfEVidO3cOdAhCCCFE0JPpB61ATk5Os9/T0tIoKSmhY8eOVFZWkp+fT21trZbgNjQ04PF4sFgs2gguRVG03kyfz6fNqm3yW7NpRWCFhYUFOgQhhBAi6EmltpXr3bs3SUlJKIpCbGwsfr8fs9lMaGgoDQ0N1NbW4vP58Pl8GAwGFEVpVqn9dcX2eKGhoafrMsRvKCsrC3QIQgghRNCTpLYVysrKomPHjmRkZDB58mTuvvturZfWaDQyatQorrnmGq0tQVEUbV5tUlISqqricDhQFEVbutCU7Or1vxTvnU5noC5RHCchISHQIQghhBBBT5LaVignJ4err76a8vJyADp06EB8fDx33XUX0dHRhIeHY7VaCQ8PJzs7G51OR4cOHTjjjDPo0qULSUlJmM1mUlNTueWWW7RtY127diUkJASTyUTXrl3p1asXiqJoa1qb+m6bDqeJ06OmpibQIQghhBBBT3pqW6kBAwawePFiRowYQbdu3bS+S7vdzu7du9m1axe1tbWsWrWKxMTEZifoVbXx7zLFxcW8++672m27du0iLS2NsrIydu3apT2+sLAQ+KXvVvpvT6/IyMhAhyCEEEIEPUlqW6Hc3FwKCwtZuHAhTqeTSy+9lLVr13LeeefhcrnYtGkTJpNJm3hQWlqKoih4vV58Ph+JiYmUlZVpI730ej1Go5GGhgYOHDigvU/btm05fPhwoC5T/H92u10OiwkhhBCnIO0HfwKVlZVs3LiR0NBQfD4flZWVOJ1ObYuYx+PB4/EQEhICNPbKNh0C0+v1eL1e7HY7F154odZTq9PpWkxom5Y8iNNHElohhBDi1CSpbeX8fj8+n4933nkHr9eL0WjEbDbTtm1b4JeT823atGHcuHFA49pVu92uHSBrkpeXh8fjafF9mh7XtWvXP/JyRAtkSYYQQghxapLUtkImk0k7JJafn094eDjfffcdNTU1WK1WwsLC2LBhA4BWeT1y5Ahffvkl8EuS5Pf7uf/++7Xfjx/v9eu+2abft27d+sddmGiRzWYLdAhCCCFE0JOkthWqqanhhx9+YMSIEXz66afU1dVhMpmw2Wz4/X6sVqv22H79+gGNI7vy8/MBtGkHAFOnTtV+rq+vB357dq04/SorKwMdghBCCBH0JKlthUJDQ7nyyitZvHgxEydOJDExkeuuuw6AuLg4EhISaNOmDREREWzatAlo3CLWtC43LCysWW9s031NkpKSms2rFYGVlpYW6BCEEEKIoCdJbSvX1Dowbtw4FEUhOjqaIUOGkJWVRWxsLIqiaI9t2ihmt9vx+Xxan6yqqoSFhWmLGUpKSrTe2qbnN82qFaffxo0bAx2CEEIIEfQkqQ2w3NxcRo4cyYgRI/j+++/p37+/dt8DDzzAhg0bOHDgADfccAM33XQTY8aM4YcffuCDDz4gKyuLK664gqNHj1JXV8e1117L3r17SU9P58CBAxw7dkzbCmY0GvF4PKiqit/vx+Vy4fP50Ov1+Hw+6uvrqa6uJioqStsydnw1t2lWrTj92rVrF+gQhBBCiKAnSW0QsFgsLF68WOt//bXvvvuObt268d5773H77beTnJyMy+VCp9MxYMAAVFXls88+01oNNm3axI4dO1BVlYyMDBRFwefz4fV68Xg8JCUlAY1V3tTUVO19nE6nlvgCuFwuOXkfBBITEwMdghBCCBH0JKkNAh06dGjx9qaE8rrrriM6OppbbrmFRYsWERoayvDhw7nzzjs5dOgQOp0ORVFISUkhMjKSyy67jNDQUCIiIti/fz86nU4b9xUdHU3btm21toLq6mrt57i4OOrq6rT5tseLjo7+g65enEptbW2gQxBCCCGCniS1QeD4Q1oejwebzYbL5WLfvn1A4/zYrKws5s2bxyWXXML69espKSkhISGBOXPmYDKZWLVqFSkpKTidTux2O3a7nSuuuIKRI0diMpno1KmTtmhh586dWsJ86623au9dUVGBwWBoMcbq6uo/8BMQvyUqKirQIQghhBBBT464B5nRo0czfPhw2rRpQ0pKCgDdu3fn4YcfZsaMGaiqSr9+/aitrWXZsmXMmzePiIgIampq+P777zEYDISGhqLT6ViwYAHQOGO2Xbt2HDp0CJ/Ph9FoRFEU/H4/M2bMQK/X43a7URQFt9utxdJU4YXGiQsNDQ2n/wMRuFwu2eQmhBBCnILil6bJVmfPnj3U1dWRnZ2t3Zabm0thYSETJkwA4M0332Tp0qWYTCY6dOjAzTffTF5eHosWLUJVVXQ6HRkZGdTU1FBUVNQsgY2JiaGyshK9Xt9sCoL8oxIYu3fvbjbFoonT6WTHjh0BiEgIIYQInO7du2MymU64XSq1rdDq1auJi4trltTm5ORoP+fm5vLdd9+RmprKvffey4QJEzj77LM5++yz2bJlC23btuW8885j586dfPLJJxgMBs4880wOHDhATU0NNTU1QGMrRFNiKwlt4Hz33XfNpmIIIYQQ4kSS1AaJu+++m9GjR3PWWWexbds2Zs6cyeuvv86kSZM4fPgwXq+XcePGkZWVxYcffojBYKBbt244HA5effVVdDodbdu25dlnn2Xz5s3s2bMHn8/HSy+9RG1tLaWlpRQWFrJz50527drF559/TlJSEv379+ezzz5j+/bteDwe7ZBYU2W2qVIrAufcc88NdAhCCCFE0JODYkFi2LBhfPjhhwB8+OGHXH/99SxdupTo6GiWLFnCe++9x2uvvYbBYGDo0KGMHTuWM888kyeeeII33niDhQsX4nQ6uemmmwDQ6/UMHTqU6dOn4/V6efrpp6msrESn0zFr1iwmTZrEkSNHaN++PTqdjoiICCwWixaPVGaFEEII0ZpIUhskBgwYwPbt26mpqeHHH39k4MCB7N+/X2sxCA8PJz09ncOHD2vPqaqqoqysjPvvv59Ro0axc+dO7TBXamoq0dHR3H333QDcddddhISE4PP5mD17NqtWrUJRFLp06YLBYKCurk4bHXX8oaSWejnF6dW0DEMIIYQQJyftB0FCVVUuueQSnn76afr27cvIkSOprKxk7dq19OjRg9DQUDZs2IDVamXfvn243W4+/fRTvF4vdXV1REVFUVNTg9/vp7S0FJ/PR/v27bX2geuuu06rvm7dupXo6Gj8fj9PPvkkDocDg8GA0WjE6XTicrm0uKRiG3gFBQX07t070GEIIYQQQU0qtUHk2muvZc2aNcTFxdGtWzc++ugj0tPTue2227jxxhu5+OKLWbBgAUOGDMFqtXLnnXfSrl077HY7dXV1qKrKueeey0MPPYTD4WDBggU0NDSgKAqJiYnN5uEWFxejKAomk4mQkBBUVdVW6orgIgmtEEIIcWqS1AaR5ORk8vPzueWWW4iOjuaOO+7AbDbzyiuvsGTJEgwGAw899BB+vx+9Xs/s2bMpLi7mlVdeYcGCBfTs2ZNt27aRn5+PXq9n6dKl9OnTB4vFQq9evbRDYEajEZ1Ox8UXX8yAAQNwu90kJydrSe/JWg70einsB8LxlXMhhBBCtEyS2iD06w1i7777Lrm5uURERPDKK6/QuXNnPB4Pc+fOpUOHDrz66qvk5uZis9nIzs7m73//Ow6HA7/fj6IoGI1G9u3bp20Li4yMxOv1smrVKj7++GO8Xi9+v19Lev1+v/ZYs9kMNLZHyAKAwJBtbkIIIcSpyfKF02zTpk1ERESQmZnJ3XffzRtvvHHCY4qKihgxYgTJycmEhIRw9tln8+OPP7J161b8fj/JyckUFxeTkpJCaWkpACaTCbfbjc/nIzQ0lLq6OuLi4oiOjqaoqIi4uDgOHTr0mz2yxy9YkGULweObb74hPj7+hNtl+YIQQoi/opMtX5BK7Wm2fPlyysrKAFpMaAHatWvHt99+ywcffMDChQtJTU1FVVV++ukn8vLycDqdPPHEE5SWltK1a1fatm3LWWedxfjx47FYLERHR9O7d28SExOpqqoiMTGRgwcPEhISQvv27YHGymtTu0GbNm0AtMpu088iOEiFXAghhDg1aZL8L+Tm5pKXl4fVaqW6upq77rqLiy++mFWrVrFo0SLtcdOnT2fv3r1MmzYNg8HAueeey9dff01+fj6dOnVi2LBhfPvtt2zdupUXXngBv99PYmIi06ZN49Zbb+Xpp59mxYoVrF+/nqqqKnJycpg8eTIWi4Uvv/wSu93Onj17UFWVwsJCDh06REVFBRaLhZqaGqqrq/F4PPTq1Yv9+/cTFxeHzWYDGpPapskIR44c0WJuSmYNBgNut1u7XVVVrT1BnF7yuQshhBCnJpXa/5Ldbue9995jzpw5TJkyBY/Hw8GDB5k9ezYLFiygQ4cOfPPNN0Dj18Tvv/8+d999NwMGDODhhx8mJSVFe60nnniCl156iX//+9/069eP/fv3N3svg8FAZmYmL7/8Mk8++SRWqxW/3090dDRffvklPXr0ICoqiuuvv56QkBB0Oh2qquL3+3G5XJSXlxMWFkaHDh20/sxTbQo7PqEFSawCqaCgINAhCCGEEEFPKrX/pezsbFRVJS4uDovFQlVVFbGxsTz66KOEhYVRWFhIr169AOjQocNvvlZlZSXp6ekAjBw58oT709PTKSgo4L777qOwsJAuXbqwYcMGfD4fF198MbW1tXi9XpYtW4bVasVms2G1WqmpqQFg7969JCYmsnnz5v947W1ISAgOh+M//0DEH6ZTp06BDkEIIYQIelKp/S/l5+cDUFFRgdVqJTQ0lNdff51XX32V559/HpPJpH2Vf/x82JYOYCUkJHDw4EEAZs+ezZo1a5rdf/ToUbKzs7niiiswm81MmTIFi8WC2+2mY8eO+P1+7r33Xt566y2MRqNWZdXpdJx55pkoisLBgwe1kVxNkw10Ot1Jr08S2uARFhYW6BCEEEKIoCeV2v9SRUUFY8aMob6+nqeeeorw8HD69OnD0KFDMZvNWCwWysrKtENYTXr27Mm0adOa3f7MM88wadIkVFUlPj6esWPHMn/+fO3+4uJiDhw4gE6n48orr6R9+/baytuGhgbatWvH2rVrOXjwIKqq0rlzZ2JiYjh06BDXX389M2bMwO/3ExkZqa3CBfB6vf/x9co0hMAJCQkJdAhCCCFE0JORXv+F3NxcCgsLmTBhwh/+XjNmzCAuLo4RI0ZoB9TefPNN5s2bx4wZM1i3bh1nnXWWNtpp3LhxXH311bRp04apU6dy4403cvToUT777DNGjRrFCy+8QFhYGFar9Q+PXfw+vv/+e2JiYk64XUZ6CSGE+Cs62UgvqdS2MsdXiP/5z38SHh6OXq8/oUJ84MABDh06xPPPP4/T6WTEiBH4fD78fr82AUG0DtHR0YEOQQghhAh6ktT+F3JycrSfTzbea+PGjbz66qvodDratm3Ls88+yyeffMJXX32Fw+GgqKiIW2+9lZycHEaNGkVmZiZ79+7FarUyffp04uLiuO+++7BarTgcDjp27EhBQQG1tbUsXboUgBtuuIHMzEwsFgvDhg3jn//8Jz169GDlypVYrVYURWHw4MGsXLmSRYsWaTNoDQaDrF4VQgghxJ+KHBT7Hfx6vJfb7eaJJ57gjTfeYOHChSQmJvLhhx8CYLVamTVrFjNnzmT27Nnaa/To0YO5c+fSv39/PvvsM4qKiqioqODtt9/mlVdeweFw0LlzZ6qqqqitrWXfvn1ER0eTkJAANB4mCg0N5c477+Ttt9/m6NGjeL1eVq5cSXh4OKmpqVofryS0rUthYWGgQxBCCCGCnlRqfwe/Hu9VVlZGWVkZ999/P9A4SaB///60a9eOzMxMAJKTk5sll2eccQYASUlJVFRU0LlzZ0aOHMmDDz6Ix+Nh1KhRXHvttZSWlvLpp59y5MgRrrvuumZxDBw4kI8++ojzzjsPo9GoVWQdDgd1dXXaZipZpNC6WCyWQIcghBBCBD1Jan8Hvx7vlZSURFJSEm+99RYRERHk5eVhNps5duyY1gJwKnv27MFmszF79mzKysq44YYbGDRoENdeey0TJkygoaGBhx56qNlz6uvrKS4u5vDhw+h0OkJDQ6mtrSUjIwO73Y5Op6OoqKjZFANJcIOf2WwOdAhCCCFE0JOk9nfw6/FeOp2Oxx9/nPHjx+P3+wkLC+Mf//gHx44d+49fs3379rz55pv8z//8DwaDgXvvvReAxMREwsLC6NWrlzZ39vg46uvrOXToEL1792br1q1ERkayd+9eLQ6/398ssY6IiKC+vr5ZYiuJbnA5fs6xEEIIIVomI73+j07neK8mt912G5MmTSItLe2kcQwfPpw9e/ZgMBiIi4vj4MGDPPjgg7z66qvEx8djMBgoLy/H4/FgNBqx2+0YjUZMJhMul4uOHTtSWFjIqlWruOeee9ixYwfx8fGoqkppaelpu1YBu3btajGxlZFeQggh/opONtJLSkCtiMPhICcnh8zMzGYJbUtCQkI477zzyMzM5LXXXkOn0/HBBx+g0+kwm8107NgRvV5PTEwMAwYMwGKxkJqaSnh4OO3btyc9PZ0LLriAiRMnUlBQgE6no7a2VhLaAKivrw90CEIIIUTQk/aD/6Pjx3v90UJCQsjNzW3xviuvvJJJkyZx/fXXc+jQIdxuNz6fj5SUFD766CM8Hg+DBg3ixx9/JD8/nyNHjuByuWhoaMBgMBAZGUlycjJ+v5/t27dTXFxMaGgoNTU1QOP2sfDw8BYnJ5ysXcFsNmO323/Xz+CvKDw8PNAhCCGEEEFPkto/iaVLlxIdHU3Pnj3Zv38/33zzDREREeTn53Pw4EEAjh07xuHDh9Hr9XTq1ImCggI8Hg/btm2jrKyMo0ePNpthO3HiRB5//HF0Oh1AsxW7xztZ/60ktL+PI0eOnLIyL4QQQvzVSfvBn8T+/fvJzs5m//79nHvuuXTq1ImBAwei1+u56qqrCA0N1RJdgDfeeIOOHTsC4Ha7iY6Oxufz4XQ6UVUVv9/PSy+9BDRWaZv8p9MbxO8nOTk50CEIIYQQQU+S2j+J9PR0fvzxR9LT0/n+++8pKCjQph0MHToUv9+P3W6nuLgYr9fLVVddRVFREQBlZWWUlJQAjS0OTZVXq9V6wvvIucLTz2AwBDoEIYQQIuhJUtsK7dmzh02bNgEwePBgnE4n119/PTU1NXzyyScsXbqU0tJSpk+fjtfrJTIyEo/HAzQmpV6vF5vNhtPpBJpXYh0OhySuQaa6ujrQIQghhBBBT5LaVmj16tXs2bOHhx9+mPLycm688Ua++OILpk6dSnV1NRaLhZSUFFJSUgB45plngMaKn9/vR6fTaS0GIvjV1dUFOgQhhBAi6MlBsSDmdruZNGkShw8fxuv1Mm7cOLKysvjwww9paGjg7LPPRqfTcfToUR588EGmT59OfX09DocDp9OpVWLz8/Pxer243W6geWVWBL+wsLBAhyCEEEIEPanUBrGmiQZLlizhvffe47XXXsNgMDB06FDS09MJDw9HVVXeeOMNMjIyKCoqoq6uDqfTSVhYGF27dgVAp9PJhrBWTEZ6CSGEEKcmSW0Qa5poAI2JTXp6OocPHwYgLi6Obdu2YTKZ6NChA2VlZej1euLi4jAajZSVlbFz506gcV7s8YeNZIJB69LSbGAhhBBCNCdJbRBrmmgAjZMICgoKaNOmDYqioNPpOHbsGFVVVQwaNIjOnTsDUF5eTmhoKIqiEBISAjQmtU2tB6L1Wbp0aaBDEEIIIYKeJLVBrGmiwYgRIxg9ejR33303sbGxdO/enY0bN5KamkpoaCidO3dm8+bN2gzakSNH4vf7tUS2tLRUOxwWExMjB8RamfHjxwc6BCGEECLoSVIbxD799FOsVitGoxGPx4PZbAZg2rRpxMfH4/V6Wb9+PXFxcaiqit1ux+v1smLFCm6//XYiIiJISEjA7/cTGhpKdnY2DocDvV6PXt94RlBaEYLf1q1bAx2CEEIIEfQkqQ1ydrud9957jzlz5jBlyhQ8Hg92u53BgwfTrl07Zs6cSY8ePQgPD+eaa65Br9fz9ttvs3TpUnw+n3aYzGQy4Xa7sdvteDwePB7PfzTWqyn5FYEjB8WEEEKIU5OMJchlZ2ejqipxcXFYLBaqqqqAxoNiFRUVrFu3jrZt23LllVeyePFiIiMjueWWW6ipqSEmJoa2bdtSW1tLZWUlHo+HkJAQIiIiKC8v/48mIjQtbRCBExsbG+gQhBBCiKAnldogl5+fD0BFRQVWq1VLcFRVJTs7mx49etCjRw8uuOAC2rZti8vlYsaMGRgMBq688kqys7Pp2bOntlXM6/USExNzwvtIG0LwioyMDHQIQgghRNCTSm2Qq6ioYMyYMdTX1/PUU0+h0+ma3f/www/zwAMPsHHjRkwmE6mpqdx///2EhoayaNEi/H4/kZGR1NbWoigKRqORffv2AY2JbFP7gRweC17Hjh3TtsMJIYQQomWS1Aa57OxsJkyY0Oy2tWvXNvs9KSkJk8nEvHnzuOCCC/B6vTzwwAPMmjWL0tJSQkJCMBgMuN1unE4ner2eiIgIGhoa8Hg86HQ6vF4vqqrKkoYgJJVaIYQQ4tSk/aAVy83N5a677qKsrIxdu3axZs0aIiMjqaqqQqfTMXjwYBRFoaGhQRvvNWDAAAAMBoNWnW1amysJbXCSw3pCCCHEqcl/LYNYTk7OKR9jt9tZu3YtVVVVDBs2jKysLAoKCpgzZw6VlZUYDAYGDx7M8uXLMRgMvPvuu/To0YPY2Fjq6uoAiI+PJz09nU2bNmE2m6mvr0dVVXQ6nSxtEEIIIUSrIJXaVmrDhg0sXryY7Oxs7r33XuLi4jCZTNhsNrxeL9XV1ZhMJpxOJzU1NVqLQb9+/fB6vVitVu21ysvL+eGHH/B6vdTX1wMQGhoqCW2QKCkpCXQIQgghRNCTpLaVy8/P54033qCiooLKykocDgcGg4EPPviAL774AoBDhw7h9/tRFIXQ0FBiYmJwOp1a24HZbD5h+oHNZjvt1yJaJj21QgghxKlJ+0EQyc3NJS8vD6vVSnV1NXfddRcXX3wxU6dO5YMPPsDv9+P3+3niiSdITk6mvr6effv2kZmZSVhYGAaDgR07dqAoChdeeKHWM7t3714AjEYjR48eRafTNZt8YLfbA3bN4tSa/vIhhBBCiJOTpDbING0Qa+qRraurY/78+YSGhpKcnEynTp149tlnuf322zlw4ADx8fF4PB7Gjx/Pxx9/THl5OR6PR0tYzWYzdruduLg4bXGDJEmtS3V1tSxgEEIIIU5B2g+CSGFhIRUVFaiqytNPP43FYqGwsBCAcePG0bZtWzZu3AjAnDlzMJlMXHPNNRgMBvbv309dXR1Op5OYmBhCQkIAcDgc6PV67Ha7Nuv017NuRXDr1KlToEMQQgghgp4ktUGmtrYWgKeffhqr1crBgwcB2LJlC2+99RbQuCghLCwMVVUZPXo0BoOB7du3ExERgcViQVEUbe6soij4fD5CQkKorq4GGkd3yQax1qOmpibQIQghhBBBT9oP/mAn65P99ttvee211zCZTERFRfHiiy8C4HQ6GTNmDD/++CMvvfQSL7zwAgCbNm0iMzMTgEceeYRXXnkFo9HIkCFDcDqd2Gw2fD4fXq8Xo9HYrAVBURSqqqpISkrCZrPJ9rBWpqysjKioqECHIYQQQgQ1qdSeBk19snPmzGHKlCm43W6eeOIJ3njjDRYuXEh2djYzZ84EICYmhnnz5hEVFcVVV11FVlYWERERxMTEoNfrefPNN9m0aRP33HMPGRkZREREEB4ezuTJk3n44YfR6/VER0fj9/tJTU3FYDAQFRVFWFgYcXFxMsi/FUpNTQ10CEIIIUTQk6T2NMjOzkZVVeLi4rBYLFRUVBAeHk5iYqJ2f9OEgpbExsZy+eWXk5CQwHnnnceBAwcYOXIkS5cu5aGHHuLmm2+mU6dO5OXlAY0joHQ6HZdddhmRkZHMmTOHPn368Mgjj5CUlERISAiqqnLFFVcAYDKZtPcyGo3az9J7Gxw8Hk+gQxBCCCGCniS1p0F+fj4AFRUVWK1WEhISsFqtlJWVAbBx40bat2/PgAEDtBaDJoqikJCQwIQJE7Tb0tPT2b59O9DYa7tq1SpeeOEFLrroIgwGAxdccAE6nY7t27dTV1fH/fffz/bt29m0aRNutxuHwwH8kiwdPw0hOjpa+1mmJASHhoaGQIcghBBCBD1Jav8ADzzwAC6XS/u9oqKCq6++mssvv5ynnnoKnU7H888/zz333MMNN9zA999/z5133tnia/Xs2ZOioiJ++OEHKisrgcae2lmzZjFq1Ch+/vlnDAYDNpuNl19+mdTUVIqKiujUqRNHjx7F5XJhsVgYO3Ysb7/9trYxzOfzcfToUaB5JbC0tBSDwfBHfTTiv5CUlBToEIQQQoigp/jl1NAfKjc3l8LCQs466yxWrFjBlClT/lfPnzhxIpdddhkDBw78zdefMGHCCYfSoqOj2bFjB9HR0Rw7dgyv18v/a+/+Y6Ks4ziAv+8nCAhIhXMxyQtoNKKQYjgUyz9SmZCxVtmGNFwl0RIDjTgIGVRojuWwXyMZC+VX6/7ox2hFsSSrazHSwVLsrAjiNzi5I+6eu3v6Q3ni4MiS0/Nx79dfd889+96HD+h9+PL9fr6RkZEwmUxwOBzw9fXF9PQ0VCoV1Go1rFary9gzx+ySd33++ecIDw+fd91qtaKrq8sLEREREXlPTEyMy9LJGdw1hIU7FGzZsgW33XYbtFotSktLodfrpbZYRUVFuOOOO1BQUIDe3l5YrVbs2LEDKSkp2LBhA1paWtDX14e33noLDocDJpNJOu60paUFtbW1UCqViI+PR35+PqqqqtDX14exsTH8+eefeOmll7Bs2TK0t7eju7sbERERSE1NRWJiIoaGhnD69GloNBqIogir1Qqj0YjR0VEMDAxAqVRCq9XizJkzAACz2Sx9rb/99pu0rGBmGYLD4XC71IAF7fVh9pIQIiIico/LDy6Z26HAbrdjamoKzz77LCorK/HOO+8gMTERdXV1KCsrk/rIGo1GHD58GNXV1fMKw0OHDqG0tBRtbW2Ii4sDcLHnaFVVFWpra9HQ0IChoSGcOHECwMVNWu+99x70ej1qa2sRExODdevWYc+ePdLBCVNTU3A4HNi+fTvsdjsqKiqg0WjQ29sLtVoNjUaDrKwsxMbGQqm8+O1Vq9XS4yVLlkjxLbQRbPZmMfI+bhQjIiK6PM7UXjK3Q8HMkbKrVq0CAPT09OD7779HS0sLAODChQsICAhAcXExiouLYTabkZaW5jLm2bNnERsbCwBYvXo1zp07h97eXoyPj+Ppp58GAFgsFvzxxx8AgOjoaAAX11DOXpM7N876+npEREQgMDAQkZGR0lKCiYkJ2Gw2HDt2DE6nE06nE8DFzWbBwcEYHx+XDndQqVTw8fHB1NTUvPfQaDQLvj9deyEhId4OgYiI6LrHovaSuR0KbrrpJgCQZjh1Oh3S0tKQmpqKsbExfPDBBxgeHkZ3dzfefPNNWK1WrF+/Hg899JA0pk6nQ2dnJ5KTk6W1j2FhYVixYgVqamqg0WhgMBgQHR2N1tZWt6d8KRQKl8MSuru7ERoaCpPJBIvFApPJBJVKBYfDIR2usGLFClgsFpjNZpjNZgQFBUmbzPz9/S97AIOvry8sFgsASGMvFA9530Jri+jKdHR0ID4+3tth3FCYU89jTj2POfU8T+f0cntJWNReMjo6iszMTExOTkodCmbbuXMn9Ho9mpubYTab8dxzz+GWW27ByMgItm7dCj8/P2RlZbkcblBSUoLdu3fjyJEjCAkJgY+PD0JCQvDkk08iIyMDDocDt956KzZv3rxgXHfffTcOHjyIsLAwKU6lUon3338fCoUCer0eDocDpaWl+Pjjj2EymTA8PIwlS5ZIx+ROTExAFEUolUoEBwdLp4+5axUVHR3t0jPX4XC4FLIsaK+98+fP80QxIiKiy2D3A7h2ELheCYKApKQkPProo1Kng7kxz72m1+tx9uxZNDc3u9zz2WefISoqasFxFmIwGNDU1IQLFy5geHgYQUFBEARBKrQjIiIgiiJ++eUXaDQaTE9PIygoCNPT07Db7dKMb3BwMARBkGaDAUiz1LOP9v0/P5pzZ5RnaDQaCILwn8fxFLVaveBaWKVSKS0Nmf04NjYWp06dcrk3Li4OjY2NbseZ+Y2VM7Wexdkaz2NOPY859Tzm1POu1kwtux/ImM1mQ2ZmJmJiYv71vvT0dJfn8fHxHt85f999980rmpOTk12KZ4PBgNraWkxNTWHTpk3Iz89HVlYWQkNDUVFR4VJYt7W1QaFQ4JNPPpHu+eKLL6DRaHDzzTe7XB8cHMS5c+eQlpaGEydO4Pz58xgfH8euXbvw4IMPYuvWrdBqtQgLC0N4eDgGBgZw9OhRGAwGFBUVwel0Yvny5Vi/fj1OnjyJxx57DL6+vjh8+DASEhKQkJAgFfgbNmxAUlISUlJSsGbNGgBAUlISxsbGkJiYiLGxMeTm5mLZsmXYsWMHYmNj8cMPP+Dhhx/G8uXL0dbWhunpaWRnZ6O0tBRqtRp+fn7Yu3cvUlJSFszvgQMH0NraiqCgIISHh2Pt2rUe/f4RERHdqFjUYn4xeL3RarVoaGhwufZfYnZ3T3p6usv1xXztM0WzTqdzKZ5nxpw9A1xTU+N2jE8//VR6PHNPe3u71BFi9vXZs8ruZpYLCwvdzjqnp6fjlVdewbZt2+a9ZjAYkJKSMu/66OgopqenpYIWAPLy8tyO39nZCQCorKzEjz/+iL1792LLli146qmnUFJSAqVSibvuugtvv/32ZTtL9Pf3Y2hoCAEBARBF8V8LYCIiIvoHlx8QyRSXH1wd/BOk5zGnnseceh5z6nnXevkB+9QSERERkeyxqCUiIiIi2WNRS0RERESyx6KWiIiIiGSPRS0RERERyR6LWiIiIiKSPfapJZKpmW58NpvNy5HceKxWq7dDuOEwp57HnHoec+p5nszpzOfdQt1o2aeWSKYmJyfR09Pj7TCIiIiuqaioKCxdunTedRa1RDLldDphsVig0WigUCi8HQ4REdFVJYoiBEGAv78/lMr5K2hZ1BIRERGR7HGjGBERERHJHotaIiIiIpI9FrVEREREJHssaomIiIhI9tinlkhmnE4n9u3bhzNnzkCr1aK8vBzh4eHeDkuWBEFAYWEh+vv7YbPZkJ2djYiICBQUFEChUCAyMhIlJSVud9nSwsbGxpCeno6amhqo1Wrm0wPeffddfPXVVxAEAdu2bUNCQgLzugiCIKCgoAD9/f1QKpUoKyvjz+oinDx5EgcPHkRdXR1+//13t3lsbm5GY2Mj1Go1srOz8cADD3g8Dn63iGSmtbUVNpsNTU1NyMvLQ0VFhbdDkq2PPvoIwcHBqK+vR3V1NcrKyvDaa68hNzcX9fX1EEURX375pbfDlBVBEPDyyy/D19cXAJhPDzAajejs7ERDQwPq6uowODjIvC7S119/DbvdjsbGRuTk5OCNN95gTq9QdXU1ioqKpEMW3OVxZGQEdXV1aGxsxJEjR1BZWXlVDg5iUUskMx0dHVi3bh0A4J577kFXV5eXI5KvTZs2YdeuXdJzlUqF7u5uJCQkAACSk5Px7bffeis8Wdq/fz8ef/xxhIaGAgDz6QHffPMNoqKikJOTg507d+L+++9nXhdp1apVcDgccDqdMJvNUKvVzOkVWrlyJaqqqqTn7vJ46tQpxMXFQavVYunSpVi5ciVOnz7t8VhY1BLJjNlsRkBAgPRcpVLBbrd7MSL58vf3R0BAAMxmM55//nnk5uZCFEXpMAt/f39MTk56OUr5MBgMCAkJkX7pAsB8esDExAS6urpw6NAhlJaWIj8/n3ldJD8/P/T392Pz5s0oLi5GRkYGc3qFNm7cCLX6n9Ws7vJoNptdTgDz9/eH2Wz2eCxcU0skMwEBAbBYLNJzp9Pp8h8K/T8DAwPIycnBE088gdTUVLz++uvSaxaLBYGBgV6MTl4+/PBDKBQKfPfdd/j555/x4osvYnx8XHqd+bwywcHB0Ol00Gq10Ol08PHxweDgoPQ68/r/1dbWYu3atcjLy8PAwAAyMzMhCIL0OnN65WavQ57J49zPLYvF4vaY20W/t8dHJKKravXq1Th+/DgA4KeffkJUVJSXI5Kv0dFRZGVlYc+ePXjkkUcAAHfeeSeMRiMA4Pjx47j33nu9GaKsHDt2DEePHkVdXR2io6Oxf/9+JCcnM5+LFB8fj/b2doiiiKGhIfz1119Ys2YN87oIgYGBUlEVFBQEu93Of/se4i6PsbGx6OjogNVqxeTkJEwm01X57OIxuUQyM9P9oKenB6Io4tVXX8Xtt9/u7bBkqby8HC0tLdDpdNI1vV6P8vJyCIIAnU6H8vJyqFQqL0YpTxkZGdi3bx+USiWKi4uZz0U6cOAAjEYjRFHE7t27ERYWxrwugsViQWFhIUZGRiAIArZv346YmBjm9Ar19fXhhRdeQHNzM3799Ve3eWxubkZTUxNEUcQzzzyDjRs3ejwOFrVEREREJHtcfkBEREREsseiloiIiIhkj0UtEREREckei1oiIiIikj0WtUREREQkeyxqiYiIiEj2WNQSERERkeyxqCUiIiIi2fsb5o3tAAhsndkAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"importance = featureImportance(X, y, modelsSelectedCalibrated)"
]
},
{
"cell_type": "code",
"execution_count": 187,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AdaBoost-GradientBoosting</th>\n",
" <th>AdaBoost-RandomForest</th>\n",
" <th>AdaBoost-DecisionTree</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>STATUS_POL</th>\n",
" <td>100.00</td>\n",
" <td>100.00</td>\n",
" <td>100.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BIO_PIC</th>\n",
" <td>12.10</td>\n",
" <td>51.32</td>\n",
" <td>27.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TEXT_WORDS</th>\n",
" <td>6.92</td>\n",
" <td>31.52</td>\n",
" <td>27.13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STATUS_PEM</th>\n",
" <td>4.91</td>\n",
" <td>64.22</td>\n",
" <td>45.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>STATUS_REM</th>\n",
" <td>3.71</td>\n",
" <td>37.15</td>\n",
" <td>9.87</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AdaBoost-GradientBoosting AdaBoost-RandomForest \\\n",
"STATUS_POL 100.00 100.00 \n",
"BIO_PIC 12.10 51.32 \n",
"TEXT_WORDS 6.92 31.52 \n",
"STATUS_PEM 4.91 64.22 \n",
"STATUS_REM 3.71 37.15 \n",
"\n",
" AdaBoost-DecisionTree \n",
"STATUS_POL 100.00 \n",
"BIO_PIC 27.35 \n",
"TEXT_WORDS 27.13 \n",
"STATUS_PEM 45.75 \n",
"STATUS_REM 9.87 "
]
},
"execution_count": 187,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"importance = importance.sort_values(by=[col for col in importance.columns],ascending=False).round(2)\n",
"importance.head()"
]
}
],
"metadata": {
"interpreter": {
"hash": "63fd5069d213b44bf678585dea6b12cceca9941eaf7f819626cde1f2670de90d"
},
"kernelspec": {
"display_name": "Python 3.9.6 64-bit",
"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": "3.8.8"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}