AIM-PIbd-31-Anisin-R-S/lab_3/lab3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Лабораторная 3\n",
    "Датасет: Набор данных для анализа и прогнозирования сердечного приступа"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['HeartDisease', 'BMI', 'Smoking', 'AlcoholDrinking', 'Stroke',\n",
      "       'PhysicalHealth', 'MentalHealth', 'DiffWalking', 'Sex', 'AgeCategory',\n",
      "       'Race', 'Diabetic', 'PhysicalActivity', 'GenHealth', 'SleepTime',\n",
      "       'Asthma', 'KidneyDisease', 'SkinCancer'],\n",
      "      dtype='object')\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 319795 entries, 0 to 319794\n",
      "Data columns (total 18 columns):\n",
      " #   Column            Non-Null Count   Dtype  \n",
      "---  ------            --------------   -----  \n",
      " 0   HeartDisease      319795 non-null  object \n",
      " 1   BMI               319795 non-null  float64\n",
      " 2   Smoking           319795 non-null  object \n",
      " 3   AlcoholDrinking   319795 non-null  object \n",
      " 4   Stroke            319795 non-null  object \n",
      " 5   PhysicalHealth    319795 non-null  float64\n",
      " 6   MentalHealth      319795 non-null  float64\n",
      " 7   DiffWalking       319795 non-null  object \n",
      " 8   Sex               319795 non-null  object \n",
      " 9   AgeCategory       319795 non-null  object \n",
      " 10  Race              319795 non-null  object \n",
      " 11  Diabetic          319795 non-null  object \n",
      " 12  PhysicalActivity  319795 non-null  object \n",
      " 13  GenHealth         319795 non-null  object \n",
      " 14  SleepTime         319795 non-null  float64\n",
      " 15  Asthma            319795 non-null  object \n",
      " 16  KidneyDisease     319795 non-null  object \n",
      " 17  SkinCancer        319795 non-null  object \n",
      "dtypes: float64(4), object(14)\n",
      "memory usage: 43.9+ MB\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>HeartDisease</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Smoking</th>\n",
       "      <th>AlcoholDrinking</th>\n",
       "      <th>Stroke</th>\n",
       "      <th>PhysicalHealth</th>\n",
       "      <th>MentalHealth</th>\n",
       "      <th>DiffWalking</th>\n",
       "      <th>Sex</th>\n",
       "      <th>AgeCategory</th>\n",
       "      <th>Race</th>\n",
       "      <th>Diabetic</th>\n",
       "      <th>PhysicalActivity</th>\n",
       "      <th>GenHealth</th>\n",
       "      <th>SleepTime</th>\n",
       "      <th>Asthma</th>\n",
       "      <th>KidneyDisease</th>\n",
       "      <th>SkinCancer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>16.60</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>55-59</td>\n",
       "      <td>White</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>20.34</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>80 or older</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>7.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>26.58</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Male</td>\n",
       "      <td>65-69</td>\n",
       "      <td>White</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fair</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>24.21</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>75-79</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Good</td>\n",
       "      <td>6.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>23.71</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Female</td>\n",
       "      <td>40-44</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>8.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  HeartDisease    BMI Smoking AlcoholDrinking Stroke  PhysicalHealth  \\\n",
       "0           No  16.60     Yes              No     No             3.0   \n",
       "1           No  20.34      No              No    Yes             0.0   \n",
       "2           No  26.58     Yes              No     No            20.0   \n",
       "3           No  24.21      No              No     No             0.0   \n",
       "4           No  23.71      No              No     No            28.0   \n",
       "\n",
       "   MentalHealth DiffWalking     Sex  AgeCategory   Race Diabetic  \\\n",
       "0          30.0          No  Female        55-59  White      Yes   \n",
       "1           0.0          No  Female  80 or older  White       No   \n",
       "2          30.0          No    Male        65-69  White      Yes   \n",
       "3           0.0          No  Female        75-79  White       No   \n",
       "4           0.0         Yes  Female        40-44  White       No   \n",
       "\n",
       "  PhysicalActivity  GenHealth  SleepTime Asthma KidneyDisease SkinCancer  \n",
       "0              Yes  Very good        5.0    Yes            No        Yes  \n",
       "1              Yes  Very good        7.0     No            No         No  \n",
       "2              Yes       Fair        8.0    Yes            No         No  \n",
       "3               No       Good        6.0     No            No        Yes  \n",
       "4              Yes  Very good        8.0     No            No         No  "
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import featuretools as ft\n",
    "import time\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "df = pd.read_csv(\".//static//csv//heart_2020_cleaned.csv\")\n",
    "\n",
    "print(df.columns)\n",
    "df.info()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Бизнес цели и цели технического проекта\n",
    "1. Улучшение профилактики сердечно-сосудистых заболеваний\n",
    "\n",
    "   - Бизнес-цель: Повышение точности прогнозирования риска сердечно-сосудистых заболеваний среди пациентов для более раннего вмешательства и снижения частоты обострений. Определение основных факторов риска, чтобы медперсонал мог предоставлять более целенаправленные рекомендации по улучшению здоровья.\n",
    "\n",
    "   - Цель технического проекта: Разработка классификационной модели для предсказания вероятности сердечно-сосудистых заболеваний на основе данных (возраст, индекс массы тела, физическая активность, курение и т. д.), что поможет выделить группы высокого риска. Интеграция этой модели в систему поддержки принятия решений для врачей, чтобы улучшить качество и своевременность рекомендаций.\n",
    "\n",
    "2. Снижение расходов на лечение сердечно-сосудистых заболеваний\n",
    "\n",
    "   - Бизнес-цель: Оптимизация затрат на лечение сердечно-сосудистых заболеваний путем эффективного распределения ресурсов и проведения профилактических мер среди целевых групп.\n",
    "\n",
    "   - Цель технического проекта: Создание системы оценки индивидуального риска сердечно-сосудистых заболеваний для пациентов, которая позволит медицинским учреждениям и страховым компаниям выделять целевые группы для проведения превентивных мероприятий, тем самым сокращая затраты на лечение."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверка на пустые значения и дубликаты"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Пустые значения по столбцам:\n",
      "HeartDisease        0\n",
      "BMI                 0\n",
      "Smoking             0\n",
      "AlcoholDrinking     0\n",
      "Stroke              0\n",
      "PhysicalHealth      0\n",
      "MentalHealth        0\n",
      "DiffWalking         0\n",
      "Sex                 0\n",
      "AgeCategory         0\n",
      "Race                0\n",
      "Diabetic            0\n",
      "PhysicalActivity    0\n",
      "GenHealth           0\n",
      "SleepTime           0\n",
      "Asthma              0\n",
      "KidneyDisease       0\n",
      "SkinCancer          0\n",
      "dtype: int64\n",
      "\n",
      "Количество дубликатов: 18078\n",
      "\n",
      "Статистический обзор данных:\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>BMI</th>\n",
       "      <th>PhysicalHealth</th>\n",
       "      <th>MentalHealth</th>\n",
       "      <th>SleepTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>319795.000000</td>\n",
       "      <td>319795.00000</td>\n",
       "      <td>319795.000000</td>\n",
       "      <td>319795.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>28.325399</td>\n",
       "      <td>3.37171</td>\n",
       "      <td>3.898366</td>\n",
       "      <td>7.097075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.356100</td>\n",
       "      <td>7.95085</td>\n",
       "      <td>7.955235</td>\n",
       "      <td>1.436007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>12.020000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24.030000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>27.340000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>31.420000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>94.850000</td>\n",
       "      <td>30.00000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 BMI  PhysicalHealth   MentalHealth      SleepTime\n",
       "count  319795.000000    319795.00000  319795.000000  319795.000000\n",
       "mean       28.325399         3.37171       3.898366       7.097075\n",
       "std         6.356100         7.95085       7.955235       1.436007\n",
       "min        12.020000         0.00000       0.000000       1.000000\n",
       "25%        24.030000         0.00000       0.000000       6.000000\n",
       "50%        27.340000         0.00000       0.000000       7.000000\n",
       "75%        31.420000         2.00000       3.000000       8.000000\n",
       "max        94.850000        30.00000      30.000000      24.000000"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_values = df.isnull().sum()\n",
    "print(\"Пустые значения по столбцам:\")\n",
    "print(null_values)\n",
    "\n",
    "duplicates = df.duplicated().sum()\n",
    "print(f\"\\nКоличество дубликатов: {duplicates}\")\n",
    "\n",
    "print(\"\\nСтатистический обзор данных:\")\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Пустых значений нет, но есть дубликаты, удаляем их"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Количество дубликатов: 0\n"
     ]
    }
   ],
   "source": [
    "df = df.drop_duplicates()\n",
    "duplicates = df.duplicated().sum()\n",
    "print(f\"\\nКоличество дубликатов: {duplicates}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Преобразуем строковые значение в столбце 'Сердечный приступ' в числовые значения. Это понадобится для расчёта качества набора признаков."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [],
   "source": [
    "map_stroke_to_int = {'No': 0, 'Yes': 1}\n",
    "\n",
    "df['Stroke'] = df['Stroke'].map(map_stroke_to_int).astype('int32')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создание выборок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: (147840, 17)\n",
      "Размер контрольной выборки: (63361, 17)\n",
      "Размер тестовой выборки: (90516, 17)\n"
     ]
    }
   ],
   "source": [
    "# Разделение данных на признаки (X) и целевую переменную (y)\n",
    "# В данном случае мы хотим предсказать 'stroke'\n",
    "X = df.drop(columns=['Stroke'])\n",
    "y = df['Stroke']\n",
    "\n",
    "# Разбиение данных на обучающую и тестовую выборки\n",
    "# Сначала разделим на обучающую и тестовую\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
    "\n",
    "# Затем разделим обучающую выборку на обучающую и контрольную\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.3)\n",
    "\n",
    "# Проверка размеров выборок\n",
    "print(\"Размер обучающей выборки:\", X_train.shape)\n",
    "print(\"Размер контрольной выборки:\", X_val.shape)\n",
    "print(\"Размер тестовой выборки:\", X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оценим сбалансированность выборок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение классов в обучающей выборке:\n",
      "Stroke\n",
      "0    0.960045\n",
      "1    0.039955\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "Распределение классов в контрольной выборке:\n",
      "Stroke\n",
      "0    0.95977\n",
      "1    0.04023\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "Распределение классов в тестовой выборке:\n",
      "Stroke\n",
      "0    0.96014\n",
      "1    0.03986\n",
      "Name: proportion, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABboAAAHyCAYAAAAtJXgGAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABp3ElEQVR4nO3deXxM1//H8XcS2SO2RBJEYl9raSxF7SE0ii7U8q2gRYsu9KstbUV1SVVrKVqlpQstpaXfVquW0qqmFFXUUkvUnohdkJA5vz88Mj9jJsRSk9u+no/HPB6ZM+fe+7kzmTl33nPnjIcxxggAAAAAAAAAAIvydHcBAAAAAAAAAADcCIJuAAAAAAAAAIClEXQDAAAAAAAAACyNoBsAAAAAAAAAYGkE3QAAAAAAAAAASyPoBgAAAAAAAABYGkE3AAAAAAAAAMDSCLoBAAAAAAAAAJZG0A0AAAAA18Fmsyk9PV27du1ydykAAAD/egTdAAAAAJBHhw4d0pNPPqmoqCj5+PgoNDRUVatW1cmTJ91dGgAAwL9aAXcXAAAAcLN98MEH6tWrl/26r6+vSpcurdatW+uFF15QWFiYG6sDYFU7duxQ8+bNdf78eT3++OO6/fbbVaBAAfn7+yswMNDd5QEAAPyrEXQDAIB/rJEjR6pMmTI6d+6cfvrpJ73zzjv65ptvtGnTJgUEBLi7PAAW069fP/n4+OiXX35RyZIl3V0OAAAALkHQDQAA/rHatm2rOnXqSJIefvhhFStWTGPGjNGXX36prl27urk6AFaydu1aff/991q0aBEhNwAAQD7EHN0AAOBfo0WLFpKklJQUSdLRo0f13//+V7fddpuCgoIUHBystm3b6vfff3da9ty5cxoxYoQqVqwoPz8/RURE6N5779XOnTslSbt375aHh0eul2bNmtnXtXz5cnl4eGj27NkaNmyYwsPDFRgYqPbt22vv3r1O2161apXatGmjQoUKKSAgQE2bNtXKlStd7mOzZs1cbn/EiBFOfWfMmKGYmBj5+/uraNGi6tKli8vtX2nfLmWz2TRu3DhVq1ZNfn5+CgsLU79+/XTs2DGHftHR0WrXrp3TdgYOHOi0Tle1jx492uk+laTMzEwlJiaqfPny8vX1VWRkpJ5++mllZma6vK8udfn9FhISovj4eG3atClPy1avXl1r165Vw4YN5e/vrzJlymjy5MkO/bKysjR8+HDFxMSoUKFCCgwMVOPGjbVs2TKHftu2bVOLFi0UHh5u349HHnlER48eddp2z549r/p49+zZU9HR0Q7L7d27V/7+/vLw8NDu3bsl/f/j/MEHHzj0HTFihMvHZeDAgU71tGvXzmFbOet84403crn3nNc/ffp0eXh4aNq0aQ79Xn31VXl4eOibb77JdV3Sxf+vnPvB09NT4eHheuCBB7Rnz54bquuXX36Rn5+fdu7cqWrVqsnX11fh4eHq16+fy8dmzpw59udXSEiI/vOf/2j//v0OfXr27KmgoCDt2rVLcXFxCgwMVIkSJTRy5EgZY5zqvfSxOXXqlGJiYlSmTBkdPHjQ3v7GG2+oYcOGKlasmPz9/RUTE6O5c+c6bPdG72MAAID8iDO6AQDAv0ZOKF2sWDFJ0q5duzR//nx16tRJZcqUUWpqqt599101bdpUmzdvVokSJSRJ2dnZateunZYuXaouXbroiSee0KlTp7R48WJt2rRJ5cqVs2+ja9euuuuuuxy2O3ToUJf1vPLKK/Lw8NAzzzyjtLQ0jRs3TrGxsVq/fr38/f0lSd9//73atm2rmJgYJSYmytPTU9OnT1eLFi20YsUK1atXz2m9pUqVUlJSkiTp9OnTevTRR11u+4UXXlDnzp318MMP6/Dhw5owYYKaNGmi3377TYULF3Zapm/fvmrcuLEk6YsvvtC8efMcbu/Xr599fvTHH39cKSkpmjhxon777TetXLlS3t7eLu+Ha3H8+HH7vl3KZrOpffv2+umnn9S3b19VqVJFGzdu1NixY/Xnn39q/vz5V1135cqV9dxzz8kYo507d2rMmDG66667HALS3Bw7dkx33XWXOnfurK5du+qzzz7To48+Kh8fH/Xu3VuSdPLkSb333nvq2rWr+vTpo1OnTun9999XXFycVq9erVq1akmSMjIyVKpUKd19990KDg7Wpk2bNGnSJO3fv19fffWV07ZDQkI0duxY+/UHH3zwqvUOHz5c586du2o/d+jVq5e++OILDR48WK1atVJkZKQ2btyoF198UQ899JDT88uVxo0bq2/fvrLZbNq0aZPGjRunAwcOaMWKFddd15EjR3Tu3Dk9+uijatGihR555BHt3LlTkyZN0qpVq7Rq1Sr5+vpK+v/fCahbt66SkpKUmpqq8ePHa+XKlU7Pr+zsbLVp00Z33HGHXn/9dS1cuFCJiYm6cOGCRo4c6bKW8+fP67777tOePXu0cuVKRURE2G8bP3682rdvr+7duysrK0uzZs1Sp06d9PXXXys+Pv6m3ccAAAD5jgEAAPiHmT59upFklixZYg4fPmz27t1rZs2aZYoVK2b8/f3Nvn37jDHGnDt3zmRnZzssm5KSYnx9fc3IkSPtbdOmTTOSzJgxY5y2ZbPZ7MtJMqNHj3bqU61aNdO0aVP79WXLlhlJpmTJkubkyZP29s8++8xIMuPHj7evu0KFCiYuLs6+HWOMOXPmjClTpoxp1aqV07YaNmxoqlevbr9++PBhI8kkJiba23bv3m28vLzMK6+84rDsxo0bTYECBZzat2/fbiSZDz/80N6WmJhoLj2UXLFihZFkZs6c6bDswoULndqjoqJMfHy8U+0DBgwwlx+eXl77008/bYoXL25iYmIc7tOPP/7YeHp6mhUrVjgsP3nyZCPJrFy50ml7l2ratKnD+owxZtiwYUaSSUtLu+qyksybb75pb8vMzDS1atUyxYsXN1lZWcYYYy5cuGAyMzMdlj127JgJCwszvXv3vuI2+vfvb4KCgpzau3fvbsqUKePQdvl9lpCQYKKiouzXN23aZDw9PU3btm2NJJOSkmKMMeavv/4yksy0adMc1nf5Y52zjQEDBjjVEx8f77CtKz0vrrT+gwcPmqJFi5pWrVqZzMxMU7t2bVO6dGlz4sSJXNeTIyoqyiQkJDi0devWzQQEBNxQXTnXW7ZsaS5cuGBvz3m9mTBhgjHGmKysLFO8eHFTvXp1c/bsWXu/r7/+2kgyw4cPt7clJCQYSeaxxx6zt9lsNhMfH298fHzM4cOHHeqdPn26sdlspnv37iYgIMCsWrXKqe4zZ844XM/KyjLVq1c3LVq0cGi/kfsYAAAgP2LqEgAA8I8VGxur0NBQRUZGqkuXLgoKCtK8efPs8+v6+vrK0/Pi4VB2draOHDmioKAgVapUSevWrbOv5/PPP1dISIgee+wxp21cPqXDtejRo4cKFixov37//fcrIiLCPm3A+vXrtX37dnXr1k1HjhxRenq60tPTlZGRoZYtW+rHH3+UzWZzWOe5c+fk5+d3xe1+8cUXstls6ty5s32d6enpCg8PV4UKFZym0sjKypIk+9mqrsyZM0eFChVSq1atHNYZExOjoKAgp3WeP3/eoV96evpVzzDev3+/JkyYoBdeeEFBQUFO269SpYoqV67ssM6c6Wou374rOTUdPnxYycnJmjdvnmrUqKGQkJCrLlugQAH169fPft3Hx0f9+vVTWlqa1q5dK0ny8vKSj4+PpItnoB89elQXLlxQnTp1HP7fcpw4cUKpqalaunSpFixYoCZNmjj1ycrKuuLj4srQoUN1++23q1OnTg7toaGhkqR9+/blaT3nzp1zegzPnz/vsu+ZM2eUnp6uY8eOOUzJkZvw8HBNmjRJixcvVuPGjbV+/XpNmzZNwcHBeaotMzNT6enpSktL0+LFi/X999+rZcuWN1yXJA0ePFheXl726w8++KDCwsK0YMECSdKaNWuUlpam/v37OzwX4+PjVblyZXu/S106DUzOtDBZWVlasmSJU98hQ4Zo5syZ+uyzz1x+oyPn2yDSxW8anDhxQo0bN3b6H7vR+xgAACC/YeoSAADwjzVp0iRVrFhRBQoUUFhYmCpVqmQPtqWLYeP48eP19ttvKyUlRdnZ2fbbcqY3kS5OeVKpUiUVKHBzD50qVKjgcN3Dw0Ply5e3z5m8fft2SVJCQkKu6zhx4oSKFCliv56enu603stt375dxphc+10+xcjx48clySlcvnydJ06cUPHixV3enpaW5nB90aJF9mA1rxITE1WiRAn169fPac7h7du3a8uWLbmu8/Ltu/Lzzz87LF+hQgXNnz8/Tx9mlChRQoGBgQ5tFStWlHRxfuU77rhDkvThhx/qzTff1NatWx1C4TJlyjitMy4uTqtWrZIktWnTRrNnz3bqc/z48Ss+Lpf76aef9NVXX2np0qVOU7L4+/urdu3amjJlimJjY+3/H2fOnHG5rvfff1/vv/++U3tUVJRTW2JiohITEyVJfn5+atGihcaNG3fF/9UuXbpoxowZWrBggfr27esyqM7NrFmzNGvWLPv1unXr6r333ruhunL+DypXruzQ7uXlpQoVKtift3/99ZckqVKlSk7rqFy5sn766SeHNk9PT5UtW9ah7dL/nUu9++67+uWXXyTJae77HF9//bVefvllrV+/3mF+elf/xzdyHwMAAOQ3BN0AAOAfq169eqpTp06ut7/66qt64YUX1Lt3b7300ksqWrSoPD099eSTTzqdKe0OOTWMHj3aPn/z5S4NObOysnTw4EG1atXqquv18PDQt99+63Bmqqt1StKhQ4ckXTwD9ErrLF68uGbOnOny9ssD6Pr16+vll192aJs4caK+/PJLl8tv2bJFH3zwgWbMmOFyrm+bzabbbrtNY8aMcbl8ZGRkrrXnqFGjht58801J0uHDh/XWW2+pWbNmWrdu3RX3Pa9mzJihnj17qmPHjhoyZIiKFy8uLy8vJSUl2eePv9SECROUnp6uzZs3KykpSY888ohmzJjh0OfQoUMug+XcPPPMM4qLi1OLFi2cfnRSkiZPnqwOHTqoYcOGV11Xhw4dnH6Q8vnnn7f/v1yqb9++6tSpk7Kzs7VlyxaNGDFCHTt21B9//JHr+o8cOaI1a9ZIkjZv3iybzebwQdWVtG7dWkOGDJF08Qz1UaNGqXnz5lqzZo3DGc/XUtely7nLL7/8oldeeUW//vqrBg0apDZt2jh842DFihVq3769mjRporffflsRERHy9vbW9OnT9cknnzit70buYwAAgPyGoBsAAPxrzZ07V82bN3c6K/X48eMO4VG5cuW0atUqnT9//qb8oGKOnDO2cxhjtGPHDtWoUcO+XUkKDg5WbGzsVdf3+++/6/z581cM93PWa4xRmTJl7GeOXsnmzZvl4eHh8gzVS9e5ZMkSNWrUKE+BYEhIiNM+XekHI4cOHapatWrpgQceyHX7v//+u1q2bHnd08kUKVLEoaZmzZqpRIkSmj59eq4/KJrjwIEDysjIcDir+88//5QkRUdHS7r4/1a2bFl98cUXDjXmnFF8ubp160qS2rZtq+LFi6tHjx567rnnVKVKFUkXp1rZsWOH2rRpk6f9mz9/vpKTk11Ok5KjXr162rVrlzZs2KBTp05Jkj766CN9/PHHTn1LlSrl9BiOGzfOZdBdoUIFe9+4uDidOXNGzz333BV/6HPAgAE6deqUkpKSNHToUI0bN06DBw/O075GREQ41FapUiU1bNhQ8+fPV9euXa+rrpyz7rdt2+ZwBrbNZtP27dtVu3ZtSf9/Rvu2bdvsU+fk2LZtm9MHEzabTbt27XJ4Ll7+v5Ojd+/eGjZsmA4cOKCqVatq0KBBDo/N559/Lj8/P3333XcOU9pMnz7d5f10I/cxAABAfsPH9QAA4F/Ly8vLaV7eOXPmaP/+/Q5t9913n9LT0zVx4kSndeR1Xl9XPvroI3uYKF0MQg8ePKi2bdtKkmJiYlSuXDm98cYbOn36tNPyhw8fdqrdy8tL7dq1u+J27733Xnl5eenFF190qt8YoyNHjtivX7hwQZ9//rnq1at3xSkyOnfurOzsbL300ktOt124cME+/cn1SE5O1pdffqnXXnst1xC7c+fO2r9/v6ZOnep029mzZ5WRkXHN2z179qwkOUz/kJsLFy7o3XfftV/PysrSu+++q9DQUMXExEiS/ez5S+/zVatWKTk5+arrT09Pd6rlyy+/1NmzZ53CVFeys7M1bNgwdevWLddvB+Tw9/dX/fr1FRsbq9jYWKdpNW6GnG8ruPpGgXTxuTB79my99tprevbZZ9WlSxc9//zz9gD4WuX1sbxSXS1btpSvr6/eeusth298zJw5U6mpqfbnXZ06dVS8eHFNnjzZYXvffvuttmzZovj4eKd1X/raYozRxIkT5e3t7TSVSOPGjSVdnCpn1KhRmjFjhhYtWmS/3cvLSx4eHg7TMO3evdvlh0g3+z4GAABwN87oBgAA/1rt2rXTyJEj1atXLzVs2FAbN27UzJkznYK9Hj166KOPPtLgwYO1evVqNW7cWBkZGVqyZIn69++vDh06XNf2ixYtqjvvvFO9evVSamqqxo0bp/Lly6tPnz6SLs7d+95776lt27aqVq2aevXqpZIlS2r//v1atmyZgoOD9dVXXykjI0OTJk3SW2+9pYoVK2r58uX2beQE5Bs2bFBycrIaNGigcuXK6eWXX9bQoUO1e/dudezYUQULFlRKSormzZunvn376r///a+WLFmiF154QRs2bNBXX311xX1p2rSp+vXrp6SkJK1fv16tW7eWt7e3tm/frjlz5mj8+PG6//77r+t+WrRokVq1anXFs9offPBBffbZZ3rkkUe0bNkyNWrUSNnZ2dq6das+++wzfffdd1c90z01NdU+NUh6erreffddFShQ4KofHEj/Hzzu3r1bFStW1OzZs7V+/XpNmTLF/i2Adu3a6YsvvtA999yj+Ph4paSkaPLkyapatarDBxkjR47U/v37Vb16dfn6+mrdunWaPn26atSooRo1aujMmTNKTEzU22+/rYYNG6p169ZXrW/fvn3y8fGx/9DprbZt2zYtXLhQNptNmzdv1ujRo1W3bl37D8NeKi0tTY8++qiaN29unxpl4sSJWrZsmXr27KmffvrpqtNr7Nq1y/5Y7t+/XxMnTlRwcLBTcHwtdRUtWlTPP/+8XnjhBcXFxalDhw7atWuXJk6cqJo1a+rhhx+WdHGO+1GjRqlXr15q2rSpunbtqtTUVI0fP17R0dEaNGiQw3r9/Py0cOFCJSQkqH79+vr222+1YMECDRs27Irz2Pft21effPKJHnnkEW3atEkBAQGKj4/XmDFj1KZNG3Xr1k1paWmaNGmSypcvrw0bNtzU+xgAACDfMQAAAP8w06dPN5LMr7/+esV+586dM0899ZSJiIgw/v7+plGjRiY5Odk0bdrUNG3a1KHvmTNnzHPPPWfKlCljvL29TXh4uLn//vvNzp07jTHGpKSkGElm9OjRTtupVq2aw/qWLVtmJJlPP/3UDB061BQvXtz4+/ub+Ph489dffzkt/9tvv5l7773XFCtWzPj6+pqoqCjTuXNns3TpUodtX+2SkJDgsN7PP//c3HnnnSYwMNAEBgaaypUrmwEDBpht27YZY4x57LHHTJMmTczChQudakpMTDSuDiWnTJliYmJijL+/vylYsKC57bbbzNNPP20OHDhg7xMVFWXi4+Odlh0wYIDTOiUZDw8Ps3btWod2V49RVlaWGTVqlKlWrZrx9fU1RYoUMTExMebFF180J06ccNre5eu79L4qXLiwadSokfnmm2+uuFzOstWqVTNr1qwxDRo0MH5+fiYqKspMnDjRoZ/NZjOvvvqqiYqKMr6+vqZ27drm66+/NgkJCSYqKsreb+7cuaZu3bomODjY+Pv7m/Lly5unnnrKHD582BhjzL59+0xkZKR58sknXe6XJJOYmGi/npCQYCSZJ554wqFfzvMkJSXlivvn6rGWZAYMGODUNz4+3mFfLv/f9PT0NKVKlTIJCQlm3759Ltd/7733moIFC5rdu3c7rPvLL780ksyoUaOuWG9UVJTDNkNCQkzr1q1NcnLyDdWVY9KkSaZy5crG29vbhIWFmX79+pkjR4449Zs9e7apXbu28fX1NUWLFjXdu3e3rztHQkKCCQwMNDt37jStW7c2AQEBJiwszCQmJprs7GyneqdPn+6w/LZt24yfn58ZNGiQve399983FSpUML6+vqZy5cpm+vTpN/0+BgAAyI88jLmB79sCAADgmi1fvlzNmzfXnDlzrvss50vt3r1bZcqUUUpKitOcvjlGjBih3bt3u/wBQtyYZs2aKT09XZs2bXJ3KbCYnj17au7cuS6nJgIAAMC14ftoAAAAAAAAAABLY45uAAAAiwsKClL37t2v+GORNWrUUIkSJW5hVQAAAABw6xB0AwAAWFxISIj9h/dyc++9996iagAAAADg1mOObgAAAAAAAACApTFHNwAAAAAAAADA0gi6AQAAAAAAAACWRtANAAAAAAAAALA0gm4AAAAAAAAAgKURdAMAAAAAAAAALI2gGwAAAAAAAABgaQTdAAAAAAAAAABLI+gGAAAAAAAAAFgaQTcgKSMjQ3v37tWxY8fcXQpuIh5XAACsyxijo0ePavv27e4uBQCAfySbzab09HTt2rXL3aUANwVBN/615syZo5YtW6pgwYIKCgpS6dKl9frrr7u7LNwgHlcAAKzr1KlTev7551WpUiX5+PioWLFiqlixorZt2+bu0gAA+Ec4dOiQnnzySUVFRcnHx0ehoaGqWrWqTp486e7SgBtWwN0FADfDH3/8oaSkJC1btkzp6ekqVqyYmjdvrmHDhqlatWpO/Z999lmNGjVKHTp00NSpUxUSEiIPDw9VrFjRDdXjZuFxBQD3++CDD9SrVy/9+uuvqlOnjsNtU6dOVd++fdWhQwd9/vnn8vLyclOVyI+OHDmipk2bas+ePXrsscfUqFEj+fj4yNvbW9HR0e4uDwBwGQ8Pjzz1W7ZsmZo1a/b3FoM82bFjh5o3b67z58/r8ccf1+23364CBQrI399fgYGB7i4PuGEE3bC8L774Ql27dlXRokX10EMPqUyZMtq9e7fef/99zZ07V7NmzdI999xj7//DDz9o1KhRSkpK0rPPPuvGynEz8bgCQP42b948Pfroo2rcuLFmzZpFyA0nQ4YM0cGDB5WcnOzyRAUAQP7y8ccfO1z/6KOPtHjxYqf2KlWq3MqycAX9+vWTj4+PfvnlF5UsWdLd5QA3nYcxxri7COB67dy5UzVq1FDp0qX1448/KjQ01H5benq6GjdurL1792rDhg0qW7asJOnuu+/W0aNHtXLlSneVjb8BjysA5A+uzuhevny52rRpo4oVK2rFihUqVKiQm6tEfpOWlqaIiAhNnjxZffr0cXc5AIDrMHDgQE2aNEnETPnT2rVrVadOHS1atEitWrVydznA34I5umFpo0eP1pkzZzRlyhSHkFuSQkJC9O677yojI8NhjuZffvlF1atXV5cuXVS0aFH5+/urbt26mj9/vr3P6dOnFRgYqCeeeMJpm/v27ZOXl5eSkpIkST179nT5dVoPDw+NGDHCfv2vv/5S//79ValSJfn7+6tYsWLq1KmTdu/e7bDc8uXL5eHhoeXLl9vbfv31V7Vq1UoFCxZUYGCgmjVrphUrVjgs98EHH8jDw0Nr1qyxt6WnpzvVIUnt2rVzqnnFihXq1KmTSpcuLV9fX0VGRmrQoEE6e/as077NnTtXderUUcGCBeXh4WG/vPHGG059XdWYcwkICNBtt92m9957z6Ffz549FRQUdMV1Xb5feXlcc6Slpemhhx5SWFiY/Pz8VLNmTX344YcOfXbv3m3fp7FjxyoqKkr+/v5q2rSpNm3a5FTv5ffnjBkz5Onpqddee83etmHDBvXs2VNly5aVn5+fwsPD1bt3bx05cuSK+woAVrZ+/Xp16NBBERER+u6771yG3HPmzFFMTIz8/f0VEhKi//znP9q/f79Dn9zGhrlz5zqMm82aNXMYa1xdcnh4eGjgwIGaOXOmKlWqJD8/P8XExOjHH3902s5vv/2mtm3bKjg4WEFBQWrZsqV++eUXl/ucWw0ffPCBQ5/q1atf9f7LqfFyrsbyN954Qw0bNlSxYsXk7++vmJgYzZ0712nZ06dP66mnnlLZsmXl7e3tUGN6evoV67l830JCQhQfH+80NuZWd46cY4Kc46Bff/1VNptNWVlZqlOnjvz8/FSsWDF17dpVe/bscVr++++/V+PGjRUYGKjChQurQ4cO2rJli0OfESNGyMPDQ1u3blXnzp0VHBysYsWK6YknntC5c+ec6r30uOLChQu66667VLRoUW3evNnePn36dLVo0ULFixeXr6+vqlatqnfeeeeK9xkA4KLMzEwlJiaqfPny9vecTz/9tDIzM536zpgxQ/Xq1VNAQICKFCmiJk2aaNGiRZKk6OjoK47zl46PGRkZeuqppxQZGSlfX19VqlRJb7zxhlMYf+nyXl5eKlmypPr27avjx4/b+2RlZWn48OGKiYlRoUKFFBgYqMaNG2vZsmVO9ee85yxdurS8vLzs677a+9zL98/T01Ph4eF64IEHHMbDS9+v5iZnHMzxyy+/yM/PTzt37lS1atXk6+ur8PBw9evXT0ePHnVa/lqOz3bt2qW4uDgFBgaqRIkSGjlypMN9nFPvpcdCp06dUkxMjMqUKaODBw/a2/N6PAO4wtQlsLSvvvpK0dHRaty4scvbmzRpoujoaC1YsMDeduTIEU2ZMkVBQUF6/PHHFRoaqhkzZujee+/VzJkz1bVrVwUFBemee+7R7NmzNWbMGIevV3/66acyxqh79+7XVOuvv/6qn3/+WV26dFGpUqW0e/duvfPOO2rWrJk2b96sgIAAl8vt2LFDzZo1U0BAgIYMGaKAgABNnTpVsbGxWrx4sZo0aXJNdeRmzpw5OnPmjB599FEVK1ZMq1ev1oQJE7Rv3z7NmTPH3i85OVmdO3dWzZo19dprr6lQoUJKT0/XoEGD8rytsWPHKiQkRCdPntS0adPUp08fRUdHKzY29rrrz8vjKklnz55Vs2bNtGPHDg0cOFBlypTRnDlz1LNnTx0/ftzpw42PPvpIp06d0oABA3Tu3DmNHz9eLVq00MaNGxUWFuaylkWLFql3794aOHCgwzQqixcv1q5du9SrVy+Fh4frjz/+0JQpU/THH3/ol19+yfMcdwBgFTt37lSbNm3k6+ur7777ThEREU59cs4Ar1u3rpKSkpSamqrx48dr5cqV+u2331S4cOFr2uZzzz2nhx9+WJLs41Pfvn1zPVb44YcfNHv2bD3++OPy9fXV22+/rTZt2mj16tX2IPqPP/5Q48aNFRwcrKefflre3t5699131axZM/3www+qX7++03orV66s5557zqGOv9v48ePVvn17de/eXVlZWZo1a5Y6deqkr7/+WvHx8fZ+Q4YM0eTJk/XQQw+pUaNG8vb21hdffKF58+blaTs5+2aM0c6dOzVmzBjdddddLgPpvMr50HfgwIGKiYnRa6+9psOHD+utt97STz/9pN9++00hISGSpCVLlqht27YqW7asRowYobNnz2rChAlq1KiR1q1b5/QBQOfOnRUdHa2kpCT98ssveuutt3Ts2DF99NFHudbz8MMPa/ny5Vq8eLGqVq1qb3/nnXdUrVo1tW/fXgUKFNBXX32l/v37y2azacCAAde9/wDwT2ez2dS+fXv99NNP6tu3r6pUqaKNGzdq7Nix+vPPPx1OUHrxxRc1YsQINWzYUCNHjpSPj49WrVql77//Xq1bt9a4ceN0+vRpSdKWLVv06quvatiwYfYpUnLCZGOM2rdvr2XLlumhhx5SrVq19N1332nIkCHav3+/xo4d61DjPffco3vvvVcXLlxQcnKypkyZorNnz9qnYjl58qTee+89de3aVX369NGpU6f0/vvvKy4uTqtXr1atWrXs60pISNCSJUv02GOPqWbNmvLy8tKUKVO0bt26PN1fjRs3Vt++fWWz2bRp0yaNGzdOBw4ccDrh7VocOXJE586d06OPPqoWLVrokUce0c6dOzVp0iStWrVKq1atkq+vr6RrOz7Lzs5WmzZtdMcdd+j111/XwoULlZiYqAsXLmjkyJEuazl//rzuu+8+7dmzRytXrnQ4Rszr8QzgkgEs6vjx40aS6dChwxX7tW/f3kgyJ0+eNMYYI8lIMsuXL7f3OXPmjKlSpYoJDw83WVlZxhhjvvvuOyPJfPvttw7rq1GjhmnatKn9eq9evUzp0qWdtivJJCYmOmzjcsnJyUaS+eijj+xty5YtM5LMsmXLjDHG3HfffcbLy8ts2rTJ3ic9Pd0UK1bMxMTE2NumT59uJJlff/3V3nb48GGnOowxJj4+3kRFRTm0uaovKSnJeHh4mL/++sveNnToUCPJHDx40N6WkpJiJJnRo0c7reNSOTWmpKTY2/78808jybz++uv2toSEBBMYGHjFdV2+X3l9XMeNG2ckmRkzZtj7ZWVlmQYNGpigoCD7/0nOPvn7+5t9+/bZ+65atcpIMoMGDXKoN+f+XLNmjQkKCjKdOnUy2dnZDjW7uo8//fRTI8n8+OOPV9xfALCKnNf6r7/+2pQrV85IMq1bt3bZNysryxQvXtxUr17dnD171t7+9ddfG0lm+PDh9rbcxoY5c+Y4jJuXynktnz59usvt54wda9assbf99ddfxs/Pz9xzzz32to4dOxofHx+zc+dOe9uBAwdMwYIFTZMmTZzW26hRI9O8efMr1tG0aVNTrVo1l3VdXuOAAQOc2vMylmdlZZnq1aubFi1aOLRHRESYuLg4h7bExEQjyRw+fPiK9TRt2tThOMgYY4YNG2YkmbS0tKvWnePyY4Kc61WrVnXYj5zjoqeeesreVqtWLVO8eHFz5MgRe9vvv/9uPD09TY8ePZz2qX379g7b7t+/v5Fkfv/9d4d6c44rhg4dary8vMz8+fOd6nY1lsfFxZmyZcvmuq8A8G8xYMAAk1vM9PHHHxtPT0+zYsUKh/bJkycbSWblypXGGGO2b99uPD09zT333OP0fspmszmt9/L3z5eaP3++kWRefvllh/b777/feHh4mB07dtjbXL1vbtiwoalatar9+oULF0xmZqZDn2PHjpmwsDDTu3dve9vZs2eNp6en6devn0PfvLzPNcaYqKgok5CQ4NDWrVs3ExAQYL+el/fgOePg5ddbtmxpLly4YG/PGYMnTJhgjLn24zNJ5rHHHrO32Ww2Ex8fb3x8fOzHFZceC9lsNtO9e3cTEBBgVq1a5VR3Xo9nAFeYugSWderUKUlSwYIFr9gv5/aTJ0/a2+rWraumTZvar/v7+6t///46dOiQ/RPW2NhYlShRQjNnzrT327RpkzZs2KD//Oc/9rbixYsrLS1NWVlZV6zD39/f/vf58+d15MgRlS9fXoULF3b5qe6JEyeUlpamxYsXKy4uzuFHmYoVK6aePXtq7dq1Sk1NveJ28+rS+jIyMpSenq6GDRvKGKPffvvNftupU6fk6el5zWfYXerYsWNKT0/Xrl27NHbsWHl5eTk8HjnS09OVnp7u9PXi3OTlcf3mm28UHh5uP8Nbkry9vfX444/r9OnT+uGHHxzW2bFjR4cf6ahXr57q16+vb775xmn7u3btUnx8vGrVqqWPP/5Ynp6OL7GX3sfnzp1Tenq67rjjDknK8yf7AGAVPXv21N69e9WtWzctWrTI4dtBOdasWaO0tDT1799ffn5+9vb4+HhVrlzZ4RtZOXLGhpxLzvHA9WrQoIFiYmLs10uXLq0OHTrou+++U3Z2trKzs7Vo0SJ17NjR/nsfkhQREaFu3brpp59+cjjGkC5+tTnnjKgryc7Otu/HlY4jcsaMSy/nz5936nfpOHPs2DGdOHFCjRs3dhpjTp06pWLFil21vtycP39e6enpOnz4sJKTkzVv3jzVqFHDfsb15XUfOXJENpstT+seMGCAw340a9ZMMTEx9v+FgwcPav369erZs6eKFi1q71ejRg21atXK5fh8+ZnWjz32mCS57Dtx4kQlJSXprbfeUocOHZxuv7S2EydOKD09XU2bNtWuXbt04sSJPO0jAPwbzZkzR1WqVFHlypUdxrMWLVpIkn36j/nz58tms2n48OFO76eu9Ruw33zzjby8vPT44487tD/11FMyxujbb791aD9z5ozS09N16NAhff755/r999/VsmVL++1eXl7y8fGRdPEM9aNHj+rChQuqU6eOw1ibkZEhm812Q2NtZmam0tPT7ZnA999/71DL5TUfO3Ysz3OjDx482OFb6w8++KDCwsLsY+31HJ9dOl1ZzvRlWVlZWrJkiVPfIUOGaObMmfrss89Ur149p9vzejwDuELQDcvKCbCv9gbXVSBeuXJlp345X3PKmSvS09NT3bt31/z583XmzBlJ0syZM+Xn56dOnTrZl2vYsKHOnTun559/Xvv27bMP2Jc7e/ashg8fbp8bLCQkRKGhoTp+/LjLN0YdO3ZUWFiYTp48qUqVKl213hu1Z88e+5vGoKAghYaG2kPjS+tr0KCBbDabnnjiCe3cudM+qF6L22+/XaGhoSpXrpymTZumiRMnOg1wGRkZCg0NVWhoqPz9/VW6dGmNHz/+iuvNy+P6119/qUKFCk4HTTn9/vrrL4f2ChUqOK2zYsWKTvd7RkaG4uLilJqaqqNHj7o8CDt69KieeOIJhYWFyd/fX6GhoSpTpowk8eYYwD/O0aNHNWPGDH344YeqVauWnnjiCafXupzXXFfjXOXKlZ1eky8dG3IuvXv3vqE6c3udP3PmjA4fPqzDhw/rzJkzuY7FNptNe/fudWg/fvx4nubg3Lp1q8NYV6lSJX3yySdO/d5//32n/c6Zp/RSX3/9te644w75+fmpaNGiCg0N1TvvvON0vzdo0EDz5s3T3LlzdfDgQaWnp9uPdfLi559/VmhoqIoXL66GDRvqwoULmjNnjtPYl1N3SEiI/P391aRJE4ffErlUzrK5jeWXjuOS6/+ZKlWqKD09XRkZGQ7tlz/G5cqVk6enp9NY/u2339qnMHM1V6kkrVy5UrGxsfa5wUNDQzVs2DBJjOUAcCXbt2/XH3/84TSeVaxYUdLFOa2li9OeeXp6Okwbdb3++usvlShRwunkuNze+40ePVqhoaGKiIjQ/fffr8aNG2vUqFEOfT788EPVqFHD/lsSoaGhWrBggcMYUKxYMVWoUEHvvfeeFi1apLS0NKWnp7ucizw3s2bNUmhoqMLCwtS6dWtFRkY6/baVJCUmJio0NFRFixZVQECA4uPjtX37dpfrzG2s9fLyUoUKFfI01ro6PvP09HQ4GUCS/XG9fKx999139eabb0pSrjlCXo9nAFeYoxuWVahQIUVERGjDhg1X7LdhwwaVLFlSwcHBkhw/HbyaHj16aPTo0Zo/f766du2qTz75RO3atXP4Ia327durd+/eGj16tEaPHp3ruh577DFNnz5dTz75pBo0aKBChQrJw8NDXbp0cXmW0xtvvKEKFSq4PJvoZsvOzlarVq109OhRPfPMM6pcubICAwO1f/9+9ezZ06G+Ll26aN26dZowYYKmTJlyXdubMWOGwsLCdO7cOX3//fcaMGCA/Pz81LNnT3sfPz8/ffXVV5Iuflgxbdo0Pfnkk4qIiFDnzp2d1nktj+vfIT09XYGBgfrqq6/UsWNHJSUlKTEx0aFP586d9fPPP2vIkCGqVauWgoKCZLPZ1KZNmzyf6QYAVjF69Gj7B8NTpkzRHXfcoaFDh+rtt9++7nVeOjbkWLFiRa7zP7rLoUOHFBcXd9V+0dHRmjp1qqSL82a+9dZbevDBB1W2bFn7N34kqUOHDk4/7Pj888/r0KFD9usrVqxQ+/bt1aRJE7399tuKiIiQt7e3pk+f7hSeT5kyRV27dnX44P5a1KhRw/4mNWce7WbNmmndunUKDw93qtsYo5SUFI0cOVLt2rVz+Qb8Vo7juZ0RuHr1avXp00eBgYF6+eWX1alTJ4c3+Tt37lTLli1VuXJljRkzRpGRkfLx8dE333yjsWPHMpYDwBXYbDbddtttGjNmjMvbIyMjb3FFzh588EH16NFDNptNu3bt0ksvvaR27dppyZIl8vDw0IwZM9SzZ0917NhRQ4YMUfHixeXl5aWkpCTt3LnTYV2zZ89W9+7dnY4HAgMD81RL69atNWTIEEnSvn37NGrUKDVv3lxr1qxxGDP79u2rTp06KTs7W1u2bNGIESPUsWNH/fHHH07rdPd7ZuniD2K+8sor+vXXXzVo0CC1adPG4Rth13I8A7hC0A1La9eunaZOnaqffvpJd955p9PtK1as0O7du9WvXz97W5kyZbRt2zanvlu3bpUkhx8wql69umrXrq2ZM2eqVKlS2rNnjyZMmOC07Pvvv6/hw4dr586d9jc5rVq1cugzd+5cJSQk2N8YShe/0nvprzhfKiYmRk2bNlVQUFCe671eGzdu1J9//qkPP/xQPXr0sLcvXrzYqa+np6feeOMNbdy4USkpKXr77beVmprqMJ3L1TRq1Mhed7t27fTHH38oKSnJIej28vJy+HHK+Ph4FS1aVAsXLnQZdOf1cY2KitKGDRtks9kczurO6RcVFeWwvKs343/++afT/R4QEKCFCxeqcuXKGjRokF599VV17tzZfrbAsWPHtHTpUr344osaPnz4FdcPAP8El/5Yct26dTVgwABNmjRJPXr0sIe4Oa+527Zts391Oce2bducXpMvHxsk5TqO5lVur/MBAQEKDQ2VdPE1PrcxxtPT0+HN+b59+3Tq1Cn76/+VBAYGOuxP48aNVbJkSS1atMgh6C5VqpTTfo8bN84h6P7888/l5+en7777zmHalOnTpzttNzo6WjNmzNBtt92m3r17q2PHjvroo4/sP7Z1NUWKFHGop1mzZipRooSmT5+uoUOH5lp3UFCQunfv7jAlWo6cbzi5+l/YunWrwzie0+9yW7duVUhIiFOIsH37dvv6pYs/9G2z2ZzG8latWumdd97RuXPnNH/+fPXt21fLly+3B+NfffWVMjMz9b///U+lS5e2L5fzdXsAQO7KlStnnwrkSlOQlCtXTjabTZs3b3b4ccfrERUVpSVLlujUqVMOZ3Xn9t6vbNmyDuNWoUKF1K1bN/3yyy9q0KCB5s6dq7Jly+qLL75w2IfLT3CSpNq1a2vq1Klq3LixRo4cqTvuuEOjR4/WypUr81R7RESEQy2VKlVSw4YN7Sfh5ahQoYK9X1xcnM6cOaPnnnvO5Q9EXzrWXnoGts1m0/bt21W7dm2H+yWvx2c5HwzknMUtXTyWkpzzit69e2vYsGE6cOCAqlatqkGDBjkcf1zL8QzgClOXwNKGDBkif39/9evXT0eOHHG47ejRo3rkkUcUEBBg/yRUku666y6tXr1aP//8s73t3LlzeueddxQeHu4wT6d08VPdRYsWady4cSpWrJjatm3rspaoqCi1aNFCsbGxTm9GpYtvzi+fM2vChAnKzs7Odf88PDzUunVrfffdd9qyZYvDvn344YeqU6eOwsLCcl0+r3Lm57q0PmNMrlOFTJgwQd9//71mzpyp2NhYNWrU6Ia2f/bs2at+jSuntkvnErtUXh/Xu+66S4cOHdLs2bPt/S5cuKAJEyYoKCjIaa7w+fPna//+/fbrq1ev1qpVq5z+D0JDQ+1fARs5cqRKlSqlPn36ONV9+f/AuHHjrrjfAPBP8corrygiIkJ9+/bVhQsXJEl16tRR8eLFNXnyZIdx4Ntvv9WWLVsUHx//t9eVnJzsMOfj3r179eWXX6p169by8vKSl5eXWrdurS+//NLh67epqan65JNPdOedd9q/NSZd/KqxJKc3hnmR82F5bmPdlXh5ecnDw8PhuGL37t2aP3++U98LFy6oe/fuqlatmsaOHavY2Finrxxfi7Nnz0rSVcfyK+1f7dq1FR4e7vS/sGLFCq1Zs0bt2rWTdPGNf61atfThhx86fMixadMmLVq0SHfddZfTuidNmuRwPeekhcvH8oYNG8rLy0uBgYGaPHmyfvzxR/sZ95fWfelYfuLECd58A0AedO7cWfv373d4Xc1x9uxZ+7RTHTt2lKenp0aOHOn0TZm8zkGd46677lJ2drYmTpzo0D527Fh5eHjk+t7+0rqk/x/fXI0Dq1atUnJystOyJ0+e1IMPPqj27dvr+eefV2xsrCIiIq6p/ivVkpsrjbUtW7aUr6+v3nrrLYf7dubMmUpNTbWPtddzfHbpfWyM0cSJE+Xt7e00r3jjxo0lSSVKlNCoUaM0Y8YMh+nYruV4BnCFM7phaRUqVNCHH36o7t2767bbbtNDDz2kMmXKaPfu3Xr//feVnp6uTz/9VOXKlbMv8/TTT2vmzJlq27atHn/8cYWEhGjGjBnavHmzZs6cqQIFHJ8W3bp109NPP6158+bp0Ucflbe393XV2q5dO3388ccqVKiQqlatquTkZC1ZsuSqP1Dx0ksv6bvvvlPTpk312GOPKSAgQFOnTtXx48c1d+5cp/7Jycn2OcJzfhxrx44dWrhwob3P4cOHdfbsWS1cuFBt2rRR5cqVVa5cOf33v//V/v37FRwcrM8//9zlnFl//PGHnn76aY0YMUJ169a9rvti/vz5CgkJsU9dsmLFCj355JMOfbKzs+01nzp1StOnT1dGRoY6duzocp15fVz79u2rd9991/5jntHR0Zo7d65WrlypcePGOc3fVr58ed1555169NFHlZmZaf/A4+mnn851//z9/TVlyhTFxsbqnXfeUf/+/RUcHKwmTZro9ddf1/nz5+1n7KWkpFzXfQgAVlOwYEFNmDBB9957r958800988wz8vb21qhRo9SrVy81bdpUXbt2VWpqqsaPH6/o6GgNGjTob6+revXqiouL0+OPPy5fX1/71Covvviivc/LL7+sxYsX684771T//v1VoEABvfvuu8rMzNTrr78u6WLwnZiYqPfee09dunRxOdf05U6fPm0f644ePaq33npL3t7e1xXwx8fHa8yYMWrTpo26deumtLQ0TZo0SeXLl3ea5u3FF1/Uxo0b9dtvv13XcU1qaqpmzJgh6eLUXe+++64KFChgf4OcY8+ePVq4cKF96pJXXnlFUVFRql27ttOZ9AUKFNDrr7+uHj16qHHjxurevbt9WpRSpUrpmWeesfcdPXq02rZtqwYNGuihhx7S2bNnNWHCBBUqVEgjRoxwqjclJUXt27dXmzZtlJycrBkzZqhbt26qWbNmrvsYFxen//znP3r66ad19913KyIiQq1bt5aPj4/uvvtu9evXT6dPn9bUqVNVvHhxHTx48JrvRwD4N3nwwQf12Wef6ZFHHtGyZcvUqFEjZWdna+vWrfrss8/03XffqU6dOipfvryee+45vfTSS2rcuLHuvfde+fr66tdff1WJEiWUlJSU523efffdat68uZ577jnt3r1bNWvW1KJFi/Tll1/qySefdMgJpIvTns6YMUPGGO3cudM+BtWpU0fSxff0X3zxhe655x7Fx8crJSVFkydPVtWqVXX69GmHdQ0YMEBnz551Oa92Xuzatcs+1u7fv18TJ05UcHCwU3C8bds2LVy40H4W/OjRo1W3bl2VLFnSaZ1FixbV888/rxdeeEFxcXHq0KGDdu3apYkTJ6pmzZp6+OGHJemaj8/8/Py0cOFCJSQkqH79+vr222+1YMECDRs2zP7tOFf69u2rTz75RI888og2bdpkn2M8r8czgEsG+AfYsGGD6dq1q4mIiDDe3t4mPDzcdO3a1WzcuNFl/507d5r777/fFCpUyPj5+Zm6deua+fPn57r+u+66y0gyP//8c55rkmQSExPt148dO2Z69eplQkJCTFBQkImLizNbt241UVFRJiEhwd5v2bJlRpJZtmyZvW3t2rWmdevWJigoyAQEBJgmTZqYH374wWF706dPN5Ku+ZJj8+bNJjY21gQFBZmQkBDTp08f8/vvvxtJZvr06cYYY86dO2dq1Khh7rzzTnPhwgX7sikpKUaSGT169BXvk8tr9PHxMeXLlzfDhw83586ds/dLSEhw6BcUFGRuv/128/HHH+d6/xqT98c1NTXV/lj4+PiY2267zb6PrvbpzTffNJGRkcbX19c0btzY/P777w59ExISTFRUlNN2evXqZYKDg82+ffuMMcbs27fP3HPPPaZw4cKmUKFCplOnTubAgQMu9wUArCrntf7XX391eXuHDh1MQECA2bVrl71t9uzZpnbt2sbX19cULVrUdO/e3f7amSMhIcEEBgY6rW/OnDlO42aOnNfyy1/jc0gyAwYMMDNmzDAVKlQwvr6+pnbt2i7XtW7dOhMXF2cfi5s3b+5wXLBy5UpTvnx5M2LECJOZmXnVOpo2beow1hUuXNg0atTIfPvtty5rvFx8fLzT2PP+++/b96Ny5cpm+vTpJjEx0WG8X7FihfHy8jLvvvuuw7I5/Q4fPuzyvrpa3d98841T3TkXDw8PEx4ebu69916zZcsWY8z//5+kpKQ4LPfZZ585/C907drV/PXXX051LFmyxDRq1Mj4+/ub4OBgc/fdd5vNmze73KfNmzeb+++/3xQsWNAUKVLEDBw40Jw9e9ap3svH4vT0dBMaGmruuecee9v//vc/U6NGDePn52eio6PNqFGjzLRp01zuCwD82wwYMMBhzLlcVlaWGTVqlKlWrZrx9fU1RYoUMTExMebFF180J06ccOg7bdo0+3hQpEgR07RpU7N48WKndbp6/3ypU6dOmUGDBpkSJUoYb29vU6FCBTN69Ghjs9kc+l1t3DLGGJvNZl599VUTFRVlP2b4+uuvnd4Pfvrpp8bDw8MsXLjQYRu5HctcLioqyqGekJAQ07p1a5OcnGzvk3NskXPx9PQ0pUqVMgkJCfZjqMuPAXJMmjTJVK5c2Xh7e5uwsDDTr18/c+TIEad+13J8tnPnTtO6dWsTEBBgwsLCTGJiosnOznaq9/Jjsm3bthk/Pz8zaNAge1tejmeA3HgYc43f/QD+he655x5t3LhRO3bscHcpN83u3btVpkyZa/76179Fzv0zevRo/fe//3V3OQCAv4GHh4cGDBjg9JVm/DOMGDFCL774og4fPuzwQ1cAAODm6Nmzp+bOnet0RjvgLszRDVzFwYMHtWDBAj344IPuLgUAAAAAAACAC8zRDeQiJSVFK1eu1HvvvSdvb2/169fP3SXdVP7+/oqLi3N3GQAAAAAAAMAN44xuIBc//PCDHnzwQaWkpOjDDz9UeHi4u0u6qcLCwhx+oBIAAAAAAACwKuboBgAAAAAAAABYGmd0AwAAAAAAAAAsjaAbAAAAAAAAAGBpbv0xyh9//FGjR4/W2rVrdfDgQc2bN08dO3a84jLLly/X4MGD9ccffygyMlLPP/+8evbsmedt2mw2HThwQAULFpSHh8eN7QAAAC4YY3Tq1CmVKFFCnp58pny9GLMBAH8nxuubg/EaAPB3upbx2q1Bd0ZGhmrWrKnevXvr3nvvvWr/lJQUxcfH65FHHtHMmTO1dOlSPfzww4qIiFBcXFyetnngwAFFRkbeaOkAAFzV3r17VapUKXeXYVmM2QCAW4Hx+sYwXgMAboW8jNduDbrbtm2rtm3b5rn/5MmTVaZMGb355puSpCpVquinn37S2LFj8xx0FyxYUNLFOyc4OPjaiwYA4CpOnjypyMhI+5iD68OYDQD4OzFe3xyM1wCAv9O1jNduDbqvVXJysmJjYx3a4uLi9OSTT+a6TGZmpjIzM+3XT506JUkKDg5mEAYA/K34+u61YcwGALgD4/W1YbwGALhDXsZrS01EdujQIYWFhTm0hYWF6eTJkzp79qzLZZKSklSoUCH7ha9UAQCQPzFmAwCQ/zFeAwDyK0sF3ddj6NChOnHihP2yd+9ed5cEAABcYMwGACD/Y7wGAORXlpq6JDw8XKmpqQ5tqampCg4Olr+/v8tlfH195evreyvKAwAAN4AxGwCA/I/xGgCQX1nqjO4GDRpo6dKlDm2LFy9WgwYN3FQRAAAAAAAAAMDd3Bp0nz59WuvXr9f69eslSSkpKVq/fr327Nkj6eJXonr06GHv/8gjj2jXrl16+umntXXrVr399tv67LPPNGjQIHeUDwAAAAAAAADIB9wadK9Zs0a1a9dW7dq1JUmDBw9W7dq1NXz4cEnSwYMH7aG3JJUpU0YLFizQ4sWLVbNmTb355pt67733FBcX55b6AQAAAAAAAADu59Y5ups1ayZjTK63f/DBBy6X+e233/7GqgAAAAAAAAAAVmKpOboBAAAAAAAAALgcQTcAAAAAAAAAwNIIugEAAAAAAAAAlkbQDQAAAAAAAACwNIJuAAAAAAAAAIClEXQDAAAAAAAAACyNoBsAAAAAAAAAYGkE3QAAAAAAAAAASyvg7gL+KWKGfOTuEgC7taN7uLsEAMiXGK+RnzBeA4BrjNfIbxizAWvgjG4AAAAAAAAAgKURdAMAAAAAAAAALI2gGwAAAAAAAABgaQTdAAAAAAAAAABLI+gGAAAAAAAAAFgaQTcAAAAAAAAAwNIIugEAAAAAAAAAlkbQDQAAAAAAAACwNIJuAAAAAAAAAIClEXQDAAAAAAAAACyNoBsAAAAAAAAAYGkE3QAAAAAAAAAASyPoBgAAAAAAAABYGkE3AAAAAAAAAMDSCLoBAAAAAAAAAJZG0A0AAAAAAAAAsDSCbgAAAAAAAACApRF0AwAAAAAAAAAsjaAbAAAAAAAAAGBpBN0AAAAAAAAAAEsj6AYAAAAAAAAAWBpBNwAAAAAAAADA0gi6AQAAAAAAAACWRtANAAAAAAAAALA0gm4AAAAAAAAAgKURdAMAAAAAAAAALI2gGwAAAAAAAABgaQTdAAAAAAAAAABLI+gGAAAAAAAAAFgaQTcAAAAAAAAAwNIIugEAAAAAAAAAlkbQDQAAAAAAAACwNIJuAAAAAAAAAIClEXQDAAAAAAAAACyNoBsAAAAAAAAAYGkE3QAAAAAAAAAASyPoBgAAAAAAAABYGkE3AAAAAAAAAMDSCLoBAAAAAAAAAJZG0A0AAAAAAAAAsDSCbgAAAAAAAACApRF0AwAAAAAAAAAsjaAbAAAAAAAAAGBpBN0AAAAAAAAAAEsj6AYAAAAAAAAAWBpBNwAAAAAAAADA0gi6AQAAAAAAAACWRtANAAAAAAAAALA0gm4AAAAAAAAAgKURdAMAAAAAAAAALI2gGwAAAAAAAABgaQTdAAAAAAAAAABLI+gGAAAAAAAAAFgaQTcAAAAAAAAAwNIIugEAAAAAAAAAlkbQDQAAAAAAAACwNLcH3ZMmTVJ0dLT8/PxUv359rV69+or9x40bp0qVKsnf31+RkZEaNGiQzp07d4uqBQAAAAAAAADkN24NumfPnq3BgwcrMTFR69atU82aNRUXF6e0tDSX/T/55BM9++yzSkxM1JYtW/T+++9r9uzZGjZs2C2uHAAAAAAAAACQX7g16B4zZoz69OmjXr16qWrVqpo8ebICAgI0bdo0l/1//vlnNWrUSN26dVN0dLRat26trl27XvUscAAAAAAAAADAP5fbgu6srCytXbtWsbGx/1+Mp6diY2OVnJzscpmGDRtq7dq19mB7165d+uabb3TXXXflup3MzEydPHnS4QIAAPIfxmwAAPI/xmsAQH7ltqA7PT1d2dnZCgsLc2gPCwvToUOHXC7TrVs3jRw5Unfeeae8vb1Vrlw5NWvW7IpTlyQlJalQoUL2S2Rk5E3dDwAAcHMwZgMAkP8xXgMA8iu3/xjltVi+fLleffVVvf3221q3bp2++OILLViwQC+99FKuywwdOlQnTpywX/bu3XsLKwYAAHnFmA0AQP7HeA0AyK8KuGvDISEh8vLyUmpqqkN7amqqwsPDXS7zwgsv6MEHH9TDDz8sSbrtttuUkZGhvn376rnnnpOnp3Nu7+vrK19f35u/AwAA4KZizAYAIP9jvAYA5FduO6Pbx8dHMTExWrp0qb3NZrNp6dKlatCggctlzpw54xRme3l5SZKMMX9fsQAAAAAAAACAfMttZ3RL0uDBg5WQkKA6deqoXr16GjdunDIyMtSrVy9JUo8ePVSyZEklJSVJku6++26NGTNGtWvXVv369bVjxw698MILuvvuu+2BNwAAAAAAAADg38WtQfcDDzygw4cPa/jw4Tp06JBq1aqlhQsX2n+gcs+ePQ5ncD///PPy8PDQ888/r/379ys0NFR33323XnnlFXftAgAAAAAAAADAzdwadEvSwIEDNXDgQJe3LV++3OF6gQIFlJiYqMTExFtQGQAAAAAAAADACtw2RzcAAAAAAAAAADcDQTcAAAAAAAAAwNIIugEAAAAAAAAAlkbQDQAAAAAAAACwNIJuAAAAAAAAAIClEXQDAAAAAAAAACyNoBsAAAAAAAAAYGkE3QAAAAAAAAAASyPoBgAAAAAAAABYGkE3AAAAAAAAAMDSCLoBAAAAAAAAAJZG0A0AAAAAAAAAsDSCbgAAAAAAAACApRF0AwAAAAAAAAAsjaAbAAAAAAAAAGBpBN0AAAAAAAAAAEsj6AYAAAAAAAAAWBpBNwAAAAAAAADA0gi6AQAAAAAAAACWRtANAAAAAAAAALA0gm4AAAAAAAAAgKURdAMAAAAAAAAALI2gGwAAAAAAAABgaQTdAAAAAAAAAABLI+gGAAAAAAAAAFgaQTcAAAAAAAAAwNIIugEAAAAAAAAAlkbQDQAAAAAAAACwNIJuAAAAAAAAAIClEXQDAAAAAAAAACyNoBsAAAAAAAAAYGkE3QAAAAAAAAAASyPoBgAAAAAAAABYGkE3AAAAAAAAAMDSCLoBAAAAAAAAAJZG0A0AAAAAAAAAsDSCbgAAAAAAAACApRF0AwAAAAAAAAAsjaAbAAAAAAAAAGBpBN0AAAAAAAAAAEsj6AYAAAAAAAAAWBpBNwAAAAAAAADA0gi6AQAAAAAAAACWRtANAAAAAAAAALA0gm4AAAAAAAAAgKURdAMAAAAAAAAALI2gGwAAAAAAAABgaQTdAAAAAAAAAABLI+gGAAAAAAAAAFgaQTcAAAAAAAAAwNIIugEAAAAAAAAAlkbQDQAAAAAAAACwNIJuAAAAAAAAAIClEXQDAAAAAAAAACyNoBsAAAAAAAAAYGkE3QAAAAAAAAAASyPoBgAAAAAAAABYGkE3AAAAAAAAAMDSCLoBAAAAAAAAAJZG0A0AAAAAAAAAsDSCbgAAAAAAAACApRF0AwAAAAAAAAAsjaAbAAAAAAAAAGBpBN0AAAAAAAAAAEsj6AYAAAAAAAAAWJrbg+5JkyYpOjpafn5+ql+/vlavXn3F/sePH9eAAQMUEREhX19fVaxYUd98880tqhYAAAAAAAAAkN8UcOfGZ8+ercGDB2vy5MmqX7++xo0bp7i4OG3btk3Fixd36p+VlaVWrVqpePHimjt3rkqWLKm//vpLhQsXvvXFAwAAAAAAAADyBbcG3WPGjFGfPn3Uq1cvSdLkyZO1YMECTZs2Tc8++6xT/2nTpuno0aP6+eef5e3tLUmKjo6+lSUDAAAAAAAAAPIZt01dkpWVpbVr1yo2Nvb/i/H0VGxsrJKTk10u87///U8NGjTQgAEDFBYWpurVq+vVV19Vdnb2rSobAAAAAAAAAJDPuO2M7vT0dGVnZyssLMyhPSwsTFu3bnW5zK5du/T999+re/fu+uabb7Rjxw71799f58+fV2JiostlMjMzlZmZab9+8uTJm7cTAADgpmHMBgAg/2O8BgDkV27/McprYbPZVLx4cU2ZMkUxMTF64IEH9Nxzz2ny5Mm5LpOUlKRChQrZL5GRkbewYgAAkFeM2QAA5H+M1wCA/MptQXdISIi8vLyUmprq0J6amqrw8HCXy0RERKhixYry8vKyt1WpUkWHDh1SVlaWy2WGDh2qEydO2C979+69eTsBAABuGsZsAADyP8ZrAEB+5bag28fHRzExMVq6dKm9zWazaenSpWrQoIHLZRo1aqQdO3bIZrPZ2/78809FRETIx8fH5TK+vr4KDg52uAAAgPyHMRsAgPyP8RoAkF+5deqSwYMHa+rUqfrwww+1ZcsWPfroo8rIyFCvXr0kST169NDQoUPt/R999FEdPXpUTzzxhP78808tWLBAr776qgYMGOCuXQAAAAAAAAAAuJnbfoxSkh544AEdPnxYw4cP16FDh1SrVi0tXLjQ/gOVe/bskafn/2fxkZGR+u677zRo0CDVqFFDJUuW1BNPPKFnnnnGXbsAAAAAAAAAAHAztwbdkjRw4EANHDjQ5W3Lly93amvQoIF++eWXv7kqAAAAAAAAAIBVuHXqEgAAAAAAAAAAbhRBNwAAAAAAAADA0gi6AQAAAAAAAACWRtANAAAAAAAAALA0gm4AAAAAAAAAgKURdAMAAAAAAAAALI2gGwAAAAAAAABgaQTdAAAAAAAAAABLI+gGAAAAAAAAAFgaQTcAAAAAAAAAwNIIugEAAAAAAAAAlkbQDQAAAAAAAACwNIJuAAAAAAAAAIClEXQDAAAAAAAAACyNoBsAAAAAAAAAYGkFrnfBt95664q3P/7449e7agAAAAAAAAAA8uy6g+4nn3xSpUqVkpeXlyRp7969ioiIUIECBeTh4UHQDQAAAAAAAAC4Ja476JakNWvWqHjx4pKkggUL6ocfflDZsmVvSmEAAAAAAAAAAOTFdc/R7eXlpezsbPv17OxsJScn35SiAAAAAAAAAADIq+sOukuVKqWlS5dKkn7++WfZbDYNHjxYw4YNkzHmphUIAAAAAAAAAMCVXHfQ3a9fP/Xs2VOVK1dWixYt1KdPH61Zs0ZLlixRq1atbmaNAAAAAAAAAADk6rrn6H722Wd1++236/fff1eZMmV03333ycPDQytWrNATTzxxM2sEAAAAAAAAACBXN/RjlK1bt1br1q0d2nx9fTV58uQbKgoAAAAAAAAAgLy67qD75MmTV7w9ODj4elcNAAAAAAAAAECeXXfQXbhwYXl4eDi1G2Pk4eGh7OzsGyoMAAAAAAAAAIC8uKGpS+bOnauiRYverFoAAAAAAAAAALhmNxR0N2rUSMWLF79ZtQAAAAAAAAAAcM1uKOjevHmzjhw5osDAQIWHh8vHx+dm1QUAAAAAAAAAQJ543sjCLVu2VLVq1VSmTBkFBgbqtttu09ixY29WbQAAAAAAAAAAXNV1n9GdkpIiY4zOnz+vkydP6sCBA1q9erVeeOEFXbhwQUOGDLmZdQIAAAAAAAAA4NJ1B91RUVEO12NiYnT33XerYsWKGjlyJEE3AAAAAAAAAOCWuKE5ul3p0qWLqlWrdrNXCwAAAAAAAACASzccdK9du1ZbtmyRJFWtWlW33367br/99hsuDAAAAAAAAACAvLjuoDstLU1dunTR8uXLVbhwYUnS8ePH1bx5c82aNUuhoaE3q0YAAAAAAAAAAHLleb0LPvbYYzp16pT++OMPHT16VEePHtWmTZt08uRJPf744zezRgAAAAAAAAAAcnXdZ3QvXLhQS5YsUZUqVextVatW1aRJk9S6deubUhwAAAAAAAAAAFdz3Wd022w2eXt7O7V7e3vLZrPdUFEAAAAAAAAAAOTVdQfdLVq00BNPPKEDBw7Y2/bv369BgwapZcuWN6U4AAAAAAAAAACu5rqD7okTJ+rkyZOKjo5WuXLlVK5cOZUpU0YnT57UhAkTbmaNAAAAAAAAAADk6rrn6I6MjNS6deu0ZMkSbd26VZJUpUoVtWjRQvv27dOePXvk5eWlkiVL3rRiAQAAAAAAAAC43HUH3ZLk4eGhVq1aqVWrVva2tLQ0lSlTRsYYhYeHO0xtAgAAAAAAAADAzXbNQXfRokWveLsxRpL4QUoAAAAAAAAAwC1xzUH38ePHNW7cOBUqVCjX2wcPHnzDhQEAAAAAAAAAkBfXNXVJly5dVLx4cZe3paamEnQDAAAAAAAAAG4ZT3cXAAAAAAAAAADAjbiuM7qTk5NVtGhR+fr6qmDBgoqIiFDhwoVvcmkAAAAAAAAAAFzddQXd99xzj/1vDw8PSVJoaKgaNmyouLi4m1MZAAAAAAAAAAB5cM1B97FjxyRJFy5cUGZmpo4ePar9+/dr8+bNWrp0qfr373/TiwQAAAAAAAAAIDfXPEd3oUKFVKhQIRUrVkwlSpRQ9erVFRcXp0GDBunrr7/WlClTZIxRixYtdP/99/8dNQMAAAAAAAAAYHddU5dcSffu3VWgwMXV+vv73+zVAwAAAAAAAADg4KYH3X5+fkpISLjZqwUAAAAAAAAAwKVrnroEAAAAAAAAAID8hKAbAAAAAAAAAGBpBN0AAAAAAAAAAEsj6AYAAAAAAAAAWBpBNwAAAAAAAADA0gi6AQAAAAAAAACWRtANAAAAAAAAALA0gm4AAAAAAAAAgKURdAMAAAAAAAAALI2gGwAAAAAAAABgaQTdAAAAAAAAAABLI+gGAAAAAAAAAFgaQTcAAAAAAAAAwNIIugEAAAAAAAAAlkbQDQAAAAAAAACwtHwRdE+aNEnR0dHy8/NT/fr1tXr16jwtN2vWLHl4eKhjx45/b4EAAAAAAAAAgHzL7UH37NmzNXjwYCUmJmrdunWqWbOm4uLilJaWdsXldu/erf/+979q3LjxLaoUAAAAAAAAAJAfuT3oHjNmjPr06aNevXqpatWqmjx5sgICAjRt2rRcl8nOzlb37t314osvqmzZsrewWgAAAAAAAABAfuPWoDsrK0tr165VbGysvc3T01OxsbFKTk7OdbmRI0eqePHieuihh666jczMTJ08edLhAgAA8h/GbAAA8j/GawBAfuXWoDs9PV3Z2dkKCwtzaA8LC9OhQ4dcLvPTTz/p/fff19SpU/O0jaSkJBUqVMh+iYyMvOG6AQDAzceYDQBA/sd4DQDIr9w+dcm1OHXqlB588EFNnTpVISEheVpm6NChOnHihP2yd+/ev7lKAABwPRizAQDI/xivAQD5VQF3bjwkJEReXl5KTU11aE9NTVV4eLhT/507d2r37t26++677W02m02SVKBAAW3btk3lypVzWMbX11e+vr5/Q/UAAOBmYswGACD/Y7wGAORXbj2j28fHRzExMVq6dKm9zWazaenSpWrQoIFT/8qVK2vjxo1av369/dK+fXs1b95c69ev5ytTAAAAAAAAAPAv5NYzuiVp8ODBSkhIUJ06dVSvXj2NGzdOGRkZ6tWrlySpR48eKlmypJKSkuTn56fq1as7LF+4cGFJcmoHAAAAAAAAAPw7uD3ofuCBB3T48GENHz5chw4dUq1atbRw4UL7D1Tu2bNHnp6WmkocAAAAAAAAAHALuT3olqSBAwdq4MCBLm9bvnz5FZf94IMPbn5BAAAAAAAAAADL4FRpAAAAAAAAAIClEXQDAAAAAAAAACyNoBsAAAAAAAAAYGkE3QAAAAAAAAAASyPoBgAAAAAAAABYGkE3AAAAAAAAAMDSCLoBAAAAAAAAAJZG0A0AAAAAAAAAsDSCbgAAAAAAAACApRF0AwAAAAAAAAAsjaAbAAAAAAAAAGBpBN0AAAAAAAAAAEsj6AYAAAAAAAAAWBpBNwAAAAAAAADA0gi6AQAAAAAAAACWRtANAAAAAAAAALA0gm4AAAAAAAAAgKURdAMAAAAAAAAALI2gGwAAAAAAAABgaQTdAAAAAAAAAABLI+gGAAAAAAAAAFgaQTcAAAAAAAAAwNIIugEAAAAAAAAAlkbQDQAAAAAAAACwNIJuAAAAAAAAAIClEXQDAAAAAAAAACyNoBsAAAAAAAAAYGkE3QAAAAAAAAAASyPoBgAAAAAAAABYGkE3AAAAAAAAAMDSCLoBAAAAAAAAAJZG0A0AAAAAAAAAsDSCbgAAAAAAAACApRF0AwAAAAAAAAAsjaAbAAAAAAAAAGBpBN0AAAAAAAAAAEsj6AYAAAAAAAAAWBpBNwAAAAAAAADA0gi6AQAAAAAAAACWRtANAAAAAAAAALA0gm4AAAAAAAAAgKURdAMAAAAAAAAALI2gGwAAAAAAAABgaQTdAAAAAAAAAABLI+gGAAAAAAAAAFgaQTcAAAAAAAAAwNIIugEAAAAAAAAAlkbQDQAAAAAAAACwNIJuAAAAAAAAAIClEXQDAAAAAAAAACyNoBsAAAAAAAAAYGkE3QAAAAAAAAAASyPoBgAAAAAAAABYGkE3AAAAAAAAAMDSCLoBAAAAAAAAAJZG0A0AAAAAAAAAsDSCbgAAAAAAAACApRF0AwAAAAAAAAAsjaAbAAAAAAAAAGBpBN0AAAAAAAAAAEsj6AYAAAAAAAAAWBpBNwAAAAAAAADA0gi6AQAAAAAAAACWRtANAAAAAAAAALA0gm4AAAAAAAAAgKXli6B70qRJio6Olp+fn+rXr6/Vq1fn2nfq1Klq3LixihQpoiJFiig2NvaK/QEAAAAAAAAA/2xuD7pnz56twYMHKzExUevWrVPNmjUVFxentLQ0l/2XL1+url27atmyZUpOTlZkZKRat26t/fv33+LKAQAAAAAAAAD5gduD7jFjxqhPnz7q1auXqlatqsmTJysgIEDTpk1z2X/mzJnq37+/atWqpcqVK+u9996TzWbT0qVLb3HlAAAAAAAAAID8oIA7N56VlaW1a9dq6NCh9jZPT0/FxsYqOTk5T+s4c+aMzp8/r6JFi7q8PTMzU5mZmfbrJ0+evLGiAQDA34IxGwCA/I/xGgCQX7n1jO709HRlZ2crLCzMoT0sLEyHDh3K0zqeeeYZlShRQrGxsS5vT0pKUqFCheyXyMjIG64bAADcfIzZAADkf4zXAID8yu1Tl9yI1157TbNmzdK8efPk5+fnss/QoUN14sQJ+2Xv3r23uEoAAJAXjNkAAOR/jNcAgPzKrVOXhISEyMvLS6mpqQ7tqampCg8Pv+Kyb7zxhl577TUtWbJENWrUyLWfr6+vfH19b0q9AADg78OYDQBA/sd4DQDIr9x6RrePj49iYmIcfkgy54clGzRokOtyr7/+ul566SUtXLhQderUuRWlAgAAAAAAAADyKbee0S1JgwcPVkJCgurUqaN69epp3LhxysjIUK9evSRJPXr0UMmSJZWUlCRJGjVqlIYPH65PPvlE0dHR9rm8g4KCFBQU5Lb9AAAAAAAAAAC4h9uD7gceeECHDx/W8OHDdejQIdWqVUsLFy60/0Dlnj175On5/yeev/POO8rKytL999/vsJ7ExESNGDHiVpYOAAAAAAAAAMgH3B50S9LAgQM1cOBAl7ctX77c4fru3bv//oIAAAAAAAAAAJbh1jm6AQAAAAAAAAC4UQTdAAAAAAAAAABLI+gGAAAAAAAAAFgaQTcAAAAAAAAAwNIIugEAAAAAAAAAlkbQDQAAAAAAAACwNIJuAAAAAAAAAIClEXQDAAAAAAAAACyNoBsAAAAAAAAAYGkE3QAAAAAAAAAASyPoBgAAAAAAAABYGkE3AAAAAAAAAMDSCLoBAAAAAAAAAJZG0A0AAAAAAAAAsDSCbgAAAAAAAACApRF0AwAAAAAAAAAsjaAbAAAAAAAAAGBpBN0AAAAAAAAAAEsj6AYAAAAAAAAAWBpBNwAAAAAAAADA0gi6AQAAAAAAAACWRtANAAAAAAAAALA0gm4AAAAAAAAAgKURdAMAAAAAAAAALI2gGwAAAAAAAABgaQTdAAAAAAAAAABLI+gGAAAAAAAAAFgaQTcAAAAAAAAAwNIIugEAAAAAAAAAlkbQDQAAAAAAAACwNIJuAAAAAAAAAIClEXQDAAAAAAAAACyNoBsAAAAAAAAAYGkE3QAAAAAAAAAASyPoBgAAAAAAAABYGkE3AAAAAAAAAMDSCLoBAAAAAAAAAJZG0A0AAAAAAAAAsDSCbgAAAAAAAACApRF0AwAAAAAAAAAsjaAbAAAAAAAAAGBpBN0AAAAAAAAAAEsj6AYAAAAAAAAAWBpBNwAAAAAAAADA0gi6AQAAAAAAAACWRtANAAAAAAAAALA0gm4AAAAAAAAAgKURdAMAAAAAAAAALI2gGwAAAAAAAABgaQTdAAAAAAAAAABLI+gGAAAAAAAAAFgaQTcAAAAAAAAAwNIIugEAAAAAAAAAlkbQDQAAAAAAAACwtALuLgDAv1PMkI/cXQJgt3Z0D3eXAAD5EuM18hPGawDIHWM28hN3jdmc0Q0AAAAAAAAAsDSCbgAAAAAAAACApRF0AwAAAAAAAAAsjaAbAAAAAAAAAGBpBN0AAAAAAAAAAEsj6AYAAAAAAAAAWBpBNwAAAAAAAADA0gi6AQAAAAAAAACWRtANAAAAAAAAALA0gm4AAAAAAAAAgKXli6B70qRJio6Olp+fn+rXr6/Vq1dfsf+cOXNUuXJl+fn56bbbbtM333xziyoFAAAAAAAAAOQ3bg+6Z8+ercGDBysxMVHr1q1TzZo1FRcXp7S0NJf9f/75Z3Xt2lUPPfSQfvvtN3Xs2FEdO3bUpk2bbnHlAAAAAAAAAID8wO1B95gxY9SnTx/16tVLVatW1eTJkxUQEKBp06a57D9+/Hi1adNGQ4YMUZUqVfTSSy/p9ttv18SJE29x5QAAAAAAAACA/MCtQXdWVpbWrl2r2NhYe5unp6diY2OVnJzscpnk5GSH/pIUFxeXa38AAAAAAAAAwD9bAXduPD09XdnZ2QoLC3NoDwsL09atW10uc+jQIZf9Dx065LJ/ZmamMjMz7ddPnDghSTp58uSNlO4kO/PsTV0fcCNu9v/334HnDPKTm/2cyVmfMeamrvef7laM2bz2ID9hvAauDeN1/sB4jX8jxmzg2tzM58y1jNduDbpvhaSkJL344otO7ZGRkW6oBrg1Ck14xN0lAJbydz1nTp06pUKFCv0t6/4nYszGvw3jNXBtGK/zB8Zr/BsxZgPX5u94zuRlvHZr0B0SEiIvLy+lpqY6tKempio8PNzlMuHh4dfUf+jQoRo8eLD9us1m09GjR1WsWDF5eHjc4B7gZjp58qQiIyO1d+9eBQcHu7scIN/jOZN/GWN06tQplShRwt2lWApjtjXw2gNcG54z+Rfj9fVhvLYOXn+Aa8NzJn+6lvHarUG3j4+PYmJitHTpUnXs2FHSxUFy6dKlGjhwoMtlGjRooKVLl+rJJ5+0ty1evFgNGjRw2d/X11e+vr4ObYULF74Z5eNvEhwczAsKcA14zuRPnBl27RizrYXXHuDa8JzJnxivrx3jtfXw+gNcG54z+U9ex2u3T10yePBgJSQkqE6dOqpXr57GjRunjIwM9erVS5LUo0cPlSxZUklJSZKkJ554Qk2bNtWbb76p+Ph4zZo1S2vWrNGUKVPcuRsAAAAAAAAAADdxe9D9wAMP6PDhwxo+fLgOHTqkWrVqaeHChfYfnNyzZ488PT3t/Rs2bKhPPvlEzz//vIYNG6YKFSpo/vz5ql69urt2AQAAAAAAAADgRm4PuiVp4MCBuU5Vsnz5cqe2Tp06qVOnTn9zVbjVfH19lZiY6PQ1OACu8ZwB4A689gDXhucMAHfh9Qe4NjxnrM/DGGPcXQQAAAAAAAAAANfL8+pdAAAAAAAAAADIvwi6AQAAAAAAAACWRtANAAAAAAAAALA0gm4AAAAAAAAAgKURdCNfmDRpkqKjo+Xn56f69etr9erV7i4JyLd+/PFH3X333SpRooQ8PDw0f/58d5cE4F+EMRvIG8ZrAO7EeA3kHWP2PwdBN9xu9uzZGjx4sBITE7Vu3TrVrFlTcXFxSktLc3dpQL6UkZGhmjVratKkSe4uBcC/DGM2kHeM1wDchfEauDaM2f8cHsYY4+4i8O9Wv3591a1bVxMnTpQk2Ww2RUZG6rHHHtOzzz7r5uqA/M3Dw0Pz5s1Tx44d3V0KgH8Bxmzg+jBeA7iVGK+B68eYbW2c0Q23ysrK0tq1axUbG2tv8/T0VGxsrJKTk91YGQAAuBRjNgAA+R/jNYB/M4JuuFV6erqys7MVFhbm0B4WFqZDhw65qSoAAHA5xmwAAPI/xmsA/2YE3QAAAAAAAAAASyPohluFhITIy8tLqampDu2pqakKDw93U1UAAOByjNkAAOR/jNcA/s0IuuFWPj4+iomJ0dKlS+1tNptNS5cuVYMGDdxYGQAAuBRjNgAA+R/jNYB/swLuLgAYPHiwEhISVKdOHdWrV0/jxo1TRkaGevXq5e7SgHzp9OnT2rFjh/16SkqK1q9fr6JFi6p06dJurAzAPx1jNpB3jNcA3IXxGrg2jNn/HB7GGOPuIoCJEydq9OjROnTokGrVqqW33npL9evXd3dZQL60fPlyNW/e3Kk9ISFBH3zwwa0vCMC/CmM2kDeM1wDcifEayDvG7H8Ogm4AAAAAAAAAgKUxRzcAAAAAAAAAwNIIugEAAAAAAAAAlkbQDQAAAAAAAACwNIJuAAAAAAAAAIClEXQDAAAAAAAAACyNoBsAAAAAAAAAYGkE3QAAAAAAAAAASyPoBnBDRowYoVq1arm7DAAAcAWM1wAAWANjNnD9CLqBf6DDhw/r0UcfVenSpeXr66vw8HDFxcVp5cqVkiQPDw/Nnz/fvUUCAPAvx3gNAIA1MGYD1lDA3QUAuPnuu+8+ZWVl6cMPP1TZsmWVmpqqpUuX6siRI3leR1ZWlnx8fP7GKgEA+HdjvAYAwBoYswFr4Ixu4B/m+PHjWrFihUaNGqXmzZsrKipK9erV09ChQ9W+fXtFR0dLku655x55eHjYr+d8Peq9995TmTJl5OfnJ0nas2ePOnTooKCgIAUHB6tz585KTU3Ndfs7d+5U2bJlNXDgQBljlJmZqf/+978qWbKkAgMDVb9+fS1fvvxvvhcAAMjfGK8BALAGxmzAOgi6gX+YoKAgBQUFaf78+crMzHS6/ddff5UkTZ8+XQcPHrRfl6QdO3bo888/1xdffKH169fLZrOpQ4cOOnr0qH744QctXrxYu3bt0gMPPOBy2xs2bNCdd96pbt26aeLEifLw8NDAgQOVnJysWbNmacOGDerUqZPatGmj7du3/z13AAAAFsB4DQCANTBmAxZiAPzjzJ071xQpUsT4+fmZhg0bmqFDh5rff//dfrskM2/ePIdlEhMTjbe3t0lLS7O3LVq0yHh5eZk9e/bY2/744w8jyaxevdq+XM2aNc3KlStNkSJFzBtvvGHv+9dffxkvLy+zf/9+h221bNnSDB069GbuMgAAlsN4DQCANTBmA9bAGd3AP9B9992nAwcO6H//+5/atGmj5cuX6/bbb9cHH3xwxeWioqIUGhpqv75lyxZFRkYqMjLS3la1alUVLlxYW7Zssbft2bNHrVq10vDhw/XUU0/Z2zdu3Kjs7GxVrFjR/il4UFCQfvjhB+3cufPm7TAAABbEeA0AgDUwZgPWwI9RAv9Qfn5+atWqlVq1aqUXXnhBDz/8sBITE9WzZ89clwkMDLyubYWGhqpEiRL69NNP1bt3bwUHB0uSTp8+LS8vL61du1ZeXl4OywQFBV3XtgAA+CdhvAYAwBoYs4H8jzO6gX+JqlWrKiMjQ5Lk7e2t7Ozsqy5TpUoV7d27V3v37rW3bd68WcePH1fVqlXtbf7+/vr666/l5+enuLg4nTp1SpJUu3ZtZWdnKy0tTeXLl3e4hIeH3+Q9BADA+hivAQCwBsZsIP8h6Ab+YY4cOaIWLVpoxowZ2rBhg1JSUjRnzhy9/vrr6tChgyQpOjpaS5cu1aFDh3Ts2LFc1xUbG6vbbrtN3bt317p167R69Wr16NFDTZs2VZ06dRz6BgYGasGCBSpQoIDatm2r06dPq2LFiurevbt69OihL774QikpKVq9erWSkpK0YMGCv/V+AAAgP2O8BgDAGhizAesg6Ab+YYKCglS/fn2NHTtWTZo0UfXq1fXCCy+oT58+mjhxoiTpzTff1OLFixUZGanatWvnui4PDw99+eWXKlKkiJo0aaLY2FiVLVtWs2fPznXb3377rYwxio+PV0ZGhqZPn64ePXroqaeeUqVKldSxY0f9+uuvKl269N+y/wAAWAHjNQAA1sCYDViHhzHGuLsIAAAAAAAAAACuF2d0AwAAAAAAAAAsjaAbAAAAAAAAAGBpBN0AAAAAAAAAAEsj6AYAAAAAAAAAWBpBNwAAAAAAAADA0gi6AQAAAAAAAACWRtANAAAAAAAAALA0gm4AAAAAAAAAgKURdAMAAAAAAAAALI2gGwAAAAAAAABgaQTdAAAAAAAAAABLI+gGAAAAAAAAAFja/wEEz+1yPQCYPQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1800x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Функция для анализа сбалансированности\n",
    "def analyze_balance(y_train, y_val, y_test, y_name):\n",
    "    # Распределение классов\n",
    "    print(\"Распределение классов в обучающей выборке:\")\n",
    "    print(y_train.value_counts(normalize=True))\n",
    "    \n",
    "    print(\"\\nРаспределение классов в контрольной выборке:\")\n",
    "    print(y_val.value_counts(normalize=True))\n",
    "    \n",
    "    print(\"\\nРаспределение классов в тестовой выборке:\")\n",
    "    print(y_test.value_counts(normalize=True))\n",
    "\n",
    "    # Создание фигуры и осей для трех столбчатых диаграмм\n",
    "    fig, axes = plt.subplots(1, 3, figsize=(18, 5), sharey=True)\n",
    "    fig.suptitle('Распределение в различных выборках')\n",
    "\n",
    "    # Обучающая выборка\n",
    "    sns.barplot(x=y_train.value_counts().index, y=y_train.value_counts(normalize=True), ax=axes[0])\n",
    "    axes[0].set_title('Обучающая выборка')\n",
    "    axes[0].set_xlabel(y_name)\n",
    "    axes[0].set_ylabel('Доля')\n",
    "\n",
    "    # Контрольная выборка\n",
    "    sns.barplot(x=y_val.value_counts().index, y=y_val.value_counts(normalize=True), ax=axes[1])\n",
    "    axes[1].set_title('Контрольная выборка')\n",
    "    axes[1].set_xlabel(y_name)\n",
    "\n",
    "    # Тестовая выборка\n",
    "    sns.barplot(x=y_test.value_counts().index, y=y_test.value_counts(normalize=True), ax=axes[2])\n",
    "    axes[2].set_title('Тестовая выборка')\n",
    "    axes[2].set_xlabel(y_name)\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "analyze_balance(y_train, y_val, y_test, 'Stroke')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выборки несбалансированны. Необходимо сбалансировать обучающую и контрольную выборки, чтобы получить лучшие результаты при обучении модели. Для балансировки применим RandomOverSampler:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение классов в обучающей выборке:\n",
      "Stroke\n",
      "0    0.5\n",
      "1    0.5\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "Распределение классов в контрольной выборке:\n",
      "Stroke\n",
      "0    0.5\n",
      "1    0.5\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "Распределение классов в тестовой выборке:\n",
      "Stroke\n",
      "0    0.96014\n",
      "1    0.03986\n",
      "Name: proportion, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1800x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ros = RandomOverSampler(random_state=42)\n",
    "\n",
    "# Применение RandomOverSampler для балансировки выборок\n",
    "X_train_resampled, y_train_resampled = ros.fit_resample(X_train, y_train)\n",
    "X_val_resampled, y_val_resampled = ros.fit_resample(X_val, y_val)\n",
    "\n",
    "# Проверка сбалансированности после RandomOverSampler\n",
    "analyze_balance(y_train_resampled, y_val_resampled, y_test, 'Stroke')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Перейдем к конструированию признаков.\n",
    "Применим унитарное кодирование категориальных признаков (one-hot encoding), переведя их в бинарные вектора:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     BMI  PhysicalHealth  MentalHealth  SleepTime  HeartDisease_Yes  \\\n",
      "0  26.50             5.0           0.0        7.0             False   \n",
      "1  33.91             0.0           0.0        7.0             False   \n",
      "2  42.57             4.0           5.0        6.0             False   \n",
      "3  32.08             0.0           0.0        6.0             False   \n",
      "4  15.78             1.0           3.0        6.0             False   \n",
      "\n",
      "   Smoking_Yes  AlcoholDrinking_Yes  DiffWalking_Yes  Sex_Male  \\\n",
      "0        False                False            False      True   \n",
      "1        False                False            False      True   \n",
      "2        False                False            False      True   \n",
      "3        False                False            False      True   \n",
      "4        False                False            False      True   \n",
      "\n",
      "   AgeCategory_25-29  ...  Diabetic_Yes  Diabetic_Yes (during pregnancy)  \\\n",
      "0              False  ...         False                            False   \n",
      "1              False  ...         False                            False   \n",
      "2              False  ...         False                            False   \n",
      "3              False  ...         False                            False   \n",
      "4              False  ...         False                            False   \n",
      "\n",
      "   PhysicalActivity_Yes  GenHealth_Fair  GenHealth_Good  GenHealth_Poor  \\\n",
      "0                  True           False           False           False   \n",
      "1                  True           False           False           False   \n",
      "2                  True           False            True           False   \n",
      "3                  True           False           False           False   \n",
      "4                  True           False            True           False   \n",
      "\n",
      "   GenHealth_Very good  Asthma_Yes  KidneyDisease_Yes  SkinCancer_Yes  \n",
      "0                 True       False              False           False  \n",
      "1                 True       False              False           False  \n",
      "2                False       False              False           False  \n",
      "3                 True       False              False           False  \n",
      "4                False       False              False           False  \n",
      "\n",
      "[5 rows x 37 columns]\n"
     ]
    }
   ],
   "source": [
    "# Определение категориальных признаков\n",
    "categorical_features = ['HeartDisease', 'Smoking', 'AlcoholDrinking',\n",
    "       'DiffWalking', 'Sex', 'AgeCategory',\n",
    "       'Race', 'Diabetic', 'PhysicalActivity', 'GenHealth',\n",
    "       'Asthma', 'KidneyDisease', 'SkinCancer']\n",
    "\n",
    "# Применение one-hot encoding к обучающей выборке\n",
    "X_train_encoded = pd.get_dummies(X_train_resampled, columns=categorical_features, drop_first=True)\n",
    "\n",
    "# Применение one-hot encoding к контрольной выборке\n",
    "X_val_encoded = pd.get_dummies(X_val_resampled, columns=categorical_features, drop_first=True)\n",
    "\n",
    "# Применение one-hot encoding к тестовой выборке\n",
    "X_test_encoded = pd.get_dummies(X_test, columns=categorical_features, drop_first=True)\n",
    "\n",
    "print(X_train_encoded.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Далее применим дискретизацию к числовым признакам "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     BMI  PhysicalHealth  MentalHealth  SleepTime  HeartDisease_Yes  \\\n",
      "0  26.50             5.0           0.0        7.0             False   \n",
      "1  33.91             0.0           0.0        7.0             False   \n",
      "2  42.57             4.0           5.0        6.0             False   \n",
      "3  32.08             0.0           0.0        6.0             False   \n",
      "4  15.78             1.0           3.0        6.0             False   \n",
      "\n",
      "   Smoking_Yes  AlcoholDrinking_Yes  DiffWalking_Yes  Sex_Male  \\\n",
      "0        False                False            False      True   \n",
      "1        False                False            False      True   \n",
      "2        False                False            False      True   \n",
      "3        False                False            False      True   \n",
      "4        False                False            False      True   \n",
      "\n",
      "   AgeCategory_25-29  ...  Diabetic_Yes (during pregnancy)  \\\n",
      "0              False  ...                            False   \n",
      "1              False  ...                            False   \n",
      "2              False  ...                            False   \n",
      "3              False  ...                            False   \n",
      "4              False  ...                            False   \n",
      "\n",
      "   PhysicalActivity_Yes  GenHealth_Fair  GenHealth_Good  GenHealth_Poor  \\\n",
      "0                  True           False           False           False   \n",
      "1                  True           False           False           False   \n",
      "2                  True           False            True           False   \n",
      "3                  True           False           False           False   \n",
      "4                  True           False            True           False   \n",
      "\n",
      "   GenHealth_Very good  Asthma_Yes  KidneyDisease_Yes  SkinCancer_Yes  \\\n",
      "0                 True       False              False           False   \n",
      "1                 True       False              False           False   \n",
      "2                False       False              False           False   \n",
      "3                 True       False              False           False   \n",
      "4                False       False              False           False   \n",
      "\n",
      "       BMI_binned  \n",
      "0      Overweight  \n",
      "1           Obese  \n",
      "2  Severely Obese  \n",
      "3           Obese  \n",
      "4     Underweight  \n",
      "\n",
      "[5 rows x 38 columns]\n"
     ]
    }
   ],
   "source": [
    "bmi_bins = [0, 18.5, 25, 30, 40, 60]\n",
    "bmi_labels = [\"Underweight\", \"Normal\", \"Overweight\", \"Obese\", \"Severely Obese\"]\n",
    "\n",
    "\n",
    "X_train_encoded['BMI_binned'] = pd.cut(X_train_encoded['BMI'], bins=bmi_bins, labels=bmi_labels)\n",
    "X_val_encoded['BMI_binned'] = pd.cut(X_val_encoded['BMI'], bins=bmi_bins, labels=bmi_labels)\n",
    "\n",
    "X_test_encoded['BMI_binned'] = pd.cut(X_test_encoded['BMI'], bins=bmi_bins, labels=bmi_labels)\n",
    "\n",
    "print(X_train_encoded.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Применим ручной синтез признаков. К примеру, можно создать фактор риска для сердечных заболеваний: комбинированный признак на основе факторов риска, таких как курение, диабет, употребление алкоголя и наличие болезней."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     BMI  PhysicalHealth  MentalHealth  SleepTime  HeartDisease_Yes  \\\n",
      "0  26.50             5.0           0.0        7.0             False   \n",
      "1  33.91             0.0           0.0        7.0             False   \n",
      "2  42.57             4.0           5.0        6.0             False   \n",
      "3  32.08             0.0           0.0        6.0             False   \n",
      "4  15.78             1.0           3.0        6.0             False   \n",
      "\n",
      "   Smoking_Yes  AlcoholDrinking_Yes  DiffWalking_Yes  Sex_Male  \\\n",
      "0        False                False            False      True   \n",
      "1        False                False            False      True   \n",
      "2        False                False            False      True   \n",
      "3        False                False            False      True   \n",
      "4        False                False            False      True   \n",
      "\n",
      "   AgeCategory_25-29  ...  PhysicalActivity_Yes  GenHealth_Fair  \\\n",
      "0              False  ...                  True           False   \n",
      "1              False  ...                  True           False   \n",
      "2              False  ...                  True           False   \n",
      "3              False  ...                  True           False   \n",
      "4              False  ...                  True           False   \n",
      "\n",
      "   GenHealth_Good  GenHealth_Poor  GenHealth_Very good  Asthma_Yes  \\\n",
      "0           False           False                 True       False   \n",
      "1           False           False                 True       False   \n",
      "2            True           False                False       False   \n",
      "3           False           False                 True       False   \n",
      "4            True           False                False       False   \n",
      "\n",
      "   KidneyDisease_Yes  SkinCancer_Yes      BMI_binned  RiskFactor  \n",
      "0              False           False      Overweight           0  \n",
      "1              False           False           Obese           0  \n",
      "2              False           False  Severely Obese           0  \n",
      "3              False           False           Obese           0  \n",
      "4              False           False     Underweight           0  \n",
      "\n",
      "[5 rows x 39 columns]\n"
     ]
    }
   ],
   "source": [
    "X_train_encoded['RiskFactor'] = ((X_train_encoded['Smoking_Yes'] == True) | \n",
    "                      (X_train_encoded['Diabetic_Yes'] == True) | \n",
    "                      (X_train_encoded['AlcoholDrinking_Yes'] == True) | \n",
    "                      (X_train_encoded['KidneyDisease_Yes'] == True) | \n",
    "                      (X_train_encoded['SkinCancer_Yes'] == True)).astype(int)\n",
    "\n",
    "print(X_train_encoded.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Используем масштабирование признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     BMI  PhysicalHealth  MentalHealth  SleepTime  HeartDisease_Yes  \\\n",
      "0  26.50       -0.099915     -0.540452  -0.070949             False   \n",
      "1  33.91       -0.581538     -0.540452  -0.070949             False   \n",
      "2  42.57       -0.196239      0.006442  -0.646839             False   \n",
      "3  32.08       -0.581538     -0.540452  -0.646839             False   \n",
      "4  15.78       -0.485213     -0.212315  -0.646839             False   \n",
      "\n",
      "   Smoking_Yes  AlcoholDrinking_Yes  DiffWalking_Yes  Sex_Male  \\\n",
      "0        False                False            False      True   \n",
      "1        False                False            False      True   \n",
      "2        False                False            False      True   \n",
      "3        False                False            False      True   \n",
      "4        False                False            False      True   \n",
      "\n",
      "   AgeCategory_25-29  ...  PhysicalActivity_Yes  GenHealth_Fair  \\\n",
      "0              False  ...                  True           False   \n",
      "1              False  ...                  True           False   \n",
      "2              False  ...                  True           False   \n",
      "3              False  ...                  True           False   \n",
      "4              False  ...                  True           False   \n",
      "\n",
      "   GenHealth_Good  GenHealth_Poor  GenHealth_Very good  Asthma_Yes  \\\n",
      "0           False           False                 True       False   \n",
      "1           False           False                 True       False   \n",
      "2            True           False                False       False   \n",
      "3           False           False                 True       False   \n",
      "4            True           False                False       False   \n",
      "\n",
      "   KidneyDisease_Yes  SkinCancer_Yes      BMI_binned  RiskFactor  \n",
      "0              False           False      Overweight           0  \n",
      "1              False           False           Obese           0  \n",
      "2              False           False  Severely Obese           0  \n",
      "3              False           False           Obese           0  \n",
      "4              False           False     Underweight           0  \n",
      "\n",
      "[5 rows x 39 columns]\n"
     ]
    }
   ],
   "source": [
    "numerical_features = ['PhysicalHealth', 'MentalHealth', 'SleepTime']\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_encoded[numerical_features] = scaler.fit_transform(X_train_encoded[numerical_features])\n",
    "X_val_encoded[numerical_features] = scaler.transform(X_val_encoded[numerical_features])\n",
    "X_test_encoded[numerical_features] = scaler.transform(X_test_encoded[numerical_features])\n",
    "\n",
    "print(X_train_encoded.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "И также попробуем сконструировать признаки, используя фреймворк Featuretools:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\User\\Desktop\\aim\\aimvenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         BMI  PhysicalHealth  MentalHealth  SleepTime  HeartDisease_Yes  \\\n",
      "index                                                                     \n",
      "0      26.50       -0.099915     -0.540452  -0.070949             False   \n",
      "1      33.91       -0.581538     -0.540452  -0.070949             False   \n",
      "2      42.57       -0.196239      0.006442  -0.646839             False   \n",
      "3      32.08       -0.581538     -0.540452  -0.646839             False   \n",
      "4      15.78       -0.485213     -0.212315  -0.646839             False   \n",
      "\n",
      "       Smoking_Yes  AlcoholDrinking_Yes  DiffWalking_Yes  Sex_Male  \\\n",
      "index                                                                \n",
      "0            False                False            False      True   \n",
      "1            False                False            False      True   \n",
      "2            False                False            False      True   \n",
      "3            False                False            False      True   \n",
      "4            False                False            False      True   \n",
      "\n",
      "       AgeCategory_25-29  ...  PhysicalActivity_Yes  GenHealth_Fair  \\\n",
      "index                     ...                                         \n",
      "0                  False  ...                  True           False   \n",
      "1                  False  ...                  True           False   \n",
      "2                  False  ...                  True           False   \n",
      "3                  False  ...                  True           False   \n",
      "4                  False  ...                  True           False   \n",
      "\n",
      "       GenHealth_Good  GenHealth_Poor  GenHealth_Very good  Asthma_Yes  \\\n",
      "index                                                                    \n",
      "0               False           False                 True       False   \n",
      "1               False           False                 True       False   \n",
      "2                True           False                False       False   \n",
      "3               False           False                 True       False   \n",
      "4                True           False                False       False   \n",
      "\n",
      "       KidneyDisease_Yes  SkinCancer_Yes      BMI_binned  RiskFactor  \n",
      "index                                                                 \n",
      "0                  False           False      Overweight           0  \n",
      "1                  False           False           Obese           0  \n",
      "2                  False           False  Severely Obese           0  \n",
      "3                  False           False           Obese           0  \n",
      "4                  False           False     Underweight           0  \n",
      "\n",
      "[5 rows x 39 columns]\n"
     ]
    }
   ],
   "source": [
    "data = X_train_encoded.copy()\n",
    "\n",
    "es = ft.EntitySet(id=\"patients\")\n",
    "\n",
    "es = es.add_dataframe(dataframe_name=\"strokes_data\", dataframe=data, index=\"index\", make_index=True)\n",
    "\n",
    "feature_matrix, feature_defs = ft.dfs(\n",
    "    entityset=es, \n",
    "    target_dataframe_name=\"strokes_data\",\n",
    "    max_depth=1\n",
    ")\n",
    "\n",
    "print(feature_matrix.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Оценка качества набора признаков\n",
    "Представим основные оценки качества наборов признаков:\n",
    "\n",
    "- Предсказательная способность (для задачи классификации) Метрики: Accuracy, Precision, Recall, F1-Score, ROC AUC\n",
    "\n",
    "    Методы: Обучение модели на обучающей выборке и оценка на контрольной и тестовой выборках.\n",
    "\n",
    "- Скорость вычисления\n",
    "\n",
    "    Методы: Измерение времени выполнения генерации признаков и обучения модели.\n",
    "\n",
    "- Надежность\n",
    "\n",
    "    Методы: Кросс-валидация, анализ чувствительности модели к изменениям в данных.\n",
    "\n",
    "- Корреляция\n",
    "\n",
    "    Методы: Анализ корреляционной матрицы признаков, удаление мультиколлинеарных признаков.\n",
    "\n",
    "- Цельность\n",
    "\n",
    "    Методы: Проверка логической связи между признаками и целевой переменной, интерпретация результатов модели."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Время обучения модели: 36.52 секунд\n"
     ]
    }
   ],
   "source": [
    "X_train_encoded = pd.get_dummies(X_train_encoded, drop_first=True)\n",
    "X_val_encoded = pd.get_dummies(X_val_encoded, drop_first=True)\n",
    "X_test_encoded = pd.get_dummies(X_test_encoded, drop_first=True)\n",
    "\n",
    "all_columns = X_train_encoded.columns\n",
    "X_train_encoded = X_train_encoded.reindex(columns=all_columns, fill_value=0)\n",
    "X_val_encoded = X_val_encoded.reindex(columns=all_columns, fill_value=0)\n",
    "X_test_encoded = X_test_encoded.reindex(columns=all_columns, fill_value=0)\n",
    "\n",
    "# Выбор модели\n",
    "model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "\n",
    "# Начинаем отсчет времени\n",
    "start_time = time.time()\n",
    "model.fit(X_train_encoded, y_train_resampled)\n",
    "\n",
    "# Время обучения модели\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "print(f'Время обучения модели: {train_time:.2f} секунд')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Importance:\n",
      "                             feature  importance\n",
      "0                                BMI    0.194905\n",
      "3                          SleepTime    0.086714\n",
      "1                     PhysicalHealth    0.074056\n",
      "4                   HeartDisease_Yes    0.065638\n",
      "7                    DiffWalking_Yes    0.057683\n",
      "2                       MentalHealth    0.057339\n",
      "8                           Sex_Male    0.028562\n",
      "20           AgeCategory_80 or older    0.025721\n",
      "29              PhysicalActivity_Yes    0.023747\n",
      "27                      Diabetic_Yes    0.023346\n",
      "30                    GenHealth_Fair    0.022295\n",
      "34                        Asthma_Yes    0.019722\n",
      "19                 AgeCategory_75-79    0.017912\n",
      "5                        Smoking_Yes    0.017702\n",
      "37                        RiskFactor    0.017532\n",
      "31                    GenHealth_Good    0.016946\n",
      "18                 AgeCategory_70-74    0.015593\n",
      "33               GenHealth_Very good    0.015544\n",
      "25                        Race_White    0.014721\n",
      "39             BMI_binned_Overweight    0.014350\n",
      "17                 AgeCategory_65-69    0.014142\n",
      "36                    SkinCancer_Yes    0.014002\n",
      "32                    GenHealth_Poor    0.013788\n",
      "40                  BMI_binned_Obese    0.012988\n",
      "38                 BMI_binned_Normal    0.012010\n",
      "16                 AgeCategory_60-64    0.011894\n",
      "35                 KidneyDisease_Yes    0.011588\n",
      "15                 AgeCategory_55-59    0.010550\n",
      "22                        Race_Black    0.009165\n",
      "23                     Race_Hispanic    0.008975\n",
      "6                AlcoholDrinking_Yes    0.008943\n",
      "14                 AgeCategory_50-54    0.008495\n",
      "13                 AgeCategory_45-49    0.006740\n",
      "11                 AgeCategory_35-39    0.006491\n",
      "26  Diabetic_No, borderline diabetes    0.006442\n",
      "12                 AgeCategory_40-44    0.006333\n",
      "9                  AgeCategory_25-29    0.006128\n",
      "24                        Race_Other    0.005832\n",
      "10                 AgeCategory_30-34    0.005631\n",
      "41         BMI_binned_Severely Obese    0.004984\n",
      "21                        Race_Asian    0.002756\n",
      "28   Diabetic_Yes (during pregnancy)    0.002092\n"
     ]
    }
   ],
   "source": [
    "# Получение важности признаков\n",
    "importances = model.feature_importances_\n",
    "feature_names = X_train_encoded.columns\n",
    "\n",
    "# Сортировка признаков по важности\n",
    "feature_importance = pd.DataFrame({'feature': feature_names, 'importance': importances})\n",
    "feature_importance = feature_importance.sort_values(by='importance', ascending=False)\n",
    "\n",
    "print(\"Feature Importance:\")\n",
    "print(feature_importance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9562618763533519\n",
      "Precision: 0.10204081632653061\n",
      "Recall: 0.012472283813747228\n",
      "F1 Score: 0.02222771054581378\n",
      "ROC AUC: 0.5039578706315019\n",
      "Cross-validated Accuracy: 0.9940253495420259\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy: 0.9997146540973558\n",
      "Train Precision: 0.9994296336980861\n",
      "Train Recall: 1.0\n",
      "Train F1 Score: 0.9997147354964131\n",
      "Train ROC AUC: 0.9997146540973557\n"
     ]
    }
   ],
   "source": [
    "# Предсказание и оценка\n",
    "y_pred = model.predict(X_test_encoded)\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "roc_auc = roc_auc_score(y_test, y_pred)\n",
    "\n",
    "print(f\"Accuracy: {accuracy}\")\n",
    "print(f\"Precision: {precision}\")\n",
    "print(f\"Recall: {recall}\")\n",
    "print(f\"F1 Score: {f1}\")\n",
    "print(f\"ROC AUC: {roc_auc}\")\n",
    "\n",
    "# Кросс-валидация\n",
    "scores = cross_val_score(model, X_train_encoded, y_train_resampled, cv=5, scoring='accuracy')\n",
    "accuracy_cv = scores.mean()\n",
    "print(f\"Cross-validated Accuracy: {accuracy_cv}\")\n",
    "\n",
    "# Анализ важности признаков\n",
    "feature_importances = model.feature_importances_\n",
    "feature_names = X_train_encoded.columns\n",
    "\n",
    "importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': feature_importances})\n",
    "importance_df = importance_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "sns.barplot(x='Importance', y='Feature', data=importance_df)\n",
    "plt.title('Feature Importance')\n",
    "plt.show()\n",
    "\n",
    "# Проверка на переобучение\n",
    "y_train_pred = model.predict(X_train_encoded)\n",
    "\n",
    "accuracy_train = accuracy_score(y_train_resampled, y_train_pred)\n",
    "precision_train = precision_score(y_train_resampled, y_train_pred)\n",
    "recall_train = recall_score(y_train_resampled, y_train_pred)\n",
    "f1_train = f1_score(y_train_resampled, y_train_pred)\n",
    "roc_auc_train = roc_auc_score(y_train_resampled, y_train_pred)\n",
    "\n",
    "print(f\"Train Accuracy: {accuracy_train}\")\n",
    "print(f\"Train Precision: {precision_train}\")\n",
    "print(f\"Train Recall: {recall_train}\")\n",
    "print(f\"Train F1 Score: {f1_train}\")\n",
    "print(f\"Train ROC AUC: {roc_auc_train}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "aimvenv",
   "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": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}