AIM-PIbd-31-Sagirov-M-M/Lab_3/Lab_3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Написал лабу, не сохранил, а коммиту видимо оказалось лень сохранять. Переписываю по новой (плачу)\n",
    "Бизнес-цели:\n",
    "Определить, какие факторы влияют на величину страховых взносов (Charges) — помощь в разработке персонализированных страховых предложений.\n",
    "Предсказать вероятность того, что человек является курильщиком (Smoker) — для выявления групп риска и оптимизации страховых тарифов.\n",
    "Технические цели:\n",
    "Построить модель регрессии для предсказания величины взносов на основе других признаков.\n",
    "Построить модель классификации для определения вероятности курения на основе доступных данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Регрессия:\n",
      "Обучающая выборка: (1663, 6), Контрольная: (554, 6), Тестовая: (555, 6)\n",
      "Классификация:\n",
      "Обучающая выборка: (1663, 5), Контрольная: (554, 5), Тестовая: (555, 5)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Загрузка датасета\n",
    "df = pd.read_csv(\"..//datasets//Lab_1//Medical_insurance.csv\", sep=\",\")\n",
    "\n",
    "# Удаление пропусков и выбор целевых данных для бизнес-целей\n",
    "columns_to_use = [\"age\", \"sex\", \"bmi\", \"children\", \"smoker\", \"region\", \"charges\"]\n",
    "filtered_data = df[columns_to_use].dropna()\n",
    "\n",
    "# Цель 1: Регрессия (Charges)\n",
    "X_reg = filtered_data.drop(columns=[\"charges\"])\n",
    "y_reg = filtered_data[\"charges\"]\n",
    "\n",
    "# Цель 2: Классификация (Smoker)\n",
    "X_clf = filtered_data.drop(columns=[\"smoker\", \"charges\"])\n",
    "y_clf = filtered_data[\"smoker\"]\n",
    "\n",
    "# Разбиение данных для регрессии\n",
    "X_reg_train, X_reg_temp, y_reg_train, y_reg_temp = train_test_split(X_reg, y_reg, test_size=0.4, random_state=42)\n",
    "X_reg_val, X_reg_test, y_reg_val, y_reg_test = train_test_split(X_reg_temp, y_reg_temp, test_size=0.5, random_state=42)\n",
    "\n",
    "# Разбиение данных для классификации\n",
    "X_clf_train, X_clf_temp, y_clf_train, y_clf_temp = train_test_split(X_clf, y_clf, test_size=0.4, random_state=42, stratify=y_clf)\n",
    "X_clf_val, X_clf_test, y_clf_val, y_clf_test = train_test_split(X_clf_temp, y_clf_temp, test_size=0.5, random_state=42, stratify=y_clf_temp)\n",
    "\n",
    "# Проверка размера выборок\n",
    "print(\"Регрессия:\")\n",
    "print(f\"Обучающая выборка: {X_reg_train.shape}, Контрольная: {X_reg_val.shape}, Тестовая: {X_reg_test.shape}\")\n",
    "print(\"Классификация:\")\n",
    "print(f\"Обучающая выборка: {X_clf_train.shape}, Контрольная: {X_clf_val.shape}, Тестовая: {X_clf_test.shape}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оцениваем сбалансированность"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение классов для классификации:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение целевой переменной для регрессии:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Функция для визуализации распределения целевой переменной\n",
    "def plot_balance(y, title):\n",
    "    counts = y.value_counts()\n",
    "    plt.bar(counts.index.astype(str), counts.values)\n",
    "    plt.title(title)\n",
    "    plt.xlabel(\"Классы\")\n",
    "    plt.ylabel(\"Количество\")\n",
    "    plt.show()\n",
    "\n",
    "# Для классификации (Smoker)\n",
    "print(\"Распределение классов для классификации:\")\n",
    "plot_balance(y_clf_train, \"Обучающая выборка (Классификация)\")\n",
    "plot_balance(y_clf_val, \"Контрольная выборка (Классификация)\")\n",
    "plot_balance(y_clf_test, \"Тестовая выборка (Классификация)\")\n",
    "\n",
    "# Для регрессии (Charges) - визуализация распределения через гистограмму\n",
    "print(\"Распределение целевой переменной для регрессии:\")\n",
    "plt.hist(y_reg_train, bins=30, alpha=0.7, label=\"Обучающая\")\n",
    "plt.hist(y_reg_val, bins=30, alpha=0.7, label=\"Контрольная\")\n",
    "plt.hist(y_reg_test, bins=30, alpha=0.7, label=\"Тестовая\")\n",
    "plt.title(\"Распределение Charges (Регрессия)\")\n",
    "plt.xlabel(\"Значение Charges\")\n",
    "plt.ylabel(\"Частота\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Несбалансированно, нужно исправлять"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение классов после балансировки:\n",
      "smoker\n",
      "no     50.0\n",
      "yes    50.0\n",
      "Name: proportion, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "# Кодирование категориальных признаков\n",
    "label_encoders = {}\n",
    "for column in [\"sex\", \"region\"]:  # Категориальные столбцы\n",
    "    le = LabelEncoder()\n",
    "    X_clf_train[column] = le.fit_transform(X_clf_train[column])\n",
    "    label_encoders[column] = le\n",
    "\n",
    "# Применение SMOTE\n",
    "smote = SMOTE(random_state=42)\n",
    "X_clf_train_balanced, y_clf_train_balanced = smote.fit_resample(X_clf_train, y_clf_train)\n",
    "\n",
    "# Проверка нового распределения классов\n",
    "print(\"Распределение классов после балансировки:\")\n",
    "balanced_counts = y_clf_train_balanced.value_counts(normalize=True) * 100\n",
    "print(balanced_counts)\n",
    "\n",
    "# Визуализация распределения после балансировки\n",
    "plot_balance(y_clf_train_balanced, \"Обучающая выборка после балансировки (Классификация)\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вот теперь лучше, идем дальше, балансируем другую"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "До балансировки: (1663, 6) (1663,)\n",
      "После балансировки: (1665, 6) (1665,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import KBinsDiscretizer, StandardScaler\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "label_encoders = {}\n",
    "for column in [\"sex\", \"region\",\"smoker\"]:  # Категориальные столбцы\n",
    "    le = LabelEncoder()\n",
    "    X_reg_train[column] = le.fit_transform(X_reg_train[column])\n",
    "    label_encoders[column] = le\n",
    "\n",
    "# Дискретизация целевой переменной (разбиение на интервалы)\n",
    "kbins = KBinsDiscretizer(n_bins=5, encode='ordinal', strategy='quantile', random_state=222)  # 5 бинов\n",
    "y_reg_binned = kbins.fit_transform(y_reg_train.values.reshape(-1, 1)).ravel()\n",
    "\n",
    "# Масштабирование признаков\n",
    "scaler = StandardScaler()\n",
    "X_reg_train_scaled = scaler.fit_transform(X_reg_train)  # Масштабируем данные\n",
    "\n",
    "# Применение RandomOverSampler для сбалансированного распределения бинов\n",
    "ros = RandomOverSampler(random_state=42)\n",
    "X_reg_balanced, y_reg_binned_balanced = ros.fit_resample(X_reg_train_scaled, y_reg_binned)\n",
    "\n",
    "# Преобразование сбалансированных данных обратно в непрерывные значения (если требуется)\n",
    "y_reg_balanced = kbins.inverse_transform(y_reg_binned_balanced.reshape(-1, 1)).ravel()\n",
    "\n",
    "# Проверка форм данных\n",
    "print(\"До балансировки:\", X_reg_train.shape, y_reg_train.shape)\n",
    "print(\"После балансировки:\", X_reg_balanced.shape, y_reg_balanced.shape)\n",
    "\n",
    "# Визуализация распределения целевой переменной\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.hist(y_reg_train, bins=30, alpha=0.7)\n",
    "plt.title(\"До балансировки (Charges)\")\n",
    "plt.xlabel(\"Charges\")\n",
    "plt.ylabel(\"Частота\")\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.hist(y_reg_balanced, bins=30, alpha=0.7, color=\"orange\")\n",
    "plt.title(\"После балансировки (Charges)\")\n",
    "plt.xlabel(\"Charges\")\n",
    "plt.ylabel(\"Частота\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Отлично, приступаем к конструированию признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "После One-Hot Encoding:\n",
      "        age       bmi  children    smoker  sex_0.9501294624541127  \\\n",
      "0  1.266573  0.156259 -0.919952 -0.514431                    True   \n",
      "1  0.475349  0.280861  0.721550 -0.514431                   False   \n",
      "2 -0.531664 -1.137311 -0.099201 -0.514431                   False   \n",
      "3  0.187631 -0.740551 -0.099201  1.943897                    True   \n",
      "4 -1.179029  0.133306 -0.919952  1.943897                   False   \n",
      "\n",
      "   region_-0.4674261144497567  region_0.43960962455834773  \\\n",
      "0                        True                       False   \n",
      "1                       False                       False   \n",
      "2                       False                        True   \n",
      "3                       False                        True   \n",
      "4                       False                       False   \n",
      "\n",
      "   region_1.3466453635664521  \n",
      "0                      False  \n",
      "1                      False  \n",
      "2                      False  \n",
      "3                      False  \n",
      "4                       True  \n",
      "После дискретизации:\n",
      "        age       bmi  children    smoker  sex_0.9501294624541127  \\\n",
      "0  1.266573  0.156259 -0.919952 -0.514431                    True   \n",
      "1  0.475349  0.280861  0.721550 -0.514431                   False   \n",
      "2 -0.531664 -1.137311 -0.099201 -0.514431                   False   \n",
      "3  0.187631 -0.740551 -0.099201  1.943897                    True   \n",
      "4 -1.179029  0.133306 -0.919952  1.943897                   False   \n",
      "\n",
      "   region_-0.4674261144497567  region_0.43960962455834773  \\\n",
      "0                        True                       False   \n",
      "1                       False                       False   \n",
      "2                       False                        True   \n",
      "3                       False                        True   \n",
      "4                       False                       False   \n",
      "\n",
      "   region_1.3466453635664521  Age_bins  BMI_bins  \n",
      "0                      False         4         2  \n",
      "1                      False         3         2  \n",
      "2                      False         1         1  \n",
      "3                      False         2         1  \n",
      "4                       True         0         2  \n",
      "После синтеза признаков:\n",
      "        age       bmi  children    smoker  sex_0.9501294624541127  \\\n",
      "0  1.266573  0.156259 -0.919952 -0.514431                    True   \n",
      "1  0.475349  0.280861  0.721550 -0.514431                   False   \n",
      "2 -0.531664 -1.137311 -0.099201 -0.514431                   False   \n",
      "3  0.187631 -0.740551 -0.099201  1.943897                    True   \n",
      "4 -1.179029  0.133306 -0.919952  1.943897                   False   \n",
      "\n",
      "   region_-0.4674261144497567  region_0.43960962455834773  \\\n",
      "0                        True                       False   \n",
      "1                       False                       False   \n",
      "2                       False                        True   \n",
      "3                       False                        True   \n",
      "4                       False                       False   \n",
      "\n",
      "   region_1.3466453635664521  Age_bins  BMI_bins  Children_BMI  Age_BMI_ratio  \n",
      "0                      False         4         2     -0.143751       1.095406  \n",
      "1                      False         3         2      0.202656       0.371116  \n",
      "2                      False         1         1      0.112822       3.871955  \n",
      "3                      False         2         1      0.073463       0.723190  \n",
      "4                       True         0         2     -0.122635      -1.040345  \n",
      "Масштабирование завершено.\n",
      "Сгенерированные признаки:\n",
      "                age       bmi  children    smoker  sex_0.9501294624541127  \\\n",
      "charge_id                                                                   \n",
      "0          1.266573  0.156259 -0.919952 -0.514431                    True   \n",
      "1          0.475349  0.280861  0.721550 -0.514431                   False   \n",
      "2         -0.531664 -1.137311 -0.099201 -0.514431                   False   \n",
      "3          0.187631 -0.740551 -0.099201  1.943897                    True   \n",
      "4         -1.179029  0.133306 -0.919952  1.943897                   False   \n",
      "\n",
      "           region_-0.4674261144497567  region_0.43960962455834773  \\\n",
      "charge_id                                                           \n",
      "0                                True                       False   \n",
      "1                               False                       False   \n",
      "2                               False                        True   \n",
      "3                               False                        True   \n",
      "4                               False                       False   \n",
      "\n",
      "           region_1.3466453635664521  Age_bins  BMI_bins  Children_BMI  \\\n",
      "charge_id                                                                \n",
      "0                              False         4         2     -0.143751   \n",
      "1                              False         3         2      0.202656   \n",
      "2                              False         1         1      0.112822   \n",
      "3                              False         2         1      0.073463   \n",
      "4                               True         0         2     -0.122635   \n",
      "\n",
      "           Age_BMI_ratio  \n",
      "charge_id                 \n",
      "0               1.095406  \n",
      "1               0.371116  \n",
      "2               3.871955  \n",
      "3               0.723190  \n",
      "4              -1.040345  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\repos\\AIM\\AIM-PIbd-31-Sagirov-M-M\\aimenv\\Lib\\site-packages\\featuretools\\entityset\\entityset.py:1733: UserWarning: index charge_id not found in dataframe, creating new integer column\n",
      "  warnings.warn(\n",
      "c:\\repos\\AIM\\AIM-PIbd-31-Sagirov-M-M\\aimenv\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import featuretools as ft\n",
    "# One-Hot Encoding для категориальных признаков\n",
    "categorical_columns = [\"sex\", \"region\"]\n",
    "X_reg_balanced_df = pd.DataFrame(X_reg_balanced, columns=X_reg_train.columns)\n",
    "X_encoded = pd.get_dummies(X_reg_balanced_df, columns=categorical_columns, drop_first=True)\n",
    "\n",
    "print(\"После One-Hot Encoding:\")\n",
    "print(X_encoded.head())\n",
    "\n",
    "# Дискретизация числовых признаков\n",
    "X_encoded[\"Age_bins\"] = pd.cut(X_encoded[\"age\"], bins=5, labels=False)\n",
    "X_encoded[\"BMI_bins\"] = pd.cut(X_encoded[\"bmi\"], bins=5, labels=False)\n",
    "\n",
    "print(\"После дискретизации:\")\n",
    "print(X_encoded.head())\n",
    "\n",
    "# Добавление новых признаков\n",
    "X_encoded[\"Children_BMI\"] = X_encoded[\"children\"] * X_encoded[\"bmi\"]  # Произведение детей и BMI\n",
    "X_encoded[\"Age_BMI_ratio\"] = X_encoded[\"age\"] / (X_encoded[\"bmi\"] + 1)  # Возраст на индекс массы тела\n",
    "\n",
    "print(\"После синтеза признаков:\")\n",
    "print(X_encoded.head())\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "\n",
    "# Нормализация\n",
    "scaler_minmax = MinMaxScaler()\n",
    "X_normalized = scaler_minmax.fit_transform(X_encoded)\n",
    "\n",
    "# Стандартизация\n",
    "scaler_standard = StandardScaler()\n",
    "X_standardized = scaler_standard.fit_transform(X_encoded)\n",
    "\n",
    "print(\"Масштабирование завершено.\")\n",
    "\n",
    "# Создание объекта EntitySet\n",
    "es = ft.EntitySet(id=\"charge_data\")\n",
    "es = es.add_dataframe(dataframe_name=\"charge_data\", dataframe=X_encoded, index=\"charge_id\")\n",
    "\n",
    "# Применяем deep feature synthesis для создания новых признаков\n",
    "features, feature_names = ft.dfs(entityset=es, target_dataframe_name=\"charge_data\", max_depth=2)\n",
    "\n",
    "print(\"Сгенерированные признаки:\")\n",
    "print(features.head())\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Появляются предупреждения, о том что в EntitySet только один датафрейм, игнорируем\n",
    "\n",
    "Делаем то же самое для другого датасета"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "После One-Hot Encoding:\n",
      "   age     bmi  children  sex_1  region_1  region_2  region_3\n",
      "0   41  32.200         1  False     False     False      True\n",
      "1   64  22.990         0  False     False      True     False\n",
      "2   54  27.645         1  False      True     False     False\n",
      "3   28  26.315         3  False      True     False     False\n",
      "4   27  30.400         3  False      True     False     False\n",
      "После дискретизации:\n",
      "   age     bmi  children  sex_1  region_1  region_2  region_3  Age_bins  \\\n",
      "0   41  32.200         1  False     False     False      True         2   \n",
      "1   64  22.990         0  False     False      True     False         4   \n",
      "2   54  27.645         1  False      True     False     False         3   \n",
      "3   28  26.315         3  False      True     False     False         1   \n",
      "4   27  30.400         3  False      True     False     False         0   \n",
      "\n",
      "   BMI_bins  \n",
      "0         2  \n",
      "1         0  \n",
      "2         1  \n",
      "3         1  \n",
      "4         1  \n",
      "После синтеза признаков:\n",
      "   age     bmi  children  sex_1  region_1  region_2  region_3  Age_bins  \\\n",
      "0   41  32.200         1  False     False     False      True         2   \n",
      "1   64  22.990         0  False     False      True     False         4   \n",
      "2   54  27.645         1  False      True     False     False         3   \n",
      "3   28  26.315         3  False      True     False     False         1   \n",
      "4   27  30.400         3  False      True     False     False         0   \n",
      "\n",
      "   BMI_bins  Age_BMI  Age_BMI_ratio  \n",
      "0         2  1320.20       1.273292  \n",
      "1         0  1471.36       2.783819  \n",
      "2         1  1492.83       1.953337  \n",
      "3         1   736.82       1.064032  \n",
      "4         1   820.80       0.888158  \n",
      "        age       bmi  children  sex_1  region_1  region_2  region_3  \\\n",
      "0  0.500000  0.443474         1  False     False     False      True   \n",
      "1  1.000000  0.191972         0  False     False      True     False   \n",
      "2  0.782609  0.319088         1  False      True     False     False   \n",
      "3  0.217391  0.282769         3  False      True     False     False   \n",
      "4  0.195652  0.394320         3  False      True     False     False   \n",
      "\n",
      "   Age_bins  BMI_bins   Age_BMI  Age_BMI_ratio  \n",
      "0         2         2  0.403768       0.303438  \n",
      "1         4         0  0.462857       0.829753  \n",
      "2         3         1  0.471249       0.540387  \n",
      "3         1         1  0.175725       0.230526  \n",
      "4         0         1  0.208553       0.169246  \n",
      "Сгенерированные признаки:\n",
      "                age       bmi  children  sex_1  region_1  region_2  region_3  \\\n",
      "smoker_id                                                                      \n",
      "0          0.500000  0.443474         1  False     False     False      True   \n",
      "1          1.000000  0.191972         0  False     False      True     False   \n",
      "2          0.782609  0.319088         1  False      True     False     False   \n",
      "3          0.217391  0.282769         3  False      True     False     False   \n",
      "4          0.195652  0.394320         3  False      True     False     False   \n",
      "\n",
      "           Age_bins  BMI_bins   Age_BMI  Age_BMI_ratio  \n",
      "smoker_id                                               \n",
      "0                 2         2  0.403768       0.303438  \n",
      "1                 4         0  0.462857       0.829753  \n",
      "2                 3         1  0.471249       0.540387  \n",
      "3                 1         1  0.175725       0.230526  \n",
      "4                 0         1  0.208553       0.169246  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\repos\\AIM\\AIM-PIbd-31-Sagirov-M-M\\aimenv\\Lib\\site-packages\\featuretools\\entityset\\entityset.py:1733: UserWarning: index smoker_id not found in dataframe, creating new integer column\n",
      "  warnings.warn(\n",
      "c:\\repos\\AIM\\AIM-PIbd-31-Sagirov-M-M\\aimenv\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import featuretools as ft\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "# One-Hot Encoding для категориальных признаков\n",
    "categorical_columns = [\"sex\", \"region\"]\n",
    "X_clf_balanced_df = pd.DataFrame(X_clf_train_balanced, columns=X_clf_train.columns)\n",
    "X_encoded = pd.get_dummies(X_clf_balanced_df, columns=categorical_columns, drop_first=True)\n",
    "\n",
    "print(\"После One-Hot Encoding:\")\n",
    "print(X_encoded.head())\n",
    "\n",
    "# Дискретизация числовых признаков\n",
    "X_encoded[\"Age_bins\"] = pd.cut(X_encoded[\"age\"], bins=5, labels=False)\n",
    "X_encoded[\"BMI_bins\"] = pd.cut(X_encoded[\"bmi\"], bins=5, labels=False)\n",
    "\n",
    "print(\"После дискретизации:\")\n",
    "print(X_encoded.head())\n",
    "\n",
    "# Синтез новых признаков\n",
    "X_encoded[\"Age_BMI\"] = X_encoded[\"age\"] * X_encoded[\"bmi\"]  \n",
    "X_encoded[\"Age_BMI_ratio\"] = X_encoded[\"age\"] / (X_encoded[\"bmi\"] + 1e-6)  #чтоб не бесконечность\n",
    "\n",
    "print(\"После синтеза признаков:\")\n",
    "print(X_encoded.head())\n",
    "\n",
    "\n",
    "# Масштабирование признаков: Стандартизация\n",
    "scaler = StandardScaler()\n",
    "X_encoded[['age', 'bmi', 'Age_BMI', 'Age_BMI_ratio']] = scaler.fit_transform(X_encoded[['age', 'bmi', 'Age_BMI', 'Age_BMI_ratio']])\n",
    "\n",
    "# Масштабирование признаков: Нормализация\n",
    "scaler_minmax = MinMaxScaler()\n",
    "X_encoded[['age', 'bmi', 'Age_BMI', 'Age_BMI_ratio']] = scaler_minmax.fit_transform(X_encoded[['age', 'bmi', 'Age_BMI', 'Age_BMI_ratio']])\n",
    "\n",
    "# Проверка после масштабирования\n",
    "print(X_encoded.head())\n",
    "\n",
    "\n",
    "# Создание объекта EntitySet\n",
    "es = ft.EntitySet(id=\"smoker_data\")\n",
    "es = es.add_dataframe(dataframe_name=\"smoker_data\", dataframe=X_encoded, index=\"smoker_id\")\n",
    "\n",
    "# Применяем deep feature synthesis для создания новых признаков\n",
    "features, feature_names = ft.dfs(entityset=es, target_dataframe_name=\"smoker_data\", max_depth=2)\n",
    "\n",
    "print(\"Сгенерированные признаки:\")\n",
    "print(features.head())\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Те же самые предупреждения, не обращаем внимания. Приступаем к оценке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8882882882882883\n",
      "ROC-AUC: 0.8528610899771755\n",
      "Время обучения модели: 0.2450 секунд\n",
      "Время предсказания: 0.0123 секунд\n",
      "Средняя точность по кросс-валидации: 0.9989\n",
      "Корреляция признаков с целевой переменной:\n",
      "smoker      1.000000\n",
      "sex         0.082326\n",
      "bmi         0.011489\n",
      "children    0.006362\n",
      "region     -0.006751\n",
      "age        -0.023286\n",
      "Name: smoker, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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21hnIDACXL1/GTz/9hF9//dXg/TY8PT213e6GDx8OZ2dnfPvtt1i6dKk2xtnZudz/3a+//qrz+++//w6VSoU9e/boDPitaMai5s2bY8eOHbCyssKOHTvw+uuv49q1a9orEQ++jh68olhUVITIyMhy7VEqlTrLunXrhjp16mDz5s0ICgqqcP+J6L+BXXroP+XTTz+Fh4cHXn/9dZSUlAAA2rRpA1dXV6xduxYqlUobu3//foSEhGDgwIHltvP1119rfxYEAV9//TWkUmm5ftu//vqrTh/8c+fO4Z9//kH//v0BAK6urujWrRvWrVuHhISEcs/zcDeFn376CQDKfTHRJygoCDExMfjhhx/Qq1cvnb7Pj2PRokVwc3PDlClT9P7dw8MDgYGB2Lp1q04yfuPGDRw6dAgDBgyolnZU5H5yJzwwTaogCFi1apXe+NWrV+Po0aP48ccf0atXL3Ts2LHKzymRSDBu3Dhcu3ZNbxcjY9pVWfHx8di9e7f29+zsbGzbtg2BgYHlKruPOndVpW+f/vnnH5w5c6Zatv+wM2fO6IwNuHfvHn777Tf06dOnXFK/YMECdOzYEYMHD6709rOyslBUVKTzPlBZ+o5FVlYWNm/erDf+mWeegY2NDSwsLLBhwwZERUXpXKnq1asXLC0t8dVXX+lsc+PGjcjKytL7vvSg+1O9GrMvRGR+WOGn/xQ7OzusXr0aw4cPxxdffIH58+dDKpVi6dKlmDhxIrp27YoxY8Zop+X09vYuN0WjXC7HgQMHMH78eLRr1w779+/H3r178e6775arMjZq1AidOnXC1KlToVKpsHLlSjg5OWHevHnamDVr1qBTp05o3rw5Jk+eDB8fHyQlJeHMmTOIjY3F1atXkZSUhODgYGzYsAGjR4+Gn5+fwf08fPgwvvzyS3z//fflrlA8LDc3FwcOHNBZdr86f+LECUilUp2Bx4cOHcKPP/6oHaypz7Jly9C/f3906NABkyZN0k7LqVAosHDhQoPteVx+fn5o2LAh3nnnHcTFxcHe3h6//PKL3r75N2/exLx587Bw4UI8++yzj/W8H3/8MebOnQulUvnY7aqKxo0bY9KkSTh//jzc3NywadMmJCUl6U00K3PugNLuRw//T1y5cgVA6ZfWunXrolGjRnj++eexa9cuDBs2DAMHDkRkZCTWrl0Lf39/5ObmPtZ+6RMQEIC+ffvqTMsJQG+3tUOHDuHUqVMVbuv69euYM2eOdt78+Ph4bNq0CRqNBmPGjKly2/r06QNLS0sMGjQIb7zxBnJzc7F+/Xq4urrq/TL/8H7Nnz8fS5YswejRo9GiRQu4uLggKCgIixYtQr9+/TB48GCEhobim2++wbPPPlvuKlRSUhJ++OEHAKUD29etWweJRKL3HgJE9B9UO5MDET1Z96chfHBKxAcNGTJEsLa2FiIiIrTLtm/fLrRq1UqQyWSCo6OjMHbsWJ0pNQWhbMrC8PBwoU+fPoK1tbXg5uYmBAcH60yd9+DUjl988YXg5eUlyGQyoXPnzsLVq1fLtSc8PFwYN26c4O7uLkilUsHT01N4/vnntdMDnjp1SmjUqJGwcOHCctMgPjwtZ2pqqlCnTh1hzJgxOnEVTcuJf6c7rOhxf7v3j2lgYKCg0WgqfP77Dh8+LHTs2FGwsrIS7O3thUGDBgm3bt3SiXlS03LeunVL6NWrl2Brays4OzsLkydPFq5evaqzzcLCQqFFixZCp06dhJKSknL7U9lpOSv6H9P398q062F4xLScAwcOFA4ePCi0aNFCkMlkgp+fn845frAtlTl396eeNfS43x6NRiN89tlnQv369QWZTCa0atVK+OOPP4Tx48cL9evXN3j8Hmz/wyqalnP69OnCDz/8IPj6+mqf7+FpNu//Tw0ZMkRn+f3///vx8fHxwuDBgwU3NzdBKpUKHh4ewvPPPy+cPHnS6Dbu2bNHaNGihSCXywVvb29h6dKlwqZNm8pNcfvgtJz3FRYWCn5+fsKzzz6r8//49ddfC35+foJUKhXc3NyEqVOnChkZGTrrPvw6dnBwEDp27Cjs27evXLuJ6L9JJAgPXCskIoMmTJiAnTt3PrJ6GRUVhQYNGmDZsmXV0m++tnh7e2PhwoXl7gpKTwdvb28EBATgjz/+qLHn7NatG7p16/bEr9Q8TCQSYfr06Trd6YiIqHLYh5+IiIiIyIwx4SeiCnXt2lWn/z5R27Zty00VS0RETzcO2iWiCm3durW2m0BPmc8//7y2m0BERFXEPvxERERERDXkr7/+wrJly3Dx4kUkJCRg9+7dGDp0qMF1jh8/jtmzZ+PmzZvw8vLC+++/X6XxdezSQ0RERERUQ/Ly8tCyZUusWbOmUvGRkZEYOHAgunfvjitXrmDmzJl47bXXcPDgwUo/Jyv8RERERES1QCQSPbLCP3/+fOzduxc3btzQLhs9ejQyMzPL3TOlIqzwExEREREZSaVSITs7W+dRnXe5PnPmDHr16qWzrG/fvlW6q/lTM2h3r7RJbTeBKrC433e13QSqQNMOAbXdBDIgMTq5tptAFcjPyqntJlAFVHkFtd0EMuDk711ruwl61WYeef69MeXu+B0cHFxt9ytJTEyEm5ubzjI3NzdkZ2ejoKAAVlZWj9zGU5PwExERERGZmqCgIMyePVtnmUwmq6XW6MeEn4iIiIjISDKZ7Ikm+O7u7khKStJZlpSUBHt7+0pV9wEm/ERERERk4kRSUW034Ynp0KED9u3bp7Pszz//RIcOHSq9DQ7aJSIiIiKqIbm5ubhy5QquXLkCoHTazStXriAmJgZAaRehcePGaeOnTJmCiIgIzJs3D7dv38Y333yDn3/+GbNmzar0c7LCT0REREQmzUJiOhX+CxcuoHv37trf7/f/Hz9+PLZs2YKEhARt8g8ADRo0wN69ezFr1iysWrUKdevWxYYNG9C3b99KPycTfiIiIiKiGtKtWzcYug3Wli1b9K5z+fJlo5+TCT8RERERmTSRlL3UDeHRISIiIiIyY0z4iYiIiIjMGLv0EBEREZFJM6VBu7WBFX4iIiIiIjPGCj8RERERmTRzvvFWdWCFn4iIiIjIjDHhJyIiIiIyY+zSQ0REREQmjYN2DWOFn4iIiIjIjLHCT0REREQmjYN2DWOFn4iIiIjIjDHhJyIiIiIyY+zSQ0REREQmjYN2DWOFn4iIiIjIjLHCT0REREQmTSRmhd8QVviJiIiIiMwYK/xEREREZNIsWOE3iBV+IiIiIiIzxoSfiIiIiMiMsUsPEREREZk0kQW79BjCCj8RERERkRljhZ+IiIiITJpIzBq2ITw6RERERERmjAk/EREREZEZY5ceIiIiIjJpnIffMFb4iYiIiIjMGCv8RERERGTSOC2nYazwExERERGZMVb4iYiIiMiksQ+/YazwExERERGZMSb8RERERERmjF16iIiIiMikidilxyBW+ImIiIiIzNhjVfjDwsIQHh6OLl26wMrKCoIgQCTiNywiIiIiqjkiC9awDTHq6KSlpaFXr15o3LgxBgwYgISEBADApEmTMGfOnGptIBERERERGc+ohH/WrFmQSCSIiYmBtbW1dvmoUaNw4MCBamscERERERE9HqO69Bw6dAgHDx5E3bp1dZb7+voiOjq6WhpGRERERFQZvNOuYUZV+PPy8nQq+/elp6dDJpM9dqOIiIiIiKh6GFXh79y5M7Zt24aPP/4YACASiaDRaPD555+je/fu1drA2ubYqQ185kyC4pkAyOu44sKIaUjac8TwOl3awn/5Atj6+6LwXgLCFn+L2G27dWLqT30JPrMnQebuguxrt3Fz5sfIOn/9Se6KWZs01huD+rjDzkaC6yHZWP7NXcQmFBhcZ/iAOhgz3AuOSkuER+biy3VhCLmbo/373Om+aNNSCWdHS+QXqnEjJBvfbo1ATKzh7VKpbq1l6NvOCgpbC9xLKsH/HcpHVEKJ3tg6zmIM7mKF+u4SODuI8dOfeThyvlAnxtdLgr7tS2Mc7CywZmc2rtwproldMUsDuikxvLcjlAoJImNVWPdTIu5GFVYY3/EZO7w8xAWuTlLEJxdhy65kXLyRpzd22kvu6N9VifU/J2LPkYwntQtma0gfV7w4yAOOCinCY/KxenM0QsP1H2sA6NJOiYkv1oW7iwyxiYVY/797OHclSydmwkhPDOjhAlsbCW6E5mDVxijEJaqe9K6Yrer+zLGzlWDSS95o20oJNxcZMrOL8dfZVGz4IQp5+eqa2CWTxzvtGmZUhf/zzz/Hd999h/79+6OoqAjz5s1DQEAA/vrrLyxdurS621irxDbWyL4WihtvLapUvJV3XTy7Zx3Sjv+Dk22GIHL1VjRf9wmce3fSxniM7I+my4Jw95M1ONl2GHKu3Ua7vRth6eL4pHbDrI0d4YUXnvfE8m/u4vV3LqOgUI0VHzWHpbTiF3+PTi6Y8VpDbP6/KEyaeRFhkblY8VFzOCik2pjQsFx8tioUY6edx5zg6xCJgC8/agFOBPBobZpa4sWeNvj9ZAE+3pSF2GQ1Zo62g521/nNiKQVSMzXYdTwfmbkavTEyqQixySX438GKEx+qnE5t7PDaC674v72pmPlpJCJjC/HRW/WgsBPrjffzscLc1zxx6FQm3v4kEmev5OK9qV6oV6f8Fd32gXZo4mOFtAx+GTNGtw6OmPJKPWzbGYcpQTcQHp2PpUFN4GCvvz7n39gW77/VCPuPpeCNBTdw6kIGPnrHF951rbQxowd7YFg/N6zcEIUZ799EoUqDJUFNIDXwHkkVexKfOc6OlnB2ssSaTRF4ZcYFfLoyFO2fccSCt5rU1G6RmTMqdQkICMCdO3fQqVMnDBkyBHl5eRg+fDguX76Mhg0bVncba1XKwb9wJ3glkn47XKn4+q+PRkFkLELmLUXu7QhEf/MjEn85iAZvT9DGNJg5Efc2/ozYrbuQGxKO69OCoc4vhNeEEU9oL8zbyMGe2PZzNE7+k4bwqDx88uVtODnK0Lm9c4XrjB5aF78fTMC+I0mIupePZd/cRaFKg+d7u2tj9hxMwNWbWUhMVuFOeC7W/xAFNxc53F3lNbFbJq13Wzn+vqLC6WsqJKSq8cP+PBSVAB1b6u/yF5Wgxs6j+Th/qwglJYLemBsRxfj1RAEu3yl6kk3/TxjaywkHT2biyOks3Esowjc/JkJVpEHv5xz0xg/u6YhLN3Ox+1A6YhOL8OOeFITHFOL5bkqdOEcHCd4Y7YYvNsahRK3/PJJhLwx0x76jKTh4IhXRcYVYuSEKqiIN+nVz0Rs/vL8bzl/Nws9/JCImvhBbfo7D3ch8DO3rphPzw+54nL6YiYiYAixdEwFnpSU6tVHq3SYZ9iQ+cyJj8vH+4ls4dT4N8YmFuHQtE999H4mObZ0gZpGpUkQWolp7mAKj/40UCgXee+89/Pzzz9i3bx8++eQTeHh4VGfbTJJD+0CkHj2jsyzlz5NQtg8EAIikUiieaYbUI6fLAgQBqUdPw6F9qxpsqXmo4yaHs6MM56+UdRvIy1fj1p1sBPjZ611HIhGhcSM7XLhato4gABeuZKBZE/3ryGUWGNDLHfGJBUhO5WVwQ8QWQH0PCUKiyhJzAUBIZBEaekorXpFqhEQMNKonx9WQsislggBcuZ2HJj5Wetfx87HCldu6V1Yu38qF3wPxIhEwe2Id7DqUhpgEfikzhkQsQuMGNrh0vaw7jiAAl65nw7+xrd51/H1tcfG6bvedC1eztPEerjI4KS1x6Xq29u95BWqEhOVWuE2qWE195gCAjY0EefklUOu/6ElUJUb14b927Zre5SKRCHK5HPXq1fvPDt6VuTlDlZSqs0yVlAqpwg4WchmkSgUsJBKoktMeikmDTROfmmyqWXBUWgIAMjJ1uw9kZBZp//Ywhb0UErEI6Q91OUjPLEb9urqD0YcNqIOpE3xgbSVGdGw+Zn5wrcIKNJWytRZBbCFCdp7uccrOE+DuZBqVEHNmbyuBWCxCRo5uv+DMbDXquut/33awlyAzu3y8g6LsI2REXydoNAJ+P8o++8ZS2P97brJ0x7pkZBXDy1P/lUVHBykysorLxTv+21VE6SDVLns45v7fqPKe9GdO2ToSTBhVH78fTKiGVhMZmfAHBgZq76grCKUf6g/eYVcqlWLUqFFYt24d5PLyb1IqlQoqlW6VtFjQQCridSsyrHdXV8yd3lj7+7yPnuxA50PHk3D+cgacHC0xZlhdfDzfH1PnXUZRMZN+ovsa1pNjcA9HzPw0srabQlStavozBwCsrcRY9mFzRN3Lx8b/carzyuKddg0zKuHfvXs35s+fj7lz56Jt27YAgHPnzuGLL75AcHAwSkpKsGDBArz//vtYvnx5ufUXL16MRYt0B8GOETlirLji/m+mQpWUCpmb7n7I3JxRnJUDTaEKRakZ0JSUQObq9FCME1SJulcGqLyT59Jw684F7e+W0tIXuNJBirSMsm4ESgdLhEXk6t1GVnYxStQCHJW61S3Hh7YBlF6qzcsvQGxCAW6GZmP//3VElw7OOPxXSnXtktnJzReg1giwt9Gt5tvblK/6U83Lzi2BWi1A+dAAXQd7cbnK8n2Z2SVwsC8fn/lvfDNfKyjsxNi0uJH272KxCK++4IbBPRzx2nvh1bwX5ikr+99zo9D9aFYqpEjP1D8IOj2zGEqFtHz8vxX9+5Xoh7ehVEgRHp1fnc03SzX9mWNlJcYXi5ojv0CNdz+9ATXHwlA1Merr0KeffopVq1Zh0qRJaN68OZo3b45Jkybhyy+/xBdffIGxY8di9erV2L17t971g4KCkJWVpfN40cI8ZqjJPHsFTj3a6yxz7vkcMs5eAQAIxcXIunQTzj06lAWIRHDq3gGZZy/XYEtNU0GBGnEJhdpHZEw+UtNVaNOybPCZtZUY/o3tceN2tt5tlJQIuBOWg9YtytYRiYDWLZW4Gap/HQAQ/RsnlbKKYIhaA0QnlKCpd9mHmwhAU28pwuM4c0ttK1EDYTGFaNHURrtMJAJa+tkgNEL/tIK3IwrQ0s9GZ1lgUxvc/jf+2NlsvPlxJN76pOyRllGM3YfSEPzVvSe3M2amRC3gTmQeWgUotMtEIqBVgD1u3dGfTN66m4tnAnT7gbduURafkKxCWkaRToy1lQWaNrKtcJtUpiY/c6ytxPjyoxYoKREw/5MbvJJcRRy0a5hRFf7r16+jfv365ZbXr18f16+XXu4KDAxEQoL+vmcymaxcH/+ntTuP2MYaNo3qaX+3blAX9i39UJSehcJ7CWjyyWzIPd1wdeJ8AED0dz+h/rSx8Fs8F/e2/ALn7u3hMbI/zg9+Q7uNyJWb0XLTUmRevIGs89fg/dZ4SGyscG/rrhrfP3OwY08cxo+qh3vxBUhIKsRrL3sjLV2Fv8+WXTFZ+UkL/HUmFbv2xgMAfvo1Fu/N8sPtsByE3MnBi0M8YSW3wN7DiQBKB2b16OyC85czkJldDBcnGV5+wQsqlQZnLqTXyn6akj/PFeLVQbaISlAjMr4EvdrKYSkV4dS10q58rw6yRUaOBruPl1YYxRalc/EDpQMXlXYW8HIVo7BYQEpG6Yg1mRRwVZZVmZ0VYni5apBXKCA9m6PaquLXw2mYNaEOwqIKcSeqAEN6OkJuaYHDpzMBALMmeCAtswTbfi29krXnSDoWv1MfQ3s54sL1XHR+1h6N6lvh6x9KXy85eWrk5On28S9RC8jILkFcEgfwVsXOvYmYP9UHdyLycDssFyMGuEMus8DBE6XnYv40H6SmF2HjT7EAgF37k/Dlh34YOdAdZy9novtzTmjsY4MV30Vpt7lrfxLGDquD2MRCJCarMPHFukjNKMLJCxxvYYwn8ZlzP9mXySzw0RchsLESw8aq9P0uM7sYGr7F0WMyKuH38/PDkiVL8N1338HSsnSQSnFxMZYsWQI/Pz8AQFxcHNzc3AxtxiQoWgegw5Hvtb/7L38XAHBv2y5cmxQEmYcLrLzKZicqiIrF+cFvwP+LIHi/OQ6FsYm4/sb7SP3zpDYmYcd+WLo4onHwW6U33roagnPPv4aihwbyUuX8+Ms9yOVizJvRGLY2Ely/lYU5wdd1qiOe7lZwsC+rOB89mQIHhRSvjfWGo7L0Uuyc4Ovay9+qYg1aNlPgxcF1YWcrQXpmEa7ezMKUeZeRmcUq9aNcCCmCnXU+hnSxgr1N6Y23Vm3PQc6/XXoc7S20438AwMHOAh++5qD9vW97K/Rtb4XQ6GIs/7G0AlbfQ4K5L5dVPkf1Lq04n75WiM1/cG7+qjh5IQcK22SMHewCpb0YEbEqBH8Vg8x/B/K6OErxwOnB7YgCLN8Qh5eHuGDcUBfEJxfh02/vISaeM1ZVt+Nn0ksHbI70hNKhtNvNgiWh2u5Wrs6WOq+dW3dy8enqcLw6qi5eHV0XcYmF+HD5XUQ9cIPAn/YkQC6zwOzJ3rC1luB6aA6CltxBMSvIRnkSnzlNGtqi2b+z/Py8vp3O870w6SwSk/lao8cjEh5856ik06dPY/DgwbCwsECLFi0AlFb91Wo1/vjjD7Rv3x7ff/89EhMTMXfu3Eptc6+UN5d4Wi3u911tN4Eq0LRDQG03gQxIjE6u7SZQBfKzch4dRLVClce7mT/NTv7etbaboNfNIT1q7bmb/Xa01p67soyq8D/33HOIjIzEjz/+iDt37gAARo4ciZdeegl2dnYAgFdeeaX6WklEREREREYxKuEHADs7O3Tp0gXe3t4oKirto3ns2DEAwODBg6undUREREREj2Aqg2dri1EJf0REBIYNG4br169DJBJBEASdefjVarWBtYmIiIiIqKYYNTXO22+/jQYNGiA5ORnW1ta4ceMGTpw4gTZt2uD48ePV3EQiIiIiooqJLCxq7WEKjKrwnzlzBkePHoWzszMsLCwgFovRqVMnLF68GG+99RYuX+Z88kRERERETwOjvpao1Wrt4FxnZ2fEx5fOM1u/fn2EhoZWX+uIiIiIiOixGFXhDwgIwNWrV9GgQQO0a9cOn3/+OSwtLfHdd9/Bx8enuttIRERERFQhDto1zKiE//3330deXumNbj766CM8//zz6Ny5M5ycnLB9+/ZqbSARERERERnPqIS/b9++2p8bNWqE27dvIz09HUqlUme2HiIiIiKiJ40VfsOMnof/YY6OjtW1KSIiIiIiqiamMZcQEREREREZpdoq/EREREREtYFdegxjhZ+IiIiIyIyxwk9EREREJs1U7nhbW3h0iIiIiIjMGCv8RERERGTSLMTsw28IK/xERERERGaMCT8RERERkRljlx4iIiIiMmmcltMwVviJiIiIiMwYK/xEREREZNI4LadhPDpERERERGaMCT8RERERkRljlx4iIiIiMmkctGsYK/xERERERGaMFX4iIiIiMmms8BvGCj8RERERkRljhZ+IiIiITBqn5TSMR4eIiIiIyIwx4SciIiIiMmPs0kNEREREJo2Ddg1jhZ+IiIiIyIyxwk9EREREJo2Ddg3j0SEiIiIiMmNM+ImIiIiIzBi79BARERGRaRNx0K4hrPATEREREZkxVviJiIiIyKRxWk7DWOEnIiIiIjJjTPiJiIiIiMwYu/QQERERkUnjPPyG8egQEREREZkxVviJiIiIyKRx0K5hrPATEREREZkxVviJiIiIyKSxD79hPDpERERERGbsqanwL+73XW03gSoQdOD12m4CVWC91++13QQyoKhQVdtNoAqIRKx3Pa28W/jWdhOIzM5Tk/ATERERERmDg3YNY4mDiIiIiMiMscJPRERERCaNFX7DWOEnIiIiIjJjTPiJiIiIiMwYu/QQERERkWnjPPwG8egQEREREZkxVviJiIiIyKSJRBy0awgr/EREREREZowVfiIiIiIyaSL24TeIR4eIiIiIyIwx4SciIiIiMmNM+ImIiIjIpIksRLX2MMaaNWvg7e0NuVyOdu3a4dy5cwbjV65ciSZNmsDKygpeXl6YNWsWCgsLK/18TPiJiIiIiGrI9u3bMXv2bAQHB+PSpUto2bIl+vbti+TkZL3x//vf/7BgwQIEBwcjJCQEGzduxPbt2/Huu+9W+jmZ8BMRERGRabOwqL1HFa1YsQKTJ0/GxIkT4e/vj7Vr18La2hqbNm3SG3/69Gl07NgRL730Ery9vdGnTx+MGTPmkVcFdA5PlVtJREREREQAAJVKhezsbJ2HSqXSG1tUVISLFy+iV69e2mUWFhbo1asXzpw5o3ed5557DhcvXtQm+BEREdi3bx8GDBhQ6TYy4SciIiIiMtLixYuhUCh0HosXL9Ybm5qaCrVaDTc3N53lbm5uSExM1LvOSy+9hI8++gidOnWCVCpFw4YN0a1bN3bpISIiIqL/jtoctBsUFISsrCydR1BQULXt2/Hjx/HZZ5/hm2++waVLl7Br1y7s3bsXH3/8caW3wRtvEREREREZSSaTQSaTVSrW2dkZYrEYSUlJOsuTkpLg7u6ud50PPvgAr7zyCl577TUAQPPmzZGXl4fXX38d7733HiwqMY6AFX4iIiIiMmkikUWtParC0tISrVu3xpEjR7TLNBoNjhw5gg4dOuhdJz8/v1xSLxaLAQCCIFTqeVnhJyIiIiKqIbNnz8b48ePRpk0btG3bFitXrkReXh4mTpwIABg3bhw8PT214wAGDRqEFStWoFWrVmjXrh3CwsLwwQcfYNCgQdrE/1GY8BMRERGRaTPyBli1YdSoUUhJScGHH36IxMREBAYG4sCBA9qBvDExMToV/ffffx8ikQjvv/8+4uLi4OLigkGDBuHTTz+t9HMy4SciIiIiqkEzZszAjBkz9P7t+PHjOr9LJBIEBwcjODjY6OdjH34iIiIiIjPGCj8RERERmTSREXe8/S/h0SEiIiIiMmOs8BMRERGRSROZ0KDd2sAKPxERERGRGWPCT0RERERkxtilh4iIiIhMWxXvePtfw6NDRERERGTGWOEnIiIiIpPGQbuGscJPRERERGTGWOEnIiIiItPGG28ZxKNDRERERGTGmPATEREREZkxdukhIiIiIpMmEnHQriGs8BMRERERmTFW+ImIiIjItHHQrkE8OkREREREZqzaE35BEKp7k0REREREZCSjEv4JEyYgLy+v3PKoqCh06dLlsRtFRERERFRZIgtRrT1MgVEJ/9WrV9GiRQucOXNGu2zr1q1o2bIlnJ2dq61xRERERET0eIwatHvu3Dm8++676NatG+bMmYOwsDDs378fK1aswOTJk6u7jUREREREFRNxWKohRiX8UqkUy5Ytg7W1NT7++GNIJBKcOHECHTp0qO72PTUmjfXGoD7usLOR4HpINpZ/cxexCQUG1xk+oA7GDPeCo9IS4ZG5+HJdGELu5mj/Pne6L9q0VMLZ0RL5hWrcCMnGt1sjEBNreLtUyrFTG/jMmQTFMwGQ13HFhRHTkLTniOF1urSF//IFsPX3ReG9BIQt/hax23brxNSf+hJ8Zk+CzN0F2ddu4+bMj5F1/vqT3BWz1b+LAsN6O8LBXoyoWBXW/5yCu9GFFcY/18oWLw1yhquTBAnJxdj2ayou3izffRAApoxxRb/ODti4Ixm/H8t8Qntgvgb1dMYL/V3hqJAg4l4BvvkhDqER+RXGd35WgfHDPeDmbIm4JBU2/hyP89dK38/EYmDCCA8828IeHq6WyMvX4PKtHGz8OR7pmSU1tUtmY3BvF7w4yB2OCinCY/Lx9ZZ7CA3X/zoAgC7tlJgwsg7cXWSISyzE+v+Lw7krWTox41+ogwE9nGFrI8HN0Fys2hSNuETVk94Vs9SrnTUGdraFwlaMmMRibPsjCxGxxRXGtw2Q44VednB2kCAprQQ/HczG1Ttlx15mKcKovvZo01QOW2sLpGSU4OCZPBw9V/HrkaiqjPo6VFxcjDlz5mDp0qUICgpChw4dMHz4cOzbt6+62/dUGDvCCy8874nl39zF6+9cRkGhGis+ag5LacX9tnp0csGM1xpi8/9FYdLMiwiLzMWKj5rDQSHVxoSG5eKzVaEYO+085gRfh0gEfPlRC84sVUliG2tkXwvFjbcWVSreyrsunt2zDmnH/8HJNkMQuXormq/7BM69O2ljPEb2R9NlQbj7yRqcbDsMOdduo93ejbB0cXxSu2G2Ora2xasjXPDT3jTMXhyDqDgVgt/0hMJWrDe+iY8cc171wOHTWZi9OAb/XM3FgjfqoJ6HZbnYdi1t0cRbjjQmk0bp2tYBr4+pgx9/S8T04FBE3CvAp+/4QGGnvwbk38gaQVO9ceCvNEz7MBSnL2Uh+O0GqO8pBwDILC3QqL41/rcnCdM/vIOPVkeirrsMi2b61ORumYVu7ZWY8ooXvv8lHlPevYWI6AIsWeALB/sKzo2vDd570wcHjqdiStAtnLqQiUVzGsK7rlwbM2qQO4b1c8WqjTGY8UEIClVqLFnQGFIDn2GkX7vmcowdoMDuozl4f00KYhKLMX+CE+xt9H9w+9aTYvqLSpy4kI/316TgYkghZo11RF3XsvM5doA9WvrK8O2ODMxbmYwDp/Mw/nkFnvGT1dRumQcLUe09TIBRqWWbNm2wZ88eHD9+HJ9++imOHz+OmTNnYvjw4Zg2bVp1t7HWjRzsiW0/R+PkP2kIj8rDJ1/ehpOjDJ3bVzxeYfTQuvj9YAL2HUlC1L18LPvmLgpVGjzf210bs+dgAq7ezEJisgp3wnOx/ocouLnI4e4qr3C7VCbl4F+4E7wSSb8drlR8/ddHoyAyFiHzliL3dgSiv/kRib8cRIO3J2hjGsyciHsbf0bs1l3IDQnH9WnBUOcXwmvCiCe0F+ZrSA8lDp3KxtGz2YhNLMK3/5cMVZGAns/Z640f1F2JS7fy8OvhDMQmFuF/f6Qh4l4hBnRz0IlzVEgw+UUXrNiSCLWas4IZY3g/Fxw4kYZDf6cjJl6Fr7bEQlWkQd8u+r/YDu3jggvXs7FzfwruJaiwbVciwqIKMKRX6XtgfoEGQcvC8de5TMQmqnA7PB9rvo9F4wbWcHGU6t0m6TdioBv2HU3FwRNpiIkrxMqN0VAVadCvm/7Pm+H93XD+ahZ+/iMJMfGF2LIjHmGR+RjS1/WBGFf8uDsBpy9mIjKmAEu/iYKTUoqObRxqaK/MR/+Otjh2IR9/XSpAfEoJNv+WBVWxgK6trfXG9+1gi2t3Vdh7Mg/xKSXYeTgHUfHF6N3BRhvjW88Sf1/OR0hkEVIz1Th2Ph8xicXwqVu+2EFkLKMT/itXrqB9+/YASm9nPH/+fJw5cwZ//fVXtTawttVxk8PZUYbzVzK0y/Ly1bh1JxsBfvoTF4lEhMaN7HDhatk6ggBcuJKBZk30ryOXWWBAL3fEJxYgOZWXWZ8Eh/aBSD16RmdZyp8noWwfCAAQSaVQPNMMqUdOlwUIAlKPnoZD+1Y12FLTJxEDDevJcS20rBuCIABXb+ehSQMrves0aSDHtdu6l7Av38rXiReJgJkT3PHr4QzcSyh6Mo03cxKxCL7e1rh0M1e7TBCAyzdz4d/IRu86TRvZ4PID8QBw8UYOmlYQDwA2VmJoNALy8tXV0/D/AIlYhMYNbHDpRrZ2mSAAl25kw99X/7H299WNB4Dz17Lh72sLAPBwtYST0lInJq9AjZDwPG0MVY5YDDSoI8XNsLLPaEEAboap0Kie/i+2jepJcSNc9zP9WpgKjbzKkvm7MUV4xk8OpX1pSta0gSXcnSW4HsZcgKqPUX34N27cqHd5q1atcPHixcdq0NPGUVn6oszI1O2fl5FZpP3bwxT2UkjEIqRn6K6TnlmM+nV1qwDDBtTB1Ak+sLYSIzo2HzM/uIaSElYtnwSZmzNUSak6y1RJqZAq7GAhl0GqVMBCIoEqOe2hmDTYNGHXhKqwsxVDLBYhM1s32cvKUaOum/7XjYO9BJk5D8eXQGlf1gVoeB8lNBoBf7DPvtHs7f49N1kPvadlFcPLQ38XAqVCgozs8vFKhf6PEKlUhEmj6uD42QzkF2qqp+H/AQp7CcRiETLKnZsSeNXRf+VX6SBFRpZu17bMrGI4OpQmoMp/u5EaiqHKsbO2gFgsQlbuQ+9TuRp4uFTwvmYrRnau7msgO1cNB7uyeuu237MwaagDVs93R4lagCAAG3dnIjSKRY2qEHHQrkFGJfwA8P3332Pt2rWIjIzEmTNnUL9+faxcuRINGjTAkCFDDK6rUqmgUul+c9Woi2Ahrv3LV727umLu9Mba3+d99GQHax46noTzlzPg5GiJMcPq4uP5/pg67zKKipn0Ez2ooZcMz3dTYvaS6NpuChkgFgPvTfcGAKzeGlu7jSEyAX062KCRlyW++D4NqRlq+DWwxPjBCmTkqHEznEk/VQ+jvg59++23mD17NgYMGIDMzEyo1aXfdh0cHLBy5cpHrr948WIoFAqdR2zYj8Y0pdqdPJeGiW9f0D6y/q1qKR+qhCgdLJGeof+FmJVdjBK1AEel7jqODlKkPbROXr4asQkFuHozC+8vuYV6da3RpQPvZfAkqJJSIXPTPbYyN2cUZ+VAU6hCUWoGNCUlkLk6PRTjBFWi7pUBMiwnVw21WoCDve4AXYWdGBnZ+rt4ZGaXwMHu4XiJNt6/kRUUdmJs+MQHv6z2xS+rfeHqJMWEES747uMGT2ZHzFB2zr/nRvHQe5qifKX4voysEijtHx1/P9l3c7JE0OfhrO5XUVZ2CdRqQVuVv0+pkJS7ynxfRmb5Ky0OCinS/42/f7XAUAxVTk6+Bmq1UG7iAYWtRbmq/32ZuWrY2+qmWva2YmTmlL42pBLgxd72+HF/Fi7fVuFeUgn+PJuPf64XYGAndrmqEg7aNciohH/16tVYv3493nvvPYjFZf/4bdq0wfXrj66IBwUFISsrS+dRt9FYY5pS7QoK1IhLKNQ+ImPykZquQpuWSm2MtZUY/o3tceN2tt5tlJQIuBOWg9YtytYRiYDWLZW4Gap/HQAQ/RsnlfKy1JOQefYKnHq011nm3PM5ZJy9AgAQiouRdekmnHs8ML2sSASn7h2QefZyDbbU9JWogfCYQrRoUtaFTSQCWjSxRmik/mlnQyML0cJPt8tbYNOy+OPnsjHz02jM+qzskZZZgl//zMDC1awkV1aJWsDdqHy08i9LJkQiINDfFrfC9E/9GBKWh0B/3eTjmWZ2CHkg/n6y7+kmw4LPw5CTx777VVWiFnAnMg/PBNhpl4lEQKtm9rh1V/+5uXU3D62a6Y4Na93cHrfulo65SEguQlpGEVoFlMVYW1mgaUMbbQxVjloNRMYXo1nDst4IIhHQrKEMYTH6vzyFxRSjWUPdrnIBDWUIu1da/JOIRZBIRNA8dFFfoyndNlF1MSqzjIyMRKtW5QcxymQy5OVVPFfwg3H29vY6j6ehO09FduyJw/hR9dCxrRN86tvg/dl+SEtX4e+zZVXflZ+0wPCBdbS///RrLAb19UC/Hm6oX9ca70zzhZXcAnsPJwIoHQz88gteaNLQFm4uMgT42ePjBf5QqTQ4cyG9xvfRFIltrGHf0g/2Lf0AANYN6sK+pR/kXh4AgCafzEbLzUu18dHf/QTrBl7wWzwXNk18UH/KS/AY2R+Rq7ZoYyJXbobXpBfh+cpQ2Pr5IGDNQkhsrHBv664a3Tdz8NvRDPTuqED3dvao626JKaNdIZdZ4MiZ0i+9b493x8tDyq64/H4sA638bTCkpxKeblKMHuiEhvXk2Hc8EwCQk6dBTEKRzkOtFpCZXYL4ZFYqq2LXgRT07+qEXh2V8PKQ4c3xdSGXWeDQ36XvPXNfr4eJIz208b8eSkGb5vYY0c8FXh4yvDzUHb4NrPDb4dL3QLEY+GBGAzT2tsbStdGwsBBBqZBAqZBAImbWUhW/7E3CgO4u6N3FCfXqyPH2q/Uhl1ngwInSYz1/qjcmjfbUxu/an4RnW9rjhYFu8Kojx7gRddDYxxq/HUx+ICYZY4d6oENrBRp4WWH+1AZIyyjGqQuZNb17Jm//qVx0a2ODzq2sUMdFgomDFZBZinDiYumEA2+84IAX+5R9YTt4JhctfGXo39EGHs4SDO9hBx9PKf48U5orFagEhESoMKafPZo2sISLUozOrazQqZU1Ltyq+J4lRFVlVB/+Bg0a4MqVK6hfv77O8gMHDqBp06bV0rCnyY+/3INcLsa8GY1hayPB9VtZmBN8Xaefvae7FRweuOR99GQKHBRSvDbWG45KS4RF5GJO8HXtZVlVsQYtmynw4uC6sLOVID2zCFdvZmHKvMvlBtORforWAehw5Hvt7/7L3wUA3Nu2C9cmBUHm4QIrr7KkpSAqFucHvwH/L4Lg/eY4FMYm4vob7yP1z5PamIQd+2Hp4ojGwW+V3njragjOPf8aih4ayEuPdupiLhS2qRjzvBOU9mJExqqw6Os4ZP07MNdFKYHwQFkrNKIQKzYlYOxgZ7w82AnxKcVYsi4eMZyNp9qdOJcJhb0E44Z7QKmQICKmAO8tj0BmdmkXHRdHS2ge6I1zKywfS9ZGYfwID0x4wQPxSSosWhWJ6LjShMRZaYkOzygAAN9+4qfzXHMXh+HabVaSK+v42Qwo7CWY8EIdKB2kCI/OR9CSu8j8t/uUq7NMpxp8624ePvs6EhNf9MSrozwRl6hC8BfhiIotSxa3/54IucwCs17zhq21GDdCc7FgyR0Uc6xYlf1zvRD2NlkY0dMOCjsxohOK8fmWNGTnlb5gnBViCA8c1rsxxfjm5wyM7GWPF/vYIzGtBF/+mI7Y5LLucF9vz8CoPvaY+qIStlYWSM0swY4/s3GEN96qEhFvYmSQSBCEKr/iN2zYgIULF+KLL77ApEmTsGHDBoSHh2Px4sXYsGEDRo8eXeWGdBp0osrrUM0IOvB6bTeBKrB+8u+13QQyoCCPH9hPK3URb9r2tHL3qfPoIKo1P3z6dJ6f/I0f1tpzW0/6qNaeu7KMqvC/9tprsLKywvvvv4/8/Hy89NJL8PT0xKpVq4xK9omIiIiIjMZBDwYZlfAXFBRg2LBhGDt2LPLz83Hjxg2cOnUKdevWre72ERERERHRYzAq4R8yZAiGDx+OKVOmoKioCIMHD4ZUKkVqaipWrFiBqVOnVnc7iYiIiIj0Yx9+g4w6OpcuXULnzp0BADt37oSbmxuio6Oxbds2fPXVV9XaQCIiIiIiMp5RCX9+fj7s7EqnnTp06BCGDx8OCwsLtG/fHtHRvAsmEREREdHTwqiEv1GjRvj1119x7949HDx4EH369AEAJCcnw97e/hFrExERERFVI5Go9h4mwKiE/8MPP8Q777wDb29vtGvXDh06lN6Z9NChQ3pvyEVERERERLXDqEG7L7zwAjp16oSEhAS0bNlSu7xnz54YNmxYtTWOiIiIiOhReOMtw4xK+AHA3d0d7u7uOsvatm372A0iIiIiIqLqw69DRERERERmzOgKPxERERHRU0HEGrYhPDpERERERGaMFX4iIiIiMm0WpjE9Zm1hhZ+IiIiIyIwx4SciIiIiMmPs0kNEREREJk3EQbsG8egQEREREZkxVviJiIiIyLRx0K5BrPATEREREZkxVviJiIiIyLSxD79BPDpERERERGaMCT8RERERkRljlx4iIiIiMm0iDto1hBV+IiIiIiIzxgo/EREREZk2C9awDeHRISIiIiIyY0z4iYiIiIjMGLv0EBEREZFp4zz8BvHoEBERERGZMVb4iYiIiMi0WXBaTkNY4SciIiIiMmOs8BMRERGRaWMffoN4dIiIiIiIzBgTfiIiIiIiM8YuPURERERk2kQctGsIK/xERERERGaMFX4iIiIiMm0WrGEbwqNDRERERGTGmPATEREREZkxdukhIiIiItPGQbsGscJPRERERGTGWOEnIiIiItPGO+0axKNDRERERGTGWOEnIiIiItPGaTkN4tEhIiIiIjJjTPiJiIiIiMwYu/QQERERkWnjtJwGPTUJf9MOAbXdBKrAeq/fa7sJVIHJ6wfVdhPIgBUjvq/tJlAFWndrXttNoApcOXW7tptABtWp7QaQEZ6ahJ+IiIiIyCicltMgHh0iIiIiIjPGhJ+IiIiIyIyxSw8RERERmTYO2jWIFX4iIiIiIjPGCj8RERERmTbeadcgHh0iIiIiIjPGCj8RERERmTSBffgNYoWfiIiIiMiMMeEnIiIiIjJj7NJDRERERKaNd9o1iEeHiIiIiMiMscJPRERERKaNFX6DeHSIiIiIiMwYE34iIiIiIjPGLj1EREREZNI4D79hrPATEREREZkxVviJiIiIyLRx0K5BPDpERERERGaMFX4iIiIiMm3sw28QK/xERERERGaMCT8RERERkRljlx4iIiIiMm0WrGEbwqNDRERERGTGWOEnIiIiIpPGG28Zxgo/EREREZEZY8JPRERERGTG2KWHiIiIiEwb77RrEI8OEREREZEZY8JPRERERCZNEFnU2sMYa9asgbe3N+RyOdq1a4dz584ZjM/MzMT06dPh4eEBmUyGxo0bY9++fZV+PnbpISIiIiKqIdu3b8fs2bOxdu1atGvXDitXrkTfvn0RGhoKV1fXcvFFRUXo3bs3XF1dsXPnTnh6eiI6OhoODg6Vfk4m/ERERERk2kxoWs4VK1Zg8uTJmDhxIgBg7dq12Lt3LzZt2oQFCxaUi9+0aRPS09Nx+vRpSKVSAIC3t3eVnpNdeoiIiIiIjKRSqZCdna3zUKlUemOLiopw8eJF9OrVS7vMwsICvXr1wpkzZ/Sus2fPHnTo0AHTp0+Hm5sbAgIC8Nlnn0GtVle6jUz4iYiIiIiMtHjxYigUCp3H4sWL9campqZCrVbDzc1NZ7mbmxsSExP1rhMREYGdO3dCrVZj3759+OCDD/DFF1/gk08+qXQb2aWHiIiIiEyasYNnq0NQUBBmz56ts0wmk1Xb9jUaDVxdXfHdd99BLBajdevWiIuLw7JlyxAcHFypbTDhJyIiIiIykkwmq3SC7+zsDLFYjKSkJJ3lSUlJcHd317uOh4cHpFIpxGKxdlnTpk2RmJiIoqIiWFpaPvJ52aWHiIiIiEybSFR7jyqwtLRE69atceTIEe0yjUaDI0eOoEOHDnrX6dixI8LCwqDRaLTL7ty5Aw8Pj0ol+wATfiIiIiKiGjN79mysX78eW7duRUhICKZOnYq8vDztrD3jxo1DUFCQNn7q1KlIT0/H22+/jTt37mDv3r347LPPMH369Eo/J7v0EBERERHVkFGjRiElJQUffvghEhMTERgYiAMHDmgH8sbExMDCoqwm7+XlhYMHD2LWrFlo0aIFPD098fbbb2P+/PmVfk4m/ERERERk2mpx0K4xZsyYgRkzZuj92/Hjx8st69ChA86ePWv085nW0SEiIiIioiqpdIXf0dERd+7cgbOzM5RKJUQGBimkp6dXS+OIiIiIiB5FMKE77daGSif8X375Jezs7AAAK1eufFLtISIiIiKialTphH/8+PF6fyYiIiIioqfXYw3aTU5ORnJyss68oADQokWLx2rU06Zbaxn6trOCwtYC95JK8H+H8hGVUKI3to6zGIO7WKG+uwTODmL89Gcejpwv1Inx9ZKgb/vSGAc7C6zZmY0rd4prYlfMTv8uCgzr7QgHezGiYlVY/3MK7kYXVhj/XCtbvDTIGa5OEiQkF2Pbr6m4eDNPb+yUMa7o19kBG3ck4/djmU9oD8yTY6c28JkzCYpnAiCv44oLI6Yhac8Rw+t0aQv/5Qtg6++LwnsJCFv8LWK37daJqT/1JfjMngSZuwuyr93GzZkfI+v89Se5K2ZtwkhPDOjhAlsbCW6E5mDVxijEJaoMrjOkjyteHOQBR4UU4TH5WL05GqHhZa8hqVSEqS/XQ/fnnCCVinD+aha+2hSFjCz975lUXodmYnQNlMDOSoSENAG/nSrCvWRBb6ybUoQ+z0rh6SKCo50F9pwqwsnr6nJx9jbAgHZSNKknhqUESM0SsON4EWJT9G+XKjaolzNGDnCFo0KKiHsFWLMtFqER+RXGd27rgAkjPODmbIm4JBU2bI/H+avZ2r93bKPA8z2c4ettDXs7Caa8dxsRMQU1sSvmxcQG7dY0o47OxYsXERAQAA8PD7Ro0QKBgYHaR6tWraq7jbWqTVNLvNjTBr+fLMDHm7IQm6zGzNF2sLPW31fMUgqkZmqw63g+MnM1emNkUhFik0vwv4P6E02qnI6tbfHqCBf8tDcNsxfHICpOheA3PaGwFeuNb+Ijx5xXPXD4dBZmL47BP1dzseCNOqjnUf6mFe1a2qKJtxxpmUxSjCG2sUb2tVDceGtRpeKtvOvi2T3rkHb8H5xsMwSRq7ei+bpP4Ny7kzbGY2R/NF0WhLufrMHJtsOQc+022u3dCEsXxye1G2Zt9GAPDOvnhpUbojDj/ZsoVGmwJKgJpNKK+8F26+CIKa/Uw7adcZgSdAPh0flYGtQEDvZltaNp4+qhfWsHLFp5F7MWhcBZaYmFs31rYpfMQsuGYgx6TorDF0qw6hcVEtI0mDRQBhu5/nipBEjP1mD/2RJk5+lP3q0sgWlDZVBrgE37VFi+XYU/zhQj3/B3O9KjazsHvPGSJ37YnYhpH4QiIqYAn81rqPMaeJC/rw3eneaNAyfSMPWD2zh9MQsLZzaAd92yEyqXWeDGnTxs2B5fU7tB/0FGJfyvvvoqGjdujNOnTyMiIgKRkZHaR0RERHW3sVb1bivH31dUOH1NhYRUNX7Yn4eiEqBjS/23UI5KUGPn0Xycv1WEkhL9b743Iorx64kCXL5T9CSbbvaG9FDi0KlsHD2bjdjEInz7f8lQFQno+Zy93vhB3ZW4dCsPvx7OQGxiEf73Rxoi7hViQDcHnThHhQSTX3TBii2JUKtZ/TJGysG/cCd4JZJ+O1yp+Pqvj0ZBZCxC5i1F7u0IRH/zIxJ/OYgGb0/QxjSYORH3Nv6M2K27kBsSjuvTgqHOL4TXhBFPaC/M2/D+bvhhdzxOX8xEREwBlq6JgLPSEp3aKCtc54WB7th3NAUHT6QiOq4QKzdEQVWkQb9uLgAAGysx+nd3wdrvY3DlZg7uRubj87URCGhih6aNbGpq10xa5xYS/BOixoVQNZIzBOz6qxjFJcCzfvoTytgUAXvPluBquBolGv3vV91aSZCVK2DH8WLcSxaQkSPgbqwG6dl8f6uqEf1dsf94Gg79nY6Y+EKs2nwPKpUGfbs46Y0f2scF569lY8e+ZNyLV2HrLwkIiyrA4F4u2pgjpzLw46+JuHwzp6Z2wywJENXawxQYlfBHRETg888/R7t27eDt7Y369evrPMyF2AKo7yFBSFRZYi4ACIksQkNPae01jCARAw3ryXEttOwqiSAAV2/noUkDK73rNGkgx7XbupddL9/K14kXiYCZE9zx6+EM3EvgF7Ka4tA+EKlHz+gsS/nzJJTtAwEAIqkUimeaIfXI6bIAQUDq0dNwaG9eVxVrgoerDE5KS1y6XtatIK9AjZCwXPg3ttW7jkQsQuMGNrh0PUu7TBCAS9eztev4+lhDKrHAxQe2ey++EEkpqgq3S2XEFoCniwhhsWVdcgQAd2PVqO9mfHcF//pixKYIeLm3JT4cL8fbL8jQtqn+K6FUMYlYBF9va53EXBCAyzdz0LSRtd51/BvZlEvkL1zPRlNffgGmmmXUO0jPnj1x9erV6m7LU8fWWgSxhajcZdLsPAH2Nqbxjc5c2dmKIRaLkJmt21c1K0cNpb3+DzIHewkycx6OL9GJH95HCY1GwB/ss1+jZG7OUCWl6ixTJaVCqrCDhVwGS2clLCQSqJLTHopJg8zduSabahaUDqUFi4ws3bFDGVnF2r89TGEvgVgsKtcXPyOrGI7/ruPoYImiYg3y8tUVxlDFbOSA2EKEnIe6b+cWCBV2I60MR3sR2vuLkZqlwYY/VDh7swRDOkrRujGT/qqwtxP/+xp46HWTXVLh/7fSQVLuNZOZVQJHBe97Wt0EkUWtPUyBUf9xGzZswPjx43Hjxg0EBARAKtX9Rx88eLDB9VUqFVQq3c6D6hIVxBL93WSIakJDLxme76bE7CXRtd0UomrVs6MTZk321v7+7tI7tdcYqnEiERCbosGBc6WJZ3yaGm6OFmjvL8HFO+UH+BKR+TEq4T9z5gxOnTqF/fv3l/ubSCSCWm34DWTx4sVYtEh3MF+rHnPRuud8Y5rzxOTmC1Brylfz7W3KV/2pZuXkqqFWC3B4qJqvsBMjI1v//19mdgkc7B6Ol2jj/RtZQWEnxoZPfLR/F4tFmDDCBYN6KPH6B5HVvBd0nyopFTI33Uq9zM0ZxVk50BSqUJSaAU1JCWSuTg/FOEGVqHtlgMo7fTEDIWG52t+l0tKKlFIhRXpmWbVSqZAiPFr/bCNZ2SVQqwUoH6pMPriN9MwiWEotYGMt1qnyP/w8pF9eIaDWCLB7qFeirZUIOfnGf+bk5AtIztBdPzlDg+Y+rPBXRXaO+t/XgG6RU2kvqfD/OyOzpNxrxkEhQTpnraIaZtR1iDfffBMvv/wyEhISoNFodB6PSvYBICgoCFlZWTqPwK4zjWnKE6XWANEJJWjqXfbiFgFo6i1FeBw/vGpTiRoIjylEiyZl/SZFIqBFE2uERuqfziw0shAt/HT7WQY2LYs/fi4bMz+NxqzPyh5pmSX49c8MLFwd++R2hpB59gqcerTXWebc8zlknL0CABCKi5F16Sace3QoCxCJ4NS9AzLPXq7BlpqmgkIN4pNU2kd0bAHSMorwTEDZAHdrKws0bWSLW3dy9W6jRC3gTmQeWgUotMtEIqBVgL12nbsR+Sgu0ehst66HHG4usgq3S2XUGiAuRUAjz7JEXASgkacY0Un6Z32rjKhEDVwcdAtXLg4WyMgxfpv/RSVqAXej8hHob6ddJhIBgc3sEBKm/4vyrbA8tGpmp7PsmQA7hNzlLH3VTmRRew8TYFQr09LSMGvWLLi5uRn1pDKZDPb29jqPp7U7z5/nCtE5UI4OzWVwdxJjbH8bWEpFOHWttEvSq4NsMaxbWRIptgC8XMXwchVDIhZBaWcBL1cxXJRlh1omLYsBAGdF6c+O9qbxT/O0+O1oBnp3VKB7O3vUdbfElNGukMsscORM6YDBt8e74+UhZVXj349loJW/DYb0VMLTTYrRA53QsJ4c+45nAgBy8jSISSjSeajVAjKzSxCfzC94VSG2sYZ9Sz/Yt/QDAFg3qAv7ln6Qe3kAAJp8MhstNy/Vxkd/9xOsG3jBb/Fc2DTxQf0pL8FjZH9ErtqijYlcuRlek16E5ytDYevng4A1CyGxscK9rbtqdN/Mxa79SRg7rA46tHZAAy8rLJjWEKkZRTh5IUMbs+z9JhjS11X7+869iRjYwwV9ujijXh05Zk7yhlxmgYMnUgCUDvzdfywFU1+ph0B/O/g2sMa8qQ1w804OQsKY4FTG39dK0LapGK0bi+HqIMKwLlJYSoELoaUV4VHdpejXtqxiLLYAPJxE8HASQWIhgsKm9Gcne5HONuu5WqB7Kwmc7EUIbCRGu6ZinLnJ7jxV9cv+ZAzo5oTenRzhVUeGtyZ4lb4G/iodXzT3jfp49UUPbfyvh1LQprk9RvR3hZeHDK8Mc0fjBtbYczhFG2NnI4ZPPSvU8yydqtPLQwafelblrgwQPQ6j/puGDx+OY8eOoWHDhtXdnqfOhZAi2FnnY0gXK9jblN54a9X2HOT826XH0d4CglB2qdTBzgIfvuag/b1veyv0bW+F0OhiLP+xNBGt7yHB3JfLqmSjepeO1j99rRCb/+CHYmWdupgLhW0qxjzvBKW9GJGxKiz6Og5Z/w7MdVFKIDwwTV1oRCFWbErA2MHOeHmwE+JTirFkXTxiOBtPtVO0DkCHI99rf/df/i4A4N62Xbg2KQgyDxdYeZV9KBZExeL84Dfg/0UQvN8ch8LYRFx/432k/nlSG5OwYz8sXRzROPit0htvXQ3BuedfQ9FDA3mpcn7akwC5zAKzJ3vD1lqC66E5CFpyB8XFZa+ZOm5yKOzKrnAeP5MOhb0EE0Z6QulQ2v1nwZJQnUGJ32yLgaABgmf7QioR4cK1LKzayHExlXU1XA0bOdDnWQnsrEWITxWwca8Kuf9euHSwE+HBzjn2NiLMGlk2p3vXQCm6BkoRHq/Guj2l722xKQK2HSxCv3ZS9GotQXqOgD2ni3H5LhP+qjrxTyYUdhKMG+EBpUKCiJgCvLcsHJnZpa8BVyepTk5w624eFn8bhQkveGDiSA/EJ6mwcGUkomLLbhDZ/hkF5r5eNsPhezMaAAC+35WA73cn1tCemT5BxMlUDBEJD/5nVtKnn36KlStXYuDAgWjevHm5QbtvvfVWlRsy+TN+aD+tUmJ5bp5Wk9cPqu0mkAErRnz/6CCqFa27Na/tJlAFrpy6XdtNIAMOff90ToWccfVErT23smXXWnvuyjJ6lh5bW1ucOHECJ07oHmCRSGRUwk9ERERERNXPqIQ/MrJstpL7FwhEvJRCRERERLXAVObDry1GH52NGzciICAAcrkccrkcAQEB2LBhQ3W2jYiIiIiIHpNRFf4PP/wQK1aswJtvvokOHUqnyTtz5gxmzZqFmJgYfPTRR9XaSCIiIiKiCrGniUFGJfzffvst1q9fjzFjxmiXDR48GC1atMCbb77JhJ+IiIiI6ClhVMJfXFyMNm3alFveunVrlJTw7nFEREREVHPYh98wo47OK6+8gm+//bbc8u+++w5jx4597EYREREREVH1qHSFf/bs2dqfRSIRNmzYgEOHDqF9+/YAgH/++QcxMTEYN25c9beSiIiIiIiMUumE//Llyzq/t27dGgAQHh4OAHB2doazszNu3rxZjc0jIiIiIjJMAAftGlLphP/YsWNPsh1ERERERPQEGDVol4iIiIjoacFBu4bx6BARERERmTEm/EREREREZoxdeoiIiIjItPFOuwaxwk9EREREZMZY4SciIiIikyawhm0Qjw4RERERkRljhZ+IiIiITJrAPvwGscJPRERERGTGmPATEREREZkxdukhIiIiIpPGO+0axqNDRERERGTGWOEnIiIiIpMmgIN2DWGFn4iIiIjIjDHhJyIiIiIyY+zSQ0REREQmjYN2DePRISIiIiIyY6zwExEREZFJ4512DWOFn4iIiIjIjLHCT0REREQmjdNyGsYKPxERERGRGWPCT0RERERkxtilh4iIiIhMGqflNIxHh4iIiIjIjLHCT0REREQmjYN2DWOFn4iIiIjIjDHhJyIiIiIyY+zSQ0REREQmjYN2DePRISIiIiIyY6zwExEREZFJ46Bdw1jhJyIiIiIyY6zwExEREZFJYx9+w3h0iIiIiIjMGBN+IiIiIiIzxi49RERERGTSOGjXMFb4iYiIiIjM2FNT4U+MTq7tJlAFigpVtd0EqsCKEd/XdhPIgNm/vFLbTaAKfCXbXttNoAoIGk1tN4FMkCBihd8QVviJiIiIiMwYE34iIiIiIjP21HTpISIiIiIyhiCwS48hrPATEREREZkxVviJiIiIyKQJrGEbxKNDRERERGTGWOEnIiIiIpPGG28Zxgo/EREREZEZY8JPRERERGTG2KWHiIiIiEwau/QYxgo/EREREZEZY4WfiIiIiEwaK/yGscJPRERERGTGmPATEREREZkxdukhIiIiIpPGLj2GscJPRERERGTGWOEnIiIiIpMmCKzwG8IKPxERERGRGWPCT0RERERkxtilh4iIiIhMGgftGsYKPxERERGRGWOFn4iIiIhMGiv8hrHCT0RERERkxljhJyIiIiKTxgq/YazwExERERGZMSb8RERERERmzOguPUeOHMGRI0eQnJwMjUaj87dNmzY9dsOIiIiIiCqDd9o1zKiEf9GiRfjoo4/Qpk0beHh4QCTiQSYiIiIiehoZlfCvXbsWW7ZswSuvvFLd7SEiIiIiqhINB+0aZFQf/qKiIjz33HPV3RYiIiIiIqpmRiX8r732Gv73v/9Vd1uIiIiIiKiaGdWlp7CwEN999x0OHz6MFi1aQCqV6vx9xYoV1dI4IiIiIqJH4Tz8hhmV8F+7dg2BgYEAgBs3buj8jQN4iYiIiIieHkYl/MeOHavudhARERERGYXTchr2WDfeCgsLw8GDB1FQUAAAEAShWhpFRERERETVw6iEPy0tDT179kTjxo0xYMAAJCQkAAAmTZqEOXPmVGsDiYiIiIgMESCqtYcpMCrhnzVrFqRSKWJiYmBtba1dPmrUKBw4cKDaGkdERERERI/HqD78hw4dwsGDB1G3bl2d5b6+voiOjq6WhhERERER0eMzKuHPy8vTqezfl56eDplM9tiNIiIiIiKqLA7aNcyoLj2dO3fGtm3btL+LRCJoNBp8/vnn6N69e7U1joiIiIiIHo9RFf7PP/8cPXv2xIULF1BUVIR58+bh5s2bSE9Px6lTp6q7jUREREREFTKVwbO1xagKf0BAAO7cuYNOnTphyJAhyMvLw/Dhw3H58mU0bNiwuttIRERERERGqnKFv7i4GP369cPatWvx3nvvPYk2ERERERFRNalywi+VSnHt2rUn0RYiIiIioirjoF3DjOrS8/LLL2Pjxo3V3RYiIiIiIqpmRg3aLSkpwaZNm3D48GG0bt0aNjY2On9fsWJFtTTuaTGgmxLDeztCqZAgMlaFdT8l4m5UYYXxHZ+xw8tDXODqJEV8chG27ErGxRt5emOnveSO/l2VWP9zIvYcyXhSu2C2BvV0xgv9XeGokCDiXgG++SEOoRH5FcZ3flaB8cM94OZsibgkFTb+HI/z13IAAGIxMGGEB55tYQ8PV0vk5Wtw+VYONv4cj/TMkpraJbMzYaQnBvRwga2NBDdCc7BqYxTiElUG1xnSxxUvDvKAo0KK8Jh8rN4cjdDwsteQVCrC1JfroftzTpBKRTh/NQtfbYpCRhbPU2U4dmoDnzmToHgmAPI6rrgwYhqS9hwxvE6XtvBfvgC2/r4ovJeAsMXfInbbbp2Y+lNfgs/sSZC5uyD72m3cnPkxss5ff5K7Ypb4vvZ0G9zbBSMHuv37/lSANVtjDJ6fLm0dMH6kJ9z/PT8b/i8W565ma//eqY0Dnu/lAl9va9jbSTDl3VsIjy6oiV0xK5rabsBTzqgK/40bN/DMM8/Azs4Od+7cweXLl7WPK1euVHMTa1enNnZ47QVX/N/eVMz8NBKRsYX46K16UNiJ9cb7+Vhh7mueOHQqE29/EomzV3Lx3lQv1KtT/v4E7QPt0MTHCmkZxU96N8xS17YOeH1MHfz4WyKmB4ci4l4BPn3HBwo7/d9j/RtZI2iqNw78lYZpH4bi9KUsBL/dAPU95QAAmaUFGtW3xv/2JGH6h3fw0epI1HWXYdFMn5rcLbMyerAHhvVzw8oNUZjx/k0UqjRYEtQEUmnFl167dXDElFfqYdvOOEwJuoHw6HwsDWoCB/uy8zptXD20b+2ARSvvYtaiEDgrLbFwtm9N7JJZENtYI/taKG68tahS8VbedfHsnnVIO/4PTrYZgsjVW9F83Sdw7t1JG+Mxsj+aLgvC3U/W4GTbYci5dhvt9m6EpYvjk9oNs8T3tadb1/ZKvDG2Ln7YlYCp74cgIiYfixf46rw/Pcjf1wbvzvDBgeOpmPpeCE5dyMTC2Q3hXVeujZHLLXAjNBcbfoqtqd2g/yCjEv5jx45V+Dh69Gh1t7FWDe3lhIMnM3HkdBbuJRThmx8ToSrSoPdzDnrjB/d0xKWbudh9KB2xiUX4cU8KwmMK8Xw3pU6co4MEb4x2wxcb41CiFmpgT8zP8H4uOHAiDYf+TkdMvApfbYmFqkiDvl30JxhD+7jgwvVs7NyfgnsJKmzblYiwqAIM6eUMAMgv0CBoWTj+OpeJ2EQVbofnY833sWjcwBoujtKa3DWzMby/G37YHY/TFzMREVOApWsi4Ky0RKc2ygrXeWGgO/YdTcHBE6mIjivEyg1RUBVp0K+bCwDAxkqM/t1dsPb7GFy5mYO7kfn4fG0EAprYoWkjmwq3S2VSDv6FO8ErkfTb4UrF1399NAoiYxEybylyb0cg+psfkfjLQTR4e4I2psHMibi38WfEbt2F3JBwXJ8WDHV+IbwmjHhCe2Ge+L72dBvR3w37j6Xi4F9piIkrxKpNMVCpNOjb1Ulv/LB+rjh/LQs79iYhJr4QW3fGIywqH0P6uGpjDp9Mxw+7E3DpRk5N7YZZEgRRrT2MsWbNGnh7e0Mul6Ndu3Y4d+5cpdb76aefIBKJMHTo0Co9n1EJ/3+FRAw0qifH1ZCyrgSCAFy5nYcmPlZ61/HzscKV27rddy7fyoXfA/EiETB7Yh3sOpSGmISiJ9N4MycRi+DrbY1LN3O1ywQBuHwzF/4VJH1NG9ng8gPxAHDxRo7BJNHGSgyNRkBevrp6Gv4f4uEqg5PSEpeul126zitQIyQsF/6NbfWuIxGL0LiBDS5dz9IuEwTg0vVs7Tq+PtaQSixw8YHt3osvRFKKqsLt0uNxaB+I1KNndJal/HkSyvaBAACRVArFM82QeuR0WYAgIPXoaTi0b1WDLTVtfF97upW+P1nj0o2y9x5BAC7dyIG/r/73Hv9GtuUS+QvXslmc+I/bvn07Zs+ejeDgYFy6dAktW7ZE3759kZycbHC9qKgovPPOO+jcuXOVn7PSffiHDx9e6Y3u2rWryg15GtnbSiAWi5CRo/ummJmtRl338l10AMDBXoLM7PLxDoqyQz2irxM0GgG/H2WffWPZ24khFouQmaXbHSojqxheHvrPjVIhQUZ2+XilQv/LQCoVYdKoOjh+NgP5hewdWFVKh9LqYYaec3T/bw9T2P/7mnuoL35GVjG8/u2i4OhgiaJiTblkJSOrGI4VbJcej8zNGaqkVJ1lqqRUSBV2sJDLIFUqYCGRQJWc9lBMGmyasOtIZfF97emmsKvg/Sm7GF515HrXUTpI9JzPEr5X/cetWLECkydPxsSJEwEAa9euxd69e7Fp0yYsWLBA7zpqtRpjx47FokWL8PfffyMzM7NKz1nphF+hUGh/FgQBu3fvhkKhQJs2bQAAFy9eRGZmZqW+GKhUKqhUuoP21OoiiMWWlW2OyWpYT47BPRwx89PI2m4KGSAWA+9N9wYArN7KfpWV0bOjE2ZN9tb+/u7SO7XXGCIqh+9rZM5q8067+vJamUwGmaz8F/WioiJcvHgRQUFB2mUWFhbo1asXzpw5Uy7+vo8++giurq6YNGkS/v777yq3sdIJ/+bNm7U/z58/Hy+++CLWrl0Lsbh08Kparca0adNgb2//yG0tXrwYixbpDhbzfWYamrSZUdnm1Ijs3BKo1QKUDw3QdbAXVzgbSGZ2CRzsy8dn/hvfzNcKCjsxNi1upP27WCzCqy+4YXAPR7z2Xng174V5ys5RQ60W4KDQrZIoFdIKz01GVgmU9o+Ov/+h6OZkiXlLwlgFq6TTFzMQElbWtUAqLe0xqFRIkZ5ZVuFSKqQIj9Y/o0VW9r+vuYeqkw9uIz2zCJZSC9hYi3Wq/A8/D1UfVVIqZG7OOstkbs4ozsqBplCFotQMaEpKIHN1eijGCapE3SsDVDG+rz3dsnIqeH+yl5a7knlfRmaJnvMp4XuVmdGX1wYHB2PhwoXlYlNTU6FWq+Hm5qaz3M3NDbdv39a7/ZMnT2Ljxo2PNTGOUX34N23ahHfeeUeb7AOAWCzG7NmzsWnTpkeuHxQUhKysLJ1Ho1avG9OUJ6pEDYTFFKJF07K+diIR0NLPBqER+qfMuh1RgJZ+un3zApva4Pa/8cfOZuPNjyPx1idlj7SMYuw+lIbgr+49uZ0xMyVqAXej8tHKv6zfpEgEBPrb4laY/ilQQ8LyEOiv28/ymWZ2CHkg/v6HoqebDAs+D0NOHvu4VlZBoQbxSSrtIzq2AGkZRXgmoKwIYG1lgaaNbHHrTq7ebZSoBdyJzEOrgLIriiIR0CrAXrvO3Yh8FJdodLZb10MONxdZhdulx5N59gqcerTXWebc8zlknL0CABCKi5F16Sace3QoCxCJ4NS9AzLPXq7Blpo2vq893Urfn/LRqlnZe0/p+5Mdbt3V/95zKywXrZrZ6Sx7JsBe5/xQ9ajNQbv68toHK/iPIycnB6+88grWr18PZ2fnR69QAaMS/pKSEr3fQm7fvg2N5tFVA5lMBnt7e53H09qd59fDaejbyQE92itQ190S015yh9zSAodPZwIAZk3wwLihLtr4PUfS8UwzWwzt5Yi6bpYY87wzGtW3wh/HS/vr5+SpEROv0nmUqAVkZJcgLokDeKti14EU9O/qhF4dlfDykOHN8XUhl1ng0N/pAIC5r9fDxJEe2vhfD6WgTXN7jOjnAi8PGV4e6g7fBlb47XBpBVIsBj6Y0QCNva2xdG00LCxEUCokUCokkIh5Bz9j7NqfhLHD6qBDawc08LLCgmkNkZpRhJMXysavLHu/CYb0LZuxYufeRAzs4YI+XZxRr44cMyd5Qy6zwMETKQBKB/7uP5aCqa/UQ6C/HXwbWGPe1Aa4eSeHH6KVJLaxhn1LP9i39AMAWDeoC/uWfpB7lb5emnwyGy03L9XGR3/3E6wbeMFv8VzYNPFB/SkvwWNkf0Su2qKNiVy5GV6TXoTnK0Nh6+eDgDULIbGxwr2t5jGmq6bwfe3p9sv+JAzo7ozenR1Rr44cb02s9+/7U+n4lXlTvPHqqDra+N0HkvFsCwVeGOAKLw8ZXhnugcY+1vjtUNngTDsbMRrWt9JOpVrXQ46G9a0qHIdBTx99ea2+7jwA4OzsDLFYjKSkJJ3lSUlJcHd3LxcfHh6OqKgoDBo0CBKJBBKJBNu2bcOePXsgkUgQHl65niFG/TdNnDgRkyZNQnh4ONq2bQsA+Oeff7BkyRLtAARzcfJCDhS2yRg72AVKezEiYlUI/ioGmf8O5HVxlEJ4YFbN2xEFWL4hDi8PccG4oS6ITy7Cp9/eQ0y84RsNUdWdOJcJhb0E44Z7QKmQICKmAO8tj0BmdumlbBdHSzz4/fNWWD6WrI3C+BEemPCCB+KTVFi0KhLRcaU3UXNWWqLDM6WV5W8/8dN5rrmLw3DtNqvHVfXTngTIZRaYPdkbttYSXA/NQdCSOyguLnvR1HGTQ2FXdsn7+Jl0KOwlmDDSE0qH0u4/C5aE6nRR+GZbDAQNEDzbF1KJCBeuZWHVxuga3TdTpmgdgA5Hvtf+7r/8XQDAvW27cG1SEGQeLrDyKksqC6JicX7wG/D/Igjeb45DYWwirr/xPlL/PKmNSdixH5Yujmgc/FbpjbeuhuDc86+h6KGBvGQY39eebifOZsDBToLxL9T5t3tiAd5deld7flydLCE8kBTcupuHxWsiMGGkJya+6Im4RBUWrghHVGzZzTs7tHbA3De8tb+//2bpQPdtv8Tj+10JNbNjVGMsLS3RunVrHDlyRDu1pkajwZEjRzBjRvmu7X5+frh+XfcGhu+//z5ycnKwatUqeHl5Vep5RcKD/5mVpNFosHz5cqxatQoJCaX/jB4eHnj77bcxZ84cna4+lTXojZAqr0M1o6iQX1aeViUqXhV6ms3+5ZXabgJV4KuXttd2E6gCmhJ2OXqa/flj69pugl4nb9XeFd5O/lWbZnX79u0YP3481q1bh7Zt22LlypX4+eefcfv2bbi5uWHcuHHw9PTE4sWL9a4/YcIEZGZm4tdff630cxpV4bewsMC8efMwb948ZGeXzkdbmcG6RERERET/ZaNGjUJKSgo+/PBDJCYmIjAwEAcOHNAO5I2JiYGFRfXeKuuxO4gx0SciIiKi2qSpcn+V2jVjxgy9XXgA4Pjx4wbX3bJlS5Wfr9IJf6tWrSASVW6Az6VLl6rcECIiIiIiqn6VTvjvDywgIiIiInqa1OaNt0xBpRP+4ODgJ9kOIiIiIiJ6Aqp3RAARERERET1VKl3hd3R0xJ07d+Ds7AylUmmwP396enq1NI6IiIiI6FEEgV16DKl0wv/ll1/Czq709tArV658Uu0hIiIiIqJqVOmEf/z48Xp/JiIiIiKqTVW/jex/i9Hz8Gs0GoSFhSE5ORmaB+/zDaBLly6P3TAiIiIiInp8RiX8Z8+exUsvvYTo6GgID32lEolEUKt5W2wiIiIioqeBUQn/lClT0KZNG+zduxceHh6VviEXEREREVF103AefoOMSvjv3r2LnTt3olGjRtXdHiIiIiIiqkZGzcPfrl07hIWFVXdbiIiIiIiqTBBEtfYwBZWu8F+7dk3785tvvok5c+YgMTERzZs3h1Qq1Ylt0aJF9bWQiIiIiIiMVumEPzAwECKRSGeQ7quvvqr9+f7fOGiXiIiIiGoSp+U0rNIJf2Rk5JNsBxERERERPQGVTvjr16+v/Xnx4sVwc3PTqfADwKZNm5CSkoL58+dXXwuJiIiIiMhoRg3aXbduHfz8/Motb9asGdauXfvYjSIiIiIiqiwBolp7mAKjEv7ExER4eHiUW+7i4oKEhITHbhQREREREVUPoxJ+Ly8vnDp1qtzyU6dOoU6dOo/dKCIiIiKiytIItfcwBUbdeGvy5MmYOXMmiouL0aNHDwDAkSNHMG/ePMyZM6daG0hERERERMYzKuGfO3cu0tLSMG3aNBQVFQEA5HI55s+fj6CgoGptIBERERERGc+ohF8kEmHp0qX44IMPEBISAisrK/j6+kImk1V3+4iIiIiIDDKVO97WFqMS/vtsbW3x7LPPVldbiIiIiIiomj1Wwk9EREREVNt4p13DjJqlh4iIiIiITAMr/ERERERk0jQmcgOs2sIKPxERERGRGWPCT0RERERkxtilh4iIiIhMGgftGsYKPxERERGRGWOFn4iIiIhMGm+8ZRgr/EREREREZowJPxERERGRGWOXHiIiIiIyaRoO2jWIFX4iIiIiIjPGCj8RERERmTROy2kYK/xERERERGaMCT8RERERkRljlx4iIiIiMmkCOA+/IazwExERERGZMVb4iYiIiMikcVpOw1jhJyIiIiIyY6zwExEREZFJ47SchrHCT0RERERkxp6aCn9+Vk5tN4EqIBLxe+HTqnW35rXdBDLgK9n22m4CVeCt/42q7SZQBZYN2VLbTSAyO09Nwk9EREREZAx26TGMpVsiIiIiIjPGCj8RERERmTSNwBtvGcIKPxERERGRGWPCT0RERERkxtilh4iIiIhMGgftGsYKPxERERGRGWOFn4iIiIhMGiv8hrHCT0RERERkxljhJyIiIiKTpmGF3yBW+ImIiIiIzBgTfiIiIiIiM8YuPURERERk0gTeadcgVviJiIiIiMwYK/xEREREZNI4LadhrPATEREREZkxJvxERERERGaMXXqIiIiIyKRxHn7DWOEnIiIiIjJjrPATERERkUnjoF3DWOEnIiIiIjJjrPATERERkUljhd8wVviJiIiIiMwYE34iIiIiIjPGLj1EREREZNI4LadhrPATEREREZkxVviJiIiIyKRx0K5hrPATEREREZkxJvxERERERGaMXXqIiIiIyKRpNLXdgqcbK/xERERERGaMFX4iIiIiMmkctGuYUQl/Xl4elixZgiNHjiA5ORmah66jREREVEvjiIiIiIjo8RiV8L/22ms4ceIEXnnlFXh4eEAkElV3u4iIiIiIKoUVfsOMSvj379+PvXv3omPHjtXdHiIiIiIiqkZGDdpVKpVwdHSs7rYQEREREVE1Myrh//jjj/Hhhx8iPz+/uttDRERERFQlGqH2HqbAqC49X3zxBcLDw+Hm5gZvb29IpVKdv1+6dKlaGkdERERERI/HqIR/6NCh1dwMIiIiIiLjCLU6avfpn7zGqIQ/ODi4uttBRERERERPwGPdeOvixYsICQkBADRr1gytWrWqlkYREREREVH1MCrhT05OxujRo3H8+HE4ODgAADIzM9G9e3f89NNPcHFxqc42EhERERFViPPwG2bULD1vvvkmcnJycPPmTaSnpyM9PR03btxAdnY23nrrrepuIxERERERGcmoCv+BAwdw+PBhNG3aVLvM398fa9asQZ8+faqtcUREREREj6LR1HYLnm5GJfwajabcVJwAIJVKoTHDIz6kjyteHOQBR4UU4TH5WL05GqHheRXGd2mnxMQX68LdRYbYxEKs/989nLuSpRMzYaQnBvRwga2NBDdCc7BqYxTiElVPelfMzuDeLnhxkLv23Hy95d4jz82EkXXg7iJDXGIh1v9fXLlzM/6FOhjQwxm2NhLcDM3Fqk3RPDdG6NBMjK6BEthZiZCQJuC3U0W4l6z/mqubUoQ+z0rh6SKCo50F9pwqwsnr6nJx9jbAgHZSNKknhqUESM0SsON4EWJTeC23qgb1dMYL/V3hqJAg4l4BvvkhDqERFd9bpfOzCowf7gE3Z0vEJamw8ed4nL+WAwAQi4EJIzzwbAt7eLhaIi9fg8u3crDx53ikZ5bU1C6ZBcdObeAzZxIUzwRAXscVF0ZMQ9KeI4bX6dIW/ssXwNbfF4X3EhC2+FvEbtutE1N/6kvwmT0JMncXZF+7jZszP0bW+etPclfM2sRRXni+lytsrSW4EZqNFd9FIi6x0OA6Q/u5YfTgOnB0sERYdB6+2hiF22G52r8/38sVvTo7w7eBDWysJXh+3Dnk5pd/HyQyhlFdenr06IG3334b8fHx2mVxcXGYNWsWevbsWW2Nexp06+CIKa/Uw7adcZgSdAPh0flYGtQEDvb6vyv5N7bF+281wv5jKXhjwQ2cupCBj97xhXddK23M6MEeGNbPDSs3RGHG+zdRqNJgSVATSKVP/7ROT5Nu7ZWY8ooXvv8lHlPevYWI6AIsWeBb8bnxtcF7b/rgwPFUTAm6hVMXMrFoTkN415VrY0YNcsewfq5YtTEGMz4IQaFKjSULGvPcVFHLhmIMek6KwxdKsOoXFRLSNJg0UAYbuf54qQRIz9Zg/9kSZOfpT96tLIFpQ2VQa4BN+1RYvl2FP84UI5/fxaqsa1sHvD6mDn78LRHTg0MRca8An77jA4VdBa+dRtYImuqNA3+lYdqHoTh9KQvBbzdAfc/SEyqztECj+tb4354kTP/wDj5aHYm67jIsmulTk7tlFsQ21si+Foobby2qVLyVd108u2cd0o7/g5NthiBy9VY0X/cJnHt30sZ4jOyPpsuCcPeTNTjZdhhyrt1Gu70bYeni+KR2w6yNGVoHIwa4Y8V3EZj67nUUqDRY9kFTWBr4nOj+nBOmjffGlh2xmDzvGsKj8rHs/aY6n1dymQXOXc7Ej7viamI3zI4g1N7DFBiV8H/99dfIzs6Gt7c3GjZsiIYNG6JBgwbIzs7G6tWrq7uNteqFge7YdzQFB0+kIjquECs3REFVpEG/bvoHJg/v74bzV7Pw8x+JiIkvxJaf43A3Mh9D+7rpxPywOx6nL2YiIqYAS9dEwFlpiU5tlDW1W2ZhxEA37DuaioMn0hATV4iVG6P/PTfOeuPLzk1S6bnZEY+wyHwM6ev6QIwrftydgNMXMxEZU4Cl30TBSSlFxzYONbRX5qFzCwn+CVHjQqgayRkCdv1VjOIS4Fk//QllbIqAvWdLcDVcjZIKblvYrZUEWbkCdhwvxr1kARk5Au7GapCebSLvtk+R4f1ccOBEGg79nY6YeBW+2hILVZEGfbvoTwCH9nHBhevZ2Lk/BfcSVNi2KxFhUQUY0qv0tZZfoEHQsnD8dS4TsYkq3A7Px5rvY9G4gTVcHMtfDaaKpRz8C3eCVyLpt8OViq//+mgURMYiZN5S5N6OQPQ3PyLxl4No8PYEbUyDmRNxb+PPiN26C7kh4bg+LRjq/EJ4TRjxhPbCvL0w0APf/xKLU+czEBGdj8Wrw0o/w9tW/AVq5CAP7D2cjAPHUhAdW4AV30WgUKXBgB5lnz879ybif7/G49bd3Aq3Q2QsoxJ+Ly8vXLp0CXv37sXMmTMxc+ZM7Nu3D5cuXULdunWru421RiIWoXEDG1y6XtblQxCAS9ez4d/YVu86/r62uHhdt4vIhatZ2ngPVxmclJa4dD1b+/e8AjVCwnIr3CaVpz03N8qOoyAAl25kw9/XRu86/r668QBw/lo2/H3vnxvL0nNz46FzE56njaFHE1sAni4ihMWWXYoWANyNVaO+m1FvOQAA//pixKYIeLm3JT4cL8fbL8jQtqm4Glr83yIRi+DrbY1LN8uSCkEALt/MhX8j/a+dpo1scPmmbhJy8UYOmlYQDwA2VmJoNALy2CXhiXJoH4jUo2d0lqX8eRLK9oEAAJFUCsUzzZB65HRZgCAg9ehpOLTnVNpVdf8z/OK1ss/5vHw1bt3NhX9jO73rSCQiNPGxxcVrmdplggBcvJ4J/yb61yGqbkbPwy8SidC7d2/07t27OtvzVFHYSyAWi5CRpdsHNSOrGF6e+vsmODpIkZFVXC7eUVFa5VI6SLXLHo65/zd6tLJz8/BxLIFXHf3nRukgLXcuM7OK4fjvcVcq7p+bimPo0WzkgNhChJwC3eW5BQJcHYxP+B3tRWjvL8bf10pw9FIxvFwtMKSjFGo1cPEOk8rKsrcTQywWIVPPe5CXh0zvOkqFBBnZet6zFPo/QqRSESaNqoPjZzOQX2h+47qeJjI3Z6iSUnWWqZJSIVXYwUIug1SpgIVEAlVy2kMxabBpwi5XVeWoLP0sSM98+PVQVOHnhMKu9PMq/eHXXGYx6nla6V2Hqq6Ci8P0r0on/F999RVef/11yOVyfPXVVwZjHzU1p0qlgkql2/FWoy6Chdiyss0hov8YkQiITdHgwLnSL2TxaWq4OVqgvb+ECf9TRCwG3pvuDQBYvTW2dhtD9Jh6dXbGnNfLvhgtWHy7FltDZLxKJ/xffvklxo4dC7lcji+//LLCOJFI9MiEf/HixVi0SHdAknez1+ATMLmyzakRWdklUKuFclUspUJa7tv9femZxdpKsU78v9/sM/5d7+FtKBVShEdXPEMG6So7Nw8fa4n2GD8sI7N8RdLhgfNw/2qBUiHROTcOCinCo3huKiuvEFBrBNg9VLiytRIhJ9/4EkxOvoDkDN31kzM0aO7Dbj1VkZ2jhlotwEHP+9TDV7fuy8gqgdL+0fH3k303J0vMWxLG6n4NUCWlQuamO25J5uaM4qwcaApVKErNgKakBDJXp4dinKBK1L0yQOWdOp+OkAf61EslpQNzHR0e/gy3RFiU/hnisnJKP68cH37NOVScS1DVmcrg2dpS6evrkZGRcHJy0v5c0SMiIuKR2woKCkJWVpbOw7vpeOP34gkpUQu4E5mHVgEK7TKRCGgVYI9bd/QPqrl1NxfPBNjrLGvdoiw+IVmFtIwinRhrKws0bWRb4TapvPvn5pmAsv6PIhHQqpk9bt3V/6Z7624eWjV76Nw0t9cOkEpILkJaRhFaPXxuGtpwEFUVqDVAXIqARp5libgIQCNPMaKTjE8AoxI1cHHQnQXDxcECGTlMKquiRC3gblQ+WvmXjUsRiYBAf1vcCtP/2gkJy0Ogv+44lmea2SHkgfj7yb6nmwwLPg9DTh6vutSEzLNX4NSjvc4y557PIePsFQCAUFyMrEs34dyjQ1mASASn7h2QefZyDbbUNBUUahCXWKh9RMUWlH6GNy/LC6ytxPD3tcWtOzl6t1FSIiA0IldnHZEIaN1cgVuh+tchqm7Gd6h9DDKZDPb29jqPp7U7z869iRjYwwV9ujijXh05Zk7yhlxmgYMnUgAA86f5YNLosoHKu/Yn4dmWCowc6A6vOnKMe8ETjX1s8OvBJJ2YscPqoENrBzTwssKCaQ2RmlGEkxcyanz/TNkve5MwoLsLendxQr06crz9an3IZRY4cKK0ajV/qjcmjfbUxpeeG3u8MNCt9NyMqIPGPtb47WDyAzHJGDvUAx1aK9DAywrzpzZAWkYxTl3IrOndM2l/XytB26ZitG4shquDCMO6SGEpBS6EllaER3WXol/bsqstYgvAw0kEDycRJBYiKGxKf3ayF+lss56rBbq3ksDJXoTARmK0ayrGmZtMLKtq14EU9O/qhF4dlfDykOHN8XUhl1ng0N/pAIC5r9fDxJEe2vhfD6WgTXN7jOjnAi8PGV4e6g7fBlb47XDpa00sBj6Y0QCNva2xdG00LCxEUCokUCokkIg5pW1ViG2sYd/SD/Yt/QAA1g3qwr6lH+RepeejySez0XLzUm189Hc/wbqBF/wWz4VNEx/Un/ISPEb2R+SqLdqYyJWb4TXpRXi+MhS2fj4IWLMQEhsr3Nu6q0b3zVzs3JuAV0bUxXNtlGhQzxrvvtmo9DP8XLo25otgfwzr5679fcfvCXi+lxv6dnVBPU8rzJrsA7lMjP3HUrQxjg5SNPK2hqd76Ti0BvWt0cjbGna2Rg+3JNIy6r9o9uzZepeLRCLI5XI0atQIQ4YMgaOj6c/xe/xMOhT2EkwY6QmlQ2m3mwVLQrWXsl2dLSE8cB3p1p1cfLo6HK+OqotXR9dFXGIhPlx+F1GxZSMYf9qTALnMArMne8PWWoLroTkIWnIHxcW8HlUVx89mlJ6bF+poz03QkrvI1J4bmc4gnlt38/DZ15GY+KInXh3libhEFYK/CEdUbNnNUrb/ngi5zAKzXvOGrbUYN0JzsYDnpsquhqthIwf6PCuBnbUI8akCNu5VIfffl4GDnQgPHlF7GxFmjSwbbN01UIqugVKEx6uxbk8RgNKpO7cdLEK/dlL0ai1Beo6APaeLcfkuE/6qOnEuEwp7CcYN94BSIUFETAHeWx6BzOzS146Lo6XOXStvheVjydoojB/hgQkveCA+SYVFqyIRHVf62nFWWqLDM6XVy28/8dN5rrmLw3DtNq+QVZaidQA6HPle+7v/8ncBAPe27cK1SUGQebjAyqvsy1hBVCzOD34D/l8EwfvNcSiMTcT1N95H6p8ntTEJO/bD0sURjYPfKr3x1tUQnHv+NRQ9NJCXKuf/fo2HXCbGO2/4wNZGguu3szHvkxAUPfA54ekmg+KBOfaPnU6Dg70UE0d7wdFBirCoPMz7NERn4onBfdww4UUv7e+rPw4AACz5OgwHjpd9MSD9hFodtfv0FzZEglD1Xk/du3fHpUuXoFar0aRJEwDAnTt3IBaL4efnh9DQUIhEIpw8eRL+/v6V2mbP0eeq2gyqISJRrVwIokp4pmuz2m4CGXD1TGhtN4Eq8Nb/RtV2E6gCy4Zsqe0mkAHHd3Z4dFAtWL6r9rp3vjP86c+TjGrhkCFD0KtXL8THx+PixYu4ePEiYmNj0bt3b4wZMwZxcXHo0qULZs2aVd3tJSIiIiLSoRFq72EKjEr4ly1bho8//hj29mWDGxUKBRYuXIjPP/8c1tbW+PDDD3Hx4sVqaygREREREVWdUQl/VlYWkpOTyy1PSUlBdnbpXUodHBxQVFT0eK0jIiIiInoEQai9hykwukvPq6++it27dyM2NhaxsbHYvXs3Jk2ahKFDhwIAzp07h8aNG1dnW4mIiIiIqIqMmqVn3bp1mDVrFkaPHo2SktJZHSQSCcaPH6+9KZefnx82bNhQfS0lIiIiIqIqMyrht7W1xfr16/Hll19qb7Tl4+MDW9uyG7MEBgZWSwOJiIiIiAzRmMro2VryWPMIJSYmIiEhAb6+vrC1tYURM3wSEREREdETZFTCn5aWhp49e6Jx48YYMGAAEhISAACTJk3CnDlzqrWBRERERESGcNCuYUYl/LNmzYJUKkVMTAysra21y0eNGoUDBw5UW+OIiIiIiOjxGJXwHzp0CEuXLkXdunV1lvv6+iI6OrpaGkZEREREZI7WrFkDb29vyOVytGvXDufOnaswdv369ejcuTOUSiWUSiV69eplMF4foxL+vLw8ncr+fenp6ZDJZMZskoiIiIjIKKbUpWf79u2YPXs2goODcenSJbRs2RJ9+/bVe48rADh+/DjGjBmDY8eO4cyZM/Dy8kKfPn0QFxdX6ec0KuHv3Lkztm3bpv1dJBJBo9Hg888/R/fu3Y3ZJBERERGR2VuxYgUmT56MiRMnwt/fH2vXroW1tTU2bdr0/+3dd1hT1/8H8DcjhJkwZakgTlTEVZSitVWqdVXqtipqabXDar9YtVbFVYutldra6Ra/Wvtz1NYO0YoTrYq4UEABEa1MWTIMI/f3B1+DEYgQIyHx/XqePI8599ybz71Hcj85Oeekxvrbtm3Du+++i86dOyuWvZfL5Th06FCdX1OtZTlXrlyJvn37Ijo6GqWlpZgzZw6uXLmCnJwcREVFqXNIIiIiIiK1yHVk9mxpaSnOnTuHefPmKcoMDQ3h7++PU6dO1ekYxcXFKCsrg62tbZ1ft94Jf1lZGWbMmIF9+/bh4MGDsLKyQmFhIYYPH4733nsPzs7O9T0kEREREZFOkslkkMlkSmVisbjGYe7Z2dmoqKiAo6OjUrmjoyPi4+Pr9Hpz586Fi4sL/P396xxjvRN+kUiES5cuwcbGBvPnz6/v7kREREREeiM0NBRLlixRKlu0aBEWL16s8ddasWIFduzYgSNHjsDU1LTO+6k1hn/ChAnYsGGDOrsSEREREWmUINfeY968ecjPz1d6PDxk52H29vYwMjJCRkaGUnlGRgacnJxUnuMXX3yBFStW4MCBA+jUqVO9ro9aY/jLy8uxceNG/P333+jWrRssLCyUtoeFhalzWCIiIiIinVLb8J2amJiYoFu3bjh06BACAgIAQDEBd/r06bXu9/nnn2P58uWIiIhA9+7d6x2jWgl/bGwsunbtCgC4du2a0jYDAwN1DklEREREpBZBRybtAkBwcDAmTZqE7t27w8fHB6tXr0ZRURGmTJkCAAgMDISrqytCQ0MBAJ999hlCQkKwfft2uLu7Iz09HQBgaWkJS0vLOr2mWgn/4cOH1dmNiIiIiOiZNmbMGGRlZSEkJATp6eno3Lkz9u/fr5jIm5qaCkPDqlH333//PUpLSzFy5Eil49RnnoBaCT8RERERUWMhl2s7gvqZPn16rUN4jhw5ovQ8JSXliV9PrUm7RERERESkG5jwExERERHpMQ7pISIiIiKdpkuTdrWBPfxERERERHqMPfxEREREpNPk7OBXiT38RERERER6jAk/EREREZEe45AeIiIiItJpAsf0qMQefiIiIiIiPcYefiIiIiLSaVyVUzX28BMRERER6TH28BMRERGRTpNzDL9K7OEnIiIiItJjTPiJiIiIiPQYh/QQERERkU4TOGtXJfbwExERERHpMfbwExEREZFOE+TajqBxYw8/EREREZEeY8JPRERERKTHOKSHiIiIiHSanJN2VWIPPxERERGRHmMPPxERERHpNC7LqRp7+ImIiIiI9Bh7+ImIiIhIp8nl7OFXhT38RERERER6jAk/EREREZEe45AeIiIiItJpnLOrWqNJ+GVFJdoOgWrh3qm1tkOgWlyIitd2CKSCIOdvvTdWK4dt1nYIVIvZv07WdgikUoK2AyA1NJqEn4iIiIhIHQIn7arEMfxERERERHqMCT8RERERkR7jkB4iIiIi0mlyztpViT38RERERER6jD38RERERKTTOGlXNfbwExERERHpMfbwExEREZFOYw+/auzhJyIiIiLSY0z4iYiIiIj0GIf0EBEREZFO44ge1djDT0RERESkx9jDT0REREQ6jZN2VWMPPxERERGRHmPCT0RERESkxzikh4iIiIh0miBwSI8q7OEnIiIiItJj7OEnIiIiIp0m56RdldjDT0RERESkx9jDT0REREQ6jWP4VWMPPxERERGRHmPCT0RERESkxzikh4iIiIh0Gn9pVzX28BMRERER6TH28BMRERGRTmMPv2rs4SciIiIi0mP1TvjLyspgbGyM2NjYpxEPERERERFpUL2H9IhEIjRv3hwVFRVPIx4iIiIionqRcx1+ldQa0jN//nx8/PHHyMnJ0XQ8RERERESkQWpN2v3mm2+QmJgIFxcXuLm5wcLCQml7TEyMRoIjIiIiInocTtpVTa2EPyAgQMNhEBERERHR06BWwr9o0SJNx0FEREREpBaBY/hVUntZzry8PKxfvx7z5s1TjOWPiYnBv//+q7HgiIiIiIjoyajVw3/p0iX4+/tDKpUiJSUFb731FmxtbbFnzx6kpqYiPDxc03ESEREREZEa1OrhDw4OxuTJk3H9+nWYmpoqygcNGoRjx45pLDgiIiIioseRywWtPXSBWgn/2bNnMW3atGrlrq6uSE9Pf+KgiIiIiIhIM9Qa0iMWi1FQUFCt/Nq1a3BwcHjioIiIiIiI6orLcqqmVg//q6++iqVLl6KsrAwAYGBggNTUVMydOxcjRozQaIBERERERKQ+tRL+VatWobCwEE2aNEFJSQn69OmDVq1awcrKCsuXL9d0jEREREREpCa1hvRIpVIcPHgQUVFRuHjxIgoLC9G1a1f4+/tzHVQiIiIialDMP1VTK+FfuXIlZs+eDT8/P/j5+SnKKyoqMGHCBPz0008aC5CIiIiIiNSndsJva2uLoKAgRVlFRQXGjh2L2NhYjQVHRERERPQ4glyu7RAaNbUS/j/++AP9+/eHVCrFyJEjUV5ejtGjRyM+Ph6HDx/WdIxERERERKQmtRL+5557Drt370ZAQABMTEywYcMGJCYm4vDhw3B0dNR0jEREREREpCa1En4A6Nu3L8LDwzFixAh4enri6NGjsLe312RsjUrQeHcM7e8EKwtjXI4rwBffXcfttBKV+wwf5IJxw5vB1sYESTcK8eWPiYi7fg8AYGVpjKDX3eHTxQaODmLkFZTh2D/ZWP/fFBQVVzTEKekF/x7mGNzbElJLI6SmlyH893wk3y6rtb5PR1OM9LeCvbUxMu6WY0dEAS5ekym2i00MMGaABN09TWFpbois3HJEnCpC5JnihjgdvTPU3x6jBjWBrVSE5Fsl+Db8NhKSa7+WvX2sMXmEMxztTfBvhgzrf76DsxerfvPDr7sUQ/rao7W7OSRWxnh7fjySU1X/HVLNXn3ZAaMGO8JWKkJSagm+3ZKqsm1e8LHGpFGucHrQNj/dxpmH2qZXd2sM8XeoapuPryLpJtvmSUwZ0wxD/JvA0twYsQkFCFt7A/+m31e5T8Arjhj7qgtsrU2QeLMIX29IQXxioWL7EP8m8O9tj9YtLGBhbowhgWdQyHtOndj26g6PWUGQdu0IU5cmiB7xLjJ+O6R6nxd80P6Lj2DZvjXu30pDYuj3uB3+i1Idt3deh0dwEMRODii4FI8rHyxD/tnLT/NU9JKu/OKtttR5Wc7hw4dXe4SHh8PBwQHW1taYOnWqolzfjB/RDCOHuOKL765j6ofnUXK/AmFLvWAiMqh1n769HDD9zZbY9FMKgj44h8QbhQhb6gVrqQgAYG9rAns7E3y7MRkTp0dj+eoE9Oxqi49mtG2o09J5PbxMMX6QFL9E3sOCb7OQml6GuZPtILGo+b916+YivDfaBkeji7Hg2yyci7uP/4y3RdMmVZ97xw+SwLu1GN/vzMWc1ZnYf7IIk4ZI0bWduKFOS2/06WGNaa+74r+/pOPdhQlITi3Bp3NawlpScz9D+9YW+Phdd+w/ehfvLIzHyXP5WPxBC7g3NVXUMRUbIvZaEdb/fKehTkMv9elpg2njm+K/e9LwzoI4JKcWI/Sj1qrbZroH9h/Jxjvz4xAVnYfFwS2V28bUELEJhVi/43ZDnYZeGxfgghGDnBC2NhnvfHwZJTI5Vi70VHnfeel5O7w7yR2bd97GW3MuISmlGCsXeCq1q6nYEGfO52Hbnn8b4jT0ipGFOQouJSB2xpI61Tdzb4rnfvsRd4+cxonuw3BjzRZ4/fgJ7F/upajjPGogPFfOw/VPvsUJn9dw71I8evyxASYOtk/rNOgZVeeEXyqV1vgYMGAAWrZsqVSmb0a96orw/7uJE6fvIimlCJ98GQ87WzF696z9G42xAU2xLyINfx7KQMqtYqz87jruy+QY8rITAOBGajEWhF5F1Nm7uJN+HzGX8rB26w34+djBSK1fR3j2DPSzxOHoYhyLKcGdrHJs+jUfsjIBfbqZ11h/gK8lLl2X4Y8TRbiTVY5df99Dyp0yvOxroajTurkJjp8vRtyNUmTnVeDw2WKkppfBo6lJQ52W3hgxsAn+OnIXB47nIPXOfXy16RZkMjkGvGBXY/2A/g44e6kAO//MxK07MmzZnYbElBK86l/1692HonKxbW86zl+511CnoZdGDHTEX4ezEXHsLlL/vY+vNqZWtk2fmtvmtVea4OylfOz8IwOpd+5jy647SEwpxrD+TRR1/j6Rg//+koaYWLaNJowc7Iytu28j6mwukm8WI3RNIuxtTNDLp/ZEcNRQZ/zxdyb2H87CzdslCFubjPsyOQb1rWqnXX+kY/veO7h6vbDW41DNsiKO4dqi1cj49e861XebOhYlN24jbs5nKIxPxs3vtiF9dwRazJysqNPigym4teH/cHvLHhTGJeHyu4tQUXwfzSbzR0zrSxAErT10QZ2H9GzatOlpxtFouTiawt5WjLMXchVlRcUVuHqtAB3bSXDoeFa1fYyNDdCmlRW27kpVlAkCEH0hFx3aSmp9LQsLYxQVl6OCE80fy8gIaOEiwr6jVTctQQCuJMrQqrmoxn1aNRfhr6gipbJLiTJ086zqpbyeWoqu7Uxx9Fwxcgvk8GxhAid7Y/z3z4JHD0cqGBsZoLW7OXbsy1CUCQJw/so9eLaq+QNZ+1YW2L0/U6ks+nIBnu9m/TRDfeYYGxmgTQtz7PgtTVEmCEBM7D20b20JIKPaPu1bWWLXX8rl0ZfYNk+LcxMx7GxMcO5SvqKsqLgCV68Xon0bK0RG3a22j7GxAdp6WGL7Qz33ggCcu5yH9m2tGiRuUmbdszOyI08plWUdPIH2qz4GABiIRJB27YCkz36sqiAIyI48CeueXRoyVHoGqD2GHwCysrKQkJAAAGjbti0cHBwes4fusbWp7NnNzVMeF56bV6rY9iipRARjIwPk5Crvk5NXBremNSc7UokxJo9xw76ItBq3kzIrc0MYGRkgv1B57Gl+oRzODjW3i7WlEQoKlT9NFRRWwNqq6iuV8H35CAqwxpq5TiivECAIwIZf8pCQUqr5k9BjEisjGBkZIDf/kb+bgnI0czGtcR8ba2Pk5pcrleXll8NW+kRvU/QIqZXx/9pG+VrnFpSpbJu8R9syvxy21jV/uKYnY2tTeV1zHr3v5JfWes0ftGvOo+2UV4bmrmZPJ1BSSexoD1lGtlKZLCMbIqkVDE3FENlIYWhsDFnm3Ufq3IVFW4+GDFUvCBzDr5Jad9KioiK8//77CA8Ph/x/654aGRkhMDAQa9asgbl5zUntAzKZDDKZTKlMXlEKQyPtD5t4uU8TzH6vjeL5nKVPf+KMuZkRVoZ4IeVWMTZsv/nUX49q19/XAq2amWDV1rvIzq1AuxYmmPSqFLn3KnAliUk/EWmef297zJpaleB9FBqvxWiISB+plfAHBwfj6NGj2Ldvn+KXdk+cOIEZM2Zg1qxZ+P7771XuHxoaiiVLlCe9NGs9Cc3bTlEnHI06ceYurl6LVjw3EVX2/tpYi3A3tyrhs7E2QWJyzWMg8wvKUF4hKHppHrB95BgAYGZmhFVLvFBcUoGPl8eiooKfUOviXrEcFRUCpJZGAKp6tKSWhtV6/R/IK6yAxFJ5goTE0gh59yo/tIqMgdEvS7B6ew4uJFR+IL2VUQ43ZxEG97LElaScp3MyeqjgXgUqKgTYSJX/BmwkxtV6LR/IzSuHzSO9+dZSY+Q80hNNTyb/Xvn/2kb5WttIRNW+kXkgN69cseCAor609rak+ok6m4O4h8bUi4wrJ+baWouUrrGN1ASJKUXV9geq2tX20XZ65BjUcGQZ2RA7Ks/1Ezvaoyz/HuT3ZSjNzoW8vBziJnaP1LGDLF35mwGiJ6XW9NDdu3djw4YNGDhwICQSCSQSCQYNGoR169Zh165dj91/3rx5yM/PV3o0bTVenVA0rqSkAv+m3Vc8bqQWIztHhu7eNoo65mZGaN9Ggtj4msd1l5cLuJZ4D906Ve1jYAB087bBlYSqfczNjPDl0k4oLxcw95NYlJYx2a+rigrgxp0ydGhZ9a2QgQHQoaUYiak139wSU8vQoaXyajsdW4qReKvyQ5ixkQGMjQ3w6LeCcnnlsanuyisEXE8pRuf2VWOHDQyAzh2sEJdY89KPVxOL0KWD8ljjrh2tEHe95gSH1FNeIeDajWJ06VA1n8jAAOjS0arWiZxXEwtraBsJ4hLZNppQcl+Of9PvKx4pt0twN7cUXb2qFsEwNzNC+9aWuHqt5knR5eUCEpILlfYxMAC6eUlxNYETqbUh758LsOvbU6nMvt/zyP3nAgBAKCtDfswV2Pf1rapgYAC7l3yR98/5BoxUPwhyQWsPXaBWwl9cXFzjD2w1adIExcWPX69cLBYrPig8eDSG4Ty12fnbv5g0pjn8fOzg4WaBBcHtcDdHhuP/VH0CX/1JJwwf7KJ4vmPvbQwd4IxX+jrCrak5Pny3NcxMDfHH3+kAqpJ9U7EhQr9OgIWZEWytRbC1FsGQq/TUyV9RhXixuwV6dzGDi4MxprwqhdjEAEfPVf4fnDbSGqP7VyUpEacK0am1GAP9LOBsb4zhfa3g4SrCwVOVSUuJTEBcsgzjXpHAs4UJHGyM0LuLGXp1MUf0VdVrX1N1u//KxKAX7fByL1s0cxFjxuRmMBUbIuJY5XjV2dPc8MZoZ0X9vQey0N1LghEDm6CZsxgTX3NCmxbm+O3vqonxVhZG8GhuhuaulWPNmzmL4dHcrFpvNam2+68MDHrJHi/3tkVzF1PMmNK8sm2OVrbNnLfd8caYqvezX/Zn4rlOUowc9L+2Ge6MNh7m+PVA1SRrKwsjtHQzg9v/2qapsylaurFt1LXrjzRMHNEUz3e3QYvm5vj4/VbIzi3FiTNV3zSuWtQer73ipHi+c18ahvg7YkAfBzR3NcN/3vKAqdgIfx2u+huytRahlbs5XJ0q26mFmzlauZvDypLt9DhGFuaQeLeDxLsdAMC8RVNIvNvBtFnl+1jbT4LhvekzRf2ba3fAvEUztAudDYu2HnB7+3U4jxqIG19tVtS5sXoTmgWNhuvEAFi280DHbxfD2MIMt7bsadBzI/2n1l+4r68vFi1ahPDwcJiaVr5plJSUYMmSJfD19X3M3rpn2+5bMDU1wpzpbWBpYYzLV/Mxa9FlpR55VyczWEuqvkqNPJEFa6kIb453h61N5fCfWYsuKyb/tm1piQ7tKnvY/m9dD6XXGxn0D9Izlec4UHWnL9+HxCIfI/pZQWplhJtpZfh8810UFFUO0bGXGuHh1bKup5bhu//LxSh/CUb3lyD9bjm+3JaD25lVQ0a++TkXY/pL8M5oG1iaGSI7rxw7DxbgEH94q96Ons6D1MoYgSOcYSM1RnJqCeavTEJeQeX1bmInUlrO7Or1IoR+n4LJI50xZZQz7mTIsHj1DaTcrvqw1bOrFLOnuimez5/eAgCwdU8atv6S3kBnpvuO/pMLaytjTBrpAhupCEk3S/DxZ9cfahuT6m3zbTImj3LFlNGu+DddhsVhSUpt49vNGrOnuSueL3i/ckx6+O472LqHixHU109778BUbIQPp3lU3nfiCzDnkzjl+46jGNKH1tg/fPIurCUiTBnbDLbWIiSmFGHO8jiloVqv9nfE5NHNFM/XLOsIAFjxTSL2H6m+6hxVkXbrCN9DWxXP239RudrOrfA9uBQ0D2JnB5g1q+rEKEm5jbOvTkP7VfPg/n4g7t9Ox+VpC5B98ISiTtrOv2DiYIs2i2ZU/vDWxTicGfImSjOrr8REqskFLnGoioGgxgKisbGxGDBgAGQyGby9vQEAFy9ehKmpKSIiItChQ4d6B9Jr6NF670MNw71Ta22HQLXITK2+hCI1HoKcN6DGqkzGSfiN1exfJ2s7BFJhcFmCtkOo0fAZiVp77T1ft9Laa9eVWj38HTt2xPXr17Ft2zbEx1euJjBu3DiMHz8eZmZc/ouIiIiIqLFQe9Ceubk53nrrLU3GQkRERERUb7oyeVZb1E7479y5gxMnTiAzM1OxFv8DM2bMeOLAiIiIiIjoyamV8G/evBnTpk2DiYkJ7OzsYPDQmoUGBgZM+ImIiIiowbCHXzW1Ev6FCxciJCQE8+bNgyHXkCQiIiIiarTUSviLi4sxduxYJvtEREREpHVqLDr5TFErYw8KCsLOnTs1HQsREREREWmYWj38oaGhGDJkCPbv3w8vLy+IRCKl7WFhYRoJjoiIiIiInozaCX9ERATatm0LANUm7RIRERERNZRHV4wkZWol/KtWrcLGjRsxefJkDYdDRERERESapFbCLxaL4efnp+lYiIiIiIjqjctyqqbWpN2ZM2dizZo1mo6FiIiIiIg0TK0e/jNnziAyMhK///47OnToUG3S7p49ezQSHBERERERPRm1En5ra2sMHz5c07EQEREREdWbIHDSripqJfzfffcd5HI5LCwsAAApKSnYu3cvPD09MWDAAI0GSERERERE6lMr4R82bBiGDx+Ot99+G3l5eejZsydEIhGys7MRFhaGd955R9NxEhERERHViJN2VVNr0m5MTAx69+4NANi1axccHR1x8+ZNhIeH4+uvv9ZogEREREREpD61eviLi4thZWUFADhw4ACGDx8OQ0ND9OzZEzdv3tRogEREREREqrCHXzW1evhbtWqFvXv34tatW4iIiED//v0BAJmZmZBIJBoNkIiIiIiI1KdWwh8SEoIPP/wQ7u7u6NGjB3x9fQFU9vZ36dJFowESEREREZH61BrSM3LkSPTq1QtpaWnw9vZWlPfr1w+vvfaaxoIjIiIiInocOZflVEmthB8AnJyc4OTkpFTm4+PzxAEREREREZHmqJ3wExERERE1Bpy0q5paY/iJiIiIiEg3MOEnIiIiItJjHNJDRERERDpNkHPSrirs4SciIiIiakDffvst3N3dYWpqih49euDMmTMq6+/cuRPt2rWDqakpvLy88Oeff9br9ZjwExEREZFOE+SC1h719fPPPyM4OBiLFi1CTEwMvL29MWDAAGRmZtZY/+TJkxg3bhyCgoJw/vx5BAQEICAgALGxsXV+TSb8REREREQNJCwsDG+99RamTJmC9u3b44cffoC5uTk2btxYY/2vvvoKr7zyCmbPng1PT08sW7YMXbt2xTfffFPn12TCT0REREQ6TRDkWnvUR2lpKc6dOwd/f39FmaGhIfz9/XHq1Kka9zl16pRSfQAYMGBArfVrwkm7RERERERqkslkkMlkSmVisRhisbha3ezsbFRUVMDR0VGp3NHREfHx8TUePz09vcb66enpdY6RPfxERERERGoKDQ2FVCpVeoSGhmo7LCXs4SciIiIinSbX4i/tzps3D8HBwUplNfXuA4C9vT2MjIyQkZGhVJ6RkQEnJ6ca93FycqpX/Zqwh5+IiIiISE1isRgSiUTpUVvCb2Jigm7duuHQoUOKMrlcjkOHDsHX17fGfXx9fZXqA8DBgwdrrV8T9vATERERkU7TpR/eCg4OxqRJk9C9e3f4+Phg9erVKCoqwpQpUwAAgYGBcHV1VQwLmjlzJvr06YNVq1Zh8ODB2LFjB6Kjo7F27do6vyYTfiIiIiKiBjJmzBhkZWUhJCQE6enp6Ny5M/bv36+YmJuamgpDw6pBOM8//zy2b9+OBQsW4OOPP0br1q2xd+9edOzYsc6vaSAIgvYGPT2k19Cj2g6BauHeqbW2Q6BaZKZmPL4SaY0u9Tg9a8pkpdoOgWox+9fJ2g6BVBhclqDtEGr04si6L1GpaUd21X1ojbawh5+IiIiIdJo6v3j7LOGkXSIiIiIiPcYefiIiIiLSafX9xdtnDXv4iYiIiIj0GHv4iYiIiEincQy/auzhJyIiIiLSY0z4iYiIiIj0GIf0EBEREZFO4++eqMYefiIiIiIiPdZofmlXX8hkMoSGhmLevHkQi8XaDocewfZpvNg2jRfbpnFj+zRebBtqLJjwa1hBQQGkUiny8/MhkUi0HQ49gu3TeLFtGi+2TePG9mm82DbUWHBIDxERERGRHmPCT0RERESkx5jwExERERHpMSb8GiYWi7Fo0SJOzmmk2D6NF9um8WLbNG5sn8aLbUONBSftEhERERHpMfbwExERERHpMSb8RERERER6jAk/EREREZEeY8JPRE/Niy++iA8++ECjx9y8eTOsra01ekx9lJKSAgMDA1y4cKHWOo9ey8WLF6Nz584qjzt58mQEBARoJEbSLHd3d6xevVrbYdBj8G+ItMFY2wEQEdXHmDFjMGjQIG2HoRd4LfXL2bNnYWFhoe0wiKgRYsJPRDrFzMwMZmZm2g5DLzyNa1laWgoTExONHlPfaeqaOTg4aCAa0jX8m6O64JCeOtq/fz969eoFa2tr2NnZYciQIUhKSlJsP3nyJDp37gxTU1N0794de/furfZ1emxsLAYOHAhLS0s4Ojpi4sSJyM7O1sLZ6Kddu3bBy8sLZmZmsLOzg7+/P4qKigAA69evh6enJ0xNTdGuXTt89913iv3eeOMNdOrUCTKZDEDlm2eXLl0QGBiolfPQN+Xl5Zg+fTqkUins7e2xcOFCPFgN2N3dHZ988gkCAwNhaWkJNzc3/Pbbb8jKysKwYcNgaWmJTp06ITo6WnE8DulRJpfL8fnnn6NVq1YQi8Vo3rw5li9frtienJyMl156Cebm5vD29sapU6cU2x53LSsqKhAcHKx435szZw4eXcn5xRdfxPTp0/HBBx/A3t4eAwYMAPD497sXX3wRM2bMwJw5c2BrawsnJycsXrxYMxelkavpmj3uet27dw/jx4+HhYUFnJ2d8eWXX1YbMvfokJ7U1FTF35FEIsHo0aORkZGh2P5gCNfWrVvh7u4OqVSKsWPH4t69ew1xGRqN2u4dD4befPrpp3B0dIS1tTWWLl2K8vJyzJ49G7a2tmjatCk2bdqkdLzLly+jb9++iuNNnToVhYWFtb7+2bNn4eDggM8++wwAkJeXhzfffBMODg6QSCTo27cvLl68qKj/oN3Wr1+PFi1awNTU9OlcGNIrTPjrqKioCMHBwYiOjsahQ4dgaGiI1157DXK5HAUFBRg6dCi8vLwQExODZcuWYe7cuUr75+XloW/fvujSpQuio6Oxf/9+ZGRkYPTo0Vo6I/2SlpaGcePG4Y033kBcXByOHDmC4cOHQxAEbNu2DSEhIVi+fDni4uLw6aefYuHChdiyZQsA4Ouvv0ZRURE++ugjAMD8+fORl5eHb775RpunpDe2bNkCY2NjnDlzBl999RXCwsKwfv16xfYvv/wSfn5+OH/+PAYPHoyJEyciMDAQEyZMQExMDFq2bInAwMBqiSZVmjdvHlasWIGFCxfi6tWr2L59OxwdHRXb58+fjw8//BAXLlxAmzZtMG7cOJSXl9fp2KtWrcLmzZuxceNGnDhxAjk5Ofjll1+q1duyZQtMTEwQFRWFH374oc7vd1u2bIGFhQVOnz6Nzz//HEuXLsXBgwef7ILoiIev2YoVKx57vYKDgxEVFYXffvsNBw8exPHjxxETE1Pr8eVyOYYNG4acnBwcPXoUBw8eRHJyMsaMGaNULykpCXv37sXvv/+O33//HUePHsWKFSue2nk3NqruHQAQGRmJO3fu4NixYwgLC8OiRYswZMgQ2NjY4PTp03j77bcxbdo03L59G0BlrjBgwADY2Njg7Nmz2LlzJ/7++29Mnz69xtePjIzEyy+/jOXLlyvyhlGjRiEzMxN//fUXzp07h65du6Jfv37IyclR7JeYmIjdu3djz549KufpECkIpJasrCwBgHD58mXh+++/F+zs7ISSkhLF9nXr1gkAhPPnzwuCIAjLli0T+vfvr3SMW7duCQCEhISEhgxdL507d04AIKSkpFTb1rJlS2H79u1KZcuWLRN8fX0Vz0+ePCmIRCJh4cKFgrGxsXD8+PGnHvOzoE+fPoKnp6cgl8sVZXPnzhU8PT0FQRAENzc3YcKECYptaWlpAgBh4cKFirJTp04JAIS0tDRBEARh06ZNglQqbZgTaOQKCgoEsVgsrFu3rtq2GzduCACE9evXK8quXLkiABDi4uIEQah+LRctWiR4e3srnjs7Owuff/654nlZWZnQtGlTYdiwYYqyPn36CF26dFF67bq83/Xp00fo1auXUp3nnntOmDt3bt1OXoc9es0ed70KCgoEkUgk7Ny5U7E9Ly9PMDc3F2bOnKkoc3NzE7788ktBEAThwIEDgpGRkZCamqrY/qD9z5w5IwhCZXubm5sLBQUFijqzZ88WevToocnTbdRU3TsmTZokuLm5CRUVFYqytm3bCr1791Y8Ly8vFywsLISffvpJEARBWLt2rWBjYyMUFhYq6vzxxx+CoaGhkJ6erjjusGHDhD179giWlpbCjh07FHWPHz8uSCQS4f79+0qxtGzZUvjxxx8FQahsN5FIJGRmZmrgCtCzgj38dXT9+nWMGzcOHh4ekEgkcHd3B1D5lWlCQgI6deqk9LWaj4+P0v4XL17E4cOHYWlpqXi0a9cOAJSGBpF6vL290a9fP3h5eWHUqFFYt24dcnNzUVRUhKSkJAQFBSld+08++UTpuvv6+uLDDz/EsmXLMGvWLPTq1UuLZ6NfevbsCQMDA8VzX19fXL9+HRUVFQCATp06KbY96Jn28vKqVpaZmdkQ4eqUuLg4yGQy9OvXr9Y6D19fZ2dnAHW7lvn5+UhLS0OPHj0UZcbGxujevXu1ut26dVN6Xtf3u4djexDfs9LOD1+zx12v5ORklJWVKd1XpFIp2rZtW+vx4+Li0KxZMzRr1kxR1r59e1hbWyMuLk5R5u7uDisrK8XzZ6kNgNrvHQ906NABhoZVqZKjo6PS+5ORkRHs7OwU1ywuLg7e3t5Kk6f9/Pwgl8uRkJCgKDt9+jRGjRqFrVu3Kn3rcvHiRRQWFsLOzk7p/8ONGzeU/nbc3Nw4Z4PqhZN262jo0KFwc3PDunXr4OLiArlcjo4dO6K0tLRO+xcWFmLo0KGKMXoPe3ATJvUZGRnh4MGDOHnyJA4cOIA1a9Zg/vz52LdvHwBg3bp1SonLg30ekMvliIqKgpGRERITExs09medSCRS/PvBB4OayuRyecMGpgPqMuG2Ia7loyvD1PX97uHYHsT3rLTzw9fscdfrab4nPcttANR+7zh9+jSAmq+PJq5Zy5YtYWdnh40bN2Lw4MGKYxYWFsLZ2RlHjhypts/D8224GhPVF3v46+Du3btISEjAggUL0K9fP3h6eir1ALRt2xaXL19WTPoEKifhPKxr1664cuUK3N3d0apVK6UH/3A1w8DAAH5+fliyZAnOnz+vGB/r4uKC5OTkate9RYsWin1XrlyJ+Ph4HD16FPv37682CYvU9+DG+cA///yD1q1bK33gIvW0bt0aZmZmOHTokMaPLZVK4ezsrNR+5eXlOHfu3GP35ftd/Tzuenl4eEAkEindV/Lz83Ht2rVaj+np6Ylbt27h1q1birKrV68iLy8P7du3f6rno2tqunfUNFelLjw9PXHx4kXFghEAEBUVBUNDQ6VvZOzt7REZGYnExESMHj0aZWVlACr/L6Snp8PY2Lja/wV7e/snO1F6pjHhrwMbGxvY2dlh7dq1SExMRGRkJIKDgxXbX3/9dcjlckydOhVxcXGIiIjAF198AaCqR+29995DTk4Oxo0bh7NnzyIpKQkRERGYMmWKYmgDqe/06dP49NNPER0djdTUVOzZswdZWVnw9PTEkiVLEBoaiq+//hrXrl3D5cuXsWnTJoSFhQEAzp8/j5CQEKxfvx5+fn4ICwvDzJkzkZycrOWz0g+pqakIDg5GQkICfvrpJ6xZswYzZ87Udlh6wdTUFHPnzsWcOXMQHh6OpKQk/PPPP9iwYYNGjj9z5kysWLECe/fuRXx8PN59913k5eU9dj++39XP466XlZUVJk2ahNmzZ+Pw4cO4cuUKgoKCYGhoqDRc7mH+/v7w8vLC+PHjERMTgzNnziAwMBB9+vSpcVjWs0rVvUMd48ePh6mpKSZNmoTY2FgcPnwY77//PiZOnKg0mR4AmjRpgsjISMTHxysm0/v7+8PX1xcBAQE4cOAAUlJScPLkScyfP19ptTKi+mLCXweGhobYsWMHzp07h44dO+I///kPVq5cqdgukUiwb98+XLhwAZ07d8b8+fMREhICAIpx/S4uLoiKikJFRQX69+8PLy8vfPDBB7C2tlYaH0jqkUgkOHbsGAYNGoQ2bdpgwYIFWLVqFQYOHIg333wT69evx6ZNm+Dl5YU+ffpg8+bNaNGiBe7fv48JEyZg8uTJGDp0KABg6tSpeOmllzBx4kQmJxoQGBiIkpIS+Pj44L333sPMmTMxdepUbYelNxYuXIhZs2YhJCQEnp6eGDNmjMbGYM+aNQsTJ07EpEmT4OvrCysrK7z22muP3Y/vd/VTl+sVFhYGX19fDBkyBP7+/vDz81MsNVwTAwMD/Prrr7CxscELL7wAf39/eHh44Oeff27IU2v0VN071GFubo6IiAjk5OTgueeew8iRI9GvX79aV31zcnJCZGQkLl++jPHjx0Mul+PPP//ECy+8gClTpqBNmzYYO3Ysbt68We0DA1F9GAgC17p7GrZt24YpU6YgPz+fPxJEREQaVVRUBFdXV6xatQpBQUHaDoeIGjlO2tWQ8PBweHh4wNXVFRcvXsTcuXMxevRoJvtERPTEzp8/j/j4ePj4+CA/Px9Lly4FAAwbNkzLkRGRLmDCryHp6ekICQlBeno6nJ2dMWrUKKVfuyQiInoSX3zxBRISEmBiYoJu3brh+PHjnMhJRHXCIT1ERERERHqMs6eIiIiIiPQYE34iIiIiIj3GhJ+IiIiISI8x4SciIiIi0mNM+ImIiIiI9BgTfiIiIiIiPcaEn4iIiIhIjzHhJyIiIiLSY0z4iYiIiIj02P8DXYsLoRmVAB4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          no       0.91      0.96      0.93       442\n",
      "         yes       0.79      0.62      0.69       113\n",
      "\n",
      "    accuracy                           0.89       555\n",
      "   macro avg       0.85      0.79      0.81       555\n",
      "weighted avg       0.88      0.89      0.88       555\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score, classification_report\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Кодирование категориальных признаков\n",
    "label_encoders = {}\n",
    "for column in [\"sex\", \"region\"]:  # Категориальные столбцы\n",
    "    le = LabelEncoder()\n",
    "    X_clf_test[column] = le.fit_transform(X_clf_test[column])\n",
    "    X_clf_train[column] = le.fit_transform(X_clf_train[column])\n",
    "    X_clf[column] = le.fit_transform(X_clf[column])\n",
    "    label_encoders[column] = le\n",
    "\n",
    "# 1. Оценка предсказательной способности (Accuracy и ROC-AUC для бинарной классификации)\n",
    "model = RandomForestClassifier(random_state=42)\n",
    "\n",
    "start_time = time.perf_counter()\n",
    "model.fit(X_clf_train, y_clf_train)\n",
    "end_time = time.perf_counter()\n",
    "\n",
    "train_time = end_time - start_time\n",
    "\n",
    "y_pred = model.predict(X_clf_test)\n",
    "accuracy = accuracy_score(y_clf_test, y_pred)\n",
    "roc_auc = roc_auc_score(y_clf_test, model.predict_proba(X_clf_test)[:, 1])\n",
    "\n",
    "print(f\"Accuracy: {accuracy}\")\n",
    "print(f\"ROC-AUC: {roc_auc}\")\n",
    "print(f\"Время обучения модели: {train_time:.4f} секунд\")\n",
    "\n",
    "# 2. Оценка скорости вычисления (время предсказания)\n",
    "start_time = time.perf_counter()\n",
    "y_pred = model.predict(X_clf_test)\n",
    "end_time = time.perf_counter()\n",
    "predict_time = end_time - start_time\n",
    "\n",
    "print(f\"Время предсказания: {predict_time:.4f} секунд\")\n",
    "\n",
    "# 3. Оценка надежности модели с помощью перекрестной проверки\n",
    "cv_scores = cross_val_score(model, X_clf, y_clf, cv=5, scoring='accuracy')\n",
    "mean_cv_score = np.mean(cv_scores)\n",
    "print(f\"Средняя точность по кросс-валидации: {mean_cv_score:.4f}\")\n",
    "\n",
    "# 4. Оценка корреляции признаков с целевой переменной\n",
    "# Сначала нужно закодировать целевую переменную, если она не числовая\n",
    "if y_clf.dtypes == 'object':\n",
    "    y_clf = LabelEncoder().fit_transform(y_clf)\n",
    "if isinstance(y_clf, np.ndarray):\n",
    "    y_clf = pd.Series(y_clf)\n",
    "\n",
    "# Преобразуем y_clf в DataFrame с названием 'smoker'\n",
    "y_clf = pd.Series(y_clf, name='smoker')\n",
    "\n",
    "# Объединяем X_clf с y_clf\n",
    "correlation_matrix = pd.concat([X_clf, y_clf], axis=1).corr()\n",
    "\n",
    "# Корреляция признаков с целевой переменной (столбец 'smoker')\n",
    "correlation_with_target = correlation_matrix['smoker'].sort_values(ascending=False)\n",
    "print(\"Корреляция признаков с целевой переменной:\")\n",
    "print(correlation_with_target)\n",
    "\n",
    "# Визуализация корреляции\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f')\n",
    "plt.title('Корреляционная матрица признаков')\n",
    "plt.show()\n",
    "\n",
    "# Дополнительная информация о модели\n",
    "print(classification_report(y_clf_test, y_pred))\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вроде всё, мораль такова - используйте ctrl+s"
   ]
  }
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