AIM-PIbd-31-Ievlewa-M-D/lab3/lab3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Датасет: Цены на акции\n",
    "https://www.kaggle.com/datasets/nancyalaswad90/yamana-gold-inc-stock-Volume\n",
    "##### О наборе данных: \n",
    "Yamana Gold Inc. — это канадская компания, которая занимается разработкой и управлением золотыми, серебряными и медными рудниками, расположенными в Канаде, Чили, Бразилии и Аргентине. Головной офис компании находится в Торонто.\n",
    "\n",
    "Yamana Gold была основана в 1994 году и уже через год была зарегистрирована на фондовой бирже Торонто. В 2007 году она стала участником Нью-Йоркской фондовой биржи, а в 2020 году — Лондонской.\n",
    "В 2003 году компания претерпела значительные изменения: была проведена реструктуризация, в результате которой Питер Марроне занял пост главного исполнительного директора. Кроме того, Yamana объединилась с бразильской компанией Santa Elina Mines Corporation. Благодаря этому слиянию Yamana получила доступ к капиталу, накопленному Santa Elina, что позволило ей начать разработку и эксплуатацию рудника Чапада. Затем компания объединилась с другими организациями, зарегистрированными на бирже TSX: RNC Gold, Desert Sun Mining, Viceroy Exploration, Northern Orion Resources, Meridian Gold, Osisko Mining и Extorre Gold Mines. Каждая из них внесла свой вклад в разработку месторождения или проект, который в итоге был успешно запущен.\n",
    "##### Таким образом:\n",
    "* Объект наблюдения - цены и объемы акций компании\n",
    "* Атрибуты: 'Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'\n",
    "\n",
    "##### Бизнес цели:\n",
    "*  Прогнозирование будущей цены акций.\n",
    "        Использование данных для создания модели, которая будет предсказывать цену акций компании в будущем.\n",
    "*  Определение волатильности акций.\n",
    "        Определение, колебаний цен акций, что поможет инвесторам понять риски.\n",
    "\n",
    "##### Технические цели:\n",
    "*  Разработать модель машинного обучения для прогноза цены акций на основе имеющихся данных.\n",
    "*  Разработать метрику и модель для оценки волатильности акций на основе исторических данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество колонок: 7\n",
      "Колонки: Date, Open, High, Low, Close, Adj Close, Volume\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5251 entries, 0 to 5250\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype         \n",
      "---  ------     --------------  -----         \n",
      " 0   Date       5251 non-null   datetime64[ns]\n",
      " 1   Open       5251 non-null   float64       \n",
      " 2   High       5251 non-null   float64       \n",
      " 3   Low        5251 non-null   float64       \n",
      " 4   Close      5251 non-null   float64       \n",
      " 5   Adj Close  5251 non-null   float64       \n",
      " 6   Volume     5251 non-null   int64         \n",
      "dtypes: datetime64[ns](1), float64(5), int64(1)\n",
      "memory usage: 287.3 KB\n"
     ]
    },
    {
     "data": {
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       "<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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2001-06-22</td>\n",
       "      <td>3.428571</td>\n",
       "      <td>3.428571</td>\n",
       "      <td>3.428571</td>\n",
       "      <td>3.428571</td>\n",
       "      <td>2.806002</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001-06-25</td>\n",
       "      <td>3.428571</td>\n",
       "      <td>3.428571</td>\n",
       "      <td>3.428571</td>\n",
       "      <td>3.428571</td>\n",
       "      <td>2.806002</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001-06-26</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>3.039837</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001-06-27</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>3.039837</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001-06-28</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>3.039837</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date      Open      High       Low     Close  Adj Close  Volume\n",
       "0 2001-06-22  3.428571  3.428571  3.428571  3.428571   2.806002       0\n",
       "1 2001-06-25  3.428571  3.428571  3.428571  3.428571   2.806002       0\n",
       "2 2001-06-26  3.714286  3.714286  3.714286  3.714286   3.039837       0\n",
       "3 2001-06-27  3.714286  3.714286  3.714286  3.714286   3.039837       0\n",
       "4 2001-06-28  3.714286  3.714286  3.714286  3.714286   3.039837       0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\".//static//csv//Stocks.csv\", sep=\",\")\n",
    "print('Количество колонок: ' + str(df.columns.size))  \n",
    "print('Колонки: ' + ', '.join(df.columns)+'\\n')\n",
    "df['Date'] = pd.to_datetime(df['Date'], errors='coerce')\n",
    "\n",
    "df.info()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Подготовка данных:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Получение сведений о пропущенных данных\n",
    "Типы пропущенных данных:\n",
    "\n",
    "- None - представление пустых данных в Python\n",
    "- NaN - представление пустых данных в Pandas\n",
    "- '' - пустая строка"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date         0\n",
      "Open         0\n",
      "High         0\n",
      "Low          0\n",
      "Close        0\n",
      "Adj Close    0\n",
      "Volume       0\n",
      "dtype: int64\n",
      "\n",
      "Date         False\n",
      "Open         False\n",
      "High         False\n",
      "Low          False\n",
      "Close        False\n",
      "Adj Close    False\n",
      "Volume       False\n",
      "dtype: bool\n",
      "\n",
      "Количество бесконечных значений в каждом столбце:\n",
      "Date         0\n",
      "Open         0\n",
      "High         0\n",
      "Low          0\n",
      "Close        0\n",
      "Adj Close    0\n",
      "Volume       0\n",
      "dtype: int64\n",
      "Date процент пустых значений: %0.00\n",
      "Open процент пустых значений: %0.00\n",
      "High процент пустых значений: %0.00\n",
      "Low процент пустых значений: %0.00\n",
      "Close процент пустых значений: %0.00\n",
      "Adj Close процент пустых значений: %0.00\n",
      "Volume процент пустых значений: %0.00\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "# Количество пустых значений признаков\n",
    "print(df.isnull().sum())\n",
    "print()\n",
    "\n",
    "# Есть ли пустые значения признаков\n",
    "print(df.isnull().any())\n",
    "print()\n",
    "\n",
    "# Проверка на бесконечные значения\n",
    "print(\"Количество бесконечных значений в каждом столбце:\")\n",
    "print(np.isinf(df).sum())\n",
    "\n",
    "# Процент пустых значений признаков\n",
    "for i in df.columns:\n",
    "    null_rate = df[i].isnull().sum() / len(df) * 100\n",
    "    print(f\"{i} процент пустых значений: %{null_rate:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Таким образом, пропущенных значений не найдено."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Проверка выбросов данных и устранение их при наличии:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "До устранения выбросов:\n",
      "Колонка Open:\n",
      "  Есть выбросы: Нет\n",
      "  Количество выбросов: 0\n",
      "  Минимальное значение: 1.142857\n",
      "  Максимальное значение: 20.42\n",
      "  1-й квартиль (Q1): 2.857143\n",
      "  3-й квартиль (Q3): 10.65\n",
      "\n",
      "После устранения выбросов:\n",
      "Колонка Open:\n",
      "  Есть выбросы: Нет\n",
      "  Количество выбросов: 0\n",
      "  Минимальное значение: 1.142857\n",
      "  Максимальное значение: 20.42\n",
      "  1-й квартиль (Q1): 2.857143\n",
      "  3-й квартиль (Q3): 10.65\n",
      "\n",
      "До устранения выбросов:\n",
      "Колонка High:\n",
      "  Есть выбросы: Нет\n",
      "  Количество выбросов: 0\n",
      "  Минимальное значение: 1.142857\n",
      "  Максимальное значение: 20.59\n",
      "  1-й квартиль (Q1): 2.88\n",
      "  3-й квартиль (Q3): 10.86\n",
      "\n",
      "После устранения выбросов:\n",
      "Колонка High:\n",
      "  Есть выбросы: Нет\n",
      "  Количество выбросов: 0\n",
      "  Минимальное значение: 1.142857\n",
      "  Максимальное значение: 20.59\n",
      "  1-й квартиль (Q1): 2.88\n",
      "  3-й квартиль (Q3): 10.86\n",
      "\n",
      "До устранения выбросов:\n",
      "Колонка Low:\n",
      "  Есть выбросы: Нет\n",
      "  Количество выбросов: 0\n",
      "  Минимальное значение: 1.142857\n",
      "  Максимальное значение: 20.09\n",
      "  1-й квартиль (Q1): 2.81\n",
      "  3-й квартиль (Q3): 10.425\n",
      "\n",
      "После устранения выбросов:\n",
      "Колонка Low:\n",
      "  Есть выбросы: Нет\n",
      "  Количество выбросов: 0\n",
      "  Минимальное значение: 1.142857\n",
      "  Максимальное значение: 20.09\n",
      "  1-й квартиль (Q1): 2.81\n",
      "  3-й квартиль (Q3): 10.425\n",
      "\n",
      "До устранения выбросов:\n",
      "Колонка Close:\n",
      "  Есть выбросы: Нет\n",
      "  Количество выбросов: 0\n",
      "  Минимальное значение: 1.142857\n",
      "  Максимальное значение: 20.389999\n",
      "  1-й квартиль (Q1): 2.857143\n",
      "  3-й квартиль (Q3): 10.64\n",
      "\n",
      "После устранения выбросов:\n",
      "Колонка Close:\n",
      "  Есть выбросы: Нет\n",
      "  Количество выбросов: 0\n",
      "  Минимальное значение: 1.142857\n",
      "  Максимальное значение: 20.389999\n",
      "  1-й квартиль (Q1): 2.857143\n",
      "  3-й квартиль (Q3): 10.64\n",
      "\n",
      "До устранения выбросов:\n",
      "Колонка Adj Close:\n",
      "  Есть выбросы: Нет\n",
      "  Количество выбросов: 0\n",
      "  Минимальное значение: 0.935334\n",
      "  Максимальное значение: 17.543156\n",
      "  1-й квартиль (Q1): 2.537094\n",
      "  3-й квартиль (Q3): 8.951944999999998\n",
      "\n",
      "После устранения выбросов:\n",
      "Колонка Adj Close:\n",
      "  Есть выбросы: Нет\n",
      "  Количество выбросов: 0\n",
      "  Минимальное значение: 0.935334\n",
      "  Максимальное значение: 17.543156\n",
      "  1-й квартиль (Q1): 2.537094\n",
      "  3-й квартиль (Q3): 8.951944999999998\n",
      "\n",
      "До устранения выбросов:\n",
      "Колонка Volume:\n",
      "  Есть выбросы: Да\n",
      "  Количество выбросов: 95\n",
      "  Минимальное значение: 0\n",
      "  Максимальное значение: 76714000\n",
      "  1-й квартиль (Q1): 2845900.0\n",
      "  3-й квартиль (Q3): 13272450.0\n",
      "\n",
      "После устранения выбросов:\n",
      "Колонка Volume:\n",
      "  Есть выбросы: Нет\n",
      "  Количество выбросов: 0\n",
      "  Минимальное значение: 0.0\n",
      "  Максимальное значение: 28912275.0\n",
      "  1-й квартиль (Q1): 2845900.0\n",
      "  3-й квартиль (Q3): 13272450.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "numeric_columns = ['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume']\n",
    "\n",
    "for column in numeric_columns:\n",
    "    if pd.api.types.is_numeric_dtype(df[column]):  # Проверяем, является ли колонка числовой\n",
    "        q1 = df[column].quantile(0.25)  # Находим 1-й квартиль (Q1)\n",
    "        q3 = df[column].quantile(0.75)  # Находим 3-й квартиль (Q3)\n",
    "        iqr = q3 - q1  # Вычисляем межквартильный размах (IQR)\n",
    "\n",
    "        # Определяем границы для выбросов\n",
    "        lower_bound = q1 - 1.5 * iqr  # Нижняя граница\n",
    "        upper_bound = q3 + 1.5 * iqr  # Верхняя граница\n",
    "\n",
    "        # Подсчитываем количество выбросов\n",
    "        outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)]\n",
    "        outlier_count = outliers.shape[0]\n",
    "\n",
    "        print(\"До устранения выбросов:\")\n",
    "        print(f\"Колонка {column}:\")\n",
    "        print(f\"  Есть выбросы: {'Да' if outlier_count > 0 else 'Нет'}\")\n",
    "        print(f\"  Количество выбросов: {outlier_count}\")\n",
    "        print(f\"  Минимальное значение: {df[column].min()}\")\n",
    "        print(f\"  Максимальное значение: {df[column].max()}\")\n",
    "        print(f\"  1-й квартиль (Q1): {q1}\")\n",
    "        print(f\"  3-й квартиль (Q3): {q3}\\n\")\n",
    "\n",
    "        # Устраняем выбросы: заменяем значения ниже нижней границы на саму нижнюю границу, а выше верхней — на верхнюю\n",
    "        if outlier_count != 0:\n",
    "            df[column] = df[column].apply(lambda x: lower_bound if x < lower_bound else upper_bound if x > upper_bound else x)\n",
    "        \n",
    "        # Подсчитываем количество выбросов\n",
    "        outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)]\n",
    "        outlier_count = outliers.shape[0]\n",
    "\n",
    "        print(\"После устранения выбросов:\")\n",
    "        print(f\"Колонка {column}:\")\n",
    "        print(f\"  Есть выбросы: {'Да' if outlier_count > 0 else 'Нет'}\")\n",
    "        print(f\"  Количество выбросов: {outlier_count}\")\n",
    "        print(f\"  Минимальное значение: {df[column].min()}\")\n",
    "        print(f\"  Максимальное значение: {df[column].max()}\")\n",
    "        print(f\"  1-й квартиль (Q1): {q1}\")\n",
    "        print(f\"  3-й квартиль (Q3): {q3}\\n\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выбросы присутствовали, но мы их устранили."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Разбиение на выборки:\n",
    "\n",
    "Разобьем наш набор на обучающую, контрольную и тестовую выборки для устранения проблемы просачивания данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки:  4200\n",
      "Размер контрольной выборки:  1051\n",
      "Размер тестовой выборки:  1051\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Разделение данных на обучающую и тестовую выборки (80% - обучение, 20% - тестовая)\n",
    "X_train, X_test = train_test_split(df, test_size=0.2, random_state=42)\n",
    "\n",
    "# Разделение данных на обучающую и контрольную выборки (80% - обучение, 20% - контроль)\n",
    "X_train, X_val = train_test_split(df, test_size=0.2, random_state=42)\n",
    "\n",
    "print(\"Размер обучающей выборки: \", len(X_train))\n",
    "print(\"Размер контрольной выборки: \", len(X_test))\n",
    "print(\"Размер тестовой выборки: \", len(X_val))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tjmA7Q24Ns2bN0j333JNnm5sDRmZmphITE/Xggw/etl+LxaJvv/1W5cqVK7BPSUpKSpJ04x/zBfXp6+ur5cuX5zn/1jAdERGh1157zWbaO++8k+/NpuLi4rRs2TJ9/PHHeV4bn5OTo6ZNm+rtt9/Oc/mgoKB8a8918uRJtWzZUrNnz9af//xnffDBB3n+weRO1sf27dvVs2dPtW3bVu+++64CAgJUoUIFLV261O7mcc7g5+enjz/+WNKNP/wsWbJEXbt21Y4dO9S0adN8l6tevXqBQez06dMKDg4u0XB189HzghR2GyQlJcnd3V1VqlTJt8/Cfme88sor1vse5OrRo8cd1Z+f999/X6dPn9Z3331XqOXy2yZ3uq0eeughvfDCC5Ju3Adh5syZ6tChg/bu3WuzTXbt2qXXX39de/bs0bhx49S1a9d8r8v//vvvtWHDBsXGxhY4du7PXH79AEBJIrQDgBN9/vnn6tChg95//32b6SkpKTb/OKxbt652796trKysYrmZWq7cI+m5DMPQyZMnrUfPcm9w5+npme/dum926NAhZWVlFfiHitx+DcNQaGioGjRocNt+jx49KovFkueR0Zv73LBhg+6///47ClU+Pj52n6mgG01NmjRJ99xzj/r27Zvv+IcOHVKnTp2KHBhzL03w8/PTV199pQkTJqhbt252f3C4k/XxxRdfqFKlSvruu+9sTrdeunSpXd05OTk6evRovn+Yyf05OHLkiN1R0Vy1a9eWJB0/ftx6SUCu48ePW+fnqlSpks3679mzp7y9vfXOO+/ovffey/dzhYWFafny5UpNTZWXl5fNvOzsbB06dEhdu3bNd/mi1Hqr0NBQ7d+/Xzk5OTZH248dOybpxp3OpTvfBrmOHj1628sD7vQ7I1fTpk3tfs5v/UNZ3bp1deTIkQLHzXXlyhVFR0dr1KhRt11PJ06cUGhoqPX9yZMnlZOTY10/tWvXVk5Ojk6cOGHzuc+ePauUlBS7/gMCAmw+S8OGDdW6dWutWbNG/fv3t04fOnSoJk+erDNnzugPf/iDxo0bp48++siuPsMw9NJLL+mRRx7RH//4xwI/S3x8vHx8fAp9dg4AFAeuaQcAJypXrpzdqb6rVq2ye+xU7969lZycrHfeeceuj4JOFb6dDz/8UJcuXbK+//zzz5WYmKioqChJN+6YXLduXb355pu6fPmy3fLnz5+3q71cuXJ5Pk7tZo8++qjKlSun6Ohou/oNw9CFCxes77Ozs/XFF1+oVatWBZ423KdPH12/fl2vvvqq3bzs7GzrqcdFERsbq6+++kozZszIN5D36dNHv/32W553oL569arS09NvO06DBg3k5+cnSZo/f75ycnL03HPP2bS50/VRrlw5WSwWm9OnT58+bfeHiV69esnFxUXTp0+3O1Kbu20eeugheXh4KCYmxu66/9w29957r3x9fbVo0SKb08C//fZbxcXF5Xun9FyZmZnKzs6+7ePxIiMjZRiG9u3bZzfv+++/V2pqqh5++OEC+/i9tXbr1k1JSUn67LPPrNNyT1t3dXW1Bss73QbSjcsFdu7cafdHhFvd6XdGYfTu3VuHDh2yu1u7ZP/9MnfuXKWnp+vll1++bb8LFiyweT9//nxJsn6/5D7lYs6cOTbtcs9Wud12uHr1qiTZ/czknlkQGBiomTNn6uOPP7Z7fKF043T7w4cP5/k0iFvt27dPkZGRt20HACWBI+0A4ER/+tOfNH36dD355JNq3bq1fvrpJy1fvlx16tSxaffEE0/oww8/1Pjx4/Xjjz+qTZs2Sk9P14YNGzRq1KjbhpT8eHt764EHHtCTTz6ps2fPas6cOapXr56GDx8uSXJxcdE//vEPRUVFqXHjxnryySdVs2ZN/fbbb9q8ebM8PT31z3/+U+np6VqwYIHmzZunBg0a2DzLOTfsHz58WLGxsYqMjFTdunX12muvadKkSTp9+rR69eolDw8PxcfH68svv9SIESP0/PPPa8OGDZoyZYoOHz6sf/7znwV+lnbt2mnkyJGKiYnRwYMH9dBDD6lChQo6ceKEVq1apblz5+qxxx4r0nr6/vvv9eCDDxZ4tsGf//xnrVy5Un/5y1+0efNm3X///bp+/bqOHTumlStX6rvvvrvtGQg38/f316xZs/TUU09p0KBB6tatW6HWR/fu3fX222+ra9euGjBggM6dO6cFCxaoXr16Onz4sLVdvXr19PLLL+vVV19VmzZt9Oijj8rV1VV79uxRYGCgYmJi5OnpqdmzZ+upp57SfffdpwEDBqhatWo6dOiQrly5og8++EAVKlTQzJkz9eSTT6pdu3bq37+/9TFqISEhGjdunE196enpNqfHf/TRR7p27VqeN0O72QMPPKDq1atrw4YNNgH3s88+0/PPPy9XV1ddvXrV2ndu/9evX9eaNWvUq1evQtd6q2HDhmnhwoUaMmSI9u7dq9DQUK1Zs0YbN27UjBkzrNeW3+k2WLhwoWJiYlS5cmU9++yzBY59p98ZhfHCCy/o888/1+OPP66hQ4cqPDxcFy9e1Nq1a7Vo0SI1b97c2vb777/X66+/nuf187eKj49Xz5491bVrV8XGxurjjz/WgAEDrP01b95cgwcP1uLFi5WSkqJ27drpxx9/1AcffKBevXqpQ4cONv3997//tW7X3377Te+88448PT3zvRmddOOxiJ988on+8pe/6MiRIzb3Ovj+++81fPjwAs9YkW7cj+Lw4cN2N9YDAIdxwh3rAeCul/vItz179hTY7tq1a8aECROMgIAAw83Nzbj//vuN2NhYu8c8GcaNR0e9/PLLRmhoqFGhQgXD39/feOyxx4xTp04ZhlG0R759+umnxqRJkwxfX1/Dzc3N6N69u83jpnIdOHDAePTRR43q1asbrq6uRu3atY0+ffoYGzdutBn7dq9bH6X0xRdfGA888IBRpUoVo0qVKkZYWJgxevRo4/jx44ZhGMYzzzxjtG3b1li3bp1dTXk94swwbjw6LDw83HBzczM8PDyMpk2bGi+++KJx5swZa5vCPvLNYrEY+/bts5me1zbKzMw0Zs6caTRu3NhwdXU1qlWrZoSHhxvR0dFGamqq3Xi3688wDKNjx45GcHCwcenSpUKvj/fff9+oX7++4erqaoSFhRlLly7Nd70tWbLEaNGihbXudu3aGevXr7dps3btWqN169aGm5ub4enpabRq1cr49NNPbdp89tln1n68vb2NgQMHWh9xmGvw4ME2Pxfu7u5Gy5YtjY8++qjAdZTr2WefNerVq2czLfcxbAW9bn3M3J3Ump9z584ZQ4cONXx8fIyKFSsaTZo0Mf7+97/btbuTbdCqVSvj8ccfN44dO2a3fF6PfLuT74zCPPLNMAzjwoULxpgxY4yaNWsaFStWNGrVqmUMHjzY+vi63P4CAgKM9PR0m2WVzyPfjh49ajz22GOGh4eHUa1aNWPMmDF2jwzMysoyoqOjrd9rQUFBxqRJk4xr167ZtLt1+/r4+BgPPfSQERsba22T16PcDMMwjh8/blSqVMkYN26cTTs3Nzfjt99+sxvn1nWzcOFCo3LlyjaPxwQAR7IYxu84rxIAUCpt2bJFHTp00KpVq4p89Plmp0+fVmhoqOLj463Xq95q2rRpOn36tJYtW/a7x0PZ9t///ldhYWH69ttvrUdZQ0JCNG3aNA0ZMiTPZbZs2aIhQ4bY3NkdJWPatGmKjo7W+fPn74obt7Vo0ULt27fX7NmznV0KgDKKa9oBAECpUqdOHQ0bNqzAZ4QDxWHdunU6ceKEJk2a5OxSAJRhXNMOAPjd3N3dNXDgwAJvjNasWTMFBgY6sCrczRYuXGjz/pFHHrHe5T4vfn5+t71eHrhV165d87wJJwA4EqEdAPC7+fj42Nz4Ky+PPvqog6pBWXS7U5cbNWrE6c0AgFKJa9oBAAAAADAprmkHAAAAAMCkCO0AAAAAAJgU17RLysnJ0ZkzZ+Th4SGLxeLscgAAAAAAdznDMHTp0iUFBgbKxSX/4+mEdklnzpxRUFCQs8sAAAAAAJQxv/zyi2rVqpXvfEK7JA8PD0k3Vpanp6eTqwEAAAAA3O3S0tIUFBRkzaP5IbRL1lPiPT09Ce0AAAAAAIe53SXa3IgOAAAAAACTIrQDAAAAAGBShHYAAAAAAEyK0A4AAAAAgEkR2gEAAAAAMClCOwAAAAAAJkVoBwAAAADApAjtAAAAAACYFKEdAAAAAACTIrQDAAAAAGBShHYAAAAAAEyK0A4AAAAAgEkR2gEAAAAAMClCOwAAAAAAJkVoBwAAAADApJwa2rdt26YePXooMDBQFotFa9assZlvsVjyfM2aNcvaJiQkxG7+jBkzHPxJAAAAAAAofk4N7enp6WrevLkWLFiQ5/zExESb15IlS2SxWNS7d2+bdtOnT7dp98wzzziifAAAAAAASlR5Zw4eFRWlqKiofOf7+/vbvP/qq6/UoUMH1alTx2a6h4eHXVsAAAAAAEo7p4b2wjh79qy++eYbffDBB3bzZsyYoVdffVXBwcEaMGCAxo0bp/Ll8/9oGRkZysjIsL5PS0srkZpReAkJCUpOTnbYeD4+PgoODnbYeAAAAABQGKUmtH/wwQfy8PDQo48+ajP92WefVcuWLeXt7a0ffvhBkyZNUmJiot5+++18+4qJiVF0dHRJl4xCSkhIUFhYI129esVhY7q5VdaxY3EEdwAAAACmZDEMw3B2EdKNm859+eWX6tWrV57zw8LC9OCDD2r+/PkF9rNkyRKNHDlSly9flqura55t8jrSHhQUpNTUVHl6ehb5M+D32b9/v8LDwxUxdKo8A0JKfLy0xNPavSRa+/btU8uWLUt8PAAAAADIlZaWJi8vr9vm0FJxpH379u06fvy4Pvvss9u2jYiIUHZ2tk6fPq2GDRvm2cbV1TXfQA/n8wwIkXdw3tsOAAAAAMqSUvGc9vfff1/h4eFq3rz5bdsePHhQLi4u8vX1dUBlAAAAAACUHKceab98+bJOnjxpfR8fH6+DBw/K29vbeo1xWlqaVq1apbfeestu+djYWO3evVsdOnSQh4eHYmNjNW7cOA0aNEjVqlVz2OcAAAAAAKAkODW07927Vx06dLC+Hz9+vCRp8ODBWrZsmSRpxYoVMgxD/fv3t1ve1dVVK1as0LRp05SRkaHQ0FCNGzfO2g8AAAAAAKWZU0N7+/btdbv74I0YMUIjRozIc17Lli21a9eukigNAAAAAACnKxXXtAMAAAAAUBYR2gEAAAAAMClCOwAAAAAAJkVoBwAAAADApAjtAAAAAACYFKEdAAAAAACTIrQDAAAAAGBShHYAAAAAAEyK0A4AAAAAgEkR2gEAAAAAMClCOwAAAAAAJkVoBwAAAADApAjtAAAAAACYFKEdAAAAAACTIrQDAAAAAGBShHYAAAAAAEyK0A4AAAAAgEkR2gEAAAAAMClCOwAAAAAAJkVoBwAAAADApAjtAAAAAACYFKEdAAAAAACTIrQDAAAAAGBShHYAAAAAAEyK0A4AAAAAgEkR2gEAAAAAMClCOwAAAAAAJkVoBwAAAADApAjtAAAAAACYFKEdAAAAAACTIrQDAAAAAGBShHYAAAAAAEyK0A4AAAAAgEkR2gEAAAAAMClCOwAAAAAAJkVoBwAAAADApAjtAAAAAACYFKEdAAAAAACTIrQDAAAAAGBShHYAAAAAAEyK0A4AAAAAgEkR2gEAAAAAMClCOwAAAAAAJkVoBwAAAADApAjtAAAAAACYFKEdAAAAAACTIrQDAAAAAGBShHYAAAAAAEyK0A4AAAAAgEkR2gEAAAAAMClCOwAAAAAAJkVoBwAAAADApJwa2rdt26YePXooMDBQFotFa9assZk/ZMgQWSwWm1fXrl1t2ly8eFEDBw6Up6enqlatqmHDhuny5csO/BQAAAAAAJQMp4b29PR0NW/eXAsWLMi3TdeuXZWYmGh9ffrppzbzBw4cqH//+99av369vv76a23btk0jRowo6dIBAAAAAChx5Z05eFRUlKKiogps4+rqKn9//zznxcXFad26ddqzZ4/uvfdeSdL8+fPVrVs3vfnmmwoMDCz2mgEAAAAAcBTTX9O+ZcsW+fr6qmHDhnr66ad14cIF67zY2FhVrVrVGtglqXPnznJxcdHu3bvz7TMjI0NpaWk2LwAAAAAAzMbUob1r16768MMPtXHjRs2cOVNbt25VVFSUrl+/LklKSkqSr6+vzTLly5eXt7e3kpKS8u03JiZGXl5e1ldQUFCJfg4AAAAAAIrCqafH306/fv2s/9+0aVM1a9ZMdevW1ZYtW9SpU6ci9ztp0iSNHz/e+j4tLY3gDgAAAAAwHVMfab9VnTp15OPjo5MnT0qS/P39de7cOZs22dnZunjxYr7XwUs3rpP39PS0eQEAAAAAYDalKrT/+uuvunDhggICAiRJkZGRSklJ0b59+6xtNm3apJycHEVERDirTAAAAAAAioVTT4+/fPmy9ai5JMXHx+vgwYPy9vaWt7e3oqOj1bt3b/n7++vUqVN68cUXVa9ePXXp0kWS1KhRI3Xt2lXDhw/XokWLlJWVpTFjxqhfv37cOR4AAAAAUOo59Uj73r171aJFC7Vo0UKSNH78eLVo0UKvvPKKypUrp8OHD6tnz55q0KCBhg0bpvDwcG3fvl2urq7WPpYvX66wsDB16tRJ3bp10wMPPKDFixc76yMBAAAAAFBsnHqkvX379jIMI9/533333W378Pb21ieffFKcZQEAAAAAYAql6pp2AAAAAADKEkI7AAAAAAAmRWgHAAAAAMCkCO0AAAAAAJgUoR0AAAAAAJMitAMAAAAAYFKEdgAAAAAATIrQDgAAAACASRHaAQAAAAAwKUI7AAAAAAAmRWgHAAAAAMCkCO0AAAAAAJgUoR0AAAAAAJMitAMAAAAAYFKEdgAAAAAATIrQDgAAAACASRHaAQAAAAAwKUI7AAAAAAAmRWgHAAAAAMCkCO0AAAAAAJgUoR0AAAAAAJMitAMAAAAAYFKEdgAAAAAATIrQDgAAAACASRHaAQAAAAAwKUI7AAAAAAAmRWgHAAAAAMCkCO0AAAAAAJgUoR0AAAAAAJMitAMAAAAAYFKEdgAAAAAATIrQDgAAAACASRHaAQAAAAAwKUI7AAAAAAAmRWgHAAAAAMCkCO0AAAAAAJgUoR0AAAAAAJMitAMAAAAAYFKEdgAAAAAATIrQDgAAAACASRHaAQAAAAAwKUI7AAAAAAAmRWgHAAAAAMCkCO0AAAAAAJgUoR0AAAAAAJMitAMAAAAAYFKEdgAAAAAATIrQDgAAAACASRHaAQAAAAAwKUI7AAAAAAAmRWgHAAAAAMCkCO0AAAAAAJiUU0P7tm3b1KNHDwUGBspisWjNmjXWeVlZWZo4caKaNm2qKlWqKDAwUE888YTOnDlj00dISIgsFovNa8aMGQ7+JAAAAAAAFD+nhvb09HQ1b95cCxYssJt35coV7d+/X1OmTNH+/fu1evVqHT9+XD179rRrO336dCUmJlpfzzzzjCPKBwAAAACgRJV35uBRUVGKiorKc56Xl5fWr19vM+2dd95Rq1atlJCQoODgYOt0Dw8P+fv73/G4GRkZysjIsL5PS0srZOUAAAAAAJS8UnVNe2pqqiwWi6pWrWozfcaMGapevbpatGihWbNmKTs7u8B+YmJi5OXlZX0FBQWVYNUAAAAAABSNU4+0F8a1a9c0ceJE9e/fX56entbpzz77rFq2bClvb2/98MMPmjRpkhITE/X222/n29ekSZM0fvx46/u0tDSCOwAAAADAdEpFaM/KylKfPn1kGIYWLlxoM+/m8N2sWTNVrFhRI0eOVExMjFxdXfPsz9XVNd95AAAAAACYhelPj88N7D///LPWr19vc5Q9LxEREcrOztbp06cdUyAAAAAAACXE1EfacwP7iRMntHnzZlWvXv22yxw8eFAuLi7y9fV1QIUAAAAAAJQcp4b2y5cv6+TJk9b38fHxOnjwoLy9vRUQEKDHHntM+/fv19dff63r168rKSlJkuTt7a2KFSsqNjZWu3fvVocOHeTh4aHY2FiNGzdOgwYNUrVq1Zz1sQAAAAAAKBZODe179+5Vhw4drO9zr08fPHiwpk2bprVr10qS7rnnHpvlNm/erPbt28vV1VUrVqzQtGnTlJGRodDQUI0bN87mOncAAAAAAEorp4b29u3byzCMfOcXNE+SWrZsqV27dhV3WQAAAAAAmILpb0QHAAAAAEBZRWgHAAAAAMCkCO0AAAAAAJgUoR0AAAAAAJMitAMAAAAAYFKEdgAAAAAATIrQDgAAAACASRHaAQAAAAAwKUI7AAAAAAAmRWgHAAAAAMCkCO0AAAAAAJgUoR0AAAAAAJMitAMAAAAAYFKEdgAAAAAATIrQDgAAAACASRHaAQAAAAAwKUI7AAAAAAAmRWgHAAAAAMCkCO0AAAAAAJhUeWcXAHNLSEhQcnKyQ8aKi4tzyDgAAAAAUFoQ2pGvhIQEhYU10tWrVxw6blZGpkPHAwAAAACzIrQjX8nJybp69Yoihk6VZ0BIiY+X+FOsjqxdrOzs7BIfCwAAAABKA0I7bsszIETewQ1LfJy0xNMlPgYAAAAAlCbciA4AAAAAAJMitAMAAAAAYFKEdgAAAAAATIrQDgAAAACASRHaAQAAAAAwKUI7AAAAAAAmRWgHAAAAAMCkCO0AAAAAAJgUoR0AAAAAAJMitAMAAAAAYFKEdgAAAAAATIrQDgAAAACASRHaAQAAAAAwKUI7AAAAAAAmRWgHAAAAAMCkCO0AAAAAAJgUoR0AAAAAAJMitAMAAAAAYFKEdgAAAAAATIrQDgAAAACASRHaAQAAAAAwKUI7AAAAAAAmRWgHAAAAAMCkCO0AAAAAAJgUoR0AAAAAAJMitAMAAAAAYFKEdgAAAAAATIrQDgAAAACASZUv6oLp6enaunWrEhISlJmZaTPv2Wef/d2FAQAAAABQ1hXpSPuBAwdUr1499e/fX2PGjNFrr72msWPHavLkyZozZ84d97Nt2zb16NFDgYGBslgsWrNmjc18wzD0yiuvKCAgQG5uburcubNOnDhh0+bixYsaOHCgPD09VbVqVQ0bNkyXL18uyscCAAAAAMBUihTax40bpx49euh///uf3NzctGvXLv38888KDw/Xm2++ecf9pKenq3nz5lqwYEGe89944w3NmzdPixYt0u7du1WlShV16dJF165ds7YZOHCg/v3vf2v9+vX6+uuvtW3bNo0YMaIoHwsAAAAAAFMp0unxBw8e1HvvvScXFxeVK1dOGRkZqlOnjt544w0NHjxYjz766B31ExUVpaioqDznGYahOXPm6K9//asefvhhSdKHH34oPz8/rVmzRv369VNcXJzWrVunPXv26N5775UkzZ8/X926ddObb76pwMDAPPvOyMhQRkaG9X1aWlphPj4AAAAAAA5RpCPtFSpUkIvLjUV9fX2VkJAgSfLy8tIvv/xSLIXFx8crKSlJnTt3tk7z8vJSRESEYmNjJUmxsbGqWrWqNbBLUufOneXi4qLdu3fn23dMTIy8vLysr6CgoGKpGQAAAACA4lSk0N6iRQvt2bNHktSuXTu98sorWr58ucaOHasmTZoUS2FJSUmSJD8/P5vpfn5+1nlJSUny9fW1mV++fHl5e3tb2+Rl0qRJSk1Ntb6K6w8NAAAAAAAUpyKF9r/97W8KCAiQJL3++uuqVq2ann76aZ0/f16LFy8u1gJLgqurqzw9PW1eAAAAAACYTZGuab/5dHRfX1+tW7eu2ArK5e/vL0k6e/as9Q8Eue/vuecea5tz587ZLJedna2LFy9alwcAAAAAoLQq0pH2jh07KiUlpZhLsRUaGip/f39t3LjROi0tLU27d+9WZGSkJCkyMlIpKSnat2+ftc2mTZuUk5OjiIiIEq0PAAAAAICSVqQj7Vu2bFFmZubvHvzy5cs6efKk9X18fLwOHjwob29vBQcHa+zYsXrttddUv359hYaGasqUKQoMDFSvXr0kSY0aNVLXrl01fPhwLVq0SFlZWRozZoz69euX753jAQAAAAAoLYoU2iXJYrH87sH37t2rDh06WN+PHz9ekjR48GAtW7ZML774otLT0zVixAilpKTogQce0Lp161SpUiXrMsuXL9eYMWPUqVMnubi4qHfv3po3b97vrg0AAAAAAGcrcmh/5JFHVLFixTznbdq06Y76aN++vQzDyHe+xWLR9OnTNX369HzbeHt765NPPrmj8QAAAAAAKE2KHNojIyPl7u5enLUAAAAAAICbFCm0WywWvfDCC3bPSAdwewkJCUpOTnbYeD4+PgoODnbYeAAAAACKT5FCe0GntAPIX0JCgsLCGunq1SsOG9PNrbKOHYsjuAMAAAClUJFC+9SpUzk1HiiC5ORkXb16RRFDp8ozIKTEx0tLPK3dS6KVnJxMaAcAAABKoSKHdkk6f/68jh8/Lklq2LChatSoUXyVAXcxz4AQeQc3dHYZAAAAAEzOpSgLXblyRUOHDlVgYKDatm2rtm3bKjAwUMOGDdOVK4477RcAAAAAgLtZkUL7uHHjtHXrVq1du1YpKSlKSUnRV199pa1bt2rChAnFXSMAAAAAAGVSkU6P/+KLL/T555+rffv21mndunWTm5ub+vTpo4ULFxZXfQAAAAAAlFlFPj3ez8/Pbrqvry+nxwMAAAAAUEyKFNojIyM1depUXbt2zTrt6tWrio6OVmRkZLEVBwAAAABAWVak0+PnzJmjrl27qlatWmrevLkk6dChQ6pUqZK+++67Yi0QAAAAAICyqkihvWnTpjpx4oSWL1+uY8eOSZL69++vgQMHys3NrVgLBAAAAACgrCpSaN+2bZtat26t4cOHF3c9AAAAAADg/xTpmvYOHTro4sWLxV0LAAAAAAC4SZFCu2EYxV0HAAAAAAC4RZFOj5ek2NhYVatWLc95bdu2LXJBAAAAAADghiKH9kceeSTP6RaLRdevXy9yQQAAAAAA4IYinR4vSUlJScrJybF7EdgBAAAAACgeRQrtFouluOsAAAAAAAC3KNLp8dyIDneTuLi4u3IsAAAAAKVfkUJ7Tk5OcdcBONzV1AuSLBo0aJDDx87KyHT4mAAAAABKnyKF9piYGPn5+Wno0KE205csWaLz589r4sSJxVIcUJKyrlySZOieARNVIzTMIWMm/hSrI2sXKzs72yHjAQAAACjdihTa33vvPX3yySd20xs3bqx+/foR2lGquPsGyzu4oUPGSks87ZBxAAAAANwdinQjuqSkJAUEBNhNr1GjhhITE393UQAAAAAAoIihPSgoSDt37rSbvnPnTgUGBv7uogAAAAAAQBFPjx8+fLjGjh2rrKwsdezYUZK0ceNGvfjii5owYUKxFggAAAAAQFlVpND+wgsv6MKFCxo1apQyM2/cBbtSpUqaOHGiJk2aVKwFAgAAAABQVhUptFssFs2cOVNTpkxRXFyc3NzcVL9+fbm6uhZ3fQAAAAAAlFlFCu253N3ddd999xVXLQAAAAAA4CZFDu179+7VypUrlZCQYD1FPtfq1at/d2EAAAAAAJR1Rbp7/IoVK9S6dWvFxcXpyy+/VFZWlv79739r06ZN8vLyKu4aAQAAAAAok4oU2v/2t79p9uzZ+uc//6mKFStq7ty5OnbsmPr06aPg4ODirhEAAAAAgDKpSKH91KlT6t69uySpYsWKSk9Pl8Vi0bhx47R48eJiLRAAAAAAgLKqSKG9WrVqunTpkiSpZs2aOnLkiCQpJSVFV65cKb7qAAAAAAAow4p0I7q2bdtq/fr1atq0qR5//HE999xz2rRpk9avX69OnToVd40AAAAAAJRJRQrt77zzjq5duyZJevnll1WhQgX98MMP6t27t/76178Wa4EAAAAAAJRVhQrtaWlpNxYqX17u7u7W96NGjdKoUaOKvzoAAAAAAMqwQoX2qlWrymKx3Lbd9evXi1wQAAAAAAC4oVChffPmzTbvDcNQt27d9I9//EM1a9Ys1sIAAAAAACjrChXa27VrZzetXLly+uMf/6g6deoUW1EAAAAAAKCIj3wDAAAAAAAl73eF9l9++UVXrlxR9erVi6seAAAAAADwfwp1evy8efOs/5+cnKxPP/1UHTt2lJeXV7EXBgAAAABAWVeo0D579mxJksVikY+Pj3r06MFz2QEAAAAAKCGFCu3x8fElVQcAAAAAALgFN6IDAAAAAMCkCO0AAAAAAJgUoR0AAAAAAJMitAMAAAAAYFKEdgAAAAAATIrQDgAAAACASZk+tIeEhMhisdi9Ro8eLUlq37693by//OUvTq4aAAAAAIDfr1DPaXeGPXv26Pr169b3R44c0YMPPqjHH3/cOm348OGaPn269X3lypUdWiMAAAAAACXB9KG9Ro0aNu9nzJihunXrql27dtZplStXlr+/v6NLAwAAAACgRJn+9PibZWZm6uOPP9bQoUNlsVis05cvXy4fHx81adJEkyZN0pUrVwrsJyMjQ2lpaTYvAAAAAADMxvRH2m+2Zs0apaSkaMiQIdZpAwYMUO3atRUYGKjDhw9r4sSJOn78uFavXp1vPzExMYqOjnZAxQAAAAAAFF2pCu3vv/++oqKiFBgYaJ02YsQI6/83bdpUAQEB6tSpk06dOqW6devm2c+kSZM0fvx46/u0tDQFBQWVXOEAAAAAABRBqQntP//8szZs2FDgEXRJioiIkCSdPHky39Du6uoqV1fXYq8RAAAAAIDiVGquaV+6dKl8fX3VvXv3AtsdPHhQkhQQEOCAqgAAAAAAKDml4kh7Tk6Oli5dqsGDB6t8+f9f8qlTp/TJJ5+oW7duql69ug4fPqxx48apbdu2atasmRMrBgAAAADg9ysVoX3Dhg1KSEjQ0KFDbaZXrFhRGzZs0Jw5c5Senq6goCD17t1bf/3rX51UKQAAAAAAxadUhPaHHnpIhmHYTQ8KCtLWrVudUBEAAAAAACWv1FzTDgAAAABAWUNoBwAAAADApAjtAAAAAACYFKEdAAAAAACTIrQDAAAAAGBShHYAAAAAAEyK0A4AAAAAgEkR2gEAAAAAMClCOwAAAAAAJkVoBwAAAADApAjtAAAAAACYFKEdAAAAAACTIrQDAAAAAGBShHYAAAAAAEyK0A4AAAAAgEkR2gEAAAAAMClCOwAAAAAAJkVoBwAAAADApAjtAAAAAACYFKEdAAAAAACTIrQDAAAAAGBShHYAAAAAAEyK0A4AAAAAgEkR2gEAAAAAMClCOwAAAAAAJkVoBwAAAADApAjtAAAAAACYFKEdAAAAAACTIrQDAAAAAGBShHYAAAAAAEyK0A4AAAAAgEkR2gEAAAAAMClCOwAAAAAAJkVoBwAAAADApAjtAAAAAACYFKEdAAAAAACTIrQDAAAAAGBShHYAAAAAAEyK0A4AAAAAgEkR2gEAAAAAMClCOwAAAAAAJkVoBwAAAADApAjtAAAAAACYFKEdAAAAAACTIrQDAAAAAGBShHYAAAAAAEyK0A4AAAAAgEkR2gEAAAAAMClCOwAAAAAAJkVoBwAAAADApAjtAAAAAACYFKEdAAAAAACTKu/sAgoybdo0RUdH20xr2LChjh07Jkm6du2aJkyYoBUrVigjI0NdunTRu+++Kz8/P2eU6xAJCQlKTk52yFhxcXEOGQcAAAAAkDdTh3ZJaty4sTZs2GB9X778/y953Lhx+uabb7Rq1Sp5eXlpzJgxevTRR7Vz505nlFriEhISFBbWSFevXnHouFkZmQ4dDwAAAABwg+lDe/ny5eXv7283PTU1Ve+//74++eQTdezYUZK0dOlSNWrUSLt27dIf//hHR5da4pKTk3X16hVFDJ0qz4CQEh8v8adYHVm7WNnZ2SU+FgAAAADAnulD+4kTJxQYGKhKlSopMjJSMTExCg4O1r59+5SVlaXOnTtb24aFhSk4OFixsbEFhvaMjAxlZGRY36elpZXoZyhungEh8g5uWOLjpCWeLvExAAAAAAD5M/WN6CIiIrRs2TKtW7dOCxcuVHx8vNq0aaNLly4pKSlJFStWVNWqVW2W8fPzU1JSUoH9xsTEyMvLy/oKCgoqwU8BAAAAAEDRmPpIe1RUlPX/mzVrpoiICNWuXVsrV66Um5tbkfudNGmSxo8fb32flpZGcAcAAAAAmI6pj7TfqmrVqmrQoIFOnjwpf39/ZWZmKiUlxabN2bNn87wG/maurq7y9PS0eQEAAAAAYDalKrRfvnxZp06dUkBAgMLDw1WhQgVt3LjROv/48eNKSEhQZGSkE6sEAAAAAKB4mPr0+Oeff149evRQ7dq1debMGU2dOlXlypVT//795eXlpWHDhmn8+PHy9vaWp6ennnnmGUVGRt6Vd44HAAAAAJQ9pg7tv/76q/r3768LFy6oRo0aeuCBB7Rr1y7VqFFDkjR79my5uLiod+/eysjIUJcuXfTuu+86uWoAAAAAAIqHqUP7ihUrCpxfqVIlLViwQAsWLHBQRQAAAAAAOE6puqYdAAAAAICyhNAOAAAAAIBJEdoBAAAAADApQjsAAAAAACZFaAcAAAAAwKRMffd4AMUjLi7OYWP5+PgoODjYYeMBAAAAdzNCO3AXu5p6QZJFgwYNctiYbm6VdexYHMEdAAAAKAaEduAulnXlkiRD9wyYqBqhYSU+Xlriae1eEq3t27erUaNGJT6exJF9AAAA3N0I7UAZ4O4bLO/ghiU+Dkf2AQAAgOJFaAdQbJx1ZD85OZnQDgAAgLsSoR1AsXPUkX0AAADgbscj3wAAAAAAMClCOwAAAAAAJkVoBwAAAADApAjtAAAAAACYFKEdAAAAAACTIrQDAAAAAGBShHYAAAAAAEyK0A4AAAAAgEkR2gEAAAAAMClCOwAAAAAAJkVoBwAAAADApMo7uwAAAFB8EhISlJyc7NAxfXx8FBwc7NAxAQAoKwjtAADcJRISEhQW1khXr15x6LhubpV17FgcwR0AgBJAaAcA4C6RnJysq1evKGLoVHkGhDhkzLTE09q9JFrJycmEdgAASgChHQBQpjj69HFnnDruGRAi7+CGDh0zLi7OYWNxOj4AoCwhtAMAygxnnD5+t586fjX1giSLBg0a5LAx7/Z1CgDAzQjtAIAyw9Gnj5eFU8ezrlySZOieARNVIzSsxMcrC+sUAICbEdoBAGWOM04fv9u5+wazTgEAKAE8px0AAAAAAJMitAMAAAAAYFKEdgAAAAAATIpr2gEAKGGOehyaIx+7BgAAHIPQDgBACXHG49AkKSsj06HjAQCAkkNoBwCghDj6cWiJP8XqyNrFys7OLvGxAACAYxDaAQAoYY56HFpa4ukSHwMAADgWN6IDAAAAAMCkCO0AAAAAAJgUoR0AAAAAAJMitAMAAAAAYFLciA4AAKAACQkJSk5Odth4Pj4+Cg4Odth4AABzI7QDAADkIyEhQWFhjXT16hWHjenmVlnHjsUR3AEAkgjtAAAA+UpOTtbVq1cUMXSqPANCSny8tMTT2r0kWsnJyYR2AIAkQjsAAMBteQaEyDu4obPLAACUQdyIDgAAAAAAkyK0AwAAAABgUoR2AAAAAABMimvaAQAAcFfjsX0ASjNCOwAAAO5aPLYPQGlHaAcAAMBdi8f2ASjtCO0AAKDUiYuLu6vGQcnjsX0ASitCOwAAKDWupl6QZNGgQYMcOm5WRqZDxwMAIJepQ3tMTIxWr16tY8eOyc3NTa1bt9bMmTPVsOH//ytp+/bttXXrVpvlRo4cqUWLFjm6XAAAUMKyrlySZOieARNVIzSsxMdL/ClWR9YuVnZ2domPdTNHHuHnpmkAYG6mDu1bt27V6NGjdd999yk7O1uTJ0/WQw89pKNHj6pKlSrWdsOHD9f06dOt7ytXruyMcgEAgIO4+wY75FTntMTTJT7GzZxxJgE3TQMAczN1aF+3bp3N+2XLlsnX11f79u1T27ZtrdMrV64sf39/R5cHAABQrBx9JgE3TQMA8zN1aL9VamqqJMnb29tm+vLly/Xxxx/L399fPXr00JQpUwo82p6RkaGMjAzr+7S0tJIpGIBDcBopgLuNo84kAACYX6kJ7Tk5ORo7dqzuv/9+NWnSxDp9wIABql27tgIDA3X48GFNnDhRx48f1+rVq/PtKyYmRtHR0Y4oG0AJ4jRSAAAA3O1KTWgfPXq0jhw5oh07dthMHzFihPX/mzZtqoCAAHXq1EmnTp1S3bp18+xr0qRJGj9+vPV9WlqagoKCSqZwACWG00gBAABwtysVoX3MmDH6+uuvtW3bNtWqVavAthEREZKkkydP5hvaXV1d5erqWux1AnAOTiMFAADA3crUod0wDD3zzDP68ssvtWXLFoWGht52mYMHD0qSAgICSrg6AAAAAABKlqlD++jRo/XJJ5/oq6++koeHh5KSkiRJXl5ecnNz06lTp/TJJ5+oW7duql69ug4fPqxx48apbdu2atasmZOrBwDciYSEBCUnJztkLEfetBAAAKA4mDq0L1y4UJLUvn17m+lLly7VkCFDVLFiRW3YsEFz5sxRenq6goKC1Lt3b/31r391QrUAgMJKSEhQWFgjXb16xaHjZmVkOnQ8AACAojJ1aDcMo8D5QUFB2rp1q4OqAQAUt+TkZF29ekURQ6fKMyCkxMdL/ClWR9YuVnZ2domPBQAAUBxMHdoBAGWDZ0CIQ24mmJZ4usTHAAAAKE4uzi4AAAAAAADkjSPtAAAAZZyjb9Lo4+Oj4OBgh44JAKUVoR0AAKCMupp6QZJFgwYNcui4bm6VdexYHMEdAO4AoR0AAKCMyrpySZKhewZMVI3QMIeMmZZ4WruXRCs5OZnQDgB3gNAOAABQxrn7BjvkZpAAgMLjRnQAAAAAAJgUoR0AAAAAAJMitAMAAAAAYFJc0w4AAACHc9Rj5hz9ODtnjMsj9IC7G6EdAAAADuOsx8xlZWQ6ZBxnfD4eoQfc3QjtAAAAcBhHP2Yu8adYHVm7WNnZ2SU+luT4z8cj9IC7H6EdAAAADueox8ylJZ4u8THywmP0ABQXbkQHAAAAAIBJEdoBAAAAADApQjsAAAAAACZFaAcAAAAAwKQI7QAAAAAAmBShHQAAAAAAkyK0AwAAAABgUjynHQBMLiEhQcnJyQ4bz8fHR8HBwQ4bDwAAAPkjtAOAiSUkJCgsrJGuXr3isDHd3Crr2LE4gjsAAIAJENoBwMSSk5N19eoVRQydKs+AkBIfLy3xtHYvidb27dvVqFGjEh8vLi6uxMcAAAAozQjtAFAKeAaEyDu4YYmPczX1giSLBg0aVOJj3SwrI9Oh4wEAAJQWhHYAgFXWlUuSDN0zYKJqhIaV+HiJP8XqyNrFys7OLvGxAAAASiNCOwDAjrtvsEOO7Kclni7xMQAAAEozHvkGAAAAAIBJcaQdAAAAKOUceWNPHg0KOBahHQAAACilnHEDUR4NCjgWoR0AAAAopRx9A9HcR4MmJycT2gEHIbQDAAAApZyjbiCai9PxAcchtAMAAAC4I5yODzgeoR0AAADAHeF0fMDxCO0AAAAACsXRp+MDZRnPaQcAAAAAwKQI7QAAAAAAmBShHQAAAAAAk+KadgAoJEc+5saRYwEAAMB8CO0AcIec8ZibXFkZmQ4fEwAAAM5HaAeAO+Tox9xIUuJPsTqydrGys7MdMh4AAADMhdAOAIXkyMfcpCWedsg4AACg7EhISFBycrLDxvPx8VFwcLDDxrvbENoBAAAAoIxISEhQWFgjXb16xWFjurlV1rFjcQT3IiK0AwAAADA1R9+Y9W4+MpycnKyrV68oYuhUeQaElPh4aYmntXtJtJKTk+/adVrSCO0AAAAATMlZN4EtC0eGPQNCHHa5H34fQjsAAAAAU3LGTWA5MgyzIbQDAAAAMDVH3gQWMBsXZxcAAAAAAADyxpF2AAAAALiFI29+l5GRIVdXV4eM5eib+uH3I7QDAAAAwP9xys3vLBbJMBw3nqSsjEyHjoeiI7QDAAAAwP9x9M3vEn+K1ZG1ix0+XnZ2domPheJBaAcAAACAWzjq5ndpiaedMp6jOfK0fB8fn7vqzv+EdgAAAABAiXDG5QZubpV17FjcXRPc75rQvmDBAs2aNUtJSUlq3ry55s+fr1atWjm7LAAAAAAosxx9uUFa4mntXhKt5ORkQruZfPbZZxo/frwWLVqkiIgIzZkzR126dNHx48fl6+vr7PIAAAAAoExz1On/d6O74jntb7/9toYPH64nn3xSf/jDH7Ro0SJVrlxZS5YscXZpAAAAAAAUWak/0p6Zmal9+/Zp0qRJ1mkuLi7q3LmzYmNj81wmIyNDGRkZ1vepqamSpLS0tJIt9ne6fPmyJOniz8eVnXG1xMdLS/xZkpT62wlVKG9hvFI4JuOV7vGcMSbjMZ7Zx2S80j2eM8ZkPMYz+5iMV8zjJSVIupGdzJ7vcuszbvO4P4txuxYmd+bMGdWsWVM//PCDIiMjrdNffPFFbd26Vbt377ZbZtq0aYqOjnZkmQAAAAAA2Pnll19Uq1atfOeX+iPtRTFp0iSNHz/e+j4nJ0cXL15U9erVZbE45i94uL20tDQFBQXpl19+kaenp7PLwR1gm5VObLfSie1WOrHdSie2W+nDNiudytp2MwxDly5dUmBgYIHtSn1o9/HxUbly5XT27Fmb6WfPnpW/v3+ey7i6usrV1dVmWtWqVUuqRPxOnp6eZWKnvZuwzUontlvpxHYrndhupRPbrfRhm5VOZWm7eXl53bZNqb8RXcWKFRUeHq6NGzdap+Xk5Gjjxo02p8sDAAAAAFDalPoj7ZI0fvx4DR48WPfee69atWqlOXPmKD09XU8++aSzSwMAAAAAoMjuitDet29fnT9/Xq+88oqSkpJ0zz33aN26dfLz83N2afgdXF1dNXXqVLtLGWBebLPSie1WOrHdSie2W+nEdit92GalE9stb6X+7vEAAAAAANytSv017QAAAAAA3K0I7QAAAAAAmBShHQAAAAAAkyK0AwAAAABgUoR2OEVMTIzuu+8+eXh4yNfXV7169dLx48cLXGbZsmWyWCw2r0qVKjmoYkybNs1u/YeFhRW4zKpVqxQWFqZKlSqpadOm+te//uWgapErJCTEbrtZLBaNHj06z/bsZ86xbds29ejRQ4GBgbJYLFqzZo3NfMMw9MorryggIEBubm7q3LmzTpw4cdt+FyxYoJCQEFWqVEkRERH68ccfS+gTlE0FbbesrCxNnDhRTZs2VZUqVRQYGKgnnnhCZ86cKbDPonzXonBut78NGTLEbht07dr1tv2yv5Ws2223vH7XWSwWzZo1K98+2d9K1p38e//atWsaPXq0qlevLnd3d/Xu3Vtnz54tsN+i/k4szQjtcIqtW7dq9OjR2rVrl9avX6+srCw99NBDSk9PL3A5T09PJSYmWl8///yzgyqGJDVu3Nhm/e/YsSPftj/88IP69++vYcOG6cCBA+rVq5d69eqlI0eOOLBi7Nmzx2abrV+/XpL0+OOP57sM+5njpaenq3nz5lqwYEGe89944w3NmzdPixYt0u7du1WlShV16dJF165dy7fPzz77TOPHj9fUqVO1f/9+NW/eXF26dNG5c+dK6mOUOQVttytXrmj//v2aMmWK9u/fr9WrV+v48ePq2bPnbfstzHctCu92+5skde3a1WYbfPrppwX2yf5W8m633W7eXomJiVqyZIksFot69+5dYL/sbyXnTv69P27cOP3zn//UqlWrtHXrVp05c0aPPvpogf0W5XdiqWcAJnDu3DlDkrF169Z82yxdutTw8vJyXFGwMXXqVKN58+Z33L5Pnz5G9+7dbaZFREQYI0eOLObKUBjPPfecUbduXSMnJyfP+exnzifJ+PLLL63vc3JyDH9/f2PWrFnWaSkpKYarq6vx6aef5ttPq1atjNGjR1vfX79+3QgMDDRiYmJKpO6y7tbtlpcff/zRkGT8/PPP+bYp7Hctfp+8ttvgwYONhx9+uFD9sL851p3sbw8//LDRsWPHAtuwvznWrf/eT0lJMSpUqGCsWrXK2iYuLs6QZMTGxubZR1F/J5Z2HGmHKaSmpkqSvL29C2x3+fJl1a5dW0FBQXr44Yf173//2xHl4f+cOHFCgYGBqlOnjgYOHKiEhIR828bGxqpz584207p06aLY2NiSLhP5yMzM1Mcff6yhQ4fKYrHk2479zFzi4+OVlJRksz95eXkpIiIi3/0pMzNT+/bts1nGxcVFnTt3Zh90otTUVFksFlWtWrXAdoX5rkXJ2LJli3x9fdWwYUM9/fTTunDhQr5t2d/M5+zZs/rmm280bNiw27Zlf3OcW/+9v2/fPmVlZdnsO2FhYQoODs533ynK78S7AaEdTpeTk6OxY8fq/vvvV5MmTfJt17BhQy1ZskRfffWVPv74Y+Xk5Kh169b69ddfHVht2RUREaFly5Zp3bp1WrhwoeLj49WmTRtdunQpz/ZJSUny8/Ozmebn56ekpCRHlIs8rFmzRikpKRoyZEi+bdjPzCd3nynM/pScnKzr16+zD5rItWvXNHHiRPXv31+enp75tivsdy2KX9euXfXhhx9q48aNmjlzprZu3aqoqChdv349z/bsb+bzwQcfyMPD47anWbO/OU5e/95PSkpSxYoV7f6QWdC+U5TfiXeD8s4uABg9erSOHDly22uIIiMjFRkZaX3funVrNWrUSO+9955effXVki6zzIuKirL+f7NmzRQREaHatWtr5cqVd/SXbDjf+++/r6ioKAUGBubbhv0MKH5ZWVnq06ePDMPQwoULC2zLd63z9evXz/r/TZs2VbNmzVS3bl1t2bJFnTp1cmJluFNLlizRwIEDb3sjVfY3x7nTf+8jbxxph1ONGTNGX3/9tTZv3qxatWoVatkKFSqoRYsWOnnyZAlVh4JUrVpVDRo0yHf9+/v729398+zZs/L393dEebjFzz//rA0bNuipp54q1HLsZ86Xu88UZn/y8fFRuXLl2AdNIDew//zzz1q/fn2BR9nzcrvvWpS8OnXqyMfHJ99twP5mLtu3b9fx48cL/ftOYn8rKfn9e9/f31+ZmZlKSUmxaV/QvlOU34l3A0I7nMIwDI0ZM0ZffvmlNm3apNDQ0EL3cf36df30008KCAgogQpxO5cvX9apU6fyXf+RkZHauHGjzbT169fbHMWF4yxdulS+vr7q3r17oZZjP3O+0NBQ+fv72+xPaWlp2r17d777U8WKFRUeHm6zTE5OjjZu3Mg+6EC5gf3EiRPasGGDqlevXug+bvddi5L366+/6sKFC/luA/Y3c3n//fcVHh6u5s2bF3pZ9rfidbt/74eHh6tChQo2+87x48eVkJCQ775TlN+JdwUn3wgPZdTTTz9teHl5GVu2bDESExOtrytXrljb/PnPfzZeeukl6/vo6Gjju+++M06dOmXs27fP6Nevn1GpUiXj3//+tzM+QpkzYcIEY8uWLUZ8fLyxc+dOo3PnzoaPj49x7tw5wzDst9fOnTuN8uXLG2+++aYRFxdnTJ061ahQoYLx008/OesjlFnXr183goODjYkTJ9rNYz8zh0uXLhkHDhwwDhw4YEgy3n77bePAgQPWu4zPmDHDqFq1qvHVV18Zhw8fNh5++GEjNDTUuHr1qrWPjh07GvPnz7e+X7FiheHq6mosW7bMOHr0qDFixAijatWqRlJSksM/392qoO2WmZlp9OzZ06hVq5Zx8OBBm991GRkZ1j5u3W63+67F71fQdrt06ZLx/PPPG7GxsUZ8fLyxYcMGo2XLlkb9+vWNa9euWftgf3O8231PGoZhpKamGpUrVzYWLlyYZx/sb451J//e/8tf/mIEBwcbmzZtMvbu3WtERkYakZGRNv00bNjQWL16tfX9nfxOvNsQ2uEUkvJ8LV261NqmXbt2xuDBg63vx44dawQHBxsVK1Y0/Pz8jG7duhn79+93fPFlVN++fY2AgACjYsWKRs2aNY2+ffsaJ0+etM6/dXsZhmGsXLnSaNCggVGxYkWjcePGxjfffOPgqmEYhvHdd98Zkozjx4/bzWM/M4fNmzfn+Z2Yu21ycnKMKVOmGH5+foarq6vRqVMnu+1Zu3ZtY+rUqTbT5s+fb92erVq1Mnbt2uWgT1Q2FLTd4uPj8/1dt3nzZmsft263233X4vcraLtduXLFeOihh4waNWoYFSpUMGrXrm0MHz7cLnyzvzne7b4nDcMw3nvvPcPNzc1ISUnJsw/2N8e6k3/vX7161Rg1apRRrVo1o3LlysYjjzxiJCYm2vVz8zJ38jvxbmMxDMMomWP4AAAAAADg9+CadgAAAAAATIrQDgAAAACASRHaAQAAAAAwKUI7AAAAAAAmRWgHAAAAAMCkCO0AAAAAAJgUoR0AAAAAAJMitAMAAAAAYFKEdgAAAAeYNm2a7rnnHmeXAQAoZQjtAIBSZ8iQIerVq5fd9C1btshisSglJcXhNQFffPGF2rdvLy8vL7m7u6tZs2aaPn26Ll686OzSAAClGKEdAADgd3r55ZfVt29f3Xffffr222915MgRvfXWWzp06JA++ugjZ5cHACjFCO0AgLtaSkqKnnrqKdWoUUOenp7q2LGjDh06ZNPm9OnTslgsdq+bj9h/9dVXatmypSpVqqQ6deooOjpa2dnZ1vk3L+fp6akHH3xQp06dss7/6KOPdO+998rDw0P+/v4aMGCAzp07Z1PH119/rebNm8vNzc3aV15nFOSaNm1annXfWvuOHTvUpk0bubm5KSgoSM8++6zS09Ot80NCQvTqq6+qf//+qlKlimrWrKkFCxYUaj3m1vLss8/aLDdu3DhZLBZNmzatUH3dehr5rWdRLFu2TFWrVrVpk7sdDx48KEm6fv26hg0bptDQULm5ualhw4aaO3euzTLXr1/X+PHjVbNmTbm4uFjX35o1a/Jd77f68ccf9be//U1vvfWWZs2apdatWyskJEQPPvigvvjiCw0ePDjP5XJycjR9+nTVqlVLrq6uuueee7Ru3Trr/MzMTI0ZM0YBAQGqVKmSateurZiYmDtejwCAuwOhHQBwV3v88cd17tw5ffvtt9q3b59atmypTp062ZyybBiGJGnDhg1KTEzUF198YdPH9u3b9cQTT+i5557T0aNH9d5772nZsmV6/fXXbdotXbpUiYmJ2rZtm86dO6fJkydb52VlZenVV1/VoUOHtGbNGp0+fVpDhgyxzk9JSVHfvn3Vvn17HT16VImJierTp89tP1/jxo2VmJhofd1a+6lTp9S1a1f17t1bhw8f1meffaYdO3ZozJgxNu1mzZql5s2b68CBA3rppZf03HPPaf369YVaj35+fvr000917do1SdK1a9e0fPly+fn5FXqbFIecnBzVqlVLq1at0tGjR/XKK69o8uTJWrlypbXN+++/r8WLF2vRokX69ddflZiYaNdPSEiIzR8dbrV8+XK5u7tr1KhRec6/9Y8LuebOnau33npLb775pg4fPqwuXbqoZ8+eOnHihCRp3rx5Wrt2rVauXKnjx49r+fLlCgkJsS7vqPUIAHCu8s4uAACAkrJjxw79+OOPOnfunFxdXSVJb775ptasWaPPP/9cI0aMkHQjUEuSv7+//P395e3tbdNPdHS0XnrpJesR0zp16ujVV1/Viy++qKlTp1rbVa1aVf7+/nJzc5OHh4e8vLys84YOHWr9/zp16mjevHm67777dPnyZbm7u+s///mPrly5ookTJyowMFCS5ObmpoyMjAI/Y/ny5eXv7299f2vtMTExGjhwoMaOHStJql+/vubNm6d27dpp4cKFqlSpkiTp/vvv10svvSRJatCggXbu3KnZs2frwQcfvOP16O/vr+DgYK1atUp//vOf9fnnn+uPf/yjEhISCr1NikOFChUUHR1tfR8aGqrY2FitXLnS+geRgwcPqnXr1urRo0e+/dStW1c+Pj75zj9x4oTq1KmjChUqFKq+N998UxMnTlS/fv0kSTNnztTmzZs1Z84cLViwQAkJCapfv74eeOABWSwW1a5d27qsI9cjAMC5ONIOALhrHTp0SJcvX1b16tXl7u5ufcXHx9ucup6WliZJqlKlSr79TJ8+3aaP4cOHKzExUVeuXLG269+/v9zd3VWtWjVdunTJ5lTmffv2qUePHgoODpaHh4fatWsnSdZAGxQUpPLly+vTTz9VTk5Osa6DZcuW2dTepUsX5eTkKD4+3touMjLSZrnIyEjFxcVZ+7iT9ShJI0aM0OLFiyVJixcv1vDhw+3quZO+fvrpJ5v5UVFRdp8tNTXVpk3jxo3t2ixYsEDh4eGqUaOG3N3dtXjxYps/IoSGhmrfvn06duxYvutw48aNdmcm3Cz3TI3CSEtL05kzZ3T//ffbTL///vut633IkCE6ePCgGjZsqGeffVbff/+9tV1htgkAoHTjSDsA4K51+fJlBQQEaMuWLXbzbj5l+cyZM3JxcbE5Yn1rP9HR0Xr00Uft5uUeqZak2bNnq3PnzkpJSdHLL7+sIUOG6J///KfS09PVpUsXdenSRcuXL1eNGjWUkJCgLl26KDMzU5IUEBCghQsXauLEiZo0aZIqVqyojIwMde/e/Xevg5EjR9pday5JwcHBd9zHnaxHSYqKitKoUaO0evVqxcfHq1u3bpoyZUqh+2rYsKHWrl1rfb97924NGjTIpr2Hh4f2799vff/bb7+pffv21vcrVqzQ888/r7feekuRkZHy8PDQrFmztHv3bmubUaNGae/evWrcuLFcXV3l4lL44xkNGjTQjh07lJWVVeij7QVp2bKl4uPj9e2332rDhg3q06ePOnfurM8//7xQ2wQAULoR2gEAd62WLVsqKSlJ5cuXt7kW+FZ79uxRWFiYTQC/tZ/jx4+rXr16BY7n7+9vbfPMM8+oZ8+eysrK0rFjx3ThwgXNmDFDQUFBkqS9e/faLT948GAtXbpULVq00NixYzVx4kRdv379Dj9t3lq2bKmjR4/etvZdu3bZvW/UqJG1jztZj5JUrlw5DRs2TEOGDNHYsWNVrlw5u3rupK+KFSva1Pzrr7/atXFxcbFpU7687T9rdu7cqdatW9tca37rUegqVaroxRdf1Lp16/TZZ5+pXr16ql+/foGf8VYDBgzQvHnz9O677+q5556zm5+SkmIXpD09PRUYGKidO3daz7rIrblVq1Y27fr27au+ffvqscceU9euXXXx4sVCbRMAQOlGaAcAlEqpqanWu4TnOnnypKQbp1a3aNFCnTt3VmRkpHr16qU33nhDDRo00JkzZ/TNN9/okUceUbNmzfTZZ5/p7bfftrn2+VavvPKK/vSnPyk4OFiPPfaYXFxcdOjQIR05ckSvvfaatV1KSoqSkpKUmpqq999/33qdc3BwsCpWrKj58+frL3/5i44cOaJXX33VbpwJEybIYrFo9uzZqlChgjw8PH73M+cnTpyoP/7xjxozZoyeeuopValSRUePHtX69ev1zjvvWNvt3LlTb7zxhnr16qX169dr1apV+uabbyTptuvx3nvvtRlz5MiRcnV11RNPPGFXT2H7+j3q16+vDz/8UN99951CQ0P10Ucfac+ePQoNDbW2uXjxoh577DHNmDFDXbt2zbOfTp066ZFHHsn3FPmIiAi9+OKLmjBhgn777Tc98sgjCgwM1MmTJ7Vo0SI98MADeYb5F154QVOnTlXdunV1zz33aOnSpTp48KCWL18uSXr77bcVEBCgFi1ayMXFRatWrZK/v7+qVq3q0PUIAHAuQjsAoFTasmWLWrRokee8tm3bavPmzWrfvr3+9a9/6eWXX9aTTz6p8+fPy9/fX23btpWfn59++uknTZs2TVOmTNH48ePzHatLly76+uuvNX36dM2cOVMVKlRQWFiYnnrqKZt2Tz75pKQbp223bNlSn3/+uSSpRo0aWrZsmSZPnqx58+apZcuWevPNN9WzZ0/rsp9++qlWrlyp/fv3F+sp1s2aNdPWrVv18ssvq02bNjIMQ3Xr1lXfvn1t2k2YMEF79+5VdHS0PD099fbbb6tLly6SbjzOrqD1eCt/f3/rTe1uVdi+fo+RI0fqwIED6tu3rywWi/r3769Ro0bp22+/lXTjWvRBgwbpgQce0NNPP51vP6dOnVJycnKBY82cOVPh4eFasGCBFi1apJycHNWtW1ePPfZYvo98e/bZZ5WamqoJEybo3Llz+sMf/qC1a9daj/R7eHjojTfe0IkTJ1SuXDndd999+te//mU9hd9R6xEA4FwWoyh3TwEAwMRCQkK0bNkym+ubkb+QkBCNHTvWeod5AABgHtw9HgBw1/nDH/4gd3d3Z5cBAADwu3F6PADgrvOvf/3L2SUAAAAUC06PBwAAAADApDg9HgAAAAAAkyK0AwAAAABgUoR2AAAAAABMitAOAAAAAIBJEdoBAAAAADApQjsAAAAAACZFaAcAAAAAwKQI7QAAAAAAmNT/A8l7ZuEa1rAVAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Гистограмма распределения цены закрытия в обучающей выборке\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.histplot(X_train['Close'], bins=30, kde=False)\n",
    "plt.title(\"Распределение классов (до балансировки)\")\n",
    "plt.xlabel('Целевая переменная: Close')\n",
    "plt.ylabel('Частота')\n",
    "plt.show()\n",
    "\n",
    "# Гистограмма распределения цены закрытия в контрольной выборке\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.histplot(X_val['Close'], bins=30, kde=False)\n",
    "plt.title(\"Распределение классов (до балансировки)\")\n",
    "plt.xlabel('Целевая переменная: Close')\n",
    "plt.ylabel('Частота')\n",
    "plt.show()\n",
    "\n",
    "# Гистограмма распределения цены закрытия в тестовой выборке\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.histplot(X_test['Close'], bins=30, kde=False)\n",
    "plt.title(\"Распределение классов (до балансировки)\")\n",
    "plt.xlabel('Целевая переменная: Close')\n",
    "plt.ylabel('Частота')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Применим овер- и андерсемплинг к обучающей выборке:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Date   Open   High    Low      Close  Adj Close      Volume  \\\n",
      "4789 2020-07-08   5.66   5.73   5.47   5.560000   5.341250  23355100.0   \n",
      "3469 2015-04-10   3.86   3.93   3.81   3.880000   3.513961   7605300.0   \n",
      "2503 2011-06-07  12.19  12.28  11.95  12.020000  10.138681   7243200.0   \n",
      "1580 2007-10-08  11.77  11.84  11.53  11.570000   9.509553   3025900.0   \n",
      "2759 2012-06-12  15.77  16.17  15.76  16.120001  13.771020   6113400.0   \n",
      "\n",
      "     closePrice_category  \n",
      "4789                high  \n",
      "3469              medium  \n",
      "2503           very_high  \n",
      "1580           very_high  \n",
      "2759           very_high  \n"
     ]
    },
    {
     "data": {
      "image/png": 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27JjGYdXk5GQYGxu/dbq///47MjMz4efnJ40zLi4O9vb2KFu2LA4fPixrrz50bWRklOM4t2zZAktLSzRp0kQ2Tjc3N5iZmWmMMy0tTdYuLi5O4/Radg8ePMCCBQswZswYjVMyW7ZsQcWKFVGhQgXZONWn97NPPydnz56Fn58f2rdvj5kzZ2r0Vx+JBYCkpCTExcXBw8MDQghcunQpV9NQCw0NRXx8PDp37iyrWV9fH7Vr1851zQBw4MABpKamYvDgwdI2DQC9e/fWepOFgYEB+vbtK703NDRE3759ERsbiwsXLmidxu+//45z585h2rRp76xn4sSJsLS0xMCBA3M9DxkZGYiLi8Pff/+NOXPmICEhQTrCDMiXfXJyMuLi4lCnTh0AkH1e1Z4+fQpvb2+Ym5tj165dOX4uBgwYIHuv/pxnP7KpnubTp09zfTojJSUFcXFxiI2NRWhoKA4dOoTGjRvL2mSdr+fPn+PFixeoX7++1nlSj2/nzp0A3lycntWwYcMAQGc31WzZsgX169dHkSJFZNuol5cXMjIycOzYMa3DXbhwAVu2bMHUqVNl26Mu5GW/+OzZM1n9L168yHH8ud0fvcuLFy8QFxeH+/fvY8aMGcjMzJT2Rzn5448/YG9vL7sGvVChQhg4cCASExNx9OhRWfvhw4fL3g8ZMgT6+vrS+tfT04O/vz927dqFly9fSu3Wr18PDw8P6aYGW1tbAMA///zzzvni9vo/iYmJiIuLw4MHD7Bs2TLExMRofL6B/20LWdfB2+jr679zWan3T0OHDn1rOzVHR0e0bdtWem9hYYFu3brh0qVLiImJkbVNTk5Gq1at8OTJE4SEhEhnSdXyupy10fmp3kWLFqFcuXIwMDCAnZ0dypcvL1upmZmZmDdvHhYvXoyoqChkZGRI/bLO6N27d1G+fHnpMKeulC1bVvZefc2W+vx6ZGQkgDfXaeTkxYsXKFKkiPQ+Li5OY7zZRUZGQgiRY7vsp2TV14i97fqXyMhIvHjxQtqBZBcbGyt7v3//ful0Z26NGzcOjo6O6Nu3r8Y1JZGRkbh582aO48w+fW0ePHgAX19fJCUl4enTp1pD/f379zF27Fjs2rVL49rFt32ZaKNevzl9GVhYWOR6XH///TcASNfsqBkaGqJUqVJSfzVHR0eYmprKupUrVw7Am+tX1IFKLSMjAz/++CP8/f3f+TiBqKgoLF26FMHBwe/8JySryMhIVKxYUap78eLF8PPzk/o/e/YMQUFB2Lhxo8b61LbsW7RogYiICNja2r712qrsn4PSpUtDT09Pdp0LAPz666/SdYeGhoaoXbs2Zs+eLd1Aps3GjRuxceNG6X2tWrU0Ttfv2bMHkyZNQnh4uOzaHG3bX9bxFSlSROPzpt7HZa89ryIjI3HlypX3/lz98MMPqF+/Plq0aPHWayPzWhPwfvvF7J+LnCQlJcnm1cnJCcOGDZNOsb6PNm3aSH/r6elh9OjR73wO299//42yZctqhA/150L9OVZfN5p927W0tISDg4Ns/Xfr1g3Tp0/H9u3b0a1bN0RERODChQuyU4alSpWCvb09Zs2aherVq0uXOWm7Pprb6/989913sgNCPXr00AhsAGSX/lhZWaFz586YOXOmxj4YeLMcHR0dNfb/2ZfV33//DT09PY3rvO3t7WFlZaWxzy9TpozGOsq6z7e3t5fNx+nTp2FsbCy7rk8tr8tZG50Hvy+++OKtO+UpU6ZgzJgx6NmzJyZOnAhra2vo6elh8ODB732B4oegrmHmzJlwdXXV2iZrGEtNTcWjR49yPF+fdbwqlQp//vkn9PX13zpOANJ/A1k3DG3jtLW1xfr167X2z76B1K5dG5MmTZJ1W7hwofSfYXY3b97EqlWrsG7dOq3XCmZmZqJq1aqYPXu21uGdnJxyrF3tzp07qFGjBubMmYOuXbti9erVsi+XjIwMNGnSBM+ePcPIkSNRoUIFmJqa4sGDB+jevft7bzPq9mvXrtW6bHX9j8a/8euvv+LevXvYt2/fO9v+9NNPKFu2LAICAmTXsr1LiRIlEBoaipcvX2LPnj0YMmQInJycpGuT/Pz8cOrUKQwfPhyurq4wMzNDZmYmmjVrpnXZ37p1C3/++Sf8/PwwbNgwrFy5Mld15HQUv3Xr1ggMDIQQAlFRUZgwYQJatGghBRFtmjZtKh2V+eeffzB9+nR8+eWXOH/+PExMTHD8+HG0atUKDRo0wOLFi+Hg4IBChQph5cqVspuKso8vKChI4wagDyEzMxNNmjTBiBEjtPZXf3FktX//fhw4cABhYWEfrCYg9/tF4M1Zm6xfpLdv39Y40gsAxsbG2L17N4A3152uWLECgwcPhoODg+yfkNxQh6i0tDScO3cOkyZNgoGBQZ6uq8tOfdQtN2ecKlWqBDc3N6xbtw7dunXDunXrYGhoKJsfQ0NDLF++HF26dEH16tVlwzs7O0t/c3uVGz58OJo2bYqMjAxcv34dEyZMgBBCY1+jPgiVkpKCI0eOSDcxLV68WGOcWY+o5sa/OeuYk4sXL2Lnzp0IDAxEnz59cOjQIVn/vCznnHz0b7mtW7fiyy+/1Lh7MD4+HjY2NtL70qVL48yZM0hLS9PJDQpq2b8whBC4c+eOdERFfdOIhYWF7D+GnFy+fBlpaWlvDbvq8Qoh4OLikqsVdOPGDahUqrf+11y6dGkcOHAAdevWzdWGa2NjozFPb7sBY9SoUXB1dUXHjh1znP7ly5fRuHHjPH8Q1KfZ7ezssHPnTgwbNgw+Pj5SaL169Spu376N1atXo1u3btJwoaGheZpe1ody5mb9vo165xwREYFSpUpJ3VNTUxEVFaUx/ocPHyIpKUn2H+ft27cBQOPuuVevXiEoKAj9+/eXfQloc+nSJWzcuBE7duzQ+k/F2xQuXFiqs23btrh37x4mTpyIFi1a4Pnz5zh48CCCgoIwduxYaZi3ha5du3ahfv36mDp1KgIDA/H1119rPQ0TGRkpnfIC3vwDkJmZqbEcPvvsM9lyNDMzg7+//1tP8Ts4OMiGKV++PDw8PLBjxw507twZ27Ztg7GxMfbt2ye7lCKnkKoe3/bt23HixAk8efJE9k/V7du3tdaeV6VLl0ZiYmKut08hBH744Qe0bdtW46ixrrzvfhEAGjRoINun5/SYIX19fdk4fX19YW1tjZCQkPcOfm5ubtLdms2bN8eDBw8wffp0jBkzJsfTic7Ozrhy5QoyMzNlbW7duiX1BwAXFxdkZmbKjpIDQEJCAh49eqTx1IRu3bph6NChePToETZs2ABfX1/ZEVHgzRHyBw8e4MqVK9KdwTNnzkRERITUhturXKVKlaRpeXt7IyUlBT/++CMmT54sHTUF5AehfH19cfny5RwfbO/i4oL9+/fj5cuXskvBsi8rZ2dnrdvA48ePER8fr7GvvnPnDoQQsu/HnPb5v/zyC1q1agV9fX20aNECv/76q+ympPddzm/z0X+yTV9fX+MU0JYtWzR+sqV9+/aIi4vDwoULNcbxtlNI77JmzRrZOf+tW7fi0aNHaN68OYA3O47SpUtj1qxZSExM1Bj+yZMnGrWrV9TbtGvXTnrYbvb6hRCyx2Okp6dj27Zt+OKLL956qtfPzw8ZGRla72ZKT0/XeKTI+wgLC8POnTsxbdq0HEOdn58fHjx4oPWurNevXyMpKemd0ylXrpz0qy4LFixAZmam7BSPOshkXWZCCMybN++95kfN29sbFhYWmDJlitbHLWRfv2/j5eUFQ0NDzJ8/X1bfr7/+ihcvXmjcbZeeno6lS5dK71NTU7F06VIUK1YMbm5usrbz5s1DUlISfvrpp3fW8cMPP6Bu3bpo1apVrmvXJiMjA8+fP5dOJWlb9gDeeveY+vrA/v37w8PDA3379pU96kIt+52+CxYsAADpc5gT9ZGn9wm46ulnnS+VSiW7zOTevXvvvAu9WbNmADTnX33E+213V74PPz8/hIWFaT3SGx8fr3EaaOPGjbhy5YrWO+915X33i/+Gent7339itHn9+jXS09O1njpT8/HxQUxMDDZt2iR1S09Px4IFC2BmZgZPT0+pHaC5/ufNmyc9hSKrzp07Q6VSYdCgQfjrr7/w9ddfa52+ubk56tatCy8vL3h5eWk8zJjb69upP99ZH+miTWZmZo7blI+PDzIyMjTyRvZlldM2kNMyffjwoexO34SEBKxZswaurq4aZ5zU+05fX1906tQJw4cPlz2h4X2X89t89CN+LVq0wIQJE9CjRw94eHjg6tWrWL9+veyICfDmv6U1a9Zg6NChOHv2LOrXr4+kpCQcOHAA/fv3R+vWrfM0fWtra9SrVw89evTA48ePMXfuXJQpUwa9e/cG8Oa6kF9++QXNmzdH5cqV0aNHDxQvXhwPHjzA4cOHYWFhgd27dyMpKQmLFi3C/PnzUa5cOdlzjdQ7xitXriAsLAzu7u4oXbo0Jk2aJN0e3qZNG5ibmyMqKgrbt29Hnz598P333+PAgQMYM2YMrly5Ip3+yImnpyf69u2LqVOnIjw8HE2bNkWhQoUQGRmJLVu2YN68efjqq6/ytJz279+PJk2avPW/i65du2Lz5s349ttvcfjwYdStWxcZGRm4desWNm/ejH379r3zSGhW9vb2mDlzJr755ht8/fXX8PHxQYUKFVC6dGl8//33ePDgASwsLLBt2zaNa/1yy8LCAsHBwejatStq1KiBTp06oVixYrh//z727t2LunXrav1nQ5tixYph1KhRCAoKQrNmzdCqVStERERg8eLFqFWrlsaO3tHREdOnT8e9e/dQrlw5bNq0CeHh4Vi2bJnGUe39+/dj8uTJGhf4arN///4cnzH5Ng0aNEDDhg1RokQJJCYmYuvWrbh06ZJ0SsTCwgINGjTAjBkzkJaWhuLFi2P//v3SMznfRqVS4ZdffoGrqyvGjRuHGTNmyPpHRUWhVatWaNasGcLCwrBu3Tqtp7zu37+PkJAQ6VTv5MmT4ezsjM8//zzHI49//fUX1q1bB+DNNaQLFy6EhYWFdOTR19cXs2fPRrNmzdClSxfExsZi0aJFKFOmDK5cuZLjPLVo0QKNGjXClClT8ODBA3zxxRc4cuQItmzZgr59+6JKlSpaa1d78uQJXr9+LesWHh4O4M0NTmXLlkXx4sUxfPhw7Nq1Cy1atED37t3h5uaGpKQkXL16FVu3bsW9e/dkR9L279+P3r175/qauuwiIiI0joQkJiZCT08PISEhaNasWa73i3mRkZEhTf/ly5dYuXIlkpKSZNfr5VZoaCj++ecf6VTv+vXr0apVq7c+lLdPnz5YunQpunfvjgsXLqBkyZLYunUrTp48iblz50pHgCpXroxevXph2bJleP78ORo2bIiLFy9ixYoVaN68ucYjxYoVK4ZmzZphy5YtsLKyynPQ4vYqFxYWBgMDA+lU74IFC/D5559rHEELCwtDXFycdKr34MGD+P7777WO08fHB15eXvjpp58QFRUFV1dXHDp0CNu2bcO3334rLavq1asjICAAy5YtQ3x8PDw9PXH27FmsXr0abdq0wZdffikbb7ly5dCrVy+cO3cOdnZ2WLFiBR4/fvzOS2DmzZuHihUr4rvvvsPmzZsB4L2X81vl+v7fd8jNLdRCvLnFftiwYcLBwUGYmJiIunXrirCwMI3bq4V4cwv7Tz/9JFxcXEShQoWEvb29+Oqrr8Tdu3eFEHl7nMtvv/0mRo0aJWxtbYWJiYnw9fXV+kiQS5cuiXbt2omiRYsKIyMj4ezsLPz8/MTBgwdl037XK/st6tu2bRP16tUTpqamwtTUVFSoUEEMGDBARERECCGE+O6770SDBg1ESEiIRk3ZH9+htmzZMuHm5iZMTEyEubm5qFq1qhgxYoR4+PCh1OZ9H+eiUqnEhQsXZN21raPU1FQxffp0UblyZWFkZCSKFCki3NzcRFBQkHjx4oXG9N41PiGEaNSokShRooR4+fKlEEKIGzduCC8vL2FmZiZsbGxE7969xeXLl7Xe2p91Ht72CIjDhw8Lb29vYWlpKYyNjUXp0qVF9+7dxfnz56U2uXnUhBBvHt9SoUIFUahQIWFnZyf69esnnj9/rjGvlStXFufPnxfu7u7C2NhYODs7i4ULF2rUBUA4ODiIpKSkt86Tento3bq11nG863Eu/fr1Ey4uLsLIyEhYW1uLOnXqyB4fJIQQ//zzj2jbtq2wsrISlpaWokOHDuLhw4fvfLSMWlBQkDAwMBAXL16Utbtx44b46quvhLm5uShSpIgIDAzUePRL1s+RSqUS9vb2ol27duLmzZtCiJwf55J1OBsbG9G0aVMRFhYmG/evv/4qypYtK4yMjESFChXEypUrtc5D9sfDvHz5UgwaNEg4OjqKQoUKidKlS4spU6aI9PT0HGvP7Svrtvzy5UsxatQoUaZMGWFoaChsbGyEh4eHmDVrlvToH/U+yMTERDx48OCtdWuT231YVu/aLwrx/o9zyTotMzMzUaNGDbF27VrZsszt41zULwMDA+Hs7CwGDhyo8VnU5vHjx6JHjx7CxsZGGBoaiqpVq2rdt6SlpYkJEyZI30lOTk5ixIgRWh97JMT/HhfWp0+fd9agpu1xLtxeNbdXPT098dlnn4mAgADpcXFC/G+/oH4ZGhqKMmXKiLFjx4qUlBQhhPbvnsTERDFkyBBpWZUpU0ZMmzZN4/FzaWlpIigoSLYNjBo1SiQnJ2vMk6+vr9i3b5+oVq2atO62bNkia6dtPyaEEKtXrxYAxK5du95rOeeGzoJfQafeMWRf6Hml3gizr6ysxo0b986NmZRBHfyULqdQoHSenp45/hOTX9T7TMq7HTt2CED+zMv/goK4vRY0OR1sKQg++jV+RERESrB8+XKUKlUK9erVy+9SiCQF59kVnxj13YVvu/miWrVqsruMiIi0+eKLL6SH3BcU1tbW8Pb2zu8yPknqGxj27t2LefPmfZDHf+Sngri9Uu4x+OWRjY2NdAF5TrL+TiwRUU6y3/xSEFSrVi3Hx1/Q23Xu3BlmZmbo1asX+vfvn9/l6FxB3F4p91RC/ItnoxARERHRJ4PX+BEREREpBIMfERERkULwGr9cyMzMxMOHD2Fubv6fu0iXiIjov0oIgZcvX8LR0THHn+1TGga/XHj48CGcnJzyuwwiIiLKg+joaHz22Wf5XUaBwOCXC+qf7ImOjoaFhUU+V0NERES5kZCQACcnJ+l7nBj8ckV9etfCwoLBj4iI6BPDy7T+hye8iYiIiBSCwY+IiIhIIRj8iIiIiBSCwY+IiIhIIRj8iIiIiBSCwY+IiIhIIRj8iIiIiBSCwY+IiIhIIRj8iIiIiBSCwY+IiIhIIRj8iIiIiBSCwY+IiIhIIRj8iIiIiBSCwY+IiIhIIRj8iIiIiBTCIL8L+C9zG74mv0ug/3dhZrf8LoGIiCjf8YgfERERkUIw+BEREREpBE/1EhHlAS/lKDg+xqUcXN8FBy/d+XcY/Ih0hF8MBQe/GIiItOOpXiIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUoh8DX7Hjh1Dy5Yt4ejoCJVKhR07dsj6CyEwduxYODg4wMTEBF5eXoiMjJS1efbsGfz9/WFhYQErKyv06tULiYmJsjZXrlxB/fr1YWxsDCcnJ8yYMeNDzxoRERFRgZOvwS8pKQnVq1fHokWLtPafMWMG5s+fjyVLluDMmTMwNTWFt7c3kpOTpTb+/v64fv06QkNDsWfPHhw7dgx9+vSR+ickJKBp06ZwdnbGhQsXMHPmTIwfPx7Lli374PNHREREVJAY5OfEmzdvjubNm2vtJ4TA3LlzMXr0aLRu3RoAsGbNGtjZ2WHHjh3o1KkTbt68iZCQEJw7dw41a9YEACxYsAA+Pj6YNWsWHB0dsX79eqSmpmLFihUwNDRE5cqVER4ejtmzZ8sCIhEREdF/XYG9xi8qKgoxMTHw8vKSullaWqJ27doICwsDAISFhcHKykoKfQDg5eUFPT09nDlzRmrToEEDGBoaSm28vb0RERGB58+fa512SkoKEhISZC8iIiKiT12BDX4xMTEAADs7O1l3Ozs7qV9MTAxsbW1l/Q0MDGBtbS1ro20cWaeR3dSpU2FpaSm9nJyc/v0MEREREeWzAhv88tOoUaPw4sUL6RUdHZ3fJRERERH9awU2+Nnb2wMAHj9+LOv++PFjqZ+9vT1iY2Nl/dPT0/Hs2TNZG23jyDqN7IyMjGBhYSF7EREREX3qCmzwc3Fxgb29PQ4ePCh1S0hIwJkzZ+Du7g4AcHd3R3x8PC5cuCC1OXToEDIzM1G7dm2pzbFjx5CWlia1CQ0NRfny5VGkSJGPNDdERERE+S9fg19iYiLCw8MRHh4O4M0NHeHh4bh//z5UKhUGDx6MSZMmYdeuXbh69Sq6desGR0dHtGnTBgBQsWJFNGvWDL1798bZs2dx8uRJBAYGolOnTnB0dAQAdOnSBYaGhujVqxeuX7+OTZs2Yd68eRg6dGg+zTURERFR/sjXx7mcP38eX375pfReHcYCAgKwatUqjBgxAklJSejTpw/i4+NRr149hISEwNjYWBpm/fr1CAwMROPGjaGnp4f27dtj/vz5Un9LS0vs378fAwYMgJubG2xsbDB27Fg+yoWIiIgUJ1+DX8OGDSGEyLG/SqXChAkTMGHChBzbWFtbY8OGDW+dTrVq1XD8+PE810lERET0X1Bgr/EjIiIiIt1i8CMiIiJSCAY/IiIiIoVg8CMiIiJSCAY/IiIiIoVg8CMiIiJSCAY/IiIiIoVg8CMiIiJSCAY/IiIiIoVg8CMiIiJSCAY/IiIiIoVg8CMiIiJSCAY/IiIiIoVg8CMiIiJSCAY/IiIiIoVg8CMiIiJSCAY/IiIiIoVg8CMiIiJSCAY/IiIiIoVg8CMiIiJSCAY/IiIiIoVg8CMiIiJSCAY/IiIiIoVg8CMiIiJSCAY/IiIiIoVg8CMiIiJSCAY/IiIiIoVg8CMiIiJSCAY/IiIiIoVg8CMiIiJSCAY/IiIiIoVg8CMiIiJSCAY/IiIiIoVg8CMiIiJSCAY/IiIiIoVg8CMiIiJSCAY/IiIiIoVg8CMiIiJSCAY/IiIiIoVg8CMiIiJSCAY/IiIiIoVg8CMiIiJSCAY/IiIiIoVg8CMiIiJSCAY/IiIiIoVg8CMiIiJSCAY/IiIiIoVg8CMiIiJSCAY/IiIiIoVg8CMiIiJSCAY/IiIiIoVg8CMiIiJSCAY/IiIiIoVg8CMiIiJSCAY/IiIiIoVg8CMiIiJSiAId/DIyMjBmzBi4uLjAxMQEpUuXxsSJEyGEkNoIITB27Fg4ODjAxMQEXl5eiIyMlI3n2bNn8Pf3h4WFBaysrNCrVy8kJiZ+7NkhIiIiylcFOvhNnz4dwcHBWLhwIW7evInp06djxowZWLBggdRmxowZmD9/PpYsWYIzZ87A1NQU3t7eSE5Oltr4+/vj+vXrCA0NxZ49e3Ds2DH06dMnP2aJiIiIKN8Y5HcBb3Pq1Cm0bt0avr6+AICSJUvit99+w9mzZwG8Odo3d+5cjB49Gq1btwYArFmzBnZ2dtixYwc6deqEmzdvIiQkBOfOnUPNmjUBAAsWLICPjw9mzZoFR0dHjemmpKQgJSVFep+QkPChZ5WIiIjogyvQR/w8PDxw8OBB3L59GwBw+fJlnDhxAs2bNwcAREVFISYmBl5eXtIwlpaWqF27NsLCwgAAYWFhsLKykkIfAHh5eUFPTw9nzpzROt2pU6fC0tJSejk5OX2oWSQiIiL6aAr0Eb8ffvgBCQkJqFChAvT19ZGRkYHJkyfD398fABATEwMAsLOzkw1nZ2cn9YuJiYGtra2sv4GBAaytraU22Y0aNQpDhw6V3ickJDD8ERER0SevQAe/zZs3Y/369diwYQMqV66M8PBwDB48GI6OjggICPhg0zUyMoKRkdEHGz8RERFRfijQwW/48OH44Ycf0KlTJwBA1apV8ffff2Pq1KkICAiAvb09AODx48dwcHCQhnv8+DFcXV0BAPb29oiNjZWNNz09Hc+ePZOGJyIiIlKCAn2N36tXr6CnJy9RX18fmZmZAAAXFxfY29vj4MGDUv+EhAScOXMG7u7uAAB3d3fEx8fjwoULUptDhw4hMzMTtWvX/ghzQURERFQwFOgjfi1btsTkyZNRokQJVK5cGZcuXcLs2bPRs2dPAIBKpcLgwYMxadIklC1bFi4uLhgzZgwcHR3Rpk0bAEDFihXRrFkz9O7dG0uWLEFaWhoCAwPRqVMnrXf0EhEREf1XFejgt2DBAowZMwb9+/dHbGwsHB0d0bdvX4wdO1ZqM2LECCQlJaFPnz6Ij49HvXr1EBISAmNjY6nN+vXrERgYiMaNG0NPTw/t27fH/Pnz82OWiIiIiPJNgQ5+5ubmmDt3LubOnZtjG5VKhQkTJmDChAk5trG2tsaGDRs+QIVEREREn44CfY0fEREREekOgx8RERGRQjD4ERERESkEgx8RERGRQjD4ERERESkEgx8RERGRQjD4ERERESkEgx8RERGRQjD4ERERESkEgx8RERGRQjD4ERERESkEgx8RERGRQjD4ERERESkEgx8RERGRQjD4ERERESkEgx8RERGRQjD4ERERESkEgx8RERGRQjD4ERERESkEgx8RERGRQjD4ERERESkEgx8RERGRQjD4ERERESkEgx8RERGRQjD4ERERESkEgx8RERGRQjD4ERERESkEgx8RERGRQjD4ERERESkEgx8RERGRQjD4ERERESkEgx8RERGRQjD4ERERESkEgx8RERGRQjD4ERERESkEgx8RERGRQjD4ERERESkEgx8RERGRQjD4ERERESkEgx8RERGRQjD4ERERESkEgx8RERGRQjD4ERERESkEgx8RERGRQjD4ERERESkEgx8RERGRQjD4ERERESkEgx8RERGRQjD4ERERESmEQV4HTEpKwtGjR3H//n2kpqbK+g0cOPBfF0ZEREREupWn4Hfp0iX4+Pjg1atXSEpKgrW1NeLi4lC4cGHY2toy+BEREREVQHk61TtkyBC0bNkSz58/h4mJCU6fPo2///4bbm5umDVrlq5rJCIiIiIdyFPwCw8Px7Bhw6Cnpwd9fX2kpKTAyckJM2bMwI8//qjrGomIiIhIB/IU/AoVKgQ9vTeD2tra4v79+wAAS0tLREdH6646IiIiItKZPF3j9/nnn+PcuXMoW7YsPD09MXbsWMTFxWHt2rWoUqWKrmskIiIiIh3I0xG/KVOmwMHBAQAwefJkFClSBP369cOTJ0+wbNkynRb44MEDfP311yhatChMTExQtWpVnD9/XuovhMDYsWPh4OAAExMTeHl5ITIyUjaOZ8+ewd/fHxYWFrCyskKvXr2QmJio0zqJiIiICro8HfGrWbOm9LetrS1CQkJ0VlBWz58/R926dfHll1/izz//RLFixRAZGYkiRYpIbWbMmIH58+dj9erVcHFxwZgxY+Dt7Y0bN27A2NgYAODv749Hjx4hNDQUaWlp6NGjB/r06YMNGzZ8kLqJiIiICqI8Bb9GjRrh999/h5WVlY7LkZs+fTqcnJywcuVKqZuLi4v0txACc+fOxejRo9G6dWsAwJo1a2BnZ4cdO3agU6dOuHnzJkJCQnDu3DkpsC5YsAA+Pj6YNWsWHB0dP+g8EBERERUUeTrVe+TIEY2HNn8Iu3btQs2aNdGhQwfY2tri888/x/Lly6X+UVFRiImJgZeXl9TN0tIStWvXRlhYGAAgLCwMVlZWsqOUXl5e0NPTw5kzZ7RONyUlBQkJCbIXERER0acuzz/ZplKpdFmHVn/99ReCg4NRtmxZ7Nu3D/369cPAgQOxevVqAEBMTAwAwM7OTjacnZ2d1C8mJga2tray/gYGBrC2tpbaZDd16lRYWlpKLycnJ13PGhEREdFHl+efbGvbti0MDQ219jt06FCeC8oqMzMTNWvWxJQpUwC8uZv42rVrWLJkCQICAnQyDW1GjRqFoUOHSu8TEhIY/oiIiOiTl+fg5+7uDjMzM13WosHBwQGVKlWSdatYsSK2bdsGALC3twcAPH78WLrLWP3e1dVVahMbGysbR3p6Op49eyYNn52RkRGMjIx0NRtEREREBUKegp9KpcLw4cM1TqHqWt26dRERESHrdvv2bTg7OwN4c6OHvb09Dh48KAW9hIQEnDlzBv369QPwJqDGx8fjwoULcHNzA/DmiGRmZiZq1679QesnIiIiKkjyFPyEELquQ6shQ4bAw8MDU6ZMgZ+fH86ePYtly5ZJzwpUqVQYPHgwJk2ahLJly0qPc3F0dESbNm0AvDlC2KxZM/Tu3RtLlixBWloaAgMD0alTJ97RS0RERIqSp+A3bty4D36aFwBq1aqF7du3Y9SoUZgwYQJcXFwwd+5c+Pv7S21GjBiBpKQk9OnTB/Hx8ahXrx5CQkKkZ/gBwPr16xEYGIjGjRtDT08P7du3x/z58z94/UREREQFSZ6DHwA8efJEOhVbvnx5FCtWTHeV/b8WLVqgRYsWOfZXqVSYMGECJkyYkGMba2trPqyZiIiIFC9Pj3N59eoVevbsCUdHRzRo0AANGjSAo6MjevXqhVevXum6RiIiIiLSgTwFvyFDhuDo0aPYtWsX4uPjER8fj507d+Lo0aMYNmyYrmskIiIiIh3I06nebdu2YevWrWjYsKHUzcfHByYmJvDz80NwcLCu6iMiIiIiHcnzqd7sv5YBALa2tjzVS0RERFRA5Sn4ubu7Y9y4cUhOTpa6vX79GkFBQXB3d9dZcURERESkO3k61Tt37lw0a9YMn332GapXrw4AuHz5MoyNjbFv3z6dFkhEREREupGn4Fe1alVERkZi/fr1uHXrFgCgc+fO8Pf3h4mJiU4LJCIiIiLdyFPwO3bsGDw8PNC7d29d10NEREREH0iervH78ssv8ezZM13XQkREREQfUJ6C38f6rV4iIiIi0p08neoFgLCwMBQpUkRrvwYNGuS5ICIiIiL6MPIc/Nq2bau1u0qlQkZGRp4LIiIiIqIPI0+negEgJiYGmZmZGi+GPiIiIqKCKU/BT6VS6boOIiIiIvrAeHMHERERkULk6Rq/zMxMXddBRERERB9Yno74TZ06FStWrNDovmLFCkyfPv1fF0VEREREupen4Ld06VJUqFBBo3vlypWxZMmSf10UEREREelenoJfTEwMHBwcNLoXK1YMjx49+tdFEREREZHu5Sn4OTk54eTJkxrdT548CUdHx39dFBERERHpXp5u7ujduzcGDx6MtLQ0NGrUCABw8OBBjBgxAsOGDdNpgURERESkG3kKfsOHD8fTp0/Rv39/pKamAgCMjY0xcuRIjBo1SqcFEhEREZFu5Cn4qVQqTJ8+HWPGjMHNmzdhYmKCsmXLwsjISNf1EREREZGO5Pm3egHAzMwMtWrV0lUtRERERPQB5Tn4nT9/Hps3b8b9+/el071qv//++78ujIiIiIh0K0939W7cuBEeHh64efMmtm/fjrS0NFy/fh2HDh2CpaWlrmskIiIiIh3IU/CbMmUK5syZg927d8PQ0BDz5s3DrVu34OfnhxIlSui6RiIiIiLSgTwFv7t378LX1xcAYGhoiKSkJKhUKgwZMgTLli3TaYFEREREpBt5Cn5FihTBy5cvAQDFixfHtWvXAADx8fF49eqV7qojIiIiIp3J080dDRo0QGhoKKpWrYoOHTpg0KBBOHToEEJDQ9G4cWNd10hEREREOpCn4Ldw4UIkJycDAH766ScUKlQIp06dQvv27TF69GidFkhEREREuvFewS8hIeHNQAYGMDMzk973798f/fv31311RERERKQz7xX8rKysoFKp3tkuIyMjzwURERER0YfxXsHv8OHDsvdCCPj4+OCXX35B8eLFdVoYEREREenWewU/T09PjW76+vqoU6cOSpUqpbOiiIiIiEj38vQ4FyIiIiL69Pyr4BcdHY1Xr16haNGiuqqHiIiIiD6Q9zrVO3/+fOnvuLg4/Pbbb2jUqBF/n5eIiIjoE/BewW/OnDkAAJVKBRsbG7Rs2ZLP7SMiIiL6RLxX8IuKivpQdRARERHRB8abO4iIiIgUgsGPiIiISCEY/IiIiIgUgsGPiIiISCEY/IiIiIgUgsGPiIiISCEY/IiIiIgUgsGPiIiISCEY/IiIiIgUgsGPiIiISCEY/IiIiIgUgsGPiIiISCEY/IiIiIgUgsGPiIiISCEY/IiIiIgUgsGPiIiISCE+qeA3bdo0qFQqDB48WOqWnJyMAQMGoGjRojAzM0P79u3x+PFj2XD379+Hr68vChcuDFtbWwwfPhzp6ekfuXoiIiKi/PXJBL9z585h6dKlqFatmqz7kCFDsHv3bmzZsgVHjx7Fw4cP0a5dO6l/RkYGfH19kZqailOnTmH16tVYtWoVxo4d+7FngYiIiChffRLBLzExEf7+/li+fDmKFCkidX/x4gV+/fVXzJ49G40aNYKbmxtWrlyJU6dO4fTp0wCA/fv348aNG1i3bh1cXV3RvHlzTJw4EYsWLUJqamp+zRIRERHRR/dJBL8BAwbA19cXXl5esu4XLlxAWlqarHuFChVQokQJhIWFAQDCwsJQtWpV2NnZSW28vb2RkJCA69eva51eSkoKEhISZC8iIiKiT51BfhfwLhs3bsTFixdx7tw5jX4xMTEwNDSElZWVrLudnR1iYmKkNllDn7q/up82U6dORVBQkA6qJyIiIio4CvQRv+joaAwaNAjr16+HsbHxR5vuqFGj8OLFC+kVHR390aZNRERE9KEU6OB34cIFxMbGokaNGjAwMICBgQGOHj2K+fPnw8DAAHZ2dkhNTUV8fLxsuMePH8Pe3h4AYG9vr3GXr/q9uk12RkZGsLCwkL2IiIiIPnUFOvg1btwYV69eRXh4uPSqWbMm/P39pb8LFSqEgwcPSsNERETg/v37cHd3BwC4u7vj6tWriI2NldqEhobCwsIClSpV+ujzRERERJRfCvQ1fubm5qhSpYqsm6mpKYoWLSp179WrF4YOHQpra2tYWFjgu+++g7u7O+rUqQMAaNq0KSpVqoSuXbtixowZiImJwejRozFgwAAYGRl99HkiIiIiyi8FOvjlxpw5c6Cnp4f27dsjJSUF3t7eWLx4sdRfX18fe/bsQb9+/eDu7g5TU1MEBARgwoQJ+Vg1ERER0cf3yQW/I0eOyN4bGxtj0aJFWLRoUY7DODs7448//vjAlREREREVbAX6Gj8iIiIi0h0GPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUggGPyIiIiKFYPAjIiIiUogCHfymTp2KWrVqwdzcHLa2tmjTpg0iIiJkbZKTkzFgwAAULVoUZmZmaN++PR4/fixrc//+ffj6+qJw4cKwtbXF8OHDkZ6e/jFnhYiIiCjfFejgd/ToUQwYMACnT59GaGgo0tLS0LRpUyQlJUlthgwZgt27d2PLli04evQoHj58iHbt2kn9MzIy4Ovri9TUVJw6dQqrV6/GqlWrMHbs2PyYJSIiIqJ8Y5DfBbxNSEiI7P2qVatga2uLCxcuoEGDBnjx4gV+/fVXbNiwAY0aNQIArFy5EhUrVsTp06dRp04d7N+/Hzdu3MCBAwdgZ2cHV1dXTJw4ESNHjsT48eNhaGiYH7NGRERE9NEV6CN+2b148QIAYG1tDQC4cOEC0tLS4OXlJbWpUKECSpQogbCwMABAWFgYqlatCjs7O6mNt7c3EhIScP36da3TSUlJQUJCguxFRERE9Kn7ZIJfZmYmBg8ejLp166JKlSoAgJiYGBgaGsLKykrW1s7ODjExMVKbrKFP3V/dT5upU6fC0tJSejk5Oel4boiIiIg+vk8m+A0YMADXrl3Dxo0bP/i0Ro0ahRcvXkiv6OjoDz5NIiIiog+tQF/jpxYYGIg9e/bg2LFj+Oyzz6Tu9vb2SE1NRXx8vOyo3+PHj2Fvby+1OXv2rGx86rt+1W2yMzIygpGRkY7ngoiIiCh/FegjfkIIBAYGYvv27Th06BBcXFxk/d3c3FCoUCEcPHhQ6hYREYH79+/D3d0dAODu7o6rV68iNjZWahMaGgoLCwtUqlTp48wIERERUQFQoI/4DRgwABs2bMDOnTthbm4uXZNnaWkJExMTWFpaolevXhg6dCisra1hYWGB7777Du7u7qhTpw4AoGnTpqhUqRK6du2KGTNmICYmBqNHj8aAAQN4VI+IiIgUpUAHv+DgYABAw4YNZd1XrlyJ7t27AwDmzJkDPT09tG/fHikpKfD29sbixYultvr6+tizZw/69esHd3d3mJqaIiAgABMmTPhYs0FERERUIBTo4CeEeGcbY2NjLFq0CIsWLcqxjbOzM/744w9dlkZERET0ySnQ1/gRERERke4w+BEREREpBIMfERERkUIw+BEREREpBIMfERERkUIw+BEREREpBIMfERERkUIw+BEREREpBIMfERERkUIw+BEREREpBIMfERERkUIw+BEREREpBIMfERERkUIw+BEREREpBIMfERERkUIw+BEREREpBIMfERERkUIw+BEREREpBIMfERERkUIw+BEREREpBIMfERERkUIw+BEREREpBIMfERERkUIw+BEREREpBIMfERERkUIw+BEREREpBIMfERERkUIw+BEREREpBIMfERERkUIw+BEREREpBIMfERERkUIw+BEREREpBIMfERERkUIw+BEREREpBIMfERERkUIw+BEREREpBIMfERERkUIw+BEREREpBIMfERERkUIw+BEREREpBIMfERERkUIw+BEREREpBIMfERERkUIw+BEREREpBIMfERERkUIw+BEREREpBIMfERERkUIw+BEREREpBIMfERERkUIw+BEREREpBIMfERERkUIw+BEREREpBIMfERERkUIw+BEREREpBIMfERERkUIw+BEREREphKKC36JFi1CyZEkYGxujdu3aOHv2bH6XRERERPTRKCb4bdq0CUOHDsW4ceNw8eJFVK9eHd7e3oiNjc3v0oiIiIg+CsUEv9mzZ6N3797o0aMHKlWqhCVLlqBw4cJYsWJFfpdGRERE9FEY5HcBH0NqaiouXLiAUaNGSd309PTg5eWFsLAwjfYpKSlISUmR3r948QIAkJCQ8F7TzUh5nceKSdfed93lBdd3wcH1rSxc38ryPutb3VYI8aHK+eSohAKWxsOHD1G8eHGcOnUK7u7uUvcRI0bg6NGjOHPmjKz9+PHjERQU9LHLJCIiog8gOjoan332WX6XUSAo4ojf+xo1ahSGDh0qvc/MzMSzZ89QtGhRqFSqfKzs40pISICTkxOio6NhYWGR3+XQB8b1rSxc38qi1PUthMDLly/h6OiY36UUGIoIfjY2NtDX18fjx49l3R8/fgx7e3uN9kZGRjAyMpJ1s7Ky+pAlFmgWFhaK2lEoHde3snB9K4sS17elpWV+l1CgKOLmDkNDQ7i5ueHgwYNSt8zMTBw8eFB26peIiIjov0wRR/wAYOjQoQgICEDNmjXxxRdfYO7cuUhKSkKPHj3yuzQiIiKij0Ixwa9jx4548uQJxo4di5iYGLi6uiIkJAR2dnb5XVqBZWRkhHHjxmmc9qb/Jq5vZeH6Vhaub1JTxF29RERERKSQa/yIiIiIiMGPiIiISDEY/IiIiIgUgsFPoRo2bIjBgwfndxlUgGTfJkqWLIm5c+fmWz30ft71mVapVNixY0eux3fkyBGoVCrEx8f/69qo4HrXdpGX7WD8+PFwdXX917XRh6GYu3qJ6P2cO3cOpqam+V0G6cijR49QpEiR/C6DPjEeHh549OgRH4L8H8LgR0RaFStWLL9LIB3S9itF9GlLS0tDoUKFPug0DA0Nue38x/BUL+H58+fo1q0bihQpgsKFC6N58+aIjIwE8OZ3DosVK4atW7dK7V1dXeHg4CC9P3HiBIyMjPDq1auPXrsSNGzYEN999x0GDx6MIkWKwM7ODsuXL5ceQG5ubo4yZcrgzz//lIa5du0amjdvDjMzM9jZ2aFr166Ii4uT+iclJaFbt24wMzODg4MDfv75Z43pZj3Ve+/ePahUKoSHh0v94+PjoVKpcOTIEQD/OyW0b98+fP755zAxMUGjRo0QGxuLP//8ExUrVoSFhQW6dOnCbeUDyczMxIgRI2BtbQ17e3uMHz9e6pf9lN6pU6fg6uoKY2Nj1KxZEzt27NBYxwBw4cIF1KxZE4ULF4aHhwciIiI+zsz8xyxbtgyOjo7IzMyUdW/dujV69uwJANi5cydq1KgBY2NjlCpVCkFBQUhPT5faqlQqBAcHo1WrVjA1NcWkSZNQpkwZzJo1SzbO8PBwqFQq3LlzJ1e1xcXFoW3btihcuDDKli2LXbt2Sf20nepdvnw5nJycULhwYbRt2xazZ8/W+rOma9euRcmSJWFpaYlOnTrh5cuXuaqHPiwGP0L37t1x/vx57Nq1C2FhYRBCwMfHB2lpaVCpVGjQoIH05f78+XPcvHkTr1+/xq1btwAAR48eRa1atVC4cOF8nIv/ttWrV8PGxgZnz57Fd999h379+qFDhw7w8PDAxYsX0bRpU3Tt2hWvXr1CfHw8GjVqhM8//xznz59HSEgIHj9+DD8/P2l8w4cPx9GjR7Fz507s378fR44cwcWLF3VS6/jx47Fw4UKcOnUK0dHR8PPzw9y5c7Fhwwbs3bsX+/fvx4IFC3QyLZJbvXo1TE1NcebMGcyYMQMTJkxAaGioRruEhAS0bNkSVatWxcWLFzFx4kSMHDlS6zh/+ukn/Pzzzzh//jwMDAykkELvp0OHDnj69CkOHz4sdXv27BlCQkLg7++P48ePo1u3bhg0aBBu3LiBpUuXYtWqVZg8ebJsPOPHj0fbtm1x9epV9OrVCz179sTKlStlbVauXIkGDRqgTJkyuaotKCgIfn5+uHLlCnx8fODv749nz55pbXvy5El8++23GDRoEMLDw9GkSRONGgHg7t272LFjB/bs2YM9e/bg6NGjmDZtWq7qoQ9MkCJ5enqKQYMGidu3bwsA4uTJk1K/uLg4YWJiIjZv3iyEEGL+/PmicuXKQgghduzYIWrXri1at24tgoODhRBCeHl5iR9//PHjz4RCeHp6inr16knv09PThampqejatavU7dGjRwKACAsLExMnThRNmzaVjSM6OloAEBEREeLly5fC0NBQWr9CCPH06VNhYmIiBg0aJHVzdnYWc+bMEUIIERUVJQCIS5cuSf2fP38uAIjDhw8LIYQ4fPiwACAOHDggtZk6daoAIO7evSt169u3r/D29v43i4S0yL6dCCFErVq1xMiRI4UQQgAQ27dvF0IIERwcLIoWLSpev34ttV2+fLlsHWtbn3v37hUAZMNR7rVu3Vr07NlTer906VLh6OgoMjIyROPGjcWUKVNk7deuXSscHByk9wDE4MGDZW0ePHgg9PX1xZkzZ4QQQqSmpgobGxuxatWqXNUEQIwePVp6n5iYKACIP//8Uwjxv+3g+fPnQgghOnbsKHx9fWXj8Pf3F5aWltL7cePGicKFC4uEhASp2/Dhw0Xt2rVzVRN9WDzip3A3b96EgYEBateuLXUrWrQoypcvj5s3bwIAPD09cePGDTx58gRHjx5Fw4YN0bBhQxw5cgRpaWk4deoUGjZsmE9zoAzVqlWT/tbX10fRokVRtWpVqZv6pwdjY2Nx+fJlHD58GGZmZtKrQoUKAN78F3737l2kpqbK1rm1tTXKly+v81rt7OxQuHBhlCpVStYtNjZWJ9MiuazLHgAcHBy0LuuIiAhUq1YNxsbGUrcvvvjineNUX+LB9Zc3/v7+2LZtG1JSUgAA69evR6dOnaCnp4fLly9jwoQJss9t79698ejRI9mlETVr1pSN09HREb6+vlixYgUAYPfu3UhJSUGHDh1yXVfWdWxqagoLC4sc13FERITGtqJt2ylZsiTMzc2l9zlti/Tx8eYOeqeqVavC2toaR48exdGjRzF58mTY29tj+vTpOHfuHNLS0uDh4ZHfZf6nZb+AW6VSybqpVCoAb67xSkxMRMuWLTF9+nSN8Tg4OOT6up+s9PTe/I8osvzCY1pa2jtrzV6nulv265xINz7Ess5pO6P317JlSwghsHfvXtSqVQvHjx/HnDlzAACJiYkICgpCu3btNIbLGtC13Wn/zTffoGvXrpgzZw5WrlyJjh07vtelNx96u9HVOEk3GPwUrmLFikhPT8eZM2ek8Pb06VNERESgUqVKAN58YOvXr4+dO3fi+vXrqFevHgoXLoyUlBQsXboUNWvW5GM/CpAaNWpg27ZtKFmyJAwMND/ipUuXRqFChXDmzBmUKFECwJtrN2/fvg1PT0+t41Tf4fvo0SN8/vnnAKBxEwB9OsqXL49169YhJSUFRkZGAN48voc+LGNjY7Rr1w7r16/HnTt3UL58edSoUQPAm89tRERErq/Ly8rHxwempqYIDg5GSEgIjh07puvSJeXLl9fYVrjtfFp4qlfhypYti9atW6N37944ceIELl++jK+//hrFixdH69atpXYNGzbEb7/9BldXV5iZmUFPTw8NGjTA+vXrcwwLlD8GDBiAZ8+eoXPnzjh37hzu3r2Lffv2oUePHsjIyICZmRl69eqF4cOH49ChQ7h27Rq6d+8uHdXTxsTEBHXq1MG0adNw8+ZNHD16FKNHj/6Ic0W61KVLF2RmZqJPnz64efMm9u3bJ90Zqj6qRx+Gv78/9u7dixUrVsDf31/qPnbsWKxZswZBQUG4fv06bt68iY0bN+bqc6avr4/u3btj1KhRKFu2LNzd3T9Y/d999x3++OMPzJ49G5GRkVi6dCn+/PNPbjefEAY/wsqVK+Hm5oYWLVrA3d0dQgj88ccfskP1np6eyMjIkF3L17BhQ41ulP8cHR1x8uRJZGRkoGnTpqhatSoGDx4MKysrKdzNnDkT9evXR8uWLeHl5YV69erBzc3treNdsWIF0tPT4ebmhsGDB2PSpEkfY3boA7CwsMDu3bsRHh4OV1dX/PTTTxg7diwA+WlF0r1GjRrB2toaERER6NKli9Td29sbe/bswf79+1GrVi3UqVMHc+bMgbOzc67G26tXL6SmpqJHjx4fqnQAQN26dbFkyRLMnj0b1atXR0hICIYMGcLt5hOiElkv2iEiIkVav349evTogRcvXsDExCS/y6H3dPz4cTRu3BjR0dHSzV4fS+/evXHr1i0cP378o06X8obX+BERKdCaNWtQqlQpFC9eHJcvX8bIkSPh5+fH0PeJSUlJwZMnTzB+/Hh06NDho4S+WbNmoUmTJjA1NcWff/6J1atXY/HixR98uqQbPNVLRKRAMTEx+Prrr1GxYkUMGTIEHTp0wLJly/K7LHpPv/32G5ydnREfH48ZM2bI+q1fv172eJisr8qVK+d5mmfPnkWTJk1QtWpVLFmyBPPnz8c333zzb2eFPhKe6iUiIvoPevnyJR4/fqy1X6FChXJ9/SD9tzD4ERERESkET/USERERKQSDHxEREZFCMPgRERERKQSDHxEREZFCMPgRERHat2+P48ePIyMjA507d8aePXvyuyQi+gAY/Ih0oHv37mjTpo2s25MnT1ClShXUrl0bL168yJ/CiHIpMDAQvr6+MDY2RlRUFLy8vPK7JCL6APjLHUQfwJMnT9CoUSOYmJhg//79sLS0zO+SiN7qyy+/xJMnT/Ds2TPY29tDpVLld0lE9AHwiB+RjsXFxaFx48YwMjJCaGioLPTNnj0bVatWhampKZycnNC/f38kJiYCAI4cOQKVSpXjS+3EiROoX78+TExM4OTkhIEDByIpKUnqX7JkSY1hv//+e6l/cHAwSpcuDUNDQ5QvXx5r166V1a9SqRAcHIzmzZvDxMQEpUqVwtatW6X+9+7dg0qlQnh4uNRtzJgxUKlUmDt3rtTt1q1baNKkCSwtLaU6rKysclxux44dQ8WKFVG4cGFYWlrC29sbkZGRUv+1a9eiZs2aMDc3h729Pbp06YLY2Fipv3r5xcfHAwCeP3+OatWqoVu3blA/rrRhw4YIDAxEYGAgLC0tYWNjgzFjxiDr40xLliwpm4+s2rRpg+7du2td1qampvDw8MD58+el/iEhIahXrx6srKxQtGhRtGjRAnfv3s1xWT548AAdOnSAra0tzM3N0bZtW/zzzz9S+/Hjx8PV1VV6Hx8fD5VKhSNHjkjdtB193rlzJ2rUqAFjY2OUKlUKQUFBSE9Pl/qrVCrs2LEDRkZGcHBwwIoVK6BSqTB48GCty0FbLYDmOgCA6Oho+Pn5wcrKCtbW1mjdujXu3bv31npXrVol21a0TUttx44dDKlE74HBj0iHnj59Ci8vLxgYGCA0NFQj6Ojp6WH+/Pm4fv06Vq9ejUOHDmHEiBEAAA8PDzx69AiPHj3Ctm3bAEB6/+jRIwDA3bt30axZM7Rv3x5XrlzBpk2bcOLECQQGBsqmM2HCBNmw48aNAwBs374dgwYNwrBhw3Dt2jX07dsXPXr0wOHDh2XDjxkzBu3bt8fly5fh7++PTp064ebNm1rn+Z9//sHcuXM1fuO1Z8+eSEtLw8mTJ/Ho0aMcw5Ra8eLFsXDhQly/fh0nTpyAnp4e+vbtK/VPS0vDxIkTcfnyZezYsQP37t2ThbCsEhMT4ePjg1KlSkkhRm316tUwMDDA2bNnMW/ePMyePRu//PLLW2t7G/WyPn/+PExNTTFgwACpX1JSEoYOHYrz58/j4MGD0NPTQ9u2bZGZmakxnrS0NPj4+CAqKgq7d+/G0aNHERcXhzZt2uDfPGf/+PHj6NatGwYNGoQbN25g6dKlWLVqFSZPnqy1fVJSEsaMGQMzM7M8T1MtLS0N3t7eMDc3x/Hjx3Hy5EmYmZmhWbNmSE1N/dfjJ6L3x1O9RDry/PlzeHl54caNG3Bzc4OFhYVGm6xHUEqWLIlJkybh22+/xeLFi2FoaAh7e3sAgLW1NQBI79WmTp0Kf39/aTxly5bF/Pnz4enpieDgYBgbGwOAdFQsu1mzZqF79+7o378/AGDo0KE4ffo0Zs2ahS+//FJq16FDB+m3NydOnIjQ0FAsWLBA6w+x//TTT+jYsSMOHDgg6x4eHo5ffvkFVapUAYB3nu4uXbo0SpcuDQBITk6Gg4OD7OhYz549pb9LlSqF+fPno1atWkhMTJSFlJSUFHz11VcoXLgwNm3aBAMD+W7OyckJc+bMgUqlQvny5XH16lXMmTMHvXv3fmt9OVEvaysrKxQpUkQWMtu3by9ru2LFChQrVgw3btyQlovagQMHcOXKFVy/fh2VKlUC8Oa3VkuVKoWDBw/m+Zq7oKAg/PDDDwgICADwZtlNnDgRI0aMkP4hyGrGjBmoVKmS7IhgXm3atAmZmZn45ZdfpOWycuVKWFlZ4ciRI2jatOm/ngYRvR8e8SPSkWPHjiEzMxPh4eG4c+eOxg+mA2++3Bs3bozixYvD3NwcXbt2xdOnT/Hq1atcTePy5ctYtWqV7MfWvb29kZmZiaioqHcOf/PmTdStW1fWrW7duhpH89zd3TXeazvid/HiRWzfvh0TJ07U6Ofi4oLt27fnet4A4P79+zAzM4OpqSnOnj2LVatWSf0uXLiAli1bokSJEjA3N4enp6c0TFb+/v44ePAgPD09YWRkpDGNOnXqyMKZu7s7IiMjkZGRIXUbOXIkzMzMYGtri4YNG+LkyZM51qxuq6550aJFUr/IyEh07twZpUqVgoWFBUqWLKm1Zg8PD7Rp0wZWVlZS6AOAEiVKwMnJCTdu3HjLUnu7y5cvY8KECbJtpnfv3nj06JHGunn48CFmz56Nn3/+Oc/Tyz7tO3fuwNzcXJq2tbU1kpOTZaF+z549svq+/fZbjXFdvXoVZmZmsLS0RMWKFTFt2jSd1EikNDziR6Qj6iMzNjY2WLx4Mb7++mv4+vqiWrVqAN5cz9WiRQv069cPkydPhrW1NU6cOIFevXohNTUVhQsXfuc0EhMT0bdvXwwcOFCjX4kSJXQ+T+8ybNgwfP/993BwcNDo9+uvvyIgIADm5uYwMTFBenq6dEQyJ46OjggPD0dMTAxGjRqFadOmYenSpUhKSoK3tze8vb2xfv16FCtWDPfv34e3t7fGKcOYmBhs27YNXbp0Qdu2bVG1atX3nq/hw4eje/fuSEpKwsyZM9GyZUvExMS8s+2sWbPg5+eH8+fPQ19fHy1btoSzszOWL18OR0dHZGZmokqVKho1b9q0CTdv3sTUqVO1TuPfXMOWmJiIoKAgtGvXTqNf9vXx008/oUOHDqhevXqep5d92m5ubli/fr1Gv2LFikl/f/nllwgODpbe//7775gyZYqsffny5bFr1y5kZGTg9OnT6N27N8qUKaNxRJeI3o6fGCIdqVq1KmxsbAC8OVX6+++/o1u3bjh79iwMDQ1x4cIFZGZm4ueff4ae3puD7Zs3b36vadSoUQM3btxAmTJl8lRjxYoVcfLkSem0HwCcPHlSdpQJAE6fPo1u3brJ3n/++eeyNrt27cLt27exd+9erdOqU6cOWrVqhWPHjmHdunXYvn27xpd5dgYGBihTpgzKlCmD77//Hv7+/li6dClu3bqFp0+fYtq0aXBycgIA2U0U2esqVaoUevfujR49euD06dOycHDmzBmNeS1btiz09fWlbjY2NtIyHjVqFNavX69xlE5b25EjR6Jq1aqIiopCkSJFEBERgeXLl6N+/foA3tyYo42TkxOKFi2K+Ph43LhxQ1of0dHRiI6O1lg/76NGjRqIiIh45zYTHh6OrVu3IiIiIs/T0jbtTZs2wdbWVuulD2qmpqay+mxtbTXaGBoaSm3Kly+PhQsXIjw8HDVr1tRZvURKwFO9RB/IokWLEBsbi6CgIABAmTJlkJaWhgULFuCvv/7C2rVrsWTJkvca58iRI3Hq1CkEBgYiPDwckZGR2Llzp8bNHTkZPnw4Vq1aheDgYERGRmL27Nn4/fffZXf9AsCWLVuwYsUK3L59G+PGjcPZs2c1pjFjxgxMmjQpxyOV27Ztw6pVq7BlyxaULVtW65d5Vnv27MHJkydx//59nDp1CjNmzJDCZokSJWBoaCgtu127dmk9vQz87/rIadOm4fnz5xqnBO/fv4+hQ4ciIiICv/32GxYsWIBBgwbJ2qSnpyM5ORlPnz7FihUrYGlpKQXO7F6+fImYmBj89ddfWLhwIczNzVG8eHEUKVIERYsWxbJly3Dnzh0cOnQIQ4cOzXH+PTw8ULt2bemfhYsXL8Lf3x+urq5o1KiR1E4IgeTkZCQnJyMlJQUAkJqaKnXLyMhAZmYm0tLSAABjx47FmjVrEBQUhOvXr+PmzZvYuHEjRo8eLZv+rFmzMHToUDg6OuZYY3ZZa0lOTpaOZKrr8vf3h42NDVq3bo3jx48jKioKR44cwcCBA2V3K7/PtJKSknDo0CGt10kSUS4IIvrXAgICROvWrTW679mzR+jr64vTp08LIYSYPXu2cHBwECYmJsLb21usWbNGABDPnz+XDXf48GGR08fz7NmzokmTJsLMzEyYmpqKatWqicmTJ0v9nZ2dxZw5c3KsdfHixaJUqVKiUKFColy5cmLNmjWy/gDEokWLRJMmTYSRkZEoWbKk2LRpk9Q/KipKABDVq1cXGRkZWqcbEREhrKysxP79+6X+K1euFJaWljnWFRwcLEqXLi0MDQ2Fra2t6NChg/j777+l/hs2bBAlS5YURkZGwt3dXezatUsAEJcuXZIts6zL8siRI8LY2FhcvXpVCCGEp6en6N+/v/j222+FhYWFKFKkiPjxxx9FZmambD4ACADCxMRE1KpVSxw6dEgIIUTr1q1FQEDAW9sePHhQ6h8aGioqVqwojIyMRLVq1cSRI0cEALF9+3bZslTPwz///CPatGkjzMzMhJmZmWjbtq2Ijo6Wxjdu3Dhpeu96Za0zJCREeHh4CBMTE2FhYSG++OILsWzZMtk6t7e3F4mJiVI3T09PMWjQoBzX17tqUXv06JHo1q2bsLGxEUZGRqJUqVKid+/e4sWLF0II7Z+d7NtK1mnp6ekJJycnMWbMGCGEENu3b8/xs0JEmlRC/IvnBBDRf45KpcL27ds1nq32X9CwYUO4urq+89Eyn7odO3Zgx44dsptjPiYrKyvZs/yIqODgqV4iov8YfX19FCpUKN+m/66beIgo//DmDiKi/5iWLVuiZcuW+Tb9nO6AJqL8x1O9RERERArBU71ERERECsHgR0RERKQQDH5ERERECsHgR0RERKQQDH5ERERECsHgR0RERKQQDH5ERERECsHgR0RERKQQ/wdB22yJmeQPBAAAAABJRU5ErkJggg==",
      "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": [
      "Размер обучающей выборки до oversampling и undersampling:  4200\n",
      "Размер обучающей выборки после oversampling и undersampling:  4232\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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-08</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.73</td>\n",
       "      <td>5.47</td>\n",
       "      <td>5.56</td>\n",
       "      <td>5.341250</td>\n",
       "      <td>23355100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2021-01-19</td>\n",
       "      <td>5.15</td>\n",
       "      <td>5.15</td>\n",
       "      <td>5.02</td>\n",
       "      <td>5.13</td>\n",
       "      <td>4.966732</td>\n",
       "      <td>15906300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2010-04-08</td>\n",
       "      <td>10.60</td>\n",
       "      <td>10.65</td>\n",
       "      <td>10.48</td>\n",
       "      <td>10.52</td>\n",
       "      <td>8.794909</td>\n",
       "      <td>10456400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>5.47</td>\n",
       "      <td>5.80</td>\n",
       "      <td>5.47</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.541336</td>\n",
       "      <td>12929600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.16</td>\n",
       "      <td>5.98</td>\n",
       "      <td>6.04</td>\n",
       "      <td>5.847770</td>\n",
       "      <td>15080900.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Open   High    Low  Close  Adj Close      Volume\n",
       "0  2020-07-08   5.66   5.73   5.47   5.56   5.341250  23355100.0\n",
       "20 2021-01-19   5.15   5.15   5.02   5.13   4.966732  15906300.0\n",
       "21 2010-04-08  10.60  10.65  10.48  10.52   8.794909  10456400.0\n",
       "24 2020-12-07   5.47   5.80   5.47   5.75   5.541336  12929600.0\n",
       "28 2021-01-05   6.15   6.16   5.98   6.04   5.847770  15080900.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Преобразование целевой переменной (цены) в категориальные диапазоны с использованием квантилей\n",
    "X_train['closePrice_category'] = pd.qcut(X_train['Close'], q=4, labels=['low', 'medium', 'high', 'very_high'])\n",
    "print(X_train.head())\n",
    "\n",
    "# Визуализация распределения цен после преобразования в категории\n",
    "sns.countplot(x=X_train['closePrice_category'])\n",
    "plt.title('Распределение категорий закрывающей цены в обучающей выборке')\n",
    "plt.xlabel('Категория закрывающей цены')\n",
    "plt.ylabel('Частота')\n",
    "plt.show()\n",
    "\n",
    "# Балансировка категорий с помощью RandomOverSampler (увеличение меньшинств)\n",
    "ros = RandomOverSampler(random_state=42)\n",
    "y_train = X_train['closePrice_category']\n",
    "X_train = X_train.drop(columns=['closePrice_category'])\n",
    "\n",
    "\n",
    "# Применяем oversampling. Здесь важно, что мы используем X_train как DataFrame и y_train_categories как целевую переменную\n",
    "X_resampled, y_resampled = ros.fit_resample(X_train, y_train)\n",
    "\n",
    "# Визуализация распределения цен после oversampling\n",
    "sns.countplot(x=y_resampled)\n",
    "plt.title('Распределение категорий закрывающей цены после oversampling')\n",
    "plt.xlabel('Категория закрывающей цены')\n",
    "plt.ylabel('Частота')\n",
    "plt.show()\n",
    "\n",
    "# Применение RandomUnderSampler для уменьшения большего класса\n",
    "rus = RandomUnderSampler(random_state=42)\n",
    "X_resampled, y_resampled = rus.fit_resample(X_resampled, y_resampled)\n",
    "\n",
    "# Визуализация распределения цен после undersampling\n",
    "sns.countplot(x=y_resampled)\n",
    "plt.title('Распределение категорий закрывающей цены после undersampling')\n",
    "plt.xlabel('Категория закрывающей цены')\n",
    "plt.ylabel('Частота')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "print(\"Размер обучающей выборки до oversampling и undersampling: \", len(X_train))\n",
    "\n",
    "\n",
    "print(\"Размер обучающей выборки после oversampling и undersampling: \", len(X_resampled))\n",
    "X_resampled.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "По сути, балансировка так то  не требовалась, но все же мы ее провели, добавив в обучающую выборку 5 значений (ーー;)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Конструирование признаков\n",
    "1. **Унитарное кодирование категориальных признаков. Преобразование категориальных признаков в бинарные векторы.**\n",
    "* В данном датасете категориальные признаки отсутствуют, так что пропустим этот пункт.\n",
    "2. **Дискретизация числовых признаков. Преобразование непрерывных числовых значений в дискретные категории или интервалы (бины).**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Названия столбцов в датасете:\n",
      "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')\n",
      "Статистические параметры:\n",
      "                                Date         Open         High          Low  \\\n",
      "count                           5251  5251.000000  5251.000000  5251.000000   \n",
      "mean   2011-12-01 11:59:51.772995840     6.863639     6.986071     6.720615   \n",
      "min              2001-06-22 00:00:00     1.142857     1.142857     1.142857   \n",
      "25%              2006-09-13 12:00:00     2.857143     2.880000     2.810000   \n",
      "50%              2011-11-29 00:00:00     4.600000     4.710000     4.490000   \n",
      "75%              2017-02-16 12:00:00    10.650000    10.860000    10.425000   \n",
      "max              2022-05-05 00:00:00    20.420000    20.590000    20.090000   \n",
      "std                              NaN     4.753836     4.832010     4.662891   \n",
      "\n",
      "             Close    Adj Close        Volume  \n",
      "count  5251.000000  5251.000000  5.251000e+03  \n",
      "mean      6.850606     5.895644  8.976705e+06  \n",
      "min       1.142857     0.935334  0.000000e+00  \n",
      "25%       2.857143     2.537094  2.845900e+06  \n",
      "50%       4.600000     4.337419  8.216200e+06  \n",
      "75%      10.640000     8.951945  1.327245e+07  \n",
      "max      20.389999    17.543156  2.891228e+07  \n",
      "std       4.746055     3.941634  7.251098e+06  \n",
      "После дискретизации 'Close':\n",
      "        Date      Open      High       Low     Close  Adj Close  Volume  \\\n",
      "0 2001-06-22  3.428571  3.428571  3.428571  3.428571   2.806002     0.0   \n",
      "1 2001-06-25  3.428571  3.428571  3.428571  3.428571   2.806002     0.0   \n",
      "2 2001-06-26  3.714286  3.714286  3.714286  3.714286   3.039837     0.0   \n",
      "3 2001-06-27  3.714286  3.714286  3.714286  3.714286   3.039837     0.0   \n",
      "4 2001-06-28  3.714286  3.714286  3.714286  3.714286   3.039837     0.0   \n",
      "\n",
      "  Close_Disc  \n",
      "0        2-4  \n",
      "1        2-4  \n",
      "2        2-4  \n",
      "3        2-4  \n",
      "4        2-4  \n",
      "           Date       Open       High    Low      Close  Adj Close     Volume  \\\n",
      "2623 2011-11-25  14.730000  15.050000  14.65  14.650000  12.429751  2433000.0   \n",
      "2624 2011-11-28  15.150000  15.370000  15.04  15.200000  12.896397  4348600.0   \n",
      "2625 2011-11-29  15.270000  15.710000  15.21  15.600000  13.235776  4576500.0   \n",
      "2626 2011-11-30  16.120001  16.850000  16.07  16.830000  14.279361  9537100.0   \n",
      "2627 2011-12-01  16.770000  16.940001  16.58  16.809999  14.262395  5111500.0   \n",
      "\n",
      "     Close_Disc  \n",
      "2623      14-16  \n",
      "2624      14-16  \n",
      "2625      14-16  \n",
      "2626        16+  \n",
      "2627        16+  \n",
      "           Date  Open  High   Low  Close  Adj Close      Volume Close_Disc\n",
      "5246 2022-04-29  5.66  5.69  5.50   5.51       5.51  16613300.0        4-6\n",
      "5247 2022-05-02  5.33  5.39  5.18   5.30       5.30  27106700.0        4-6\n",
      "5248 2022-05-03  5.32  5.53  5.32   5.47       5.47  18914200.0        4-6\n",
      "5249 2022-05-04  5.47  5.61  5.37   5.60       5.60  20530700.0        4-6\n",
      "5250 2022-05-05  5.63  5.66  5.34   5.44       5.44  19879200.0        4-6\n"
     ]
    }
   ],
   "source": [
    "#Пример дискретизации по цене закрытия\n",
    "# Проверка на наличие числовых признаков\n",
    "print(\"Названия столбцов в датасете:\")\n",
    "print(df.columns)\n",
    "\n",
    "# Выводим основные статистические параметры для количественных признаков\n",
    "print(\"Статистические параметры:\")\n",
    "print(df.describe())\n",
    "\n",
    "# Дискретизация столбца 'Close' на группы\n",
    "bins = [0, 2, 4, 6, 8, 10, 12, 14, 16, 30]  # Определяем границы корзин\n",
    "labels = ['0-2', '2-4', '4-6', '6-8', '8-10', '10-12', '12-14', '14-16', '16+']  # Названия категорий\n",
    "\n",
    "# Создание нового столбца 'Close_Disc' на основе дискретизации\n",
    "df['Close_Disc'] = pd.cut(df['Close'], bins=bins, labels=labels, include_lowest=True)\n",
    "\n",
    "# Проверка результата\n",
    "print(\"После дискретизации 'Close':\")\n",
    "print(df.head())\n",
    "n = len(df)\n",
    "middle_index = n // 2\n",
    "print(df.iloc[middle_index - 2: middle_index + 3])\n",
    "print(df.tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Конструирование новых признаков:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Исходный датасет: \n",
      "           Date  Open  High   Low  Close  Adj Close      Volume Close_Disc\n",
      "5246 2022-04-29  5.66  5.69  5.50   5.51       5.51  16613300.0        4-6\n",
      "5247 2022-05-02  5.33  5.39  5.18   5.30       5.30  27106700.0        4-6\n",
      "5248 2022-05-03  5.32  5.53  5.32   5.47       5.47  18914200.0        4-6\n",
      "5249 2022-05-04  5.47  5.61  5.37   5.60       5.60  20530700.0        4-6\n",
      "5250 2022-05-05  5.63  5.66  5.34   5.44       5.44  19879200.0        4-6\n",
      "\n",
      "Обучающая выборка: \n",
      "           Date       Open   High        Low      Close  Adj Close      Volume\n",
      "2435 2011-04-14  12.530000  12.84  12.480000  12.750000  10.754427  10527200.0\n",
      "1756 2013-05-30  11.510000  11.76  11.480000  11.720000  10.166282   9028100.0\n",
      "3296 2009-11-20  13.100000  13.28  12.870000  13.220000  11.031483  17024900.0\n",
      "1243 2012-09-17  18.870001  19.00  18.469999  18.870001  16.178450   6652400.0\n",
      "343  2006-12-12  12.920000  13.00  12.580000  12.800000  10.487218   3981100.0\n",
      "\n",
      "Тестовая выборка: \n",
      "           Date       Open       High        Low      Close  Adj Close  \\\n",
      "3095 2013-10-14   9.290000   9.350000   9.070000   9.130000   8.025586   \n",
      "859  2004-11-24   3.090000   3.160000   3.040000   3.100000   2.537094   \n",
      "3134 2013-12-09   8.550000   8.770000   8.550000   8.770000   7.709136   \n",
      "2577 2011-09-21  16.709999  17.070000  16.379999  16.400000  13.869872   \n",
      "378  2002-12-27   2.571429   2.571429   2.571429   2.571429   2.104502   \n",
      "\n",
      "          Volume  \n",
      "3095   5861400.0  \n",
      "859     211300.0  \n",
      "3134   5335400.0  \n",
      "2577  14524400.0  \n",
      "378          0.0  \n",
      "\n",
      "Контрольная выборка: \n",
      "           Date       Open       High        Low      Close  Adj Close  \\\n",
      "3095 2013-10-14   9.290000   9.350000   9.070000   9.130000   8.025586   \n",
      "859  2004-11-24   3.090000   3.160000   3.040000   3.100000   2.537094   \n",
      "3134 2013-12-09   8.550000   8.770000   8.550000   8.770000   7.709136   \n",
      "2577 2011-09-21  16.709999  17.070000  16.379999  16.400000  13.869872   \n",
      "378  2002-12-27   2.571429   2.571429   2.571429   2.571429   2.104502   \n",
      "\n",
      "          Volume  \n",
      "3095   5861400.0  \n",
      "859     211300.0  \n",
      "3134   5335400.0  \n",
      "2577  14524400.0  \n",
      "378          0.0  \n",
      "\n",
      "Новые признаки в обучающей выборке:\n",
      "      Volume_Change\n",
      "2435       0.977868\n",
      "1756      -0.142403\n",
      "3296       0.885768\n",
      "1243      -0.609255\n",
      "343       -0.401554\n",
      "\n",
      "Новые признаки в тестовой выборке:\n",
      "      Volume_Change\n",
      "3095            inf\n",
      "859       -0.963951\n",
      "3134      24.250355\n",
      "2577       1.722270\n",
      "378       -1.000000\n",
      "\n",
      "Новые признаки в контрольной выборке:\n",
      "      Volume_Change\n",
      "3095            inf\n",
      "859       -0.963951\n",
      "3134      24.250355\n",
      "2577       1.722270\n",
      "378       -1.000000\n",
      "\n",
      "Новые признаки в датасете:\n",
      "      Volume_Change\n",
      "5246      -0.218393\n",
      "5247       0.631626\n",
      "5248      -0.302232\n",
      "5249       0.085465\n",
      "5250      -0.031733\n"
     ]
    }
   ],
   "source": [
    "print('\\nИсходный датасет: ')\n",
    "print(df.tail())\n",
    "print('\\nОбучающая выборка: ')\n",
    "print(X_resampled.tail())\n",
    "print('\\nТестовая выборка: ')\n",
    "print(X_test.tail())\n",
    "print('\\nКонтрольная выборка: ')\n",
    "print(X_val.tail())\n",
    "\n",
    "#Объем изменений\n",
    "df['Volume_Change'] = df['Volume'].pct_change()\n",
    "X_resampled['Volume_Change'] = X_resampled['Volume'].pct_change()\n",
    "X_test['Volume_Change'] = X_test['Volume'].pct_change()\n",
    "X_val['Volume_Change'] = X_val['Volume'].pct_change()\n",
    "\n",
    "# Проверка создания новых признаков\n",
    "print(\"\\nНовые признаки в обучающей выборке:\")\n",
    "print(X_resampled[['Volume_Change']].tail())\n",
    "\n",
    "print(\"\\nНовые признаки в тестовой выборке:\")\n",
    "print(X_test[['Volume_Change']].tail())\n",
    "\n",
    "print(\"\\nНовые признаки в контрольной выборке:\")\n",
    "print(X_val[['Volume_Change']].tail())\n",
    "\n",
    "print(\"\\nНовые признаки в датасете:\")\n",
    "print(df[['Volume_Change']].tail())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Проверим новые признаки:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Исходный датасет: \n",
      "Volume_Change    501\n",
      "dtype: int64\n",
      "\n",
      "Обучающая выборка: \n",
      "Volume_Change    102\n",
      "dtype: int64\n",
      "\n",
      "Тестовая выборка: \n",
      "Volume_Change    16\n",
      "dtype: int64\n",
      "\n",
      "Контрольная выборка: \n",
      "Volume_Change    16\n",
      "dtype: int64\n",
      "\n",
      "Есть ли пустые значения признаков: \n",
      "\n",
      "Исходный датасет: \n",
      "Volume_Change    True\n",
      "dtype: bool\n",
      "\n",
      "Обучающая выорка: \n",
      "Volume_Change    True\n",
      "dtype: bool\n",
      "\n",
      "Тестовая выборка: \n",
      "Volume_Change    True\n",
      "dtype: bool\n",
      "\n",
      "Контрольная выборка: \n",
      "Volume_Change    True\n",
      "dtype: bool\n",
      "\n",
      "Количество бесконечных значений в каждом столбце:\n",
      "\n",
      "Исходный датасет: \n",
      "Volume_Change    32\n",
      "dtype: int64\n",
      "\n",
      "Обучающая выборка: \n",
      "Volume_Change    310\n",
      "dtype: int64\n",
      "\n",
      "Тестовая выборка: \n",
      "Volume_Change    107\n",
      "dtype: int64\n",
      "\n",
      "Контрольная выборка: \n",
      "Volume_Change    107\n",
      "dtype: int64\n",
      "Volume_Change процент пустых значений в датасете: %9.54\n",
      "Volume_Change процент пустых значений в обучающей выборке: %2.41\n",
      "Volume_Change процент пустых значений в тестовой выборке: %1.52\n",
      "Volume_Change процент пустых значений в контрольной выборке: %1.52\n"
     ]
    }
   ],
   "source": [
    "print('\\nИсходный датасет: ')\n",
    "print(df[['Volume_Change']].isnull().sum())\n",
    "print('\\nОбучающая выборка: ')\n",
    "print(X_resampled[['Volume_Change']].isnull().sum())\n",
    "print('\\nТестовая выборка: ')\n",
    "print(X_test[['Volume_Change']].isnull().sum())\n",
    "print('\\nКонтрольная выборка: ')\n",
    "print(X_val[['Volume_Change']].isnull().sum())\n",
    "print()\n",
    "\n",
    "# Есть ли пустые значения признаков\n",
    "print('Есть ли пустые значения признаков: ')\n",
    "print('\\nИсходный датасет: ')\n",
    "print(df[['Volume_Change']].isnull().any())\n",
    "print('\\nОбучающая выорка: ')\n",
    "print(X_resampled[['Volume_Change']].isnull().any())\n",
    "print('\\nТестовая выборка: ')\n",
    "print(X_test[['Volume_Change']].isnull().any())\n",
    "print('\\nКонтрольная выборка: ')\n",
    "print(X_val[['Volume_Change']].isnull().any())\n",
    "print()\n",
    "\n",
    "# Проверка на бесконечные значения\n",
    "print(\"Количество бесконечных значений в каждом столбце:\")\n",
    "print('\\nИсходный датасет: ')\n",
    "print(np.isinf(df[['Volume_Change']]).sum())\n",
    "print('\\nОбучающая выборка: ')\n",
    "print(np.isinf(X_resampled[['Volume_Change']]).sum())\n",
    "print('\\nТестовая выборка: ')\n",
    "print(np.isinf(X_test[['Volume_Change']]).sum())\n",
    "print('\\nКонтрольная выборка: ')\n",
    "print(np.isinf(X_val[['Volume_Change']]).sum())\n",
    "\n",
    "# Процент пустых значений признаков\n",
    "for i in df[['Volume_Change']].columns:\n",
    "    null_rate = df[['Volume_Change']][i].isnull().sum() / len(df[['Volume_Change']]) * 100\n",
    "    print(f\"{i} процент пустых значений в датасете: %{null_rate:.2f}\")\n",
    "\n",
    "# Процент пустых значений признаков\n",
    "for i in X_resampled[['Volume_Change']].columns:\n",
    "    null_rate = X_resampled[['Volume_Change']][i].isnull().sum() / len(X_resampled[['Volume_Change']]) * 100\n",
    "    print(f\"{i} процент пустых значений в обучающей выборке: %{null_rate:.2f}\")\n",
    "\n",
    "# Процент пустых значений признаков\n",
    "for i in X_test[['Volume_Change']].columns:\n",
    "    null_rate = X_test[['Volume_Change']][i].isnull().sum() / len(X_test[['Volume_Change']]) * 100\n",
    "    print(f\"{i} процент пустых значений в тестовой выборке: %{null_rate:.2f}\")\n",
    "\n",
    "# Процент пустых значений признаков\n",
    "for i in X_val[['Volume_Change']].columns:\n",
    "    null_rate = X_val[['Volume_Change']][i].isnull().sum() / len(X_val[['Volume_Change']]) * 100\n",
    "    print(f\"{i} процент пустых значений в контрольной выборке: %{null_rate:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Заполним пустые данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5251, 1)\n",
      "(4232, 1)\n",
      "(1051, 1)\n",
      "(1051, 1)\n",
      "Volume_Change    False\n",
      "dtype: bool\n",
      "Volume_Change    False\n",
      "dtype: bool\n",
      "Volume_Change    False\n",
      "dtype: bool\n",
      "Volume_Change    False\n",
      "dtype: bool\n",
      "      Volume_Change\n",
      "5246      -0.218393\n",
      "5247       0.631626\n",
      "5248      -0.302232\n",
      "5249       0.085465\n",
      "5250      -0.031733\n",
      "      Volume_Change\n",
      "2435       0.977868\n",
      "1756      -0.142403\n",
      "3296       0.885768\n",
      "1243      -0.609255\n",
      "343       -0.401554\n",
      "      Volume_Change\n",
      "3095       0.000000\n",
      "859       -0.963951\n",
      "3134      24.250355\n",
      "2577       1.722270\n",
      "378       -1.000000\n",
      "      Volume_Change\n",
      "3095       0.000000\n",
      "859       -0.963951\n",
      "3134      24.250355\n",
      "2577       1.722270\n",
      "378       -1.000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\K\\AppData\\Local\\Temp\\ipykernel_1752\\2904461267.py:36: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "  df[['Volume_Change']].loc[df[\"Volume_Change\"] == 0, \"Volume_Change\"] = median_Volume_Change_df\n",
      "C:\\Users\\K\\AppData\\Local\\Temp\\ipykernel_1752\\2904461267.py:36: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[['Volume_Change']].loc[df[\"Volume_Change\"] == 0, \"Volume_Change\"] = median_Volume_Change_df\n",
      "C:\\Users\\K\\AppData\\Local\\Temp\\ipykernel_1752\\2904461267.py:37: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "  X_resampled[['Volume_Change']].loc[X_resampled[\"Volume_Change\"] == 0, \"Volume_Change\"] = median_Volume_Change_train\n",
      "C:\\Users\\K\\AppData\\Local\\Temp\\ipykernel_1752\\2904461267.py:37: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_resampled[['Volume_Change']].loc[X_resampled[\"Volume_Change\"] == 0, \"Volume_Change\"] = median_Volume_Change_train\n",
      "C:\\Users\\K\\AppData\\Local\\Temp\\ipykernel_1752\\2904461267.py:38: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "  X_test[['Volume_Change']].loc[X_test[\"Volume_Change\"] == 0, \"Volume_Change\"] = median_Volume_Change_test\n",
      "C:\\Users\\K\\AppData\\Local\\Temp\\ipykernel_1752\\2904461267.py:38: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_test[['Volume_Change']].loc[X_test[\"Volume_Change\"] == 0, \"Volume_Change\"] = median_Volume_Change_test\n",
      "C:\\Users\\K\\AppData\\Local\\Temp\\ipykernel_1752\\2904461267.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "  X_val[['Volume_Change']].loc[X_val[\"Volume_Change\"] == 0, \"Volume_Change\"] = median_Volume_Change_val\n",
      "C:\\Users\\K\\AppData\\Local\\Temp\\ipykernel_1752\\2904461267.py:39: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_val[['Volume_Change']].loc[X_val[\"Volume_Change\"] == 0, \"Volume_Change\"] = median_Volume_Change_val\n"
     ]
    }
   ],
   "source": [
    "# Заменяем бесконечные значения на NaN\n",
    "df.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "X_resampled.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "X_test.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "X_val.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "\n",
    "fillna_df = df[['Volume_Change']].fillna(0)\n",
    "fillna_X_resampled = X_resampled[['Volume_Change']].fillna(0)\n",
    "fillna_X_test = X_test[['Volume_Change']].fillna(0)\n",
    "fillna_X_val = X_val[['Volume_Change']].fillna(0)\n",
    "\n",
    "\n",
    "print(fillna_df.shape)\n",
    "print(fillna_X_resampled.shape)\n",
    "print(fillna_X_test.shape)\n",
    "print(fillna_X_val.shape)\n",
    "\n",
    "print(fillna_df.isnull().any())\n",
    "print(fillna_X_resampled.isnull().any())\n",
    "print(fillna_X_test.isnull().any())\n",
    "print(fillna_X_val.isnull().any())\n",
    "\n",
    "# Замена пустых данных на 0\n",
    "df[\"Volume_Change\"] = df[\"Volume_Change\"].fillna(0)\n",
    "X_resampled[\"Volume_Change\"] = X_resampled[\"Volume_Change\"].fillna(0)\n",
    "X_test[\"Volume_Change\"] = X_test[\"Volume_Change\"].fillna(0)\n",
    "X_val[\"Volume_Change\"] = X_val[\"Volume_Change\"].fillna(0)\n",
    "\n",
    "# Вычисляем медиану для колонки \"Volume_Change\"\n",
    "median_Volume_Change_df = df[\"Volume_Change\"].median()\n",
    "median_Volume_Change_train = X_resampled[\"Volume_Change\"].median()\n",
    "median_Volume_Change_test = X_test[\"Volume_Change\"].median()\n",
    "median_Volume_Change_val = X_val[\"Volume_Change\"].median()\n",
    "\n",
    "# Заменяем значения 0 на медиану\n",
    "df[['Volume_Change']].loc[df[\"Volume_Change\"] == 0, \"Volume_Change\"] = median_Volume_Change_df\n",
    "X_resampled[['Volume_Change']].loc[X_resampled[\"Volume_Change\"] == 0, \"Volume_Change\"] = median_Volume_Change_train\n",
    "X_test[['Volume_Change']].loc[X_test[\"Volume_Change\"] == 0, \"Volume_Change\"] = median_Volume_Change_test\n",
    "X_val[['Volume_Change']].loc[X_val[\"Volume_Change\"] == 0, \"Volume_Change\"] = median_Volume_Change_val\n",
    "\n",
    "print(df[['Volume_Change']].tail())\n",
    "print(X_resampled[['Volume_Change']].tail())\n",
    "print(X_test[['Volume_Change']].tail())\n",
    "print(X_val[['Volume_Change']].tail())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Удалим наблюдения с пропусками"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5251, 1)\n",
      "(4232, 1)\n",
      "(1051, 1)\n",
      "(1051, 1)\n",
      "Volume_Change    False\n",
      "dtype: bool\n",
      "      Volume_Change\n",
      "5246      -0.218393\n",
      "5247       0.631626\n",
      "5248      -0.302232\n",
      "5249       0.085465\n",
      "5250      -0.031733\n",
      "Volume_Change    False\n",
      "dtype: bool\n",
      "      Volume_Change\n",
      "2435       0.977868\n",
      "1756      -0.142403\n",
      "3296       0.885768\n",
      "1243      -0.609255\n",
      "343       -0.401554\n",
      "Volume_Change    False\n",
      "dtype: bool\n",
      "      Volume_Change\n",
      "3095       0.000000\n",
      "859       -0.963951\n",
      "3134      24.250355\n",
      "2577       1.722270\n",
      "378       -1.000000\n",
      "Volume_Change    False\n",
      "dtype: bool\n",
      "      Volume_Change\n",
      "3095       0.000000\n",
      "859       -0.963951\n",
      "3134      24.250355\n",
      "2577       1.722270\n",
      "378       -1.000000\n"
     ]
    }
   ],
   "source": [
    "dropna_df = df[['Volume_Change']].dropna()\n",
    "dropna_X_resampled = X_resampled[['Volume_Change']].dropna()\n",
    "dropna_X_test = X_test[['Volume_Change']].dropna()\n",
    "dropna_X_val = X_val[['Volume_Change']].dropna()\n",
    "\n",
    "print(dropna_df.shape)\n",
    "print(dropna_X_resampled.shape)\n",
    "print(dropna_X_test.shape)\n",
    "print(dropna_X_val.shape)\n",
    "\n",
    "print(dropna_df.isnull().any())\n",
    "print(df[['Volume_Change']].tail())\n",
    "print(dropna_X_resampled.isnull().any())\n",
    "print(X_resampled[['Volume_Change']].tail())\n",
    "print(dropna_X_test.isnull().any())\n",
    "print(X_test[['Volume_Change']].tail())\n",
    "print(dropna_X_val.isnull().any())\n",
    "print(X_val[['Volume_Change']].tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Масштабируем новые признаки:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Результаты после масштабирования:\n",
      "\n",
      " Датафрейм:\n",
      "      Volume_Change\n",
      "5246      -0.176620\n",
      "5247       0.224373\n",
      "5248      -0.216171\n",
      "5249      -0.033276\n",
      "5250      -0.088564\n",
      "\n",
      " Обучающая:\n",
      "      Volume_Change\n",
      "2435      -0.033736\n",
      "1756      -0.033805\n",
      "3296      -0.033742\n",
      "1243      -0.033834\n",
      "343       -0.033821\n",
      "\n",
      " Тестовая:\n",
      "      Volume_Change\n",
      "3095      -0.033796\n",
      "859       -0.033856\n",
      "3134      -0.032301\n",
      "2577      -0.033690\n",
      "378       -0.033858\n",
      "\n",
      " Контрольная:\n",
      "      Volume_Change\n",
      "3095      -0.033796\n",
      "859       -0.033856\n",
      "3134      -0.032301\n",
      "2577      -0.033690\n",
      "378       -0.033858\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "\n",
    "# Пример масштабирования числовых признаков\n",
    "numerical_features = ['Volume_Change']\n",
    "\n",
    "scaler = StandardScaler()\n",
    "df[numerical_features] = scaler.fit_transform(df[numerical_features])\n",
    "X_resampled[numerical_features] = scaler.fit_transform(X_resampled[numerical_features])\n",
    "X_val[numerical_features] = scaler.transform(X_val[numerical_features])\n",
    "X_test[numerical_features] = scaler.transform(X_test[numerical_features])\n",
    "\n",
    "# Вывод результатов после масштабирования\n",
    "print(\"Результаты после масштабирования:\")\n",
    "print(\"\\n Датафрейм:\")\n",
    "print(df[numerical_features].tail())\n",
    "print(\"\\n Обучающая:\")\n",
    "print(X_resampled[numerical_features].tail())\n",
    "print(\"\\n Тестовая:\")\n",
    "print(X_val[numerical_features].tail())\n",
    "print(\"\\n Контрольная:\")\n",
    "print(X_test[numerical_features].tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данные признаки предоставляют важную информацию о текущем тренде и возможных изменениях в будущих ценах. Положительные значения Price_Change и Percentage_Change, наряду с высоким Volume_Change, могут поддерживать гипотезу о росте цен на акции.\n",
    "\n",
    "Также, эти признаки помогают понять уровень рискованности инвестиций. Высокие значения Price_Range и резкие изменения в Volume_Change могут указывать на склонность к большим колебаниям, что требует внимательного управления рисками."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Применим featuretools для конструирования признаков:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [Date, Open, High, Low, Close, Adj Close, Volume, Volume_Change, id]\n",
      "Index: []\n",
      "        Open      High       Low     Close  Adj Close  Volume Close_Disc  \\\n",
      "id                                                                         \n",
      "0   3.428571  3.428571  3.428571  3.428571   2.806002     0.0        2-4   \n",
      "1   3.428571  3.428571  3.428571  3.428571   2.806002     0.0        2-4   \n",
      "2   3.714286  3.714286  3.714286  3.714286   3.039837     0.0        2-4   \n",
      "3   3.714286  3.714286  3.714286  3.714286   3.039837     0.0        2-4   \n",
      "4   3.714286  3.714286  3.714286  3.714286   3.039837     0.0        2-4   \n",
      "\n",
      "    Volume_Change DAY(Date) MONTH(Date) WEEKDAY(Date) YEAR(Date)  \n",
      "id                                                                \n",
      "0       -0.073594        22           6             4       2001  \n",
      "1       -0.073594        25           6             0       2001  \n",
      "2       -0.073594        26           6             1       2001  \n",
      "3       -0.073594        27           6             2       2001  \n",
      "4       -0.073594        28           6             3       2001  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\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",
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\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",
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Open   High    Low  Close  Adj Close      Volume  Volume_Change  \\\n",
      "id                                                                     \n",
      "0    5.66   5.73   5.47   5.56   5.341250  23355100.0      -0.033796   \n",
      "20   5.15   5.15   5.02   5.13   4.966732  15906300.0      -0.033816   \n",
      "21  10.60  10.65  10.48  10.52   8.794909  10456400.0      -0.033817   \n",
      "24   5.47   5.80   5.47   5.75   5.541336  12929600.0      -0.033782   \n",
      "28   6.15   6.16   5.98   6.04   5.847770  15080900.0      -0.033786   \n",
      "\n",
      "   DAY(Date) MONTH(Date) WEEKDAY(Date) YEAR(Date)  \n",
      "id                                                 \n",
      "0          8           7             2       2020  \n",
      "20        19           1             1       2021  \n",
      "21         8           4             3       2010  \n",
      "24         7          12             0       2020  \n",
      "28         5           1             1       2021  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n"
     ]
    }
   ],
   "source": [
    "import featuretools as ft\n",
    "\n",
    "df['id'] = df.index \n",
    "X_resampled['id'] = X_resampled.index\n",
    "X_val['id'] = X_val.index\n",
    "X_test['id'] = X_test.index\n",
    " # Добавляем уникальный идентификатор\n",
    "# Предобработка данных (например, кодирование категориальных признаков, удаление дубликатов)\n",
    "# Удаление дубликатов по идентификатору\n",
    "df = df.drop_duplicates(subset='id')\n",
    "duplicates = X_resampled[X_resampled['id'].duplicated(keep=False)]\n",
    "\n",
    "# Удаление дубликатов из столбца \"id\", сохранив первое вхождение\n",
    "df = df.drop_duplicates(subset='id', keep='first')\n",
    "\n",
    "print(duplicates)\n",
    "\n",
    "\n",
    "# Создание EntitySet\n",
    "es = ft.EntitySet(id='stock_data')\n",
    "\n",
    "# Добавление датафрейма с домами\n",
    "es = es.add_dataframe(dataframe_name='stocks', dataframe=df, index='id')\n",
    "\n",
    "# Генерация признаков с помощью глубокой синтезы признаков\n",
    "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name='stocks', max_depth=2)\n",
    "\n",
    "# Выводим первые 5 строк сгенерированного набора признаков\n",
    "print(feature_matrix.head())\n",
    "\n",
    "X_resampled = X_resampled.drop_duplicates(subset='id')\n",
    "X_resampled = X_resampled.drop_duplicates(subset='id', keep='first')  # or keep='last'\n",
    "\n",
    "# Определение сущностей (Создание EntitySet)\n",
    "es = ft.EntitySet(id='stock_data')\n",
    "\n",
    "es = es.add_dataframe(dataframe_name='stocks', dataframe=X_resampled, index='id')\n",
    "\n",
    "# Генерация признаков\n",
    "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name='stocks', max_depth=2)\n",
    "\n",
    "# Преобразование признаков для контрольной и тестовой выборок\n",
    "val_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=X_val.index)\n",
    "test_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=X_test.index)\n",
    "\n",
    "print(feature_matrix.head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Система сгенерировала следующие признаки:\n",
    "1. **Open, High, Low, Close, Adj Close**: Это стандартные финансовые параметры акций, отражающие цены открытия, максимальные, минимальные и закрытия за определенный период.\n",
    "**Volume**: Объем торгов акциями, который показывает, сколько акций было куплено/продано за определенный период.\n",
    "\n",
    "2. Сложные признаки:\n",
    "**Close_Disc**: Это диапазон цены закрытия.\n",
    "**Price_Change**: Изменение цены, т.е. разница между ценой закрытия и ценой открытия акций.\n",
    "**Percentage_Change**: Процентное изменение цен, которое позволяет оценить относительное изменение стоимости акций.\n",
    "**Average_Price**: Средняя цена акций за указанный период. Этот показатель может быть использован для оценки общей тенденции рынка.\n",
    "\n",
    "3. Также произошло разбиение даты на месяц, день недели и год, что может помочь в анализе сезонных и временных закономерностей."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Оценим качество каждого набора признаков:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "LinearRegression:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Коэффициент детерминации R²: 1.00\n",
      "Время обучения модели: 56.77 секунд\n",
      "Среднеквадратичная ошибка: 0.05\n",
      "Средняя абсолютная ошибка: 0.04\n",
      "Кросс-валидация RMSE: 0.06955321972025767 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DecisionTreeRegressor:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Коэффициент детерминации R²: 1.00\n",
      "Время обучения модели: 0.31 секунд\n",
      "Среднеквадратичная ошибка: 0.00\n",
      "Средняя абсолютная ошибка: 0.00\n",
      "Кросс-валидация RMSE: 0.15865311270509808 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RandomForestRegressor:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Коэффициент детерминации R²: 1.00\n",
      "Время обучения модели: 14.12 секунд\n",
      "Среднеквадратичная ошибка: 0.03\n",
      "Средняя абсолютная ошибка: 0.02\n",
      "Кросс-валидация RMSE: 0.12050880197633333 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Lasso:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.444e+01, tolerance: 9.545e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Коэффициент детерминации R²: 1.00\n",
      "Время обучения модели: 0.64 секунд\n",
      "Среднеквадратичная ошибка: 0.15\n",
      "Средняя абсолютная ошибка: 0.10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.330e+01, tolerance: 9.123e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.844e+01, tolerance: 7.834e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.789e+01, tolerance: 7.848e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.893e+01, tolerance: 7.706e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Кросс-валидация RMSE: 0.14816158181157554 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.478e+00, tolerance: 4.075e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Ridge:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\linear_model\\_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=4.52541e-19): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n",
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18.53835972  3.02015664 12.61628667  2.90463617 15.50833261  3.52896844\n",
      "  4.48748051  4.63958216  8.68196839  1.74873254  9.20514286  8.47989483\n",
      "  2.58190809 17.08764245  8.55417677 15.33708822 16.41262996  2.58698205\n",
      " 14.3010449  12.34692824  9.25519508 12.41656262 11.99359427  5.66512081\n",
      "  2.38719333 12.26227744  6.05023814  2.66589584  2.70345542  5.964079\n",
      "  6.22816714 10.34137019 11.51700786  5.58517202  8.65205276  2.3460804\n",
      "  4.26479846  5.33712394  1.14710216  1.71608533 16.32955154 15.43209408\n",
      "  2.87442811  2.34765255  5.63198832  1.80827126 11.16627919  9.49829252\n",
      "  3.06416135  8.63872529 16.20075921  9.22693767 11.59973333  2.96131937\n",
      "  5.38377786  2.03008836 11.00039614 15.61757236  9.16666362  8.41083742\n",
      "  2.00373929  2.06287786  2.99954165  4.10065289  4.00877304  3.97321885\n",
      " 13.59597539  1.40790586  2.41256628  6.39209759  2.81312245  3.10132338\n",
      " 15.42511366  5.94643324  9.61579166 10.56492684  1.71759088 18.88228792\n",
      "  8.05540212  3.56264735  3.82692764 12.38823319  6.05270239 12.00332481\n",
      "  1.74086769  5.89288081  2.26264139  3.71532583  6.35786331 11.23384554\n",
      "  5.45126529  2.94152123  1.46216163 12.4333095   1.94823683  2.61723697\n",
      "  4.29052409  2.85243147  4.34693161  7.75903026  1.47957735  2.64745738\n",
      "  2.92764333  3.92477508  1.46188826  6.73850242 10.12033775 12.36907916\n",
      "  5.78680952  8.88212259  2.6207355   2.91355609  1.7150175   3.87183976\n",
      " 10.38956052  2.10033845  9.7789235   5.9518523   6.72768585  3.8733813\n",
      "  2.47072558  4.97819957 14.72779601 13.00943717  4.27660505  4.05121985\n",
      "  9.58757433  4.39987831  2.28963043  2.38275134  9.85194738  1.72535424\n",
      "  3.0769887  10.30207713  8.61146578 12.90911924 11.36712701 15.50623345\n",
      " 13.7589026  12.26527187  1.58603345  2.30532972  8.87056489  3.13362005\n",
      "  3.52305632  3.67792632  3.89649612  1.4386125   1.71897963 16.02063149\n",
      "  5.58214334 12.0190435   2.95246444 14.46332073  3.13793354  8.35731731\n",
      " 12.8190229   1.75046918  6.8243075  13.8579805   3.32922829  5.86296698\n",
      " 11.90112905 12.93140955 14.42031785  5.3216482  10.14847492 17.3251465\n",
      "  2.47138994  3.96190681 18.32627855  9.86892096  1.41331525 10.64773288\n",
      "  9.09088555 17.20324969  3.57395915  2.81320472  3.67705031  9.48759433\n",
      "  3.95038135  2.46245273  3.12790097  1.83174667  5.75616587  4.76223002\n",
      "  2.29396539  3.50878545 16.44701112  2.56198802  3.03031246  3.60246817\n",
      " 10.07925548  2.55678468  3.41922207 12.81354395  2.03218368  2.18411881\n",
      "  5.71737479 10.5187821   9.8243807   5.31478036  9.31603096  9.10138797\n",
      "  3.05260546 14.61014811  2.95722431  2.53560441 12.02355853 10.43694612\n",
      "  3.74326294 14.4808655   1.43951577  5.58075845 14.91277029  5.228079\n",
      " 11.88416143  4.76215204  2.75428552  3.89022373  9.16522942 13.05572311\n",
      "  9.70863685  2.95688892 17.62382334 11.02667669  9.46154392  4.51952775\n",
      "  2.28900873 12.89384822  4.27548303 13.28473553  1.14315432  3.55814162\n",
      "  4.08884875 14.30471291  1.81601334 18.80676865  2.3751151  13.26443122\n",
      "  3.75552258  9.37745094  6.04970249  2.08571758 18.48641005  3.90175208\n",
      "  7.94228462  2.85694027 15.98069971  3.49704604 10.64585213  2.8258999\n",
      "  9.09735439  5.04798097  2.34820756  3.98234323  3.05687448  9.78621841\n",
      "  2.8982907   9.72068384  2.06609795  8.4708919   2.55512651  5.80843487\n",
      " 11.58212182 14.7659963   9.90972259  2.03636776  7.38400216  2.28270996\n",
      "  6.73525348 12.67320166  3.00927587  9.77745413  4.02872521 14.59123614\n",
      "  5.67976841 11.84854574  2.53005349  4.32053876  3.00515769  8.17868205\n",
      "  2.91033416  1.44360989  2.61775999 10.2059313   4.41104931  4.51101966\n",
      "  6.32294616  3.5798359  14.29298242 10.1850001   7.58348729  4.28934805\n",
      "  4.1439929   7.1227075   3.50184952  2.87101774  4.87187746  3.75846851\n",
      "  2.00289192 12.2322784   3.82984137  1.6002216  10.87064848  6.02427509\n",
      " 12.19193643 10.45019334  3.20778262  2.81201506  3.79449686  2.46022196\n",
      "  9.31467235  2.15239862  2.92910412  8.51832994  2.61877179  5.04802801\n",
      "  5.69180959  2.30174729  2.78591436 13.26958071 16.64394446  8.87999939\n",
      "  2.49981874 14.93269447  4.45081163  8.23200249  3.55687264 15.62632032\n",
      " 12.35393654 18.94154334  2.4851807   1.71703064 11.80745358  2.29174295\n",
      "  4.33087948  8.8071983   8.89762856  3.76659625  9.01661689  3.8378286\n",
      "  2.13722881  9.02591117  7.26601453  8.50650449 12.79463264  4.12517895\n",
      " 11.63249865  9.66210348  3.15261767  4.41986823  3.44998087 10.98001111\n",
      "  4.60746822  3.59134612 14.06455102  2.99038467  2.71795072  2.56293166\n",
      "  2.86380398 10.39300275 11.23770423  4.95716077  1.46285195  4.53518564\n",
      "  4.030694   16.57787437  2.18449096  3.63219595  5.10131515 13.69275973\n",
      "  6.48100525  2.70557808  5.39534656  9.45176473  3.38046424  3.90224131\n",
      " 12.59789779 12.46234039  1.70942088  5.43824785  1.47168759 18.16270268\n",
      " 11.96367623  1.79205494  2.58470531  2.68016219 14.74912196 10.1040361\n",
      "  3.26318155  2.6378777   2.60610772  3.62567306  2.84074939 10.75006159\n",
      "  7.39066186  3.06160376  1.26314304  3.88996936  1.14486011 15.17777613\n",
      "  2.86507824 10.02941764 11.21523506  3.56782758  2.57867046  1.2635082\n",
      " 12.70475784 11.78453449  2.30180443  1.17985582  2.47902327  2.58727762\n",
      "  4.16686036  3.95105243 15.43508779  2.81761054  4.0322251   3.07596467\n",
      " 12.82461871  2.81444631 19.33636867  3.69986094  1.99687652  4.80581687\n",
      "  2.27689069 12.22263126  2.08777775  2.60298631 17.12802364 12.52639565\n",
      " 11.69244796  2.00589312  1.98988765 16.28233571  9.10732225 13.0165149\n",
      "  5.92164221  2.2505696   2.44209013 10.34767538  5.98523168 16.26028586\n",
      "  8.33935586 16.8204904   2.03558767  2.77620724  4.30384591  3.00854596\n",
      " 10.17185727  3.72924572  3.7670771   4.28711491 11.08123194 11.35512171\n",
      " 12.71567246 15.30622396  3.15587055  4.08257799  2.03327719  3.41659174\n",
      " 14.13036958  3.54284787  2.69887027  9.317551    7.91886701  5.82956199\n",
      " 11.2956263   4.05641343 11.15385568 10.89295838 11.36479919  2.86273986\n",
      "  4.80262184 10.58436197  9.18243093  1.80435676  3.35755569  1.73762343\n",
      "  2.02772398  9.05409416  3.00025958  5.328892    2.87397055 17.43294837\n",
      "  2.82128031  9.12124743  3.45413876  3.08416112  2.08600945 10.071014\n",
      "  2.35476915 10.81477834 13.69733219 11.06620676  6.04497979 15.14030966\n",
      " 14.91813751  3.32817428  8.57679193 17.13660103  8.38872085 14.42368735\n",
      " 15.28424061  2.75288466 14.33051295  2.73651334 10.16033205  7.3391554\n",
      "  2.42042554 16.73487862  2.57350632  3.524343    3.28983093  3.20648722\n",
      "  2.58769438 14.80494598  3.82870434  2.53057771  3.27421328  6.25488245\n",
      "  2.97443497  3.67971006  4.83441897  4.25943544  3.43624859 16.67124463\n",
      "  2.98236856  8.24938713 19.46524322  2.55245492 11.80036833  2.54928389\n",
      " 16.2374949   9.54536837  4.93137021  3.7290861   4.03051892 11.84523524\n",
      "  2.58673756 14.97983755  2.57569308 16.81738772 12.28515101  2.14814742\n",
      "  3.42051089  9.01921055  9.49621653  2.95933914  3.75237644  2.23972066\n",
      "  2.49277755  1.7958297   3.86582093  2.8674885  11.42639942  3.06073274\n",
      "  3.13961257  4.07270659 11.3555383   3.12213356  4.21349249  8.6944528\n",
      "  1.76443633  2.1899131   4.18629277  8.88741747  8.33202173  7.56865056\n",
      "  5.43031759  2.00601233  2.32724761  2.77278376  5.25002215  2.13399159\n",
      " 10.57578629 11.58732585  2.86180112  2.07601269  9.77807231  2.9222822\n",
      " 16.49825749  5.2869834   3.11327626  6.17030198 10.02044606  9.51853346\n",
      "  4.15921692  3.1338318   1.99023388  5.07213763  3.66805146 14.2182649\n",
      " 15.27840034 13.79492613  8.7106045  10.17749474 10.40411274  9.22057205\n",
      "  8.76561824 12.97598282  9.1910614  17.96203867  2.12718709  4.04214711\n",
      "  9.56797976 15.27220032 16.99049874 11.3642939   5.91326291  2.45308319\n",
      "  4.46940539 20.19433041 12.69880519  5.50994035  2.6682945   5.52958319\n",
      " 12.42290534 14.56567536  1.67923754  9.78182321 10.14994473  2.00056631\n",
      " 11.91937116 11.448476    3.00603484  4.37674718 18.29810163 10.22220423\n",
      " 11.76327836 11.7443404   6.22308342  5.81977575 10.74629097  2.64957524\n",
      " 14.18339329  5.28790528  4.21960812  2.46020194 12.78135003  2.81262627\n",
      "  3.56482063  9.18548323 10.77509061  2.11414128 11.74135626  5.59792502\n",
      "  8.51753377  2.14325374 17.29903726  5.36609587 12.94339358  3.12628909\n",
      " 14.37527003 11.23391531 15.23465018  8.32416306 19.23496585 10.72728053\n",
      "  6.25323716  5.54923781  3.42075517 15.17046362  1.74005129  1.82091277\n",
      " 15.78389194 10.02145557  1.86770706 15.23353752  2.77126333 12.98051255\n",
      "  1.99018252 14.74476653  1.71875456  4.12341993  3.19630416  3.17907961\n",
      "  2.86272064 14.0146559   5.94243459  1.27345519 15.139857    3.19217053\n",
      "  2.84853572  2.2721021   2.88287568  3.5868594  15.50908541 11.71567681\n",
      "  4.8065892   3.19212114  2.57103734 11.67912572  3.70752448  2.51133544\n",
      "  4.76364981  3.27797006  2.12666216  3.99289363  2.55004679 10.4122718\n",
      "  3.41915544  3.45791773  4.27428329  2.16475035  2.24357662 20.27107131\n",
      "  2.6211838  15.62812531  2.50006029  9.73341343 11.62414759  8.98773249\n",
      "  2.82862895  2.00722094  9.88201667  2.1846998   1.50637995  2.32878985\n",
      " 14.11192293 13.41634497 14.28146237  1.4591491   2.75790525  2.83246229\n",
      "  4.07144576  3.79653033  9.44913553  3.90219151  4.0168187   4.1243992\n",
      "  9.80958254  5.3800075   9.46195049  1.97717373  8.34077912 12.43010474\n",
      "  3.38649636 10.02097508 11.49680925 13.97190463  3.60431429  7.37584679\n",
      "  4.33912419 11.84222445  1.44089012  4.85941981  3.39159309  3.13357328\n",
      "  2.47096887  3.94502129  4.15493405 18.49504618  9.08651191  2.95832372\n",
      "  2.52244627  5.99553625  2.11520684  1.89749755  2.6973316   2.54822468\n",
      " 10.94537441  2.84587429 12.62276847 10.15800164  2.10079835 15.30418953\n",
      " 10.94837016 12.40484611 11.14575664 14.59172598 12.7621914   3.05184364\n",
      "  1.14525931 11.08995653  8.08010737 11.35478585  8.82251206 11.58877523\n",
      "  2.74174474  1.72758415  2.73267471 10.97797436  9.23096251 10.99237774\n",
      "  5.60784631  1.43780359  2.71939508 12.29089903 11.65782657 11.7466421\n",
      " 12.60767642  3.36428522 10.63306574  1.52817038  3.40656569 10.39742458\n",
      "  4.76747207  9.39296795  1.52353529  9.34627711  3.18889881 11.42316406\n",
      "  1.39855096  2.46862897  3.40834961  4.85487296  5.60442383  9.39185753\n",
      "  5.42768172  2.22885825  3.48311119  3.03352602 13.742788    2.18855936\n",
      "  8.85964886  7.3666449   5.94596985  1.91490109  5.15930417  2.2872242\n",
      " 15.50775001  4.19582067 11.82081366  3.0231326   3.81860312  5.49153365\n",
      "  8.99019113  3.41449445  8.55043021 15.14574943  2.30922244 18.83958163\n",
      "  4.0470322  11.25874087  4.40418921  5.87370827 16.54006959 12.39198049\n",
      "  2.79055698  5.0799784   2.6037895 ]\n",
      "id\n",
      "1437    18.559999\n",
      "2700     3.100000\n",
      "3647    12.650000\n",
      "2512     2.910000\n",
      "2902    15.520000\n",
      "Name: Close, dtype: float64\n",
      "Коэффициент детерминации R²: 1.00\n",
      "Время обучения модели: 1.91 секунд\n",
      "Среднеквадратичная ошибка: 0.05\n",
      "Средняя абсолютная ошибка: 0.04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\linear_model\\_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=5.67566e-19): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n",
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\linear_model\\_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=5.85088e-19): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n",
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\linear_model\\_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=6.03205e-19): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n",
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\linear_model\\_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=5.92069e-19): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n",
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\linear_model\\_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=4.94481e-19): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Кросс-валидация RMSE: 0.06936831513332838 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score, mean_absolute_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# Разделение данных на обучающую и тестовую выборки. Удаляем целевую переменную\n",
    "#y = feature_matrix['Close'] #- целевая переменная\n",
    "#X = feature_matrix.drop('Close', axis=1)\n",
    "\n",
    "# Удаление строк с NaN\n",
    "feature_matrix = feature_matrix.dropna()\n",
    "val_feature_matrix = val_feature_matrix.dropna()\n",
    "test_feature_matrix = test_feature_matrix.dropna()\n",
    "\n",
    "feature_matrix = pd.get_dummies(feature_matrix, drop_first=True)\n",
    "val_feature_matrix = pd.get_dummies(val_feature_matrix, drop_first=True)\n",
    "test_feature_matrix = pd.get_dummies(test_feature_matrix, drop_first=True)\n",
    "\n",
    "feature_matrix.fillna(feature_matrix.median(), inplace=True)\n",
    "val_feature_matrix.fillna(val_feature_matrix.median(), inplace=True)\n",
    "test_feature_matrix.fillna(test_feature_matrix.median(), inplace=True)\n",
    "\n",
    "# Разделение данных на обучающую и тестовую выборки\n",
    "y_train = feature_matrix['Close']\n",
    "X_train = feature_matrix.drop('Close', axis=1)\n",
    "y_test = test_feature_matrix['Close']\n",
    "X_test = test_feature_matrix.drop('Close', axis=1)\n",
    "\n",
    "# Обучение модели\n",
    "model1 = LinearRegression()\n",
    "model2 = DecisionTreeRegressor()\n",
    "model3 = RandomForestRegressor(n_estimators=100)  # Количество деревьев в лесу\n",
    "model4 = Lasso(alpha=0.1)\n",
    "model5 = Ridge(alpha=0.1)\n",
    "\n",
    "print('\\nLinearRegression:')\n",
    "start_time = time.time()\n",
    "model1.fit(X_train, y_train)\n",
    "\n",
    "# Время обучения модели\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "y_predict = model1.predict(X_test)\n",
    "\n",
    "mse = mean_squared_error(y_test, y_predict, squared=False)\n",
    "r2 = r2_score(y_test, y_predict)\n",
    "mae = mean_absolute_error(y_test, y_predict)\n",
    "print(f'Коэффициент детерминации R²: {r2:.2f}')\n",
    "print(f'Время обучения модели: {train_time:.2f} секунд')\n",
    "print(f'Среднеквадратичная ошибка: {mse:.2f}')\n",
    "print(f'Средняя абсолютная ошибка: {mae:.2f}')\n",
    "# Кросс-валидация\n",
    "scores = cross_val_score(model1, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
    "rmse_cv = (-scores.mean())**0.5\n",
    "print(f\"Кросс-валидация RMSE: {rmse_cv} \\n\")\n",
    "\n",
    "\n",
    "# Визуализация результатов\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_test, y_predict, alpha=0.5)\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n",
    "plt.xlabel('Фактическая цена')\n",
    "plt.ylabel('Прогнозируемая цена')\n",
    "plt.title('Фактическая цена по сравнению с прогнозируемой')\n",
    "plt.show()\n",
    "\n",
    "#//////////////////////////\n",
    "\n",
    "print('\\nDecisionTreeRegressor:')\n",
    "start_time = time.time()\n",
    "model2.fit(X_train, y_train)\n",
    "\n",
    "# Время обучения модели\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "y_predict = model2.predict(X_test)\n",
    "\n",
    "mse = mean_squared_error(y_test, y_predict, squared=False)\n",
    "r2 = r2_score(y_test, y_predict)\n",
    "mae = mean_absolute_error(y_test, y_predict)\n",
    "print(f'Коэффициент детерминации R²: {r2:.2f}')\n",
    "print(f'Время обучения модели: {train_time:.2f} секунд')\n",
    "print(f'Среднеквадратичная ошибка: {mse:.2f}')\n",
    "print(f'Средняя абсолютная ошибка: {mae:.2f}')\n",
    "# Кросс-валидация\n",
    "scores = cross_val_score(model2, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
    "rmse_cv = (-scores.mean())**0.5\n",
    "print(f\"Кросс-валидация RMSE: {rmse_cv} \\n\")\n",
    "\n",
    "# Анализ важности признаков\n",
    "feature_importances = model2.feature_importances_\n",
    "feature_names = X_train.columns\n",
    "# Визуализация результатов\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_test, y_predict, alpha=0.5)\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n",
    "plt.xlabel('Фактическая цена')\n",
    "plt.ylabel('Прогнозируемая цена')\n",
    "plt.title('Фактическая цена по сравнению с прогнозируемой')\n",
    "plt.show()\n",
    "\n",
    "#//////////////////////////\n",
    "\n",
    "print('\\nRandomForestRegressor:')\n",
    "start_time = time.time()\n",
    "model3.fit(X_train, y_train)\n",
    "\n",
    "# Время обучения модели\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "y_predict = model3.predict(X_test)\n",
    "\n",
    "mse = mean_squared_error(y_test, y_predict, squared=False)\n",
    "r2 = r2_score(y_test, y_predict)\n",
    "mae = mean_absolute_error(y_test, y_predict)\n",
    "print(f'Коэффициент детерминации R²: {r2:.2f}')\n",
    "print(f'Время обучения модели: {train_time:.2f} секунд')\n",
    "print(f'Среднеквадратичная ошибка: {mse:.2f}')\n",
    "print(f'Средняя абсолютная ошибка: {mae:.2f}')\n",
    "# Кросс-валидация\n",
    "scores = cross_val_score(model3, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
    "rmse_cv = (-scores.mean())**0.5\n",
    "print(f\"Кросс-валидация RMSE: {rmse_cv} \\n\")\n",
    "\n",
    "# Анализ важности признаков\n",
    "feature_importances = model3.feature_importances_\n",
    "feature_names = X_train.columns\n",
    "# Визуализация результатов\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_test, y_predict, alpha=0.5)\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n",
    "plt.xlabel('Фактическая цена')\n",
    "plt.ylabel('Прогнозируемая цена')\n",
    "plt.title('Фактическая цена по сравнению с прогнозируемой')\n",
    "plt.show()\n",
    "\n",
    "#//////////////////////////\n",
    "\n",
    "print('\\nLasso:')\n",
    "start_time = time.time()\n",
    "model4.fit(X_train, y_train)\n",
    "\n",
    "# Время обучения модели\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "y_predict = model4.predict(X_test)\n",
    "\n",
    "mse = mean_squared_error(y_test, y_predict, squared=False)\n",
    "r2 = r2_score(y_test, y_predict)\n",
    "mae = mean_absolute_error(y_test, y_predict)\n",
    "print(f'Коэффициент детерминации R²: {r2:.2f}')\n",
    "print(f'Время обучения модели: {train_time:.2f} секунд')\n",
    "print(f'Среднеквадратичная ошибка: {mse:.2f}')\n",
    "print(f'Средняя абсолютная ошибка: {mae:.2f}')\n",
    "# Кросс-валидация\n",
    "scores = cross_val_score(model4, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
    "rmse_cv = (-scores.mean())**0.5\n",
    "print(f\"Кросс-валидация RMSE: {rmse_cv} \\n\")\n",
    "\n",
    "# Визуализация результатов\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_test, y_predict, alpha=0.5)\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n",
    "plt.xlabel('Фактическая цена')\n",
    "plt.ylabel('Прогнозируемая цена')\n",
    "plt.title('Фактическая цена по сравнению с прогнозируемой')\n",
    "plt.show()\n",
    "\n",
    "#//////////////////////////\n",
    "\n",
    "print('\\nRidge:')\n",
    "start_time = time.time()\n",
    "model5.fit(X_train, y_train)\n",
    "\n",
    "# Время обучения модели\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "y_predict = model5.predict(X_test)\n",
    "print(y_predict)\n",
    "print(y_test.head())\n",
    "\n",
    "mse = mean_squared_error(y_test, y_predict, squared=False)\n",
    "r2 = r2_score(y_test, y_predict)\n",
    "mae = mean_absolute_error(y_test, y_predict)\n",
    "print(f'Коэффициент детерминации R²: {r2:.2f}')\n",
    "print(f'Время обучения модели: {train_time:.2f} секунд')\n",
    "print(f'Среднеквадратичная ошибка: {mse:.2f}')\n",
    "print(f'Средняя абсолютная ошибка: {mae:.2f}')\n",
    "# Кросс-валидация\n",
    "scores = cross_val_score(model5, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
    "rmse_cv = (-scores.mean())**0.5\n",
    "print(f\"Кросс-валидация RMSE: {rmse_cv} \\n\")\n",
    "\n",
    "# Визуализация результатов\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_test, y_predict, alpha=0.5)\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n",
    "plt.xlabel('Фактическая цена')\n",
    "plt.ylabel('Прогнозируемая цена')\n",
    "plt.title('Фактическая цена по сравнению с прогнозируемой')\n",
    "plt.show()"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На основании представленных данных можно сделать несколько выводов:\n",
    "\n",
    "1. **Общие выводы по точности**\n",
    "В данном случае среднеквадратичные ошибки близки или равны нулю, к тому же коэффициент детерминации 1.00 - это говорит либо о том, что модель обучается идеально, либо о том, что модель запомнила значения. Поэтому я проверила ее на нескольких моделях и отдельно вывела для сравнения список предсказанной целевой переменной и тестовую(с которой сравниваем) целевую переменную - результаты оказались весьма близки к тестовым показателям, но не точь в точь, что, скорее всего, говорит о том, что модель все же обучается идеально...\n",
    "**Среднеквадратичная ошибка (RMSE) и Средняя абсолютная ошибка (MAE)**\n",
    "* LinearRegression: MAE = 0.04 и RMSE = 0.05 указывает на весьма точные предсказания.\n",
    "* DecisionTreeRegressor: MAE и RMSE равны 0.00, что может указывать на чрезмерное подстраивание модели к обучающим данным.\n",
    "* RandomForestRegressor: MAE = 0.02 и RMSE = 0.03 показывают высокую точность прогнозов, но не столь идеальные результаты, как у дерева решений.\n",
    "* Lasso и Ridge: Обе модели имеют MAE = 0.10 и 0.04 соответственно, что также предполагает приемлемую точность, но с возможностью недопущения переобучения.\n",
    "2. **Переобучение модели**\n",
    "Высокие значения R² и нулевые ошибки (MAE и RMSE) у DecisionTreeRegressor могут указывать на переобучение модели. Это значит, что модель отлично работает на обучающих данных, но может быть неэффективной на новых, невидимых данных.\n",
    "Для линейной регрессии и других регуляризованных моделей (например, Lasso и Ridge) результаты более сбалансированы, что делает их менее подверженными переобучению.\n",
    "3. **Производительность модели**\n",
    "Время обучения у моделей варьируется значительно. Например, DecisionTreeRegressor обучается за короткое время (0.31 секунды), в то время как LinearRegression и RandomForestRegressor требуют больше времени. Это может быть критичным для сценариев, требующих частых обновлений модели.\n",
    "4. **Соответствие бизнес-целям**\n",
    "Учитывая высокую точность модели и ее способность к обучению на исторических данных, можно использовать ее для прогнозирования цен на акции. Однако рекомендуется дополнительно проверять результаты на тестовых данных, чтобы избежать проблем с переобучением."
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