{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Начало лабораторной работы"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Цены на кофе (12 варик)\n",
    "2. Цены на акции (13 варик)\n",
    "3. Цены на золото (14 варик)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Цены на кофе"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object') \n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df = pd.read_csv(\"C:/Users/TIGR228/Desktop/МИИ/Lab1/AIM-PIbd-31-Afanasev-S-S/static/csv/Starbucks.csv\")\n",
    "\n",
    "print(df.columns, \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Столбцы на русском"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. date: Дата\n",
    "2. open: Цена открытия\n",
    "3. high: Самая высокая цена дня\n",
    "4. low: Самая низкая цена дня\n",
    "5. Close: Цена закрытия\n",
    "6. Adj Close: Скорректированная цена закрытия\n",
    "7. Volume: Объем торгов\n",
    "\n",
    "Проблемная область: Прогнозирование динамики цен акций Starbucks на основе исторических данных о ценах и объемах торгов.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<bound method DataFrame.info of             Date       Open       High        Low      Close  Adj Close  \\\n",
      "0     1992-06-26   0.328125   0.347656   0.320313   0.335938   0.260703   \n",
      "1     1992-06-29   0.339844   0.367188   0.332031   0.359375   0.278891   \n",
      "2     1992-06-30   0.367188   0.371094   0.343750   0.347656   0.269797   \n",
      "3     1992-07-01   0.351563   0.359375   0.339844   0.355469   0.275860   \n",
      "4     1992-07-02   0.359375   0.359375   0.347656   0.355469   0.275860   \n",
      "...          ...        ...        ...        ...        ...        ...   \n",
      "8031  2024-05-17  75.269997  78.000000  74.919998  77.849998  77.849998   \n",
      "8032  2024-05-20  77.680000  78.320000  76.709999  77.540001  77.540001   \n",
      "8033  2024-05-21  77.559998  78.220001  77.500000  77.720001  77.720001   \n",
      "8034  2024-05-22  77.699997  81.019997  77.440002  80.720001  80.720001   \n",
      "8035  2024-05-23  80.099998  80.699997  79.169998  79.260002  79.260002   \n",
      "\n",
      "         Volume  \n",
      "0     224358400  \n",
      "1      58732800  \n",
      "2      34777600  \n",
      "3      18316800  \n",
      "4      13996800  \n",
      "...         ...  \n",
      "8031   14436500  \n",
      "8032   11183800  \n",
      "8033    8916600  \n",
      "8034   22063400  \n",
      "8035    4651418  \n",
      "\n",
      "[8036 rows x 7 columns]> \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(df.info, \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Объектом наблюдения является - цена акций Starbucks <br>\n",
    "Атрибуты — содержит набор информации о ценах акций Starbucks, такие как: дата, цена открытия, максимальная цена дня, минимальная цена дня, цена закрытия, скорректированная цена закрытия и объем торгов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "plt.scatter(df['Volume'], df['Close'], c=df['Close'], alpha=0.6)\n",
    "plt.colorbar(label='Close Price')\n",
    "\n",
    "plt.title(\"Зависимость цены закрытия от объема торгов\")\n",
    "plt.ylabel(\"Цена закрытия\")\n",
    "plt.xlabel(\"Объем торгов\")\n",
    "plt.grid(visible=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df['Year'] = pd.to_datetime(df['Date']).dt.year\n",
    "\n",
    "year_close = df.groupby('Year')['Close'].mean().reset_index()\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "plt.plot(year_close['Year'], year_close['Close'], marker='.')\n",
    "\n",
    "plt.title(\"Средняя цена закрытия акций Starbucks по годам\")\n",
    "plt.xlabel(\"Год\")\n",
    "plt.ylabel(\"Средняя цена закрытия\")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Связь между объектами есть. Цена связана почти со всеми характеристиками акций. Например, на графике номер один показана зависимость между ценой закрытия и объемом торгов. А на графике номер два показана зависимость средней цены закрытия от года."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Примеры бизнес-целей</h3>\n",
    "\n",
    "1. Прогнозирование динамики цен акций Starbucks на основе исторических данных о ценах и объемах торгов.\n",
    "2. Наблюдение за изменениями цен акций Starbucks с годами.\n",
    "\n",
    "Эффект для бизнеса: Оценка и оптимизация цен, оценка и планирование затрат, выявление тенденций на рынке, стратегия планирования.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Цели технического проекта</h3>\n",
    "<ul>Для первой цели:</ul>\n",
    "    <li>Вход: Исторические данные о ценах и объемах торгов</li>\n",
    "    <li>Целевой признак: Цена закрытия.</li>\n",
    "<ul>Для второй цели:</ul>\n",
    "    <li>Вход: Исторические данные о ценах и объемах торгов</li>\n",
    "    <li>Целевой признак: Год</li>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Код ниже нужен для определения проблем данных</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Максимальные значения:\n",
      " Date         2024-05-23\n",
      "Open         126.080002\n",
      "High             126.32\n",
      "Low          124.809998\n",
      "Close        126.059998\n",
      "Adj Close    118.010414\n",
      "Volume        585508800\n",
      "Year               2024\n",
      "dtype: object \n",
      "\n",
      "Столбцы с нулевыми значениями:\n",
      " Index([], dtype='object') \n",
      "\n",
      "Признаки с низкой дисперсией:\n",
      " Series([], dtype: float64) \n",
      "\n",
      "Годы:\n",
      " 0       1992\n",
      "1       1992\n",
      "2       1992\n",
      "3       1992\n",
      "4       1992\n",
      "        ... \n",
      "8031    2024\n",
      "8032    2024\n",
      "8033    2024\n",
      "8034    2024\n",
      "8035    2024\n",
      "Name: Year, Length: 8036, dtype: int32\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "max_value = df.max(axis=0)\n",
    "\n",
    "columns_with_zero = df.columns[(df == 0).any()]\n",
    "\n",
    "numeric_data = df.select_dtypes(include='number')\n",
    "shum = numeric_data.var()\n",
    "low_dispers = 0.1\n",
    "low_var_columns = shum[shum < low_dispers]\n",
    "\n",
    "df['Year'] = pd.to_datetime(df['Date']).dt.year\n",
    "print(\"Максимальные значения:\\n\", max_value, \"\\n\")\n",
    "print(\"Столбцы с нулевыми значениями:\\n\", columns_with_zero, \"\\n\")\n",
    "print(\"Признаки с низкой дисперсией:\\n\", low_var_columns, \"\\n\")\n",
    "print(\"Годы:\\n\", df['Year'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Из полученных данных выяснилось:</h4></ul> <li>Столбцы с нулевыми значениями отсутствуют, что указывает на полноту данных и отсутствие проблем с пропущенными значениями.</li> <li>Максимальные значения для различных метрик: <ul> <li>Date: 2024-05-23</li> <li>Open: 126.080002</li> <li>High: 126.32</li> <li>Low: 124.809998</li> <li>Close: 126.059998</li> <li>Adj Close: 118.010414</li> <li>Volume: 585508800</li> <li>Year: 2024</li> </ul> </li> <li>Признаки с низкой дисперсией отсутствуют, что указывает на стабильность данных и отсутствие проблем с зашумленностью.</li> <li>Годы варьируются от 1992 до 2024. Это может быть актуальной информацией для анализа временных трендов и изменений в данных за длительный период. Однако, если данные включают будущие даты (например, 2024 год), это может указывать на проблему с актуальностью данных или просачивание данных.</li> <li>Выбросы: Максимальные значения для некоторых метрик (например, Volume) могут указывать на наличие выбросов, которые могут искажать анализ и моделирование.</li> <li>Смещение: Отсутствие столбцов с нулевыми значениями и признаков с низкой дисперсией указывает на отсутствие явных проблем со смещением данных. Однако, для более точного анализа смещения необходимо провести дополнительные исследования, такие как сравнение распределений признаков в тренировочном и тестовом наборах данных.</li> <li>Просачивание данных: Наличие будущих дат (например, 2024 год) может указывать на проблему с просачиванием данных, если эти данные используются для прогнозирования будущих событий. Это может привести к некорректным результатам моделирования.</li>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ol><h3>Примеры решения проблем для набора данных</h3></ol>\n",
    "    <li>Удаление выбросов на основе значения или Volume</li>\n",
    "    <li>Удаление или обновить устаревшие даты, так как наличие будущих дат может указывать на проблему с актуальностью данных</li>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Оценка качества данных</h3>\n",
    "1. Информативность. Набор данных предоставляет достаточную информацию для анализа цен на недвижимость.\n",
    "2. Степень покрытия. Набор данных затрагивает только один райно, не включая информацию о других райнов.\n",
    "3. Соответствие реальным данным. Данные вполне кажутся реальными, не считая некоторых редких выбросов.\n",
    "4. Согласованность меток. Метки состояние и оценка вида, имеют четкие значения."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Разбиение данных на обучающую, контрольную и тестовую выборки</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Исходный размер строк: 8036 строк\n",
      "Размер обучающей выборки: 5625 строк\n",
      "Размер валидационной выборки: 1205 строк\n",
      "Размер тестовой выборки: 1206 строк\n"
     ]
    }
   ],
   "source": [
    "df_numeric = df.select_dtypes(include='number')\n",
    "\n",
    "x = df_numeric.drop(['Close'], axis=1)\n",
    "y = df_numeric['Close']\n",
    "\n",
    "x_train, x_temp, y_train, y_temp = train_test_split(x, y, test_size=0.3, random_state=14)\n",
    "\n",
    "x_val, x_test, y_val, y_test = train_test_split(x_temp, y_temp, test_size=0.5, random_state=14)\n",
    "\n",
    "print(f\"Исходный размер строк: {df_numeric.shape[0]} строк\")\n",
    "print(f\"Размер обучающей выборки: {x_train.shape[0]} строк\")\n",
    "print(f\"Размер валидационной выборки: {x_val.shape[0]} строк\")\n",
    "print(f\"Размер тестовой выборки: {x_test.shape[0]} строк\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
      "Статистические показатели для обучающей выборки:\n",
      "Среднее значение: 2.53\n",
      "Стандартное отклонение: 1.54\n",
      "Минимальное значение: -1.06\n",
      "Максимальное значение: 4.84\n",
      "Количество наблюдений: 5625\n",
      "\n",
      "Статистические показатели для валидационной выборки:\n",
      "Среднее значение: 2.44\n",
      "Стандартное отклонение: 1.58\n",
      "Минимальное значение: -1.02\n",
      "Максимальное значение: 4.79\n",
      "Количество наблюдений: 1205\n",
      "\n",
      "Статистические показатели для тестовой выборки:\n",
      "Среднее значение: 2.49\n",
      "Стандартное отклонение: 1.57\n",
      "Минимальное значение: -1.09\n",
      "Максимальное значение: 4.81\n",
      "Количество наблюдений: 1206\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Логарифмирование целевой переменной\n",
    "df['Close_log'] = np.log(df['Close'])\n",
    "\n",
    "# Выбор признаков и целевой переменной\n",
    "X = df.drop(['Close', 'Close_log'], axis=1)\n",
    "y = df['Close_log']\n",
    "\n",
    "# Выбор только числовых признаков\n",
    "X = X.select_dtypes(include='number')\n",
    "\n",
    "# Разделение данных на обучающую, валидационную и тестовую выборки\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n",
    "\n",
    "# Функция для построения гистограммы распределения целевого признака\n",
    "def plot_distribution(data, title):\n",
    "    \"\"\"Построение гистограммы распределения целевого признака\"\"\"\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    sns.histplot(data, kde=True, bins=30, color='skyblue')\n",
    "    plt.title(title)\n",
    "    plt.xlabel('Logarithm of Close Price')\n",
    "    plt.ylabel('Count')\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "# Построение гистограмм распределения целевого признака\n",
    "plot_distribution(y_train, 'Распределение логарифма цены закрытия в обучающей выборке')\n",
    "plot_distribution(y_val, 'Распределение логарифма цены закрытия в валидационной выборке')\n",
    "plot_distribution(y_test, 'Распределение логарифма цены закрытия в тестовой выборке')\n",
    "\n",
    "# Функция для вывода статистических показателей\n",
    "def get_statistics(df, name):\n",
    "    print(f\"Статистические показатели для {name} выборки:\")\n",
    "    print(f\"Среднее значение: {df.mean():.2f}\")\n",
    "    print(f\"Стандартное отклонение: {df.std():.2f}\")\n",
    "    print(f\"Минимальное значение: {df.min():.2f}\")\n",
    "    print(f\"Максимальное значение: {df.max():.2f}\")\n",
    "    print(f\"Количество наблюдений: {df.count()}\\n\")\n",
    "\n",
    "# Вывод статистических показателей для обучающей, валидационной и тестовой выборок\n",
    "get_statistics(y_train, \"обучающей\")\n",
    "get_statistics(y_val, \"валидационной\")\n",
    "get_statistics(y_test, \"тестовой\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Oversampling и undersampling</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение классов после SMOTE (oversampling):\n",
      "Close_category\n",
      "0    1157\n",
      "1    1157\n",
      "2    1157\n",
      "3    1157\n",
      "4    1157\n",
      "Name: count, dtype: int64\n",
      "Распределение классов после RandomUnderSampler (undersampling):\n",
      "Close_category\n",
      "0    1092\n",
      "1    1092\n",
      "2    1092\n",
      "3    1092\n",
      "4    1092\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Проверка наличия столбца 'Date' и создание столбца 'Year'\n",
    "if 'Date' in df.columns:\n",
    "    df['Year'] = pd.to_datetime(df['Date'], errors='coerce').dt.year\n",
    "    df = df.drop(['Date'], axis=1)\n",
    "\n",
    "# Логарифмирование целевой переменной\n",
    "df['Close_log'] = np.log(df['Close'])\n",
    "\n",
    "# Создание категорий для целевой переменной\n",
    "df['Close_category'] = pd.qcut(df['Close_log'], q=5, labels=[0, 1, 2, 3, 4])\n",
    "\n",
    "# Выбор признаков и целевой переменной\n",
    "X = df.drop(['Close', 'Close_log', 'Close_category'], axis=1)\n",
    "y = df['Close_category']\n",
    "\n",
    "# Разделение данных на обучающую и тестовую выборки\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# Применение SMOTE для oversampling\n",
    "smote = SMOTE(random_state=42)\n",
    "X_train_smote, y_train_smote = smote.fit_resample(X_train, y_train)\n",
    "\n",
    "print(\"Распределение классов после SMOTE (oversampling):\")\n",
    "print(pd.Series(y_train_smote).value_counts())\n",
    "\n",
    "# Применение RandomUnderSampler для undersampling\n",
    "undersampler = RandomUnderSampler(random_state=42)\n",
    "X_train_under, y_train_under = undersampler.fit_resample(X_train, y_train)\n",
    "\n",
    "print(\"Распределение классов после RandomUnderSampler (undersampling):\")\n",
    "print(pd.Series(y_train_under).value_counts())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Оценка сбалансированности выборок</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оценка необходимости аугментации данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Данные в выборке обучающей являются категориальными.\n",
      "Проверка необходимости аугментации для валидационной выборки:\n",
      "Среднее значение: 2.44, Стандартное отклонение: 1.58\n",
      "25-й квантиль: 1.20\n",
      "50-й квантиль (медиана): 2.53\n",
      "75-й квантиль: 4.01\n",
      "Выборка валидационной несбалансирована, рекомендуется аугментация.\n",
      "\n",
      "Данные в выборке тестовой являются категориальными.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "def check_augmentation_need(data, name):\n",
    "    \"\"\"Проверка необходимости аугментации данных\"\"\"\n",
    "    # Проверка на наличие числовых значений\n",
    "    if isinstance(data.dtype, pd.CategoricalDtype):\n",
    "        print(f\"Данные в выборке {name} являются категориальными.\")\n",
    "        return\n",
    "    elif not np.issubdtype(data.dtype, np.number):\n",
    "        print(f\"Данные в выборке {name} не являются числовыми.\")\n",
    "        return\n",
    "\n",
    "    # Проверка на наличие пустых значений\n",
    "    if data.isnull().any():\n",
    "        print(f\"Выборка {name} содержит пустые значения.\")\n",
    "        return\n",
    "\n",
    "    quantiles = data.quantile([0.25, 0.5, 0.75])\n",
    "    mean = data.mean()\n",
    "    std = data.std()\n",
    "\n",
    "    print(f\"Проверка необходимости аугментации для {name} выборки:\")\n",
    "    print(f\"Среднее значение: {mean:.2f}, Стандартное отклонение: {std:.2f}\")\n",
    "    print(f\"25-й квантиль: {quantiles[0.25]:.2f}\")\n",
    "    print(f\"50-й квантиль (медиана): {quantiles[0.5]:.2f}\")\n",
    "    print(f\"75-й квантиль: {quantiles[0.75]:.2f}\")\n",
    "\n",
    "    if std > mean * 0.5:\n",
    "        print(f\"Выборка {name} несбалансирована, рекомендуется аугментация.\\n\")\n",
    "    else:\n",
    "        print(f\"Выборка {name} сбалансирована, аугментация не требуется.\\n\")\n",
    "\n",
    "# Пример использования функции\n",
    "# y_train, y_val, y_test должны быть определены заранее\n",
    "check_augmentation_need(y_train, \"обучающей\")\n",
    "check_augmentation_need(y_val, \"валидационной\")\n",
    "check_augmentation_need(y_test, \"тестовой\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Поскольку выборка валидационной несбалансирована и демонстрирует значительный разброс значений, что подтверждается квантилями и стандартным отклонением, применение методов аугментации рекомендуется для улучшения сбалансированности и качества модели."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение 'Close_category' в обучающей выборке:\n",
      " Close_category\n",
      "2    1157\n",
      "4    1134\n",
      "1    1126\n",
      "3    1116\n",
      "0    1092\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TIGR228\\AppData\\Local\\Temp\\ipykernel_21436\\2926621768.py:29: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x=category_counts.index, y=category_counts.values, palette='viridis')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение 'Close_category' в валидационной выборке:\n",
      " Close_category\n",
      "0    263\n",
      "1    242\n",
      "3    238\n",
      "4    238\n",
      "2    224\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TIGR228\\AppData\\Local\\Temp\\ipykernel_21436\\2926621768.py:29: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x=category_counts.index, y=category_counts.values, palette='viridis')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TIGR228\\AppData\\Local\\Temp\\ipykernel_21436\\2926621768.py:29: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x=category_counts.index, y=category_counts.values, palette='viridis')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение 'Close_category' в тестовой выборке:\n",
      " Close_category\n",
      "0    253\n",
      "3    252\n",
      "1    241\n",
      "4    235\n",
      "2    225\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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zPH36tMLCwlS1alWtXbvWrX1aWpqkv7bXlcydO1dFixZVmzZt3ObZoEEDFS5c2GOeDofDrd3p06d14cKFq9Z99OhRTZkyRS+//LLb0ZeM5desWVM1atRwm2fGqSiXL/96RURE6P7773c9DwkJUa9evfT999/rxIkTmU4zbdo0xcfHa9SoUZmOz9gmBw8e1D//+U/Z7XY1bdrUrU16errHdktJSfGYV8aRf+mvr+ZPnz6tu+++WykpKdq7d69H+++++07du3dXt27dMj2ye+n8kpOTdfr0aTVp0kTGmEy/ir6apKQkScrxUdvjx49rx44d6tOnj0qUKOEaXrduXbVp08b1/pGkYsWKacuWLTp27Fim88rJe+xK5s+fL5vNlmn/XrqfuHRbJiYm6vTp02rWrJl++eUXJSYmXnM5c+fO1d13363ixYu7vQ5at26t9PR0rV+/XpK0bNky+fv7q3///q5p7Xa76+hwhuxszwyPP/64x7BevXrp2LFjbu+12bNnKzg4WN26dbvmemUmNTVVp0+f1qlTp7Ry5UqtWbNGrVq1ytG8LpfVfdbKlSt19uxZPf/88x771uzu/zO25dChQ92GP/PMM5Kk//znP27Dr7X+S5culZ+fn5588kmP+Rlj9NVXX2Vax9X2pVeTkpKi06dP68SJE5o/f75++OGHTPsjY59z+ekCVzNs2DC3508//bT8/Pxc22TVqlVKS0vTU0895frclqT+/fsrJCTEY9sVKFBAAwcOdD0PCAjQwIEDderUKcXFxbm1dTqd6tWrlzZv3qylS5e6fRsr3fjPF2/gtAQfMm3aNFWrVk0FChRQmTJlVL16dbc3Rcb5cdOnT9ehQ4fczkfMOHVB+ut0hurVq6tAgdzt/qpVq7o9t9lsqlKliuvcyf3790tSpl8jZkhMTFTx4sVdz0+fPu0x38vt379fxpgrtrv89IGMHdTVdoL79+9XYmKiQkNDMx1/6tQpt+crVqxQ6dKlr1rn5UaNGqWIiAgNHDjQ45yu/fv3a8+ePVec5+XLv15VqlTx+GDLuAL58OHDCgsLcxuXmJio1157TUOHDnV9vXy51atXKzo6WtJfYXnevHkeVzLv3bs3S9vtp59+0ksvvaQ1a9a4wuSltVzq6NGj6tixo5KTkxUfH5/pB/aRI0c0cuRIffHFFx7nI2YlkF0qJCREknJ8C6Jff/1VklS9enWPcTVr1tTy5ctdFzm9/vrr6t27t8qVK6cGDRqoQ4cO6tWrlypVqiQpZ++xKzl48KAiIiLcAmJmNmzYoFGjRmnTpk0ef5gkJiZe86Ka/fv368cff7zma/3XX39VeHi4x51hqlSp4vY8O9szQ8WKFT3atmnTRuHh4Zo9e7ZatWolp9OpTz75RPfdd1+O/5D59NNP9emnn7qeN2rUSP/3f/+Xo3ldLqv7rIzT2TI73z27fv31V9ntdo8+CAsLU7FixVx9keFa6//rr78qIiLCY/tmnIJx+fwyXG1fejUTJ050++O3ffv2mjBhgke7S7/2L1y4sDp16qRJkyZluu/LOKf48s+jokWLKjw83PV5eKXXaUBAgCpVquSxrhERER4XOl66j7503/riiy9q8+bNstlsmR4suNGfL95AuPUhjRs3dp3nlpnXXntNL7/8sh599FG98sorKlGihOx2u5566qksH63JSxk1TJw4UfXr18+0zaWBMy0tTcePH7/qOZ8Z87XZbPrqq6/k5+d31XlKch2JvDywXT7P0NBQzZ49O9Pxl+8U7rjjDo0bN85t2NSpU7V48eJMp9+zZ49iY2P18ccfZ3rurtPpVJ06dfTWW29lOn25cuWuWPuNMGHCBNntdg0bNkzx8fGZtmnUqJFWrlypP//8Ux9//LEeffRRlStXzu01XKFCBf373/92m27u3LmaMWOG63lCQoKaNWumkJAQjR07VpUrV1ZQUJC2b9+u5557zuO1feDAAd1+++2aNGmSHnnkEc2aNcst7KWnp6tNmzY6c+aMnnvuOdWoUUOFChXS0aNH1adPn2y/V2rUqCFJmZ57nNu6d++uu+++WwsXLtSKFSs0ceJETZgwQQsWLFB0dHS232PX6+DBg2rVqpVq1Kiht956S+XKlVNAQICWLl2qSZMmZWlbOp1OtWnTRsOHD890fMYHeF669OhzBj8/P/Xs2VP//ve/NX36dG3YsEHHjh3T3//+9xwvp23btq4jer///rsmTJigFi1aaNu2bZnWkB3Z3Wflpqwe8c2L9b/WvvRqHnnkEfXq1UtOp1O//PKLXnnlFd17771atWqV2zqNHDlSd999txwOh+Li4jR27FglJCRk+i1Axnp48362W7ZsUWxsrKZOnaoBAwZox44dbt9U5vfPl9xAuLWQefPmqUWLFnr//ffdhickJKhUqVKu55UrV9aWLVvkcDhy5aKoDBlHjTIYY3TgwAHVrVvXtVzpryNdrVu3vub8fvjhBzkcjqsG+oz5GmNUsWLFLH0Q7t69WzabLdMjO5fOc9WqVWratGmWdrqlSpXyWKernZQ/YsQI1a9fXw8++OAVl5/xFdmN2EkeOHBAxhi3Zf3888+S5HHxzbFjx/T2229r/PjxKlKkyBXDbcmSJV3bpFu3bqpevbomTpyozz77zNWmUKFCHtvt0l8ckv66Ajg+Pl4LFizQPffc4xqecZeQy2WcClOmTBktXrxYzzzzjDp06OD6cN+5c6d+/vlnzZo1S7169XJNt3Llykzndy3VqlVT9erVtXjxYr399tvZDo+RkZGSpH379nmM27t3r0qVKuV2xCY8PFxPPPGEnnjiCZ06dUq33367Xn31VUVHR2f7PXY1lStX1vLly3XmzJkrHr1dsmSJUlNT9cUXX6h8+fKu4Zl9rXml13HlypV17ty5a9YbGRmptWvXKiUlxe3o7YEDBzzaSVnfnlfTq1cvvfnmm1qyZIm++uorlS5dWu3atcvStJkJDw93W8/q1aurSZMmWrRo0XXfzjGr+6yM18iuXbs8jrhmV2RkpJxOp/bv3+92G66TJ08qISHB1RcZrrX+kZGRWrVqlc6ePet29Dbj1KPL5ydde196NZUqVXKrp2jRourZs6c2b96sqKgo1/A6deq42kVHR+vIkSOaNWuWLl686DHPihUrZrpNkpKSdPz4cdddUy59nWZ88yL9dVDn0KFDHu+HY8eOeXzjcKV99JgxY9S7d2/Vr19fDRs21Lhx4/TKK6+4xt/ozxdv4JxbC/Hz8/O4XcrcuXM9blnTrVs3nT59WlOnTvWYx+XTZ8eHH37o9tXsvHnzdPz4cddX0w0aNFDlypX1xhtvuN2SJMMff/zhUbufn981b6HUtWtX+fn5acyYMR71G2PcwtfFixc1f/58NW7c+KohpHv37kpPT3fbIVw6j+yce3W5TZs2afHixfrnP/95xR1L9+7ddfToUY+jmpJ0/vx5JScn53j5mTl27Jjblc1JSUn68MMPVb9+fY8j3GPGjFGZMmUyPU/xSi5cuKDk5OQc3WYm42j8pX2blpam6dOnZ9q+WrVqrq8Lp0yZIqfTqX/84x9XnZ8xRm+//Xa2a8swZswYxcfHu+4ocLkVK1boyy+/zHTa8PBw1a9fX7NmzXJ7Xe3atUsrVqxw3a0kPT3d45SJ0NBQRUREuLZrdt9jV9OtWzcZYzRmzBiPcRnbLrNtmZiYqJkzZ3pMU6hQoUzfN927d9emTZu0fPlyj3EJCQmu7dmuXTs5HA6394TT6dS0adPcpsnq9syKunXrqm7duvq///s/zZ8/Xz169MjV07kybg2VG7dfyuo+q23btipSpIjGjx/vcV1Advf/Gdsy444WGTKOCHbs2PGq01++/h06dFB6errHZ9OkSZNks9lcnyUZsrIvzY6s9ofT6ZTdbs90mVfaJm+//bbrTkXSX9fQBAQE6J133nHb7u+//74SExM9tt3Fixf1r3/9y/U8LS1N//rXv1S6dGk1aNDArW3GXS3q1aunZ599VhMmTNCuXbtc42/054s3cOTWQu69916NHTtWffv2VZMmTbRz507Nnj3b7a9C6a+jER9++KGGDh2q7777TnfffbeSk5O1atUqPfHEE7rvvvtytPwSJUrorrvuUt++fXXy5ElNnjxZVapUcV0AYrfb9X//93+Kjo5W7dq11bdvX91yyy06evSo1q5dq5CQEC1ZskTJycmaNm2a3nnnHVWrVs3tnn0ZH9g//vijNm3apKioKFWuXFnjxo3TiBEjdPjwYXXp0kVFihTRoUOHtHDhQg0YMEDPPvusVq1apZdfflk//vijlixZctV1adasmQYOHKjx48drx44datu2rfz9/bV//37NnTtXb7/9th544IEcbacVK1aoTZs2Vz1S9cgjj+jzzz/X448/rrVr16pp06ZKT0/X3r179fnnn2v58uXXPKKdHdWqVVO/fv20detWlSlTRh988IFOnjyZaUhZsWKFZs+erYCAgEznlXH7mejoaEVEROjMmTP66KOPdPz48Rx9pdukSRMVL15cvXv31pNPPimbzaaPPvooSx/EYWFhmjhxoh577DH9/e9/V4cOHVSjRg1VrlxZzz77rI4ePaqQkBDNnz8/03uBZtWDDz6onTt36tVXX9X333/vOgoVHx+vZcuWafXq1W732b3cxIkTFR0draioKPXr189166qiRYu67ot89uxZlS1bVg888IDq1aunwoULa9WqVdq6davefPNNSVl/j2VFixYt9Mgjj+idd97R/v371b59ezmdTn3zzTdq0aKFBg8erLZt2yogIECdOnXSwIEDde7cOf373/9WaGiojh8/7ja/Bg0a6N1339W4ceNUpUoVhYaGqmXLlho2bJi++OIL3XvvverTp48aNGig5ORk7dy5U/PmzdPhw4dVqlQpdenSRY0bN9YzzzyjAwcOqEaNGvriiy905swZSe5HhrOyPbOqV69eevbZZyXpuk5JkP66XePHH38s6a9zw6dOnaqQkJBcuagsq/uskJAQTZo0SY899pgaNWrkur/vDz/8oJSUFM2aNSvLy6xXr5569+6tGTNmuE4f+u677zRr1ix16dJFLVq0yNb6d+rUSS1atNCLL76ow4cPq169elqxYoUWL16sp556yuPCqKzsS6/mxx9/1McffyxjjA4ePKh33nlHZcuW9di37tixQ4ULF9bFixcVFxenDz/8UPfdd1+mp8HVrl1b/fr104wZM/Tnn3+qefPm2r59uz744ANFR0e7wm/p0qU1YsQIjRkzRu3bt1fnzp21b98+TZ8+XY0aNfJ4rUVERGjChAk6fPiwqlWrps8++0w7duzQjBkzrvoN7KhRozR//nz1799fGzZskN1uv+GfL15xY2/OgJzIuAXT1q1br9ruwoUL5plnnjHh4eEmODjYNG3a1GzatMnt9kgZUlJSzIsvvmgqVqxo/P39TVhYmHnggQfMwYMHjTE5uxXYJ598YkaMGGFCQ0NNcHCw6dixY6a3Vfr+++9N165dTcmSJU1gYKCJjIw03bt3N6tXr3Zb9rUel99WZv78+eauu+4yhQoVMoUKFTI1atQwMTExZt++fcYYY4YMGWLuueces2zZMo+aLr8VWIYZM2aYBg0amODgYFOkSBFTp04dM3z4cHPs2DFXm+zeCsxms5m4uDi34Zn1UVpampkwYYKpXbu2CQwMNMWLFzcNGjQwY8aMMYmJiR7Lu3x+2bkVWMeOHc3y5ctN3bp1TWBgoKlRo4bH7W8yXof169d3u2XQ5bcKOn/+vHnwwQdN2bJlTUBAgAkNDTUtWrRw3QInJzVu2LDB3HnnnSY4ONhERESY4cOHm+XLl3vcFimz7WiMMS1btjTly5c3Z8+eNcYYs3v3btO6dWtTuHBhU6pUKdO/f3/zww8/5Pg2TxlWr15t7rvvPhMaGmoKFChgSpcubTp16mQWL158xe2VYdWqVaZp06YmODjYhISEmE6dOpndu3e7xqemppphw4aZevXqmSJFiphChQqZevXqmenTp3vUca33WFZdvHjRTJw40dSoUcMEBASY0qVLm+joaLfX7xdffGHq1q1rgoKCTIUKFcyECRNctxu8tA9PnDhhOnbsaIoUKWIkufXT2bNnzYgRI0yVKlVMQECAKVWqlGnSpIl54403XLc5MuavWwH27NnTFClSxBQtWtT06dPHbNiwwUgyn376aba2pzH/e9//8ccfV9wGx48fN35+fqZatWrZ2naXy7jlXsajVKlSpm3btmbTpk1ZnsfVbgWWISv7LGP+6rcmTZq4tk/jxo3NJ5984jG/q90KzBhjHA6HGTNmjOuzpFy5cmbEiBFut+jLzvqfPXvWPP300yYiIsL4+/ubqlWrmokTJ3rcpiw7+9LMXFqLzWYzYWFhpmvXrmbPnj2uNhnbO+NRoEABExkZaZ588knz559/GmM8bwWWsU3Gjh3rtk2GDx+e6W3zpk6damrUqGH8/f1NmTJlzKBBg1zzvnSdateubbZt22aioqJMUFCQiYyMNFOnTnVrd6XXx9dff21sNpvrtpzGXN/niy+wGXMd30MD+uucyBYtWmju3Lk5Ppp5qcOHD6tixYo6dOiQx7lEGUaPHq3Dhw97/CoRsq9ChQq69dZbr/i1OZCfLVq0SPfff7++/fZbj1vN5YbTp08rPDxcI0eO1Msvv5zr8weupXnz5jp9+rTbqQW4Os65BQD4hMt/vjQ9PV1TpkxRSEiI6xfacltsbKzS09P1yCOP5Mn8AeQ+zrlFvlO4cGE9/PDDV73gq27duq6fEwaQdenp6de8sKxw4cK5esuw3DJkyBCdP39eUVFRSk1N1YIFC7Rx40a99tpr130rrcutWbNGu3fv1quvvqouXbpc8VskAPkP4Rb5TqlSpVwXHVxJ165db1A1gLX89ttvmf5owaVGjRqV7QuvboSWLVvqzTff1JdffqkLFy6oSpUqmjJligYPHpzryxo7dqw2btyopk2basqUKbk+fwB5h3NuAeAmcuHCBX377bdXbVOpUiWPu6wAgK8g3AIAAMAyuKAMAAAAlsE5t/rr10aOHTumIkWKWPan6AAAAHyZMUZnz55VRESE7PYrH58l3Oqvnx4tV66ct8sAAADANfz2228qW7bsFccTbiUVKVJE0l8bKyQkxMvV3DgOh0MrVqxw/UwjrI3+vrnQ3zcX+vvmcrP2d1JSksqVK+fKbVdCuNX/fpM8JCTkpgu3BQsWVEhIyE315rhZ0d83F/r75kJ/31xu9v6+1imkXFAGAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALCMAt4uwFfd2+oFb5dw3fwD/NTn8Ybq3nmsHGnp3i4nx75c/Zq3SwAAAPmEV8Pt+PHjtWDBAu3du1fBwcFq0qSJJkyYoOrVq7vaNG/eXOvWrXObbuDAgXrvvfdcz48cOaJBgwZp7dq1Kly4sHr37q3x48erQAGyOwDg6tp+OsLbJVw3f/lpQHAjdZk/Rg757sGKFT3Ge7sEWIBX09+6desUExOjRo0a6eLFi3rhhRfUtm1b7d69W4UKFXK169+/v8aOHet6XrBgQdf/09PT1bFjR4WFhWnjxo06fvy4evXqJX9/f732Gkf0AAAAbiZeDbfLli1zex4bG6vQ0FDFxcXpnnvucQ0vWLCgwsLCMp3HihUrtHv3bq1atUplypRR/fr19corr+i5557T6NGjFRAQkKfrAAAAgPwjX31vn5iYKEkqUaKE2/DZs2fr448/VlhYmDp16qSXX37ZdfR206ZNqlOnjsqUKeNq365dOw0aNEg//fSTbrvtNo/lpKamKjU11fU8KSlJkuRwOORwOLJUq3+AX/ZWLh/y97e7/eurstpnN7uM7cT2ujnQ31nnLwvsz/97fbi/j18nzus1a27W93dW19dmjDF5XEuWOJ1Ode7cWQkJCfr2229dw2fMmKHIyEhFREToxx9/1HPPPafGjRtrwYIFkqQBAwbo119/1fLly13TpKSkqFChQlq6dKmio6M9ljV69GiNGTPGY/icOXPcTnkAAABA/pCSkqKePXsqMTFRISEhV2yXb47cxsTEaNeuXW7BVvorvGaoU6eOwsPD1apVKx08eFCVK1fO0bJGjBihoUOHup4nJSWpXLlyatu27VU31qW6dx577Ub5nL+/XQ/3u12z398uh8Pp7XJy7PMvRnq7BJ/gcDi0cuVKtWnTRv7+/t4uB3mM/s66LvM9D3b4Gn/Z1Te4gWaej5NDvrs/X9RtlLdL8Ak36/s745v2a8kX4Xbw4MH68ssvtX79epUtW/aqbe+44w5J0oEDB1S5cmWFhYXpu+++c2tz8uRJSbriebqBgYEKDAz0GO7v75/lF4kv3zrrcg6H06fX52Z6Y+eG7LzO4fvo72vz5bsLXM4hp0+vD6/V7LnZ3t9ZXVevnpxjjNHgwYO1cOFCrVmzRhUrVrzmNDt27JAkhYeHS5KioqK0c+dOnTp1ytVm5cqVCgkJUa1atfKkbgAAAORPXj1yGxMTozlz5mjx4sUqUqSITpw4IUkqWrSogoODdfDgQc2ZM0cdOnRQyZIl9eOPP+rpp5/WPffco7p160qS2rZtq1q1aumRRx7R66+/rhMnTuill15STExMpkdnAQAAYF1ePXL77rvvKjExUc2bN1d4eLjr8dlnn0mSAgICtGrVKrVt21Y1atTQM888o27dumnJkiWuefj5+enLL7+Un5+foqKi9Pe//129evVyuy8uAAAAbg5ePXJ7rRs1lCtXzuPXyTITGRmppUuX5lZZAAAA8FG+fUM8AAAA4BKEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBle/YUywFfcPfAVb5dw3QIK2PVk21vV/qnXlXbR6e1ycuybf73s7RIAAPkYR24BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGfxCGQAAuGlM3vCgt0u4bjZnAVVQd03f3EfGftHb5eTYU00/y5P5cuQWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYRgFvFwAA+U39caO9XcJ1C7Tb9ULterpr4nilOp3eLifHdrw02tslAPAxHLkFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZRBuAQAAYBmEWwAAAFiGV8Pt+PHj1ahRIxUpUkShoaHq0qWL9u3b59bmwoULiomJUcmSJVW4cGF169ZNJ0+edGtz5MgRdezYUQULFlRoaKiGDRumixcv3shVAQAAQD7g1XC7bt06xcTEaPPmzVq5cqUcDofatm2r5ORkV5unn35aS5Ys0dy5c7Vu3TodO3ZMXbt2dY1PT09Xx44dlZaWpo0bN2rWrFmKjY3VyJEjvbFKAAAA8KIC3lz4smXL3J7HxsYqNDRUcXFxuueee5SYmKj3339fc+bMUcuWLSVJM2fOVM2aNbV582bdeeedWrFihXbv3q1Vq1apTJkyql+/vl555RU999xzGj16tAICAryxagAAAPACr4bbyyUmJkqSSpQoIUmKi4uTw+FQ69atXW1q1Kih8uXLa9OmTbrzzju1adMm1alTR2XKlHG1adeunQYNGqSffvpJt912m8dyUlNTlZqa6nqelJQkSXI4HHI4HFmq1T/AL/srmM/4+9vd/vVVWe2z6xFQwLe3kSQF+Nnd/vVVN6K/A+2+vY0kKeC/6xDg4+tyI/rbXxbYn//3i1h/H7+U5kb0t82Zr6JPjthMgf/96/RyMdchu/2d1fY2Y4zJSUG5zel0qnPnzkpISNC3334rSZozZ4769u3rFkQlqXHjxmrRooUmTJigAQMG6Ndff9Xy5ctd41NSUlSoUCEtXbpU0dHRHssaPXq0xowZ4zF8zpw5KliwYC6vGQAAAK5XSkqKevbsqcTERIWEhFyxXb758yUmJka7du1yBdu8NGLECA0dOtT1PCkpSeXKlVPbtm2vurEu1b3z2Lwq74bx97fr4X63a/b72+Vw+O6ffp9/kffnV7d/6vU8X0ZeC/Cz6/FWtfTe6t1KS/fd/l42eXieL+OuiePzfBl5LcBu17M16+iNPTuV5vTd/v522Ig8X0aX+Z4HO3yNv+zqG9xAM8/HyeHDh/IWdRuV58uYvrlPni8jr9lMAUX+2VW/Fl8gY/PdC+ifuDM2W+0zvmm/lnwRbgcPHqwvv/xS69evV9myZV3Dw8LClJaWpoSEBBUrVsw1/OTJkwoLC3O1+e6779zml3E3hYw2lwsMDFRgYKDHcH9/f/n7+2epZkdaepba+QKHw+nT65PVPrseaRd998PicmnpTp9enxvR36k+HAYvl+Z0+vT63Ij+dsh393+Xc8jp0+tzI/rb2H03DLr89y1tbBd9en2y299Zbe/Vk3OMMRo8eLAWLlyoNWvWqGLFim7jGzRoIH9/f61evdo1bN++fTpy5IiioqIkSVFRUdq5c6dOnTrlarNy5UqFhISoVq1aN2ZFAAAAkC949chtTEyM5syZo8WLF6tIkSI6ceKEJKlo0aIKDg5W0aJF1a9fPw0dOlQlSpRQSEiIhgwZoqioKN15552SpLZt26pWrVp65JFH9Prrr+vEiRN66aWXFBMTk+nRWQAAAFiXV8Ptu+++K0lq3ry52/CZM2eqT58+kqRJkybJbrerW7duSk1NVbt27TR9+nRXWz8/P3355ZcaNGiQoqKiVKhQIfXu3Vtjx/r+ObEAAADIHq+G26zcqCEoKEjTpk3TtGnTrtgmMjJSS5cuzc3SAAAA4IN8+4Z4AAAAwCUItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAy/BquF2/fr06deqkiIgI2Ww2LVq0yG18nz59ZLPZ3B7t27d3a3PmzBk9/PDDCgkJUbFixdSvXz+dO3fuBq4FAAAA8guvhtvk5GTVq1dP06ZNu2Kb9u3b6/jx467HJ5984jb+4Ycf1k8//aSVK1fqyy+/1Pr16zVgwIC8Lh0AAAD5UAFvLjw6OlrR0dFXbRMYGKiwsLBMx+3Zs0fLli3T1q1b1bBhQ0nSlClT1KFDB73xxhuKiIjI9ZoBAACQf3k13GbF119/rdDQUBUvXlwtW7bUuHHjVLJkSUnSpk2bVKxYMVewlaTWrVvLbrdry5Ytuv/++zOdZ2pqqlJTU13Pk5KSJEkOh0MOhyNLdfkH+OV0lfINf3+727++Kqt9dj0CCvj2NpKkAD+727++6kb0d6Ddt7eRJAX8dx0CfHxdbkR/+8sC+/P/fhHr7+OX0tyI/rY58330uSabKfC/f51eLuY6ZLe/s9reZowxOSkot9lsNi1cuFBdunRxDfv0009VsGBBVaxYUQcPHtQLL7ygwoULa9OmTfLz89Nrr72mWbNmad++fW7zCg0N1ZgxYzRo0KBMlzV69GiNGTPGY/icOXNUsGDBXF0vAAAAXL+UlBT17NlTiYmJCgkJuWK7fP3nS48ePVz/r1OnjurWravKlSvr66+/VqtWrXI83xEjRmjo0KGu50lJSSpXrpzatm171Y11qe6dx+Z4+fmFv79dD/e7XbPf3y6Hw3f/9Pv8i5F5voz2T72e58vIawF+dj3eqpbeW71baem+29/LJg/P82XcNXF8ni8jrwXY7Xq2Zh29sWen0py+29/fDhuR58voMt/zYIev8ZddfYMbaOb5ODl8+FDeom6j8nwZ0zf3yfNl5DWbKaDIP7vq1+ILZGwXvV1Ojj1xZ2y22md8034t+TrcXq5SpUoqVaqUDhw4oFatWiksLEynTp1ya3Px4kWdOXPmiufpSn+dxxsYGOgx3N/fX/7+/lmqxZGWnr3i8zGHw+nT65PVPrseaRd998PicmnpTp9enxvR36k+HAYvl+Z0+vT63Ij+dsh393+Xc8jp0+tzI/rb2H03DLr89y1tbBd9en2y299Zbe9TJ+f8/vvvio+PV3h4uCQpKipKCQkJiouLc7VZs2aNnE6n7rjjDm+VCQAAAC/x6pHbc+fO6cCBA67nhw4d0o4dO1SiRAmVKFFCY8aMUbdu3RQWFqaDBw9q+PDhqlKlitq1aydJqlmzptq3b6/+/fvrvffek8Ph0ODBg9WjRw/ulAAAAHAT8uqR223btum2227TbbfdJkkaOnSobrvtNo0cOVJ+fn768ccf1blzZ1WrVk39+vVTgwYN9M0337idUjB79mzVqFFDrVq1UocOHXTXXXdpxowZ3lolAAAAeJFXj9w2b95cV7tZw/Lly685jxIlSmjOnDm5WRYAAAB8lE+dcwsAAABcDeEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYRo7CbaVKlRQfH+8xPCEhQZUqVbruogAAAICcyFG4PXz4sNLTPX/eLzU1VUePHr3uogAAAICcyNZ9br/44gvX/5cvX66iRYu6nqenp2v16tWqUKFCrhUHAAAAZEe2wm2XLl0kSTabTb1793Yb5+/vrwoVKujNN9/MteIAAACA7MhWuHU6nZKkihUrauvWrSpVqlSeFAUAAADkRI5+fvfQoUO5XQcAAABw3XIUbiVp9erVWr16tU6dOuU6opvhgw8+uO7CAAAAgOzKUbgdM2aMxo4dq4YNGyo8PFw2my236wIAAACyLUfh9r333lNsbKweeeSR3K4HAAAAyLEc3ec2LS1NTZo0ye1aAAAAgOuSo3D72GOPac6cObldCwAAAHBdcnRawoULFzRjxgytWrVKdevWlb+/v9v4t956K1eKAwAAALIjR+H2xx9/VP369SVJu3btchvHxWUAAADwlhyF27Vr1+Z2HQAAAMB1y9E5twAAAEB+lKMjty1atLjq6Qdr1qzJcUEAAABATuUo3Gacb5vB4XBox44d2rVrl3r37p0bdQEAAADZlqNwO2nSpEyHjx49WufOnbuuggAAAICcytVzbv/+97/rgw8+yM1ZAgAAAFmWq+F206ZNCgoKys1ZAgAAAFmWo9MSunbt6vbcGKPjx49r27Ztevnll3OlMAAAACC7chRuixYt6vbcbrerevXqGjt2rNq2bZsrhQEAAADZlaNwO3PmzNyuAwAAALhuOQq3GeLi4rRnzx5JUu3atXXbbbflSlEAAABATuQo3J46dUo9evTQ119/rWLFikmSEhIS1KJFC3366acqXbp0btYIAAAAZEmO7pYwZMgQnT17Vj/99JPOnDmjM2fOaNeuXUpKStKTTz6Z2zUCAAAAWZKjI7fLli3TqlWrVLNmTdewWrVqadq0aVxQBgAAAK/J0ZFbp9Mpf39/j+H+/v5yOp3XXRQAAACQEzkKty1bttQ//vEPHTt2zDXs6NGjevrpp9WqVatcKw4AAADIjhyF26lTpyopKUkVKlRQ5cqVVblyZVWsWFFJSUmaMmVKbtcIAAAAZEmOzrktV66ctm/frlWrVmnv3r2SpJo1a6p169a5WhwAAACQHdk6crtmzRrVqlVLSUlJstlsatOmjYYMGaIhQ4aoUaNGql27tr755pu8qhUAAAC4qmyF28mTJ6t///4KCQnxGFe0aFENHDhQb731Vq4VBwAAAGRHtsLtDz/8oPbt219xfNu2bRUXF3fdRQEAAAA5ka1we/LkyUxvAZahQIEC+uOPP667KAAAACAnshVub7nlFu3ateuK43/88UeFh4dfd1EAAABATmQr3Hbo0EEvv/yyLly44DHu/PnzGjVqlO69995cKw4AAADIjmzdCuyll17SggULVK1aNQ0ePFjVq1eXJO3du1fTpk1Tenq6XnzxxTwpFAAAALiWbIXbMmXKaOPGjRo0aJBGjBghY4wkyWazqV27dpo2bZrKlCmTJ4UCAAAA15LtH3GIjIzU0qVL9eeff+rAgQMyxqhq1aoqXrx4XtQHAAAAZFmOfqFMkooXL65GjRrlZi0AAADAdcnWBWUAAABAfka4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGV4NdyuX79enTp1UkREhGw2mxYtWuQ23hijkSNHKjw8XMHBwWrdurX279/v1ubMmTN6+OGHFRISomLFiqlfv346d+7cDVwLAAAA5BdeDbfJycmqV6+epk2blun4119/Xe+8847ee+89bdmyRYUKFVK7du104cIFV5uHH35YP/30k1auXKkvv/xS69ev14ABA27UKgAAACAfKeDNhUdHRys6OjrTccYYTZ48WS+99JLuu+8+SdKHH36oMmXKaNGiRerRo4f27NmjZcuWaevWrWrYsKEkacqUKerQoYPeeOMNRURE3LB1AQAAgPd5NdxezaFDh3TixAm1bt3aNaxo0aK64447tGnTJvXo0UObNm1SsWLFXMFWklq3bi273a4tW7bo/vvvz3TeqampSk1NdT1PSkqSJDkcDjkcjizV5x/gl5PVylf8/e1u//qqrPbZ9Qgo4NvbSJIC/Oxu//qqG9HfgXbf3kaSFPDfdQjw8XW5Ef3tLwvsz//7Ray/j19KcyP62+bMt9Eny2ymwP/+dXq5mOuQ3f7OanubMcbkpKDcZrPZtHDhQnXp0kWStHHjRjVt2lTHjh1TeHi4q1337t1ls9n02Wef6bXXXtOsWbO0b98+t3mFhoZqzJgxGjRoUKbLGj16tMaMGeMxfM6cOSpYsGDurRQAAAByRUpKinr27KnExESFhIRcsZ3v//mSAyNGjNDQoUNdz5OSklSuXDm1bdv2qhvrUt07j82r8m4Yf3+7Hu53u2a/v10Oh+/+6ff5FyPzfBntn3o9z5eR1wL87Hq8VS29t3q30tJ9t7+XTR6e58u4a+L4PF9GXguw2/VszTp6Y89OpTl9t7+/HTYiz5fRZb7nwQ5f4y+7+gY30MzzcXL48KG8Rd1G5fkypm/uk+fLyGs2U0CRf3bVr8UXyNguerucHHvizthstc/4pv1a8m24DQsLkySdPHnS7cjtyZMnVb9+fVebU6dOuU138eJFnTlzxjV9ZgIDAxUYGOgx3N/fX/7+/lmqz5GWnqV2vsDhcPr0+mS1z65H2kXf/bC4XFq606fX50b0d6oPh8HLpTmdPr0+N6K/HfLd/d/lHHL69PrciP42dt8Ngy7/fUsb20WfXp/s9ndW2+fbk3MqVqyosLAwrV692jUsKSlJW7ZsUVRUlCQpKipKCQkJiouLc7VZs2aNnE6n7rjjjhteMwAAALzLq0duz507pwMHDrieHzp0SDt27FCJEiVUvnx5PfXUUxo3bpyqVq2qihUr6uWXX1ZERITrvNyaNWuqffv26t+/v9577z05HA4NHjxYPXr04E4JAAAANyGvhttt27apRYsWrucZ58H27t1bsbGxGj58uJKTkzVgwAAlJCTorrvu0rJlyxQUFOSaZvbs2Ro8eLBatWolu92ubt266Z133rnh6wIAAADv82q4bd68ua52swabzaaxY8dq7NgrX7xVokQJzZkzJy/KAwAAgI/Jt+fcAgAAANlFuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWEa+DrejR4+WzWZze9SoUcM1/sKFC4qJiVHJkiVVuHBhdevWTSdPnvRixQAAAPCmfB1uJal27do6fvy46/Htt9+6xj399NNasmSJ5s6dq3Xr1unYsWPq2rWrF6sFAACANxXwdgHXUqBAAYWFhXkMT0xM1Pvvv685c+aoZcuWkqSZM2eqZs2a2rx5s+68884bXSoAAAC8LN+H2/379ysiIkJBQUGKiorS+PHjVb58ecXFxcnhcKh169autjVq1FD58uW1adOmq4bb1NRUpaamup4nJSVJkhwOhxwOR5bq8g/wy+Ea5R/+/na3f31VVvvsegQU8O1tJEkBfna3f33VjejvQLtvbyNJCvjvOgT4+LrciP72lwX25//9ItY//38he1U3or9tznwffa7JZgr871+nl4u5Dtnt76y2txljTE4KuhG++uornTt3TtWrV9fx48c1ZswYHT16VLt27dKSJUvUt29ft5AqSY0bN1aLFi00YcKEK8539OjRGjNmjMfwOXPmqGDBgrm+HgAAALg+KSkp6tmzpxITExUSEnLFdvk63F4uISFBkZGReuuttxQcHJzjcJvZkdty5crp9OnTV91Yl+reeWzOViIf8fe36+F+t2v2+9vlcPjun36ffzEyz5fR/qnX83wZeS3Az67HW9XSe6t3Ky3dd/t72eTheb6MuyaOz/Nl5LUAu13P1qyjN/bsVJrTd/v722Ej8nwZXeZ7HuzwNf6yq29wA808HyeHDx/KW9RtVJ4vY/rmPnm+jLxmMwUU+WdX/Vp8gYztorfLybEn7ozNVvukpCSVKlXqmuHWp47NFytWTNWqVdOBAwfUpk0bpaWlKSEhQcWKFXO1OXnyZKbn6F4qMDBQgYGBHsP9/f3l7++fpVocaenZqj0/czicPr0+We2z65F20Xc/LC6Xlu706fW5Ef2d6sNh8HJpTqdPr8+N6G+HfHf/dzmHnD69Pjeiv43dd8Ogy3/f0sZ20afXJ7v9ndX2PnVyzrlz53Tw4EGFh4erQYMG8vf31+rVq13j9+3bpyNHjigqKsqLVQIAAMBb8vWR22effVadOnVSZGSkjh07plGjRsnPz08PPfSQihYtqn79+mno0KEqUaKEQkJCNGTIEEVFRXGnBAAAgJtUvg63v//+ux566CHFx8erdOnSuuuuu7R582aVLl1akjRp0iTZ7XZ169ZNqampateunaZPn+7lqgEAAOAt+Trcfvrpp1cdHxQUpGnTpmnatGk3qCIAAADkZz51zi0AAABwNYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlWCbcTps2TRUqVFBQUJDuuOMOfffdd94uCQAAADeYJcLtZ599pqFDh2rUqFHavn276tWrp3bt2unUqVPeLg0AAAA3kCXC7VtvvaX+/furb9++qlWrlt577z0VLFhQH3zwgbdLAwAAwA1UwNsFXK+0tDTFxcVpxIgRrmF2u12tW7fWpk2bMp0mNTVVqamprueJiYmSpDNnzsjhcGRtwfaLOS8633AqJSVFkkOyO71dTI7Fx8fn+TLszrQ8X0Zes6fblZKSInt6muxO+vtqCqRZoL/t/+3vtDQVoL+vLsUi+3OTIp13SKK/ryb1rMnzZeQ1m/nr8zs1wClj8931yW5/nz17VpJkzDXW2fi4o0ePGklm48aNbsOHDRtmGjdunOk0o0aNMpJ48ODBgwcPHjx4+Njjt99+u2o29PkjtzkxYsQIDR061PXc6XTqzJkzKlmypGw2mxcru7GSkpJUrlw5/fbbbwoJCfF2Ochj9PfNhf6+udDfN5ebtb+NMTp79qwiIiKu2s7nw22pUqXk5+enkydPug0/efKkwsLCMp0mMDBQgYGBbsOKFSuWVyXmeyEhITfVm+NmR3/fXOjvmwv9fXO5Gfu7aNGi12zj8xeUBQQEqEGDBlq9erVrmNPp1OrVqxUVFeXFygAAAHCj+fyRW0kaOnSoevfurYYNG6px48aaPHmykpOT1bdvX2+XBgAAgBvIEuH2wQcf1B9//KGRI0fqxIkTql+/vpYtW6YyZcp4u7R8LTAwUKNGjfI4RQPWRH/fXOjvmwv9fXOhv6/OZsy17qcAAAAA+AafP+cWAAAAyEC4BQAAgGUQbgEAAGAZhFsAAABYBuH2JjVt2jRVqFBBQUFBuuOOO/Tdd995uyTkkfXr16tTp06KiIiQzWbTokWLvF0S8sj48ePVqFEjFSlSRKGhoerSpYv27dvn7bKQR959913VrVvXdSP/qKgoffXVV94uCzfIP//5T9lsNj311FPeLiXfIdzehD777DMNHTpUo0aN0vbt21WvXj21a9dOp06d8nZpyAPJycmqV6+epk2b5u1SkMfWrVunmJgYbd68WStXrpTD4VDbtm2VnJzs7dKQB8qWLat//vOfiouL07Zt29SyZUvdd999+umnn7xdGvLY1q1b9a9//Ut169b1din5ErcCuwndcccdatSokaZOnSrpr190K1eunIYMGaLnn3/ey9UhL9lsNi1cuFBdunTxdim4Af744w+FhoZq3bp1uueee7xdDm6AEiVKaOLEierXr5+3S0EeOXfunG6//XZNnz5d48aNU/369TV58mRvl5WvcOT2JpOWlqa4uDi1bt3aNcxut6t169batGmTFysDkNsSExMl/RV4YG3p6en69NNPlZyczE/PW1xMTIw6duzo9jkOd5b4hTJk3enTp5Wenu7x621lypTR3r17vVQVgNzmdDr11FNPqWnTprr11lu9XQ7yyM6dOxUVFaULFy6ocOHCWrhwoWrVquXtspBHPv30U23fvl1bt271din5GuEWACwoJiZGu3bt0rfffuvtUpCHqlevrh07digxMVHz5s1T7969tW7dOgKuBf3222/6xz/+oZUrVyooKMjb5eRrhNubTKlSpeTn56eTJ0+6DT958qTCwsK8VBWA3DR48GB9+eWXWr9+vcqWLevtcpCHAgICVKVKFUlSgwYNtHXrVr399tv617/+5eXKkNvi4uJ06tQp3X777a5h6enpWr9+vaZOnarU1FT5+fl5scL8g3NubzIBAQFq0KCBVq9e7RrmdDq1evVqztMCfJwxRoMHD9bChQu1Zs0aVaxY0dsl4QZzOp1KTU31dhnIA61atdLOnTu1Y8cO16Nhw4Z6+OGHtWPHDoLtJThyexMaOnSoevfurYYNG6px48aaPHmykpOT1bdvX2+Xhjxw7tw5HThwwPX80KFD2rFjh0qUKKHy5ct7sTLktpiYGM2ZM0eLFy9WkSJFdOLECUlS0aJFFRwc7OXqkNtGjBih6OholS9fXmfPntWcOXP09ddfa/ny5d4uDXmgSJEiHufPFypUSCVLluS8+ssQbm9CDz74oP744w+NHDlSJ06cUP369bVs2TKPi8xgDdu2bVOLFi1cz4cOHSpJ6t27t2JjY71UFfLCu+++K0lq3ry52/CZM2eqT58+N74g5KlTp06pV69eOn78uIoWLaq6detq+fLlatOmjbdLA7yK+9wCAADAMjjnFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgCyyWazadGiRd4uAwCQCcItAFzixIkTGjJkiCpVqqTAwECVK1dOnTp10urVq71dmpukpCS9+OKLqlGjhoKCghQWFqbWrVtrwYIFyuoPT3799dey2WxKSEjI22IB4AYq4O0CACC/OHz4sJo2bapixYpp4sSJqlOnjhwOh5YvX66YmBjt3bvX2yVKkhISEnTXXXcpMTFR48aNU6NGjVSgQAGtW7dOw4cPV8uWLVWsWDFvl5ltaWlpCggI8HYZAHwcR24B4L+eeOIJ2Ww2fffdd+rWrZuqVaum2rVra+jQodq8efMVp9u5c6datmyp4OBglSxZUgMGDNC5c+dc47/++ms1btxYhQoVUrFixdS0aVP9+uuvrvGLFy/W7bffrqCgIFWqVEljxozRxYsXr7i8F154QYcPH9aWLVvUu3dv1apVS9WqVVP//v21Y8cOFS5cWJL00UcfqWHDhipSpIjCwsLUs2dPnTp1StJfQb5FixaSpOLFi8tms6lPnz6SJKfTqfHjx6tixYoKDg5WvXr1NG/ePLcavvjiC1WtWlVBQUFq0aKFZs2a5XEUeP78+apdu7YCAwNVoUIFvfnmm27zqFChgl555RX16tVLISEhGjBggFq2bKnBgwe7tfvjjz8UEBCQ746eA8inDADAxMfHG5vNZl577bVrtpVkFi5caIwx5ty5cyY8PNx07drV7Ny506xevdpUrFjR9O7d2xhjjMPhMEWLFjXPPvusOXDggNm9e7eJjY01v/76qzHGmPXr15uQkBATGxtrDh48aFasWGEqVKhgRo8enemy09PTTfHixc2AAQOuWef7779vli5dag4ePGg2bdpkoqKiTHR0tDHGmIsXL5r58+cbSWbfvn3m+PHjJiEhwRhjzLhx40yNGjXMsmXLzMGDB83MmTNNYGCg+frrr40xxvzyyy/G39/fPPvss2bv3r3mk08+MbfccouRZP78809jjDHbtm0zdrvdjB071uzbt8/MnDnTBAcHm5kzZ7rqi4yMNCEhIeaNN94wBw4cMAcOHDCzZ882xYsXNxcuXHC1e+utt0yFChWM0+m85joDAOEWAIwxW7ZsMZLMggULrtn20nA7Y8YMU7x4cXPu3DnX+P/85z/GbrebEydOmPj4eCPJFQwv16pVK49A/dFHH5nw8PBM2588edJIMm+99VYW1+x/tm7daiSZs2fPGmOMWbt2rVsgNcaYCxcumIIFC5qNGze6TduvXz/z0EMPGWOMee6558ytt97qNv7FF190m1fPnj1NmzZt3NoMGzbM1KpVy/U8MjLSdOnSxa3N+fPnTfHixc1nn33mGla3bt0rhn0AuBynJQCAlOWLsC63Z88e1atXT4UKFXINa9q0qZxOp/bt26cSJUqoT58+ateunTp16qS3335bx48fd7X94YcfNHbsWBUuXNj16N+/v44fP66UlJTrqjMuLk6dOnVS+fLlVaRIETVr1kySdOTIkStOc+DAAaWkpKhNmzZuNX344Yc6ePCgJGnfvn1q1KiR23SNGzf22C5NmzZ1G9a0aVPt379f6enprmENGzZ0axMUFKRHHnlEH3zwgSRp+/bt2rVrl+uUCQC4Fi4oAwBJVatWlc1my5OLxmbOnKknn3xSy5Yt02effaaXXnpJK1eu1J133qlz585pzJgx6tq1q8d0QUFBHsNKly6tYsWKXbPO5ORktWvXTu3atdPs2bNVunRpHTlyRO3atVNaWtoVp8s4V/g///mPbrnlFrdxgYGBWVndbLn0j4IMjz32mOrXr6/ff/9dM2fOVMuWLRUZGZnrywZgTRy5BQBJJUqUULt27TRt2jQlJyd7jL/S7bJq1qypH374wW2aDRs2yG63q3r16q5ht912m0aMGKGNGzfq1ltv1Zw5cyRJt99+u/bt26cqVap4POx2z1203W5Xjx49NHv2bB07dsxj/Llz53Tx4kXt3btX8fHx+uc//6m7775bNWrUcF1MliHjzgSXHkmtVauWAgMDdeTIEY96ypUrJ0mqXr26tm3b5javrVu3emyXDRs2uA3bsGGDqlWrJj8/v0y3ZYY6deqoYcOG+ve//605c+bo0UcfvWp7AHDj7fMiACC/OHjwoAkLCzO1atUy8+bNMz///LPZvXu3efvtt02NGjVc7XTJObfJyckmPDzcdOvWzezcudOsWbPGVKpUyXVB2S+//GKef/55s3HjRnP48GGzfPlyU7JkSTN9+nRjjDHLli0zBQoUMKNHjza7du0yu3fvNp988ol58cUXr1hnfHy8qVGjhilbtqyZNWuW+emnn8zPP/9s3n//fVOlShXz559/mlOnTpmAgAAzbNgwc/DgQbN48WJTrVo1I8l8//33xhhjfv/9d2Oz2UxsbKw5deqU61zcF1980ZQsWdLExsaaAwcOmLi4OPPOO++Y2NhY1zr5+/ub4cOHm3379pnPPvvMlC1b1khyXZQWFxfndkFZbGxspheUTZo0KdN1nDFjhgkICDDFixc358+fz25XAriJEW4B4BLHjh0zMTExJjIy0gQEBJhbbrnFdO7c2axdu9bV5tJwa4wxP/74o2nRooUJCgoyJUqUMP3793cFxRMnTpguXbqY8PBwExAQYCIjI83IkSNNenq6a/ply5aZJk2amODgYBMSEmIaN25sZsyYcdU6ExISzPPPP2+qVq1qAgICTJkyZUzr1q3NwoULXXcVmDNnjqlQoYIJDAw0UVFR5osvvnALt8YYM3bsWBMWFmZsNpsrkDudTjN58mRTvXp14+/vb0qXLm3atWtn1q1b55pu8eLFpkqVKiYwMNA0b97cvPvuu0aSWxCdN2+eqVWrlvH39zfly5c3EydOdFuHq4Xbs2fPmoIFC5onnnjiqtsBAC5nMyaHV1EAAPBfr776qt577z399ttvuTK/w4cPq3Llytq6datuv/32XJkngJsDF5QBALJt+vTpatSokUqWLKkNGzZo4sSJHj++kBMOh0Px8fF66aWXdOeddxJsAWQb4RYAkG379+/XuHHjdObMGZUvX17PPPOMRowYcd3z3bBhg1q0aKFq1ap5/CoaAGQFpyUAAADAMrgVGAAAACyDcAsAAADLINwCAADAMgi3AAAAsAzCLQAAACyDcAsAAADLINwCAADAMgi3AAAAsIz/B1YkzrN9Bg6GAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Проверка необходимости аугментации для признака 'Close_category' в обучающей выборке:\n",
      "Минимальное количество наблюдений в классе: 1092\n",
      "Максимальное количество наблюдений в классе: 1157\n",
      "Выборка 'обучающей' сбалансирована, аугментация не требуется.\n",
      "\n",
      "Проверка необходимости аугментации для признака 'Close_category' в валидационной выборке:\n",
      "Минимальное количество наблюдений в классе: 224\n",
      "Максимальное количество наблюдений в классе: 263\n",
      "Выборка 'валидационной' сбалансирована, аугментация не требуется.\n",
      "\n",
      "Проверка необходимости аугментации для признака 'Close_category' в тестовой выборке:\n",
      "Минимальное количество наблюдений в классе: 225\n",
      "Максимальное количество наблюдений в классе: 253\n",
      "Выборка 'тестовой' сбалансирована, аугментация не требуется.\n",
      "\n",
      "Распределение классов после SMOTE (oversampling):\n",
      "Close_category\n",
      "0    1157\n",
      "1    1157\n",
      "2    1157\n",
      "3    1157\n",
      "4    1157\n",
      "Name: count, dtype: int64\n",
      "Распределение классов после RandomUnderSampler (undersampling):\n",
      "Close_category\n",
      "0    1092\n",
      "1    1092\n",
      "2    1092\n",
      "3    1092\n",
      "4    1092\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "# Логарифмирование целевой переменной\n",
    "df['Close_log'] = np.log(df['Close'])\n",
    "\n",
    "# Создание категорий для целевой переменной\n",
    "df['Close_category'] = pd.qcut(df['Close_log'], q=5, labels=[0, 1, 2, 3, 4])\n",
    "\n",
    "# Выбор признаков и целевой переменной\n",
    "X = df.drop(['Close', 'Close_log', 'Close_category'], axis=1)\n",
    "y = df['Close_category']\n",
    "\n",
    "# Разделение данных на обучающую, валидационную и тестовую выборки\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n",
    "\n",
    "def analyze_close_category_distribution(data, name):\n",
    "    \"\"\"Проверка и визуализация распределения признака 'Close_category'\"\"\"\n",
    "    category_counts = data.value_counts()\n",
    "    print(f\"Распределение 'Close_category' в {name} выборке:\\n\", category_counts)\n",
    "\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    sns.barplot(x=category_counts.index, y=category_counts.values, palette='viridis')\n",
    "    plt.title(f\"Распределение признака 'Close_category' в {name} выборке\")\n",
    "    plt.xlabel('Close Category')\n",
    "    plt.ylabel('Count')\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "analyze_close_category_distribution(y_train, 'обучающей')\n",
    "analyze_close_category_distribution(y_val, 'валидационной')\n",
    "analyze_close_category_distribution(y_test, 'тестовой')\n",
    "\n",
    "def check_close_category_augmentation(data, name):\n",
    "    print(f\"Проверка необходимости аугментации для признака 'Close_category' в {name} выборке:\")\n",
    "    min_count = data.value_counts().min()\n",
    "    max_count = data.value_counts().max()\n",
    "    print(f\"Минимальное количество наблюдений в классе: {min_count}\")\n",
    "    print(f\"Максимальное количество наблюдений в классе: {max_count}\")\n",
    "\n",
    "    if max_count > min_count * 1.5:\n",
    "        print(f\"Выборка '{name}' несбалансирована, рекомендуется аугментация.\\n\")\n",
    "    else:\n",
    "        print(f\"Выборка '{name}' сбалансирована, аугментация не требуется.\\n\")\n",
    "\n",
    "check_close_category_augmentation(y_train, 'обучающей')\n",
    "check_close_category_augmentation(y_val, 'валидационной')\n",
    "check_close_category_augmentation(y_test, 'тестовой')\n",
    "\n",
    "# Применение SMOTE для oversampling\n",
    "smote = SMOTE(random_state=42)\n",
    "X_train_smote, y_train_smote = smote.fit_resample(X_train, y_train)\n",
    "\n",
    "print(\"Распределение классов после SMOTE (oversampling):\")\n",
    "print(pd.Series(y_train_smote).value_counts())\n",
    "\n",
    "# Применение RandomUnderSampler для undersampling\n",
    "undersampler = RandomUnderSampler(random_state=42)\n",
    "X_train_under, y_train_under = undersampler.fit_resample(X_train, y_train)\n",
    "\n",
    "print(\"Распределение классов после RandomUnderSampler (undersampling):\")\n",
    "print(pd.Series(y_train_under).value_counts())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В этом исследование данные сбалансированы, поэтому аугментация не требуется ."
   ]
  }
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