{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 Датасет: NASA - Nearest Earth Objects\n",
    "#### https://www.kaggle.com/datasets/sameepvani/nasa-nearest-earth-objects \n",
    "\n",
    "There is an infinite number of objects in the outer space. Some of them are closer than we think. Even though we might think that a distance of 70,000 Km can not potentially harm us, but at an astronomical scale, this is a very small distance and can disrupt many natural phenomena. These objects/asteroids can thus prove to be harmful. Hence, it is wise to know what is surrounding us and what can harm us amongst those. Thus, this dataset compiles the list of NASA certified asteroids that are classified as the nearest earth object.\n",
    "\n",
    "В космосе находится бесконечное количество объектов. Некоторые из них находятся ближе, чем мы думаем. Хотя мы можем думать, что расстояние в 70 000 км не может потенциально навредить нам, но в астрономических масштабах это очень малое расстояние и может нарушить многие природные явления. Таким образом, эти объекты/астероиды могут оказаться вредными. Следовательно, разумно знать, что нас окружает и что из этого может навредить нам. Таким образом, этот набор данных составляет список сертифицированных NASA астероидов, которые классифицируются как ближайшие к Земле объекты.\n",
    "\n",
    "- Из этого описания очевидно что объектами иследования являются околоземные объекты.\n",
    "- Атрибуты объектов: id, name, est_diameter_min, est_diameter_max, relative_velocity, miss_distance, orbiting_body, sentry_object, absolute_magnitude, hazardous\n",
    "- Очевидная цель этого датасета - это научиться определять опасность объекта автоматически.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "количество колонок: 10\n",
      "колонки: id, name, est_diameter_min, est_diameter_max, relative_velocity, miss_distance, orbiting_body, sentry_object, absolute_magnitude, hazardous\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"..//static//csv//neo_v2.csv\", sep=\",\")\n",
    "print('количество колонок: ' + str(df.columns.size))  \n",
    "print('колонки: ' + ', '.join(df.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Получение сведений о пропущенных данных\n",
    "\n",
    "Типы пропущенных данных:\n",
    "- None - представление пустых данных в Python\n",
    "- NaN - представление пустых данных в Pandas\n",
    "- '' - пустая строка"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id                    0\n",
      "name                  0\n",
      "est_diameter_min      0\n",
      "est_diameter_max      0\n",
      "relative_velocity     0\n",
      "miss_distance         0\n",
      "orbiting_body         0\n",
      "sentry_object         0\n",
      "absolute_magnitude    0\n",
      "hazardous             0\n",
      "dtype: int64\n",
      "\n",
      "id                    False\n",
      "name                  False\n",
      "est_diameter_min      False\n",
      "est_diameter_max      False\n",
      "relative_velocity     False\n",
      "miss_distance         False\n",
      "orbiting_body         False\n",
      "sentry_object         False\n",
      "absolute_magnitude    False\n",
      "hazardous             False\n",
      "dtype: bool\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Количество пустых значений признаков\n",
    "print(df.isnull().sum())\n",
    "\n",
    "print()\n",
    "\n",
    "# Есть ли пустые значения признаков\n",
    "print(df.isnull().any())\n",
    "\n",
    "print()\n",
    "\n",
    "# Процент пустых значений признаков\n",
    "for i in df.columns:\n",
    "    null_rate = df[i].isnull().sum() / len(df) * 100\n",
    "    if null_rate > 0:\n",
    "        print(f\"{i} процент пустых значений: %{null_rate:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Итог: пропущеных значений нет"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "инфографика на сайте и в datawrangelere показывает, что в столбцах orbiting_body и sentry_object у всех записей одно и тоже значение. Значит эти столбцы можно выкинуть из набора данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "количество колонок: 8\n",
      "колонки: id, name, est_diameter_min, est_diameter_max, relative_velocity, miss_distance, absolute_magnitude, hazardous\n"
     ]
    }
   ],
   "source": [
    "df = df.drop(columns=['orbiting_body'])\n",
    "df = df.drop(columns=['sentry_object'])\n",
    "print('количество колонок: ' + str(df.columns.size))  \n",
    "print('колонки: ' + ', '.join(df.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "я быстро посмотрев данные зашумленности не выявил"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "поля id и name в предсказании не помогут, но я их пока выкидывать не буду"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "посмотрим выбросы:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Колонка est_diameter_min:\n",
      "  Есть выбросы: Да\n",
      "  Количество выбросов: 8306\n",
      "  Минимальное значение: 0.0006089126\n",
      "  Максимальное значение: 37.8926498379\n",
      "  1-й квартиль (Q1): 0.0192555078\n",
      "  3-й квартиль (Q3): 0.1434019235\n",
      "\n",
      "Колонка est_diameter_max:\n",
      "  Есть выбросы: Да\n",
      "  Количество выбросов: 8306\n",
      "  Минимальное значение: 0.00136157\n",
      "  Максимальное значение: 84.7305408852\n",
      "  1-й квартиль (Q1): 0.0430566244\n",
      "  3-й квартиль (Q3): 0.320656449\n",
      "\n",
      "Колонка relative_velocity:\n",
      "  Есть выбросы: Да\n",
      "  Количество выбросов: 1574\n",
      "  Минимальное значение: 203.34643253\n",
      "  Максимальное значение: 236990.1280878666\n",
      "  1-й квартиль (Q1): 28619.02064490995\n",
      "  3-й квартиль (Q3): 62923.60463276395\n",
      "\n",
      "Колонка miss_distance:\n",
      "  Есть выбросы: Нет\n",
      "  Количество выбросов: 0\n",
      "  Минимальное значение: 6745.532515957\n",
      "  Максимальное значение: 74798651.4521972\n",
      "  1-й квартиль (Q1): 17210820.23576468\n",
      "  3-й квартиль (Q3): 56548996.45139917\n",
      "\n",
      "Колонка absolute_magnitude:\n",
      "  Есть выбросы: Да\n",
      "  Количество выбросов: 101\n",
      "  Минимальное значение: 9.23\n",
      "  Максимальное значение: 33.2\n",
      "  1-й квартиль (Q1): 21.34\n",
      "  3-й квартиль (Q3): 25.7\n",
      "\n"
     ]
    }
   ],
   "source": [
    "numeric_columns = ['est_diameter_min', 'est_diameter_max', 'relative_velocity', 'miss_distance', 'absolute_magnitude']\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(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": [
    "построим графики в надежде найти какие то зависимости опасности от других колонок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Список числовых колонок, для которых мы будем строить графики\n",
    "numeric_columns = ['est_diameter_min', 'est_diameter_max', 'relative_velocity', 'miss_distance', 'absolute_magnitude']\n",
    "\n",
    "# Создание диаграмм зависимости\n",
    "for column in numeric_columns:\n",
    "    plt.figure(figsize=(4, 8))  # Установка размера графика\n",
    "    plt.scatter(df['hazardous'], df[column], alpha=0.5)  # Создаем диаграмму рассеяния\n",
    "    plt.title(f'Зависимость {column} от hazardous')\n",
    "    plt.xlabel('hazardous (0 = нет, 1 = да)')\n",
    "    plt.ylabel(column)\n",
    "    plt.xticks([0, 1])  # Установка меток по оси X\n",
    "    plt.grid()  # Добавление сетки для удобства восприятия\n",
    "    plt.show()  # Отображение графика"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создадим выборки данных. разбивать будем относительно параметра опасный, ведь это тот самый параметр по которому наша выборка разбивается на классы. И собственно его нам и надо будет предсказывать"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scikit-learn in d:\\мии\\aim-pibd-31-kouvshinoff-t-a\\laba\\lib\\site-packages (1.5.2)\n",
      "Requirement already satisfied: numpy>=1.19.5 in d:\\мии\\aim-pibd-31-kouvshinoff-t-a\\laba\\lib\\site-packages (from scikit-learn) (2.1.1)\n",
      "Requirement already satisfied: scipy>=1.6.0 in d:\\мии\\aim-pibd-31-kouvshinoff-t-a\\laba\\lib\\site-packages (from scikit-learn) (1.14.1)\n",
      "Requirement already satisfied: joblib>=1.2.0 in d:\\мии\\aim-pibd-31-kouvshinoff-t-a\\laba\\lib\\site-packages (from scikit-learn) (1.4.2)\n",
      "Requirement already satisfied: threadpoolctl>=3.1.0 in d:\\мии\\aim-pibd-31-kouvshinoff-t-a\\laba\\lib\\site-packages (from scikit-learn) (3.5.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Функция для создания выборок\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "def split_stratified_into_train_val_test(\n",
    "    df_input,\n",
    "    stratify_colname=\"y\",\n",
    "    frac_train=0.6,\n",
    "    frac_val=0.15,\n",
    "    frac_test=0.25,\n",
    "    random_state=None,\n",
    "):\n",
    "    \"\"\"\n",
    "    Splits a Pandas dataframe into three subsets (train, val, and test)\n",
    "    following fractional ratios provided by the user, where each subset is\n",
    "    stratified by the values in a specific column (that is, each subset has\n",
    "    the same relative frequency of the values in the column). It performs this\n",
    "    splitting by running train_test_split() twice.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    df_input : Pandas dataframe\n",
    "        Input dataframe to be split.\n",
    "    stratify_colname : str\n",
    "        The name of the column that will be used for stratification. Usually\n",
    "        this column would be for the label.\n",
    "    frac_train : float\n",
    "    frac_val   : float\n",
    "    frac_test  : float\n",
    "        The ratios with which the dataframe will be split into train, val, and\n",
    "        test data. The values should be expressed as float fractions and should\n",
    "        sum to 1.0.\n",
    "    random_state : int, None, or RandomStateInstance\n",
    "        Value to be passed to train_test_split().\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    df_train, df_val, df_test :\n",
    "        Dataframes containing the three splits.\n",
    "    \"\"\"\n",
    "\n",
    "    if frac_train + frac_val + frac_test != 1.0:\n",
    "        raise ValueError(\n",
    "            \"fractions %f, %f, %f do not add up to 1.0\"\n",
    "            % (frac_train, frac_val, frac_test)\n",
    "        )\n",
    "\n",
    "    if stratify_colname not in df_input.columns:\n",
    "        raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
    "\n",
    "    X = df_input  # Contains all columns.\n",
    "    y = df_input[\n",
    "        [stratify_colname]\n",
    "    ]  # Dataframe of just the column on which to stratify.\n",
    "\n",
    "    # Split original dataframe into train and temp dataframes.\n",
    "    df_train, df_temp, y_train, y_temp = train_test_split(\n",
    "        X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
    "    )\n",
    "\n",
    "    # Split the temp dataframe into val and test dataframes.\n",
    "    relative_frac_test = frac_test / (frac_val + frac_test)\n",
    "    df_val, df_test, y_val, y_test = train_test_split(\n",
    "        df_temp,\n",
    "        y_temp,\n",
    "        stratify=y_temp,\n",
    "        test_size=relative_frac_test,\n",
    "        random_state=random_state,\n",
    "    )\n",
    "\n",
    "    assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
    "\n",
    "    return df_train, df_val, df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hazardous\n",
      "False    81996\n",
      "True      8840\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Обучающая выборка:  (54501, 6)\n",
      "hazardous\n",
      "False    49197\n",
      "True      5304\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Контрольная выборка:  (18167, 6)\n",
      "hazardous\n",
      "False    16399\n",
      "True      1768\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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Li9N6WaB032VkZCAhIQGrVq2CQCCAj49PtZaVSqUYOnQo0tPTERUVpaqlVfrnn3/g5eWFnj17qqYZGRlh6tSpSEpKUjUdKeXm5mqct7UVHBys+j+Px0NwcDCKi4tx7NgxVYwCgQAzZ85UW2727NlgjOHQoUNq06t7fgJAdnY2MjIykJubW+V85be5/Pzann9lzZs3D506dcKoUaNeGa9Sdc9f5XU2MzOz2veZCAwMVPucuLq6YujQoYiKioJcLodcLseRI0cQEBCAli1bquazs7PDuHHjEB0djZycHLUyp06dqlZT/dFHH0FPT091vpUVGRmJ6dOn4/PPP1f7bACl18KsrCyMHTtW7XgIBAJ07dq12td5oIbNH+vXr4ebmxv09PTQrFkztGrVSu3CrVAosGbNGmzYsAGJiYlqbcJld2p8fDxatWqlqjLUFVdXV7X3PB4PLi4uqjY15UVo0qRJlZaRnZ0Nc3Nz1fuMjAyNcsuLi4sDY6zS+co3U2RlZQFAlfcniIuLQ3Z2tqrKurznz5+rvT9y5Ijahb86Fi5cCHt7e0ybNk2jbTcuLg53796ttMzy669ISkoK/P39kZ+fj8zMzEoTxursDwA4cOAAlixZgpiYGLX2+LLlxsfHg8/no02bNpWWo2y28/DwqHSe5ORkAECrVq00/ubu7o7o6Gi1aY8fP1bbV3Z2dti3b1+17kEhFos19rO5ublGG2l1tr+sKVOmID4+HufPn9f48gsLC8MPP/yAe/fuoaSkRDXdyclJo5zy09LT01FYWFjh571Vq1ZqF7bk5GTw+Xy4uLiozWdrawszMzPVftbG4sWL8e6778LNzQ0eHh4YNGgQ3n//fbRv375ay3fq1En1f5FIhHXr1lXY7FmRwMBA/PvvvxCLxRV+qSQnJ2tUVQNQfTklJyerfe4++OCDaq23uvh8vtoXEwBVFbvyOpicnAx7e3u1H2DlYyyruucnALUmJDMzM4wdOxbLly9X6zSZn5//ymuVtuefUnR0NP7++28cP34cjx49emW8StU9f8teZwUCAdq3b4/vv/9e1axalvK8dHd31/hb69atsW/fPmRkZIAxhoKCggq3tXXr1lAoFHj8+DHatm2rml7+3DMyMoKdnZ1G/5GYmBjs3bsXcrkcL1680Chf+Z2o/LFYnomJSYXTK1Kjb3MvLy/V6I+KfPfdd5g/fz4++OADfPPNN7CwsACfz8esWbM0agC4oIxh+fLlGu3GSmU/RMXFxUhNTX1lO61CoQCPx8OhQ4dUvwYrKxMAnj17BqD0wlpVmTY2NpXewKX8Sdm1a1csWbJEbdq6desQGRlZ4fJ3797F9u3bsWvXrgr7ZigUCrRr1w4rV66scPnmzZtXGrvSw4cP0alTJ6xatQrvv/8+wsLCKkzoqrM/zp49i6FDh6JXr17YsGED7OzsIBQKsW3bNo3OlVxo1qwZdu3aBaA0Md26dSsGDRqE6OhotGvXrsplK/rMlKft9q9ZswZ79uzBrl27ND7ru3btwuTJkxEQEIDPP/8cNjY2EAgECA0NVSVcZZWtIamp2tRAlu+w2qtXL8THxyMyMhJHjhzBL7/8glWrVmHTpk0ICgp6ZXm7du1Cs2bNIJVKceLECXz88ccQi8Uandoqcu3aNURGRiI4OBhTp07FiRMnarpZAErb/pX9iJTeeeedWpWpa8+ePYORkVG1RlMof3gWFRXh1KlTWLFiBQBgw4YNqnnEYjH+/vtvteXOnj2r6vNRG1988QV8fX3Rt29ftZthvUp1z9+y19mnT59i6dKlGDZsWIWdGnVx3tRWbGws/Pz80K9fP3z++eeYMGGCWn8K5Xfizp07K7z+avPDX7dVBP8VERGBPn36YMuWLWrTs7KyVB18gNIeuhcvXkRJSYlOOhsqla8OZYzh4cOHql8wyg6gJiYm1eqUFRsbi5KSkioTKWW5jDE4OTmpfhVU5c6dO+DxeBVmpmXLPHbsGHr06FGtD6eVlZXGNlXVmXLevHl466238N5771W6/tjYWPTr16/GXwjKpqdmzZohMjISs2fPxuDBgzUSoursj3379kEsFuPw4cNq1bDbtm3TiFuhUODOnTuVJo7Kz8GtW7c0fkErOTo6AijtwV0+i79//77q70pisVht/w8dOhQWFhZYt25dpaMitFHd7QdKL9Bz5szBrFmzMH78eI2/R0REoGXLlvjjjz/Uju3ChQurFYu1tTUMDAwqbH64f/++2ntHR0coFArExcWpVSWnpaUhKytLbT+am5urfhUrKRP78iwsLBAYGIjAwEDk5eWhV69eCAkJqVZS0aNHD1XnsyFDhuD27dsIDQ2tVlLxyy+/YOjQoRAIBBgyZAi2bNmC//znP2rbW34fAMC9e/dUfy+rXbt2GudtdZLMyigUCiQkJKhdhx48eAAAqm12dHTEsWPHkJubq1ZbUVmMd+7cqXYzXtkfnv7+/oiNjUVUVJTaPAKBQGObyx93bc8/oPR6d+HChUqbA6tS3fO3/HXWxcUFPXr0wJkzZ9SaAoH/1fBV9nmQSCSq70VDQ8NK5+Pz+Ro/4uLi4tCnTx/V+7y8PKSmpmoMamjXrh3Cw8NhYGCA8PBwTJ06FTdu3FA1KSmvhTY2Nlp1VK5IndymWyAQaAxJCg8P1xgCNGLECGRkZGDdunUaZZRfXhs7duxQa5uLiIhAamoq/Pz8AACenp5wdnbGihUrkJeXp7F8enq6RuzKi0dVhg8fDoFAgEWLFmnEzxhT9QIGSofX7du3D15eXlVWJ44ePRpyuRzffPONxt9kMpnGSaiNCxcuIDIyEt9//32lCcPo0aORkpKCn3/+WeNvhYWFyM/Pf+V63NzcVKMB1q5dC4VCgU8++URtnuruD4FAAB6Pp/arNSkpSSNxCggIAJ/Px+LFizVqx5THZuDAgTA2NkZoaKhGvxPlPJ07d4aNjQ02bdqk1tRw6NAh3L17V220SUWKi4shk8mqNfy2Oqq7/ampqRg9ejR69uxZaR8W5ZdW2c/qxYsXceHChWrH4uvri/3796tVMd+9exeHDx9Wm1d5kVu9erXadGUNWNn96OzsjDNnzqjN99NPP2nUVJQ9n4DSmkAXF5ca7+vCwsJqL6usVfD398eYMWPw+eefq91VePDgwbh06ZLavszPz8dPP/2EFi1aVNkspytlr6uMMaxbtw5CoRD9+vVTxSiXyzWuv6tWrQKPx1NdL4HSZoFz585VWj3+KgqFokZJkrbnn1wux1dffYVx48ZV+mNCG9U9f5XXmIq20draGp07d0ZYWJhaU2Z8fDz++usv+Pn5QSAQQCAQYODAgYiMjFRrvkhLS8Ovv/6Knj17ajRD/PTTT2rNlhs3boRMJlM7dkBpU59EIgGfz8cvv/yCpKQktRohX19fmJiY4LvvvlMrT6n8d2JV6qSmYsiQIVi8eDECAwPRvXt33Lx5E7t379Zo45s4cSJ27NiBzz77DJcuXYK3tzfy8/Nx7NgxTJ8+He+++26N1m9hYYGePXsiMDAQaWlpWL16NVxcXDBlyhQAUO1YPz8/tG3bFoGBgXBwcEBKSgpOnjwJExMT/P3338jPz8f69evx448/ws3NTW1MrzIZuXHjBi5cuIBu3brB2dkZS5Yswbx585CUlISAgAAYGxsjMTERf/75J6ZOnYo5c+bg2LFjmD9/Pm7cuKFR/Veej48Ppk2bhtDQUMTExGDgwIEQCoWIi4tDeHg41qxZg5EjR9ZoPx05cgQDBgyoMjN9//33sXfvXnz44Yc4efIkevToAblcjnv37mHv3r04fPjwK2twyrK1tcXy5csRFBSECRMmYPDgwVrtD39/f6xcuRKDBg3CuHHj8Pz5c6xfvx4uLi64ceOGaj4XFxd8/fXX+Oabb+Dt7Y3hw4dDJBLh8uXLsLe3R2hoKExMTLBq1SoEBQWhS5cuGDduHMzNzREbG4uCggKEhYVBKBRi6dKlCAwMhI+PD8aOHasa0taiRQt8+umnavHl5+erVZ/u3LkTUqlU6yGEtd3+mTNnIj09HXPnzsVvv/2mVkb79u3Rvn17DBkyBH/88QeGDRsGf39/JCYmYtOmTWjTpk2FyXZFFi1ahKioKHh7e2P69OmQyWRYu3Yt2rZtqxZPhw4dMGnSJPz000/IysqCj48PLl26hLCwMAQEBKj92goKCsKHH36IESNGYMCAAYiNjcXhw4fVajkBoE2bNujduzc8PT1hYWGBK1euICIiQqMTWmX2798PKysrVfPH2bNnMWvWrGotW9aaNWvQunVrzJgxA3v37gUAfPnll9izZw/8/Pwwc+ZMWFhYICwsDImJidi3b59G52FdE4vFiIqKwqRJk9C1a1ccOnQIBw8exFdffaWqIXznnXfQp08ffP3110hKSkKHDh1w5MgRREZGYtasWapfrxs3bkRoaCgMDQ01OnVW5sKFC8jIyFA1fxw/fhxz5szReju0Pf+ePHkCfX39CjsqVkd1z19lB12gNIFfunQpTE1N0adPH1WNUFnLli3DwIED0a1bNwQFBamGlIrFYrVhuUuWLMHRo0fRs2dPTJ8+HXp6eti8eTOKioqwbNkyjXKLi4vRr18/jB49Gvfv38eGDRvQs2dPDB06tNJt9PDwwBdffIHvv/8eY8aMQfv27WFiYoKNGzfi/fffR6dOnTBmzBhYW1vj0aNHOHjwIHr06FHhj/8KVXucCKv+cCGpVMpmz57N7OzsmIGBAevRowe7cOFChcOgCgoK2Ndff82cnJyYUChktra2bOTIkaohNTUZUrpnzx42b948ZmNjwwwMDJi/v7/aMCil69evs+HDhzNLS0smEomYo6MjGz16NDt+/Ljaul/1Kj/sa9++faxnz55MIpEwiUTC3N3d2ccff8zu37/PGGNsxowZrFevXiwqKkojpoqGUDJWOrTJ09OTGRgYMGNjY9auXTs2d+5c9vTpU9U82g4p5fF47OrVq2rTKzpGxcXFbOnSpaxt27ZMJBIxc3Nz5unpyRYtWsSys7M11veq8hhjrG/fvuzNN99kubm5Wu+PLVu2MFdXVyYSiZi7uzvbtm1bpftt69atrGPHjqq4fXx82NGjR9Xm+euvv1j37t2ZgYEBMzExYV5eXmzPnj1q8/z++++qciwsLNj48eNVQ6iVlEP5lC8jIyPWqVMntnPnzir3kXLZiobY1XT7fXx8Kv28KodrKhQK9t133zFHR0cmEolYx44d2YEDBzSG1lV1DjLG2OnTp5mnpyfT19dnLVu2ZJs2baow7pKSErZo0SLVud68eXM2b948JpVK1eaTy+Xsiy++YFZWVszQ0JD5+vqyhw8fagwpXbJkCfPy8mJmZmbMwMCAubu7s2+//VZtiF1FlNcx5UtfX5+5uLiwBQsWaMRS2bLlhyeGhYUxAOyvv/5STYuPj2cjR45kZmZmTCwWMy8vL3bgwAG15epqSKlEImHx8fFs4MCBzNDQkDVr1owtXLhQY3h1bm4u+/TTT5m9vT0TCoXM1dWVLV++XG2ovZeXFxs1ahS7d+/eK+Ooat+WHb5c3SGlStqcf5988ona9MqOWUX7rTrnr6Ojo9p8VlZWbODAgezff/9ljGkOKVU6fvw469Gjh+o64+/vz27evKkRx7Vr15ivry8zMjJihoaGrE+fPuz8+fMVbtPp06fZ1KlTmbm5OTMyMmLjx49nmZmZGvGW/xxJpVLm7u7OunTpohpirozd19eXmZqaMrFYzJydndnkyZPZlStXqtx3ZfEYq0U7QwNz6tQp9OnTB+Hh4TX+9V5WUlISnJyckJiYWOmNP0JCQpCUlKRVZyBCCKkrkydPRkRERLVrm0jjo7wh2OXLl7WqKa4P9OhzQgghhOhEnfSpaCqMjIwwfvz4KjsOtm/fXnXbcUIIIeR1RklFFaysrFSddiozfPjweoqGEEIIadiaVJ8KQgghhHCH+lQQQgghRCcoqSCEEEKITlBSQQghhBCdoKSCEEIIITpBSQUhhBBCdIKSCkIIIYToBCUVhBBCCNEJSioIIYQQohOUVBBCCCFEJyipIIQQQohOUFJBCOEEj8er8hUSEsJ1iIQQLdEDxQghnEhNTVX9//fff8eCBQtw//591bSyTwdmjEEul0NPjy5ZhDRkVFNBCOGEra2t6mVqagoej6d6f+/ePRgbG+PQoUPw9PSESCRCdHQ0Jk+ejICAALVyZs2ahd69e6veKxQKhIaGwsnJCQYGBujQoQMiIiLqd+MIeU1R2k8IabC+/PJLrFixAi1btoS5uXm1lgkNDcWuXbuwadMmuLq64syZM5gwYQKsra3h4+NTxxET8nqjpIIQ0mAtXrwYAwYMqPb8RUVF+O6773Ds2DF069YNANCyZUtER0dj8+bNlFQQUscoqSCENFidO3fWav6HDx+ioKBAIxEpLi5Gx44ddRkaIaQClFQQQhosiUSi9p7P54MxpjatpKRE9f+8vDwAwMGDB+Hg4KA2n0gkqqMoCSFKlFQQQhoNa2tr3Lp1S21aTEwMhEIhAKBNmzYQiUR49OgRNXUQwgFKKgghkJbI8Sxbioy8ImTkFf/33yJkF5ZAJmcw5BdjHn8nIBABevqAQB/QEwNmjoCVa+lLX/LqFdVS3759sXz5cuzYsQPdunXDrl27cOvWLVXThrGxMebMmYNPP/0UCoUCPXv2RHZ2Ns6dOwcTExNMmjSpzmMk5HVGSQUhr5nHLwpwJzUH91Jzce9ZDu49y0VyZj4UrPJl7MVFmIetVZTKA0zsAUsXwMrtf4mGlRtg+obOYvf19cX8+fMxd+5cSKVSfPDBB5g4cSJu3rypmuebb76BtbU1QkNDkZCQADMzM3Tq1AlfffWVzuIghFSMx8o3UBJCmpTswhKcf5iBM3EZiH6YjscvCrUuw15chPMIrFkAJg6AS3/AdSDQsjcgMnrlIoSQxomSCkKaoNTsQvwV8xRRt5/hxpNsyKuqhqiGWiUVZQn0AcceQNsAoPVQwNCi9mUSQhoMSioIaSKkJXL8czMVe688xsXEF9Dlma2zpKIsvhBw7gN4jATavAsIxbotnxBS7yipIKSRe54jxS/Ridhz6RFypbI6WUedJBVlGdkCPT4BOgcCQoO6Ww8hpE5RUkFII/UoswCbzsQj4uoTFMsUdbquOk8qlCQ2QPcZQJcgQN+w7tdHCNEpSioIaWQeZRZg5dH7+PtGaq37SlRXvSUVSoZWQPdgoMsU6thJSCNCSQUhjURhsRzrTsbh57OJdV4zUV69JxVKBhZAt4+BrtMAkXH9r58QohVKKghpBP6KfYrQf+4iNVvKyfo5SyqUjO2AIauBVoO4i4EQ8kqUVBDSgKVkFWLO3lhcSMjkNA7Okwql9mMAv+8Bg+o9Bp0QUr/4XAdACKnY37FP4bf6DOcJRYNy4zdg/du4FxfHdSSEkApQUkFIA5NXJMNne2MwY8915NTRENHG7JFZZwza8gAhf91GkUzOdTiEkDIoqSCkAbn9NBv+P57FH9dSuA6lQZIZO+C9JyMBANvPJ2HExvN4xlE/E0KIJkoqCGkgom6lYuTGC0jOLOA6lAaJ8fhYIvwEqVJ91bRbKTl4d300bjzJ4i4wQogKJRWENAAbT8Xjo93XUFhC1fmViX1jArY/1XziaVpOEUZvvoB/bqZyEBUhpCxKKgjhkELBMH//LSyNuqfTZ3U0NYWWbTE+YWClf5eWKPDxr9ew/uTDeoyKEFKeHtcBEPK6UigYPtsbg/0xT7kOpUFjemLMLPoI+fKqfwMxBiw/fB9FMgU+G+BWT9ERQsqimgpCOMAYw9x9NyihqIbDttNwNKP6j0j/8XgcVh97UIcREUIqQ0kFIfWMMYav/ryFiKtPuA6lwXtp2wMfxXtpvdzqY3H48Tjdy4KQ+kZJBSH1LOSv29hz6RHXYTR4CrE5JmROBmO8Gi2/8ugDbDodr+OoCCFVoT4VhNSjDaceIuxCMtdhvJKiqABZZ3ehIO4CFAXZyLR1wuVBcnRxEFQ4/+T9hQiLLdGY3saaj9vTS58yuvtGCb48LkVeMUPgW/pY6StWzZeUpcDAnQW4MlUCE1FpErHFbCZuJ0lqtR1Lo+6hhaUhBnnY1aocQkj10LM/CKknx++mYcqOK6inp5XXSnrkUpSkJ8PCdzoERhbg3z+GnH/34s50IziYaFZwZksZCmX/2zCZAuiwKR8zvIQI6S1GRoECzVflYfu7Bmhpzof/rwXY+q4YQ9yEAIDBuwsQ1EmI4a1L3ye/MRQ+D8foZFsMhAKEf9gNHg6mOimPEFI5av4gpB48fJ6LWb/FNIqEQlFShIL752DWJxDi5h4Qmtujee/34GLBx8YrxRUuYyrmwdaIr3pdeSrHy8LSGgkASHjJYCri4T0PIbo4CNDHSYC76aWPb99zswRCAVQJhcykOd57PFxn21NYIseUHVfwPJfuvElIXaOkgpA6ll1Ygik7riK3qJE8x0MhB5gCPIFQbbKBHg/Rj6p3c64t10vQv6UAjmallxhXCz4KShiup8rxopDhcooc7ZsJ8LKQYf5JKdb5lTaFMB4fi/Rm4lmRflXFay01W4qpO67Ss0IIqWOUVBBSx+aExyIxI5/rMKqNLzKEyN4d2ed/gyw3E0whR/qN07jwRI7UvFdXtTzNVeBQnAxBnf6XGJgb8BAWYICJ+wvh9XMeJnYQwtdFD3OOSBHspY/ELAU6bs6Dy098bDqRWCfbFfM4C8ui7tdJ2YSQUtRRk5A6FHH1CY7eSeM6DK1ZDpmNzENrkLJhEsDjQ2LXEmM9hLia+upf+mExJTAT8xDgrn55GdZaiGGt/1f7cTpJhhvP5Vg7WAyXH/Ow/YM2+LBoOjK3fQ5xcw8IJGa63ixsPZeIXm7W8HGz1nnZhBBKKgipM6nZhVj0922uw6gRobkdbMd9D0WxFIriArxpJUFJxHtoaf6qu1oybI0pwfvthdAXVD4UtEjGMP0fKXYOM8DDFwrIGPDbG19CkWkOoYUDilLvw9Clq643C4wBcyNiceRTH5gaCF+9ACFEK9T8QUgdmRtxA7nSRtKPohJ8fTH0jCwgK8zD4YcyvNuq6t8hp5PlePhCgf90qvoLe8mZIgxy1kMnOwHkCqCQiXAi0xwAwBQyQKHQ2TaUl5ZT1GiTPUIaOqqpIKQO/HrxEc7GZXAdRo0VJlwFAOhZOED2MhW3T29BOysBAt8qTRbmHZMiJZdhxzADteW2XC9BVwcBPGwqvp8FANxJl+P32zJcn1Z6Dwprj14oYNHQjz0CgZE5SjKfQN/OtY62rNQf11IQ8JYDelEzCCE6RUkFITqWVVCMZYfvcR1GrSiKCpB1Jgyy3AwIxMawatMVh/tmQvjfJo3UPIZH2eq1CdlShn13SrBmkLiiIgGUNo9M/VuKlb4iSPR5UBhYYEp2ICwHd8GLoxvB5CWwGPAh9Iyt6nT7AGDJwTs45NILAn7N7thJCNFEN78iRMcWRt5qFHfN1Ia9uAjnEajzcjc2W4SlyXVbK1GVJQEemPC2I2frJ6SpoT4VhOhQQnoedl+k53pUR2LzAE4TCgBYdfQBcqSatxcnhNQMJRWE6NDyw/chawy3zeRYiYkj3ns0jOswkJlfjHUnHnIdBiFNBiUVhOjIrZRsHLr1jOswGjzGE2ChYCaeFzWMIZ1h55OQkVfEdRiENAmUVBCiI5vPJHAdQqNw5Y1J+DW14Tw1tEimwI7zSVyHQUiTQEkFITrwNKsQh26mch1Gg1dg1R4T4vtyHYaGnf8mo7CYngtCSG1RUkGIDmw/n0R9KV6BCQ3xUeE0FCka3mXnZUEJ9l55zHUYhDR6De/sJqSRyS+SYc8lGvHxKn83+win/3vXzIZoS3Qi5JQYElIrlFQQUkt/XHvS6G/HXdcy7Xww86En12FU6dGLAkQ/bLx3QSWkIaCkgpBa2h/zlOsQGjSFgRXGPp/IdRjVEhmTwnUIhDRqlFQQUgspWYW49ugl12E0aBtMZuJBvsGrZ2wAjtxOg7SEOmwSUlOUVBBSCwdin4JudF+5+OYjsCLZheswqi2vSIbjd59zHQYhjRYlFYTUwt83qOmjMiWmThidPJTrMLRGTSCE1BwlFYTU0KPMAtxKyeE6jAaJ8QT4P/4MZBY3jLtmauPUg3RqAiGkhiipIKSGaKRA5S6+EYjfU225DqNGimUKXEx8wXUYhDRKlFQQUkMXEzO5DqFByrd+C5Pie3MdRq1Ex6VzHQIhjRIlFYTU0MUE+jVbHhNKMC2/Yd41Uxv/0rElpEYa95lPCEeSM/PxLEfKdRgNzp820xH9wpTrMGrtTmoO8orohmaEaIuSCkJqgGopNKXb98Vn8R25DkMn5AqG2MdZXIdBSKNDSQUhNXAjJYvrEBoUhaEVxqWN5zoMnXqQlst1CIQ0OpRUEFIDD57lcR1Cg7LWaBbiGsldM6sr7jkdY0K0RUkFITUQ95x+xSrFNR+FVY9ach2Gzj1Mo6SCEG1RUkGIlrILSvCyoITrMBqEYrOWGJ30Dtdh1AlKHAnRHiUVhGgpKTOf6xAaBMbXwzw2Ay9L9LgOpU68LChBZl4R12EQ0qhQUkGIllKyCrkOoUG44PAB9qU14zqMOpWWQ0kFIdqgpIIQLWVR0wfyrDthUrwP12HUuazCYq5DIKRRoaSCEC3lSF/vpILpSzAlbwpKFDyuQ6lzlEASoh1KKgjRUnbh6/1Fs88qGBdeNv67ZlbHywKqqSBEG5RUEKKlnNc4qUiz7485CR24DqPeUE0FIdqhpIIQLeVIX89nQsglNhj7bBzXYdSrfHr+ByFaoaSCEC0xxrgOod4x8PCjZCYSCsRch1KvBPym32+EEF2ipIIQLekLXr/TJlWqjzVN8K6Zr0JJBSHaef2ujoTUkr4enTavCz1KKgjRCl0dCdGS8DWsqXhdCfh0rAnRBp0xhGiJkorXh1BANRWEaIOujoRoyUjcNJ91QTSZGepzHQIhjQolFYRoyc709RoB8TqzMqKkghBtUFJBiJYoqXh9WBuLuA6BkEaFkgpCtGRvZsB1CKSeONCxJkQrlFQQoiVKKl4PEn0B9akgREuUVBCiJSORHkyos2aT52Qt4ToEQhodSioIqYFWtsZch0DqmIf96/EkVkJ0iZIKQmqgjZ0J1yGQOtbWgZIKQrRFSQUhNeBBXzhNnoc9JY6EaIuSCkJqoOObZlyHQOqQHp+H1lQbRYjWKKkgpAacrY2os2YT5trMGGKhgOswCGl0KKkgpAZ4PB66OVtyHQapI73crLgOgZBGiZIKQmqon3szrkMgdaRPKxuuQyCkUaKkgpAa6uNuAx49xLLJMRbrobOjOddhENIoUVJBSA1ZG4vQ/g0zrsMgOubtagU9erw9ITVCZw4htdDfnarJmxpq+iCk5iipIKQW/NrZch0C0SEDoQCDPOiYElJTlFQQUgsuNsbU/t6EDG5nB2OxkOswCGm0KKkgpJbGer3JdQhER8Z4Nec6BEIaNUoqCKkl//Z2MDWgX7eNXUtrCbq0sOA6DEIaNUoqCKklsVCAYR0duA6D1NLozlRLQUhtUVJBiA68380RfLpnRaNlJNKjZixCdICSCkJ0wNnaCO90sOc6DFJD499+k5qwCNEBSioI0ZGZ/VwhoOqKRsdAKEBQz5Zch0FIk0BJBSE64mxthHeptqLRmdjNEdbGIq7DIKRJoKSCEB2aQbUVjYqxSA/TfJy5DoOQJkOP6wAIaUqcrCQY06U5dl98VCflK4oKkHV2FwriLkBRkA19m5Yw7z8VIjs3AABjDNnRu5EXexiKonyIHFrDYuB0CC0qH52Se/0f5F7/B7LsNACA0OpNmHUfCwPnzqp5Xhz/Gfm3joMnFMPMZxKM2vZR/S3/XjTybx2HzciFdbLNdWnWADdYSPS5DoOQJoNqKgjRsc99W9XZF1Vm1FpIk2JgNWQ27D5YB7FTR6T99n+Q5WYAAHIu7kPO1b9h4fsxbN//ATyhGM/3LgCTFVdapsDYEuY+k2A3aTXsJq2G2LEDnv+xBMXpyQCAgocXkX/3NGxGfwPz3oF4EbUW8oJsAICiKB9ZZ3bAYuBHdbK9dam1nQkmd2/BdRiENCmUVBCiY2aG+vhykLvOy1WUFKHg/jmY9QmEuLkHhOb2MOs5HkJzO+RePwTGGHKvRMK023swdH0b+jZOsBryGWR5L1Dw4EKl5Rq6dIWBcxcILRwgtHCAea+J4OuLUfT0PgCgJPMxxM3bQWTnCkkbH/D0DVW1Gi9PboNxx8HQM2lcD+Hi8YAlAR7UVEWIjlFSQUgdGN2lObq1tNRtoQo5wBTgCdSHPvL0RCh6chuy7DTI81/CoMVbqr/xRRKI7Fuh6Om9aq2CKeTIv3MaihIpRA6liZG+tROKnz2EXJqHomcPwWRF0DO3h/TJbRSnxcPY8x2dbWJ9Ge3ZHJ70zBZCdI76VBBSR0KHt8OgNWcgLVHopDy+yBAie3dkn/8NQsvmEEjMkH/3DIqe3oOeuR3keS9L55OYqS0nMDSDPD+ryrKL05PwbOccMFkxePoGsBn2NfStSm8GZdDSE5K2vfEs7FPw9PRh5f8p+EIRXhzeAEv/T0v7ZFw7AIGBCSx8g6Fv7aiT7a0rNsYifOmn+5okQgjVVBBSZ1pYSRDyTludlmk5ZDYAIGXDJDxaMQy5V/+CpHUvALWrxhdaOMAu8EfYTlwJ445+yDi4CsUZ/+tsatZzPBym/Qz7/6yHoVt3ZF8Ih7jFW+DxBci+8Dtsxy+DUfuByDy4slZx1DU+D1g95i2YU+dMQuoEJRWE1KExXm/q9LkgQnM72I77Hs0/jYDD9O2wm7gKTCGH0MwWAqPS6nxFuVoJeUEWBOVqL8rjCYQQmttDZOsCc5/J0LdxQu6VvyqctyTzMfLvnISZ9wRIH92E+A0PCAxNYejujeK0eCiKCnSxqXUiuK8rujtbcR0GIU0WJRWE1LFvh3nA2Vqi0zL5+mLoGVlALs1DYeI1GLi+DT3TZhBIzCFNjlHNpygqQNHT+xDZa1fdzxgDk5dUOD3z8HqY9w0CX98AYAowhey/K/vvv0w3zT265uVkgU/6uXIdBiFNGiUVhNQxQ309bBjvCQOhoNZlFSZcRWHCVZRkPUNh4nWk7ZkHocUbMGrXHzweD8ad30X2+d9REHcRxelJyDi4EnpGFjB066YqI+23r5Bz9W/V+5ent0P6+BZk2WkoTk/Cy9PbUfToJiRtemusPy/2MAQGJjB06QoAEDm0hjT5BopS7iHnciSElm+CLzaq9XbqmoVEHz+O6UijPQipY9RRk5B60MrWGMtHtceMPdfBWM3LURQVIOtMGGS5GRCIjWHYqjvMek0ET1B6Kpt0HQFWIkXm4bVQSPMhfqMNbEYvBk/vf30ISl4+g6gwR/Venp+NjAMrIc9/Ab5IAn3rFrAZvRgGTh3V1i3Pf4nsC3thO2G5aprIvhVMvIbhecQi8A1NYeX/ac03ro6IhXz8MqkzbE3FXIdCSJPHY6w2lzhCiDa2nUvEor/vcB3Ga0PA52HTBE8MaNOM61AIeS1Q8wch9SiwhxM+pGdN1JuQd9pQQkFIPaKkgpB69qWfO0Z0eoPrMJq8j3o74/1uLbgOg5DXCiUVhHBg6Yh28G9vx3UYTdYUbyd8UQe3SieEVI36VBDCEYWC4ev9t7DnUt080fR19XEfZ3zuSwkFIVygpIIQji2NuoeNp+K5DqPR4/GAeX7umNqL+qwQwhVKKghpADafjkfooeo99Ito0hfw8d3wdhjpSX1VCOESJRWENBD/3EzFnPBYFBTLuQ6lUbE1EWPjhE7o+CY9dZQQrlFSQUgDcv9ZLqbtvIKkzIb7/IyGxMvJAuvHdYK1sYjrUAghoKSCkAYnV1qCL/fdxMGbqVyH0qAF9miBrwe3hp6ABrER0lBQUkFIA7Xn0iN8989d5EplXIfSoNiZivHdsHbo427DdSiEkHIoqSCkAUvLkWL+/ls4cieN61AahLFezfHV4NYwFgu5DoUQUgFKKghpBP65mYoFkbeRkVfEdSiceMPcAEtHtEcPFyuuQyGEVIGSCkIaieyCEqw7GYcdF5JRJFNwHU69MDUQ4qPezpjcvQXEOnh0PCGkblFSQUgj8yxbijXH4xB+5TFkiqZ5+oqFfEzu7oSPfJxhakhNHYQ0FpRUENJIJWXkY/WxBzhwI7XJJBdiIR8jOr2BGX1dYWsq5jocQoiWKKkgpJF7li3FrxeTsefyY6TnNs4+F7YmYozv+iYmvO0Ic4k+1+EQQmqIkgpCmogSuQJRt55h57/JuJT4gutwXkko4KGHixXGdHkT/Vvb0P0mCGkCKKkgpIHYvn07Zs2ahaysrFqXlZJViEM3U3Ho1jNcf/QSDaV1RKTHh7erNfw8bNG/TTOYGlB/CUKaEj2uAyCkqZk8eTLCwsI0psfFxcHFxaVeYnAwM0CQd0sEebdEZl4RTt5Px/mHGbj+OAuJGfn1EgMA8HlAK1sTdHY0R9eWFujTygYSEV12CGmq6OwmpA4MGjQI27ZtU5tmbW3NSSyWRiKM9HxD9QTPl/nFuP74Ja4lZ+FmSjYevyjAk6xCFNdymKqBUABHS0M4WhqqEomOb5rRjaoIeY1QUkFIHRCJRLC1tVWbtnLlSmzbtg0JCQmwsLDAO++8g2XLlsHIyKjCMmJjYzFr1ixcuXIFPB4Prq6u2Lx5Mzp37gwAiI6Oxrx583DlyhVYWVlh2LBhCA0NhUQiqTI2c4k++ro3Q1/3ZqppjDGk5RTh0YsCPHlZgOzCEhSWyFFY/N9XiRxyBYNIjw+xUACJSA8mYj2YGerDwdwAjpaGsDGm0RqEvO4oqSCknvD5fPz4449wcnJCQkICpk+fjrlz52LDhg0Vzj9+/Hh07NgRGzduhEAgQExMDITC0l/98fHxGDRoEJYsWYKtW7ciPT0dwcHBCA4O1qghqQ4ejwdbUzFsTcXwcrKo1XYSQl5f1FGTEB2bPHkydu3aBbH4f7/c/fz8EB4erjZfREQEPvzwQ2RkZADQ7KhpYmKCtWvXYtKkSRrrCAoKgkAgwObNm1XToqOj4ePjg/z8fLV1E0JIfaGaCkLqQJ8+fbBx40bVe4lEgmPHjiE0NBT37t1DTk4OZDIZpFIpCgoKYGhoqFHGZ599hqCgIOzcuRP9+/fHqFGj4OzsDKC0aeTGjRvYvXu3an7GGBQKBRITE9G6deu630hCCCmHBoYTUgckEglcXFxUr6KiIgwZMgTt27fHvn37cPXqVaxfvx4AUFxcXGEZISEhuH37Nvz9/XHixAm0adMGf/75JwAgLy8P06ZNQ0xMjOoVGxuLuLg4VeJBCCH1jWoqCKkHV69ehUKhwA8//AA+vzSX37t37yuXc3Nzg5ubGz799FOMHTsW27Ztw7Bhw9CpUyfcuXOn3oaoEkJIdVBNBSH1wMXFBSUlJVi7di0SEhKwc+dObNq0qdL5CwsLERwcjFOnTiE5ORnnzp3D5cuXVc0aX3zxBc6fP4/g4GDExMQgLi4OkZGRCA4Orq9NIoQQDZRUEFIPOnTogJUrV2Lp0qXw8PDA7t27ERoaWun8AoEAmZmZmDhxItzc3DB69Gj4+flh0aJFAID27dvj9OnTePDgAby9vdGxY0csWLAA9vb29bVJhBCigUZ/EEIIIUQnqKaCEEIIITpBSQUhhBBCdIKSCkIIIYToBCUVhBBCCNEJSioIIYQQohOUVBBCCCFEJyipIIQQQohOUFJBCCGEEJ2gpIIQQgghOkFJBSGEEEJ0gpIKQgghhOgEJRWEEEII0QlKKgghhBCiE5RUEEIIIUQnKKkghBBCiE5QUkEIIYQQnaCkghBCCCE68f94mVHfV7++GAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Тестовая выборка:  (18168, 6)\n",
      "hazardous\n",
      "False    16400\n",
      "True      1768\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Вывод распределения количества наблюдений по меткам (классам)\n",
    "print(df.hazardous.value_counts())\n",
    "print()\n",
    "\n",
    "\n",
    "data = df[['est_diameter_min', 'est_diameter_max', 'relative_velocity', 'miss_distance', 'absolute_magnitude', 'hazardous']].copy()\n",
    "\n",
    "df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
    "   data, stratify_colname=\"hazardous\", frac_train=0.60, frac_val=0.20, frac_test=0.20\n",
    ")\n",
    "\n",
    "print(\"Обучающая выборка: \", df_train.shape)\n",
    "print(df_train.hazardous.value_counts())\n",
    "hazardous_counts = df_train['hazardous'].value_counts()\n",
    "plt.figure(figsize=(2, 2))# Установка размера графика\n",
    "plt.pie(hazardous_counts, labels=hazardous_counts.index, autopct='%1.1f%%', startangle=90)# Построение круговой диаграммы\n",
    "plt.title('Распределение классов hazardous в обучающей выборке')# Добавление заголовка\n",
    "plt.show()# Отображение графика\n",
    "\n",
    "print(\"Контрольная выборка: \", df_val.shape)\n",
    "print(df_val.hazardous.value_counts())\n",
    "hazardous_counts = df_val['hazardous'].value_counts()\n",
    "plt.figure(figsize=(2, 2))\n",
    "plt.pie(hazardous_counts, labels=hazardous_counts.index, autopct='%1.1f%%', startangle=90)\n",
    "plt.title('Распределение классов hazardous в контрольной выборке')\n",
    "plt.show()\n",
    "\n",
    "print(\"Тестовая выборка: \", df_test.shape)\n",
    "print(df_test.hazardous.value_counts())\n",
    "hazardous_counts = df_test['hazardous'].value_counts()\n",
    "plt.figure(figsize=(2, 2))\n",
    "plt.pie(hazardous_counts, labels=hazardous_counts.index, autopct='%1.1f%%', startangle=90)\n",
    "plt.title('Распределение классов hazardous в тестовой выборке')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "распределение плохое, соотношение классов сильно смещено, это может привести к проблемам в обучении модели, так как модель будет обучаться в основном на одном классе. В таких случаях стоит рассмотреть методы аугментации данных."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "аугментация данных оверсемплингом(Этот метод увеличивает количество примеров меньшинства)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting imblearn\n",
      "  Downloading imblearn-0.0-py2.py3-none-any.whl.metadata (355 bytes)\n",
      "Collecting imbalanced-learn (from imblearn)\n",
      "  Downloading imbalanced_learn-0.12.4-py3-none-any.whl.metadata (8.3 kB)\n",
      "Requirement already satisfied: numpy>=1.17.3 in d:\\мии\\aim-pibd-31-kouvshinoff-t-a\\laba\\lib\\site-packages (from imbalanced-learn->imblearn) (2.1.1)\n",
      "Requirement already satisfied: scipy>=1.5.0 in d:\\мии\\aim-pibd-31-kouvshinoff-t-a\\laba\\lib\\site-packages (from imbalanced-learn->imblearn) (1.14.1)\n",
      "Requirement already satisfied: scikit-learn>=1.0.2 in d:\\мии\\aim-pibd-31-kouvshinoff-t-a\\laba\\lib\\site-packages (from imbalanced-learn->imblearn) (1.5.2)\n",
      "Requirement already satisfied: joblib>=1.1.1 in d:\\мии\\aim-pibd-31-kouvshinoff-t-a\\laba\\lib\\site-packages (from imbalanced-learn->imblearn) (1.4.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in d:\\мии\\aim-pibd-31-kouvshinoff-t-a\\laba\\lib\\site-packages (from imbalanced-learn->imblearn) (3.5.0)\n",
      "Downloading imblearn-0.0-py2.py3-none-any.whl (1.9 kB)\n",
      "Downloading imbalanced_learn-0.12.4-py3-none-any.whl (258 kB)\n",
      "Installing collected packages: imbalanced-learn, imblearn\n",
      "Successfully installed imbalanced-learn-0.12.4 imblearn-0.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install imblearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Обучающая выборка после oversampling:  (100447, 6)\n",
      "hazardous\n",
      "True     51250\n",
      "False    49197\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from imblearn.over_sampling import ADASYN\n",
    "\n",
    "# Создание экземпляра ADASYN\n",
    "ada = ADASYN()\n",
    "\n",
    "# Применение ADASYN\n",
    "X_resampled, y_resampled = ada.fit_resample(df_train.drop(columns=['hazardous']), df_train['hazardous'])\n",
    "\n",
    "# Создание нового DataFrame\n",
    "df_train_adasyn = pd.DataFrame(X_resampled)\n",
    "df_train_adasyn['hazardous'] = y_resampled  # Добавление целевой переменной\n",
    "\n",
    "# Вывод информации о новой выборке\n",
    "print(\"Обучающая выборка после oversampling: \", df_train_adasyn.shape)\n",
    "print(df_train_adasyn['hazardous'].value_counts())\n",
    "hazardous_counts = df_train_adasyn['hazardous'].value_counts()\n",
    "plt.figure(figsize=(2, 2))\n",
    "plt.pie(hazardous_counts, labels=hazardous_counts.index, autopct='%1.1f%%', startangle=90)\n",
    "plt.title('Распределение классов hazardous в тренировачной выборке после ADASYN')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "P.S. можно было использовать ещё SMOTE, SVM-SMOTE, K-means SMOTE, SMOTE-N, SMOTE-NC, RandomOverSampler."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "проведём также балансировку данных методом андерсемплинга. Этот метод помогает сбалансировать выборку, уменьшая количество экземпляров класса большинства, чтобы привести его в соответствие с классом меньшинства."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Обучающая выборка после undersampling:  (10608, 6)\n",
      "hazardous\n",
      "False    5304\n",
      "True     5304\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "rus = RandomUnderSampler()# Создание экземпляра RandomUnderSampler\n",
    "\n",
    "# Применение RandomUnderSampler\n",
    "X_resampled, y_resampled = rus.fit_resample(df_train.drop(columns=['hazardous']), df_train['hazardous'])\n",
    "\n",
    "# Создание нового DataFrame\n",
    "df_train_undersampled = pd.DataFrame(X_resampled)\n",
    "df_train_undersampled['hazardous'] = y_resampled  # Добавление целевой переменной\n",
    "\n",
    "# Вывод информации о новой выборке\n",
    "print(\"Обучающая выборка после undersampling: \", df_train_undersampled.shape)\n",
    "print(df_train_undersampled['hazardous'].value_counts())\n",
    "\n",
    "# Визуализация распределения классов\n",
    "hazardous_counts = df_train_undersampled['hazardous'].value_counts()\n",
    "plt.figure(figsize=(2, 2))\n",
    "plt.pie(hazardous_counts, labels=hazardous_counts.index, autopct='%1.1f%%', startangle=90)\n",
    "plt.title('Распределение классов hazardous в тренировочной выборке после Undersampling')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "laba",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}