{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Датасет №1: [Объекты вокруг Земли](https://www.kaggle.com/datasets/sameepvani/nasa-nearest-earth-objects).\n",
    "\n",
    "### Описание датасета:\n",
    "Данный набор данных представляет собой коллекцию сведений о ближайших к Земле объектах (астероидах), сертифицированных NASA. Он содержит данные, которые могут помочь идентифицировать потенциально опасные астероиды, которые могут оказать влияние на Землю или на космические миссии. Набор данных включает в себя такие ключевые характеристики астероидов, как их размер, скорость, расстояние до Земли и информация о возможной опасности столкновения.\n",
    "\n",
    "---\n",
    "\n",
    "### Анализ сведений:\n",
    "**Проблемная область:**\n",
    "Основной проблемной областью является отслеживание и оценка рисков, связанных с приближением астероидов к Земле. С помощью данных о движении и характеристиках астероидов можно предсказать возможные столкновения и минимизировать угрозу для Земли, планируя превентивные действия.\n",
    "\n",
    "**Актуальность:**\n",
    "Набор данных высокоактуален для задач оценки рисков от космических объектов, мониторинга космического пространства и разработки превентивных мер по защите Земли. Также он важен для научных исследований в области астрономии и планетарной безопасности.\n",
    "\n",
    "**Объекты наблюдения:**\n",
    "Объектами наблюдения в данном наборе данных являются астероиды, классифицированные NASA как \"ближайшие к Земле объекты\" (Near-Earth Objects, NEO). Эти объекты могут проходить в непосредственной близости от Земли, что потенциально представляет опасность.\n",
    "\n",
    "**Атрибуты объектов:**\n",
    "- id: Уникальный идентификатор астероида.\n",
    "- name: Название, присвоенное астероиду NASA.\n",
    "- est_diameter_min: Минимальный оценочные диаметры астероида в километрах.\n",
    "- est_diameter_max: Максимальный оценочные диаметры астероида в километрах.\n",
    "- relative_velocity: Скорость астероида относительно Земли (в км/с).\n",
    "- miss_distance: Расстояние, на котором астероид пролетел мимо Земли, в километрах.\n",
    "- orbiting_body: Планета, вокруг которой вращается астероид.\n",
    "- sentry_object: Признак, указывающий на наличие астероида в системе автоматического мониторинга столкновений (система Sentry).\n",
    "- absolute_magnitude: Абсолютная величина, описывающая яркость объекта.\n",
    "- hazardous: Булев признак, указывающий, является ли астероид потенциально опасным.\n",
    "\n",
    "**Связь между объектами:**\n",
    "В данном наборе данных отсутствует явная связь между астероидами, однако на основе орбитальных параметров можно исследовать группы объектов, имеющие схожие орбиты или величины риска столкновения с Землей.\n",
    "\n",
    "---\n",
    "\n",
    "### Качество набора данных:\n",
    "**Информативность:**\n",
    "Датасет предоставляет важные сведения о ключевых характеристиках астероидов, такие как размер, скорость и расстояние от Земли, что позволяет проводить качественный анализ их потенциальной опасности.\n",
    "\n",
    "**Степень покрытия:**\n",
    "Набор данных включает данные о большом количестве астероидов (>90000 записей), что позволяет охватить значительную часть ближайших к Земле объектов. Однако не все астероиды могут быть обнаружены, так как данные зависят от возможности их наблюдения.\n",
    "\n",
    "**Соответствие реальным данным:**\n",
    "Данные в наборе предоставлены NASA, что указывает на высокую достоверность и актуальность информации. Тем не менее, параметры, такие как диаметр и расстояние, могут быть оценочными и подвергаться уточнению с новыми наблюдениями.\n",
    "\n",
    "**Согласованность меток:**\n",
    "Метрики в датасете четко обозначены, а булевы признаки, такие как \"hazardous\" (опасен или нет), соответствуют конкретным параметрам астероидов и легко интерпретируются.\n",
    "\n",
    "---\n",
    "\n",
    "### Бизес-цели:\n",
    "1. **Мониторинг космических угроз:**\n",
    "Создание системы, которая анализирует астероиды и предсказывает риски столкновения с Землей, помогая государственным агентствам и частным компаниям разрабатывать превентивные меры.\n",
    "2. **Поддержка космических миссий:**\n",
    "Предоставление точных данных для планирования и безопасного проведения космических миссий, минимизация рисков столкновения с космическими объектами.\n",
    "3. **Образовательные и научные исследования:**\n",
    "Использование данных для поддержки образовательных программ и научных исследований в области астрономии и космической безопасности.\n",
    "\n",
    "**Эффект для бизнеса:**\n",
    "Набор данных способствует развитию технологий космической безопасности, минимизирует финансовые риски от потенциальных катастроф и поддерживает стратегическое планирование космических миссий.\n",
    "\n",
    "---\n",
    "\n",
    "### Технические цели:\n",
    "1. **Моделирование риска столкновения:**\n",
    "Построение алгоритмов машинного обучения для прогнозирования вероятности столкновения астероидов с Землей.\n",
    "2. **Анализ и кластеризация астероидов:**\n",
    "Исследование взаимосвязей между астероидами, анализ орбитальных данных и выделение групп астероидов, имеющих схожие характеристики.\n",
    "3. **Оптимизация системы предупреждения угроз:**\n",
    "Создание системы раннего оповещения, которая будет автоматически анализировать данные и предупреждать о потенциальных угрозах в реальном времени.\n",
    "\n",
    "**Входные данные:**\n",
    "Диаметр, скорость, расстояние, орбитальные параметры астероидов.\n",
    "\n",
    "**Целевой признак:**\n",
    "Признак \"hazardous\" – бинарная метка, указывающая на потенциальную опасность астероида.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Выгрузка данных из файла в DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Any\n",
    "from math import ceil\n",
    "\n",
    "import pandas as pd\n",
    "from pandas import DataFrame, Series\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import ADASYN\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "df: DataFrame = pd.read_csv('..//static//csv//neo.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Краткая информация о DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 90836 entries, 0 to 90835\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   id                  90836 non-null  int64  \n",
      " 1   name                90836 non-null  object \n",
      " 2   est_diameter_min    90836 non-null  float64\n",
      " 3   est_diameter_max    90836 non-null  float64\n",
      " 4   relative_velocity   90836 non-null  float64\n",
      " 5   miss_distance       90836 non-null  float64\n",
      " 6   orbiting_body       90836 non-null  object \n",
      " 7   sentry_object       90836 non-null  bool   \n",
      " 8   absolute_magnitude  90836 non-null  float64\n",
      " 9   hazardous           90836 non-null  bool   \n",
      "dtypes: bool(2), float64(5), int64(1), object(2)\n",
      "memory usage: 5.7+ MB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>est_diameter_min</th>\n",
       "      <th>est_diameter_max</th>\n",
       "      <th>relative_velocity</th>\n",
       "      <th>miss_distance</th>\n",
       "      <th>absolute_magnitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.083600e+04</td>\n",
       "      <td>90836.000000</td>\n",
       "      <td>90836.000000</td>\n",
       "      <td>90836.000000</td>\n",
       "      <td>9.083600e+04</td>\n",
       "      <td>90836.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.438288e+07</td>\n",
       "      <td>0.127432</td>\n",
       "      <td>0.284947</td>\n",
       "      <td>48066.918918</td>\n",
       "      <td>3.706655e+07</td>\n",
       "      <td>23.527103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.087202e+07</td>\n",
       "      <td>0.298511</td>\n",
       "      <td>0.667491</td>\n",
       "      <td>25293.296961</td>\n",
       "      <td>2.235204e+07</td>\n",
       "      <td>2.894086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000433e+06</td>\n",
       "      <td>0.000609</td>\n",
       "      <td>0.001362</td>\n",
       "      <td>203.346433</td>\n",
       "      <td>6.745533e+03</td>\n",
       "      <td>9.230000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.448110e+06</td>\n",
       "      <td>0.019256</td>\n",
       "      <td>0.043057</td>\n",
       "      <td>28619.020645</td>\n",
       "      <td>1.721082e+07</td>\n",
       "      <td>21.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.748362e+06</td>\n",
       "      <td>0.048368</td>\n",
       "      <td>0.108153</td>\n",
       "      <td>44190.117890</td>\n",
       "      <td>3.784658e+07</td>\n",
       "      <td>23.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.884023e+06</td>\n",
       "      <td>0.143402</td>\n",
       "      <td>0.320656</td>\n",
       "      <td>62923.604633</td>\n",
       "      <td>5.654900e+07</td>\n",
       "      <td>25.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.427591e+07</td>\n",
       "      <td>37.892650</td>\n",
       "      <td>84.730541</td>\n",
       "      <td>236990.128088</td>\n",
       "      <td>7.479865e+07</td>\n",
       "      <td>33.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id  est_diameter_min  est_diameter_max  relative_velocity  \\\n",
       "count  9.083600e+04      90836.000000      90836.000000       90836.000000   \n",
       "mean   1.438288e+07          0.127432          0.284947       48066.918918   \n",
       "std    2.087202e+07          0.298511          0.667491       25293.296961   \n",
       "min    2.000433e+06          0.000609          0.001362         203.346433   \n",
       "25%    3.448110e+06          0.019256          0.043057       28619.020645   \n",
       "50%    3.748362e+06          0.048368          0.108153       44190.117890   \n",
       "75%    3.884023e+06          0.143402          0.320656       62923.604633   \n",
       "max    5.427591e+07         37.892650         84.730541      236990.128088   \n",
       "\n",
       "       miss_distance  absolute_magnitude  \n",
       "count   9.083600e+04        90836.000000  \n",
       "mean    3.706655e+07           23.527103  \n",
       "std     2.235204e+07            2.894086  \n",
       "min     6.745533e+03            9.230000  \n",
       "25%     1.721082e+07           21.340000  \n",
       "50%     3.784658e+07           23.700000  \n",
       "75%     5.654900e+07           25.700000  \n",
       "max     7.479865e+07           33.200000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Краткая информация о DataFrame\n",
    "df.info()\n",
    "\n",
    "# Статистическое описание числовых столбцов\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Проблема пропущенных данных:\n",
    "\n",
    "**Проблема пропущенных данных** — это отсутствие значений в наборе данных, что может искажать результаты анализа и статистические выводы.\n",
    "\n",
    "Проверка на отсутствие значений, представленная ниже, показала, что DataFrame не имеет пустых значений признаков. Нет необходимости использовать методы заполнения пропущенных данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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",
      "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"
     ]
    }
   ],
   "source": [
    "# Проверка пропущенных данных\n",
    "def check_null_columns(dataframe: DataFrame) -> None:\n",
    "    # Присутствуют ли пустые значения признаков\n",
    "    print(dataframe.isnull().any(), '\\n')\n",
    "\n",
    "    # Количество пустых значений признаков\n",
    "    print(dataframe.isnull().sum())\n",
    "\n",
    "    # Процент пустых значений признаков\n",
    "    for i in dataframe.columns:\n",
    "        null_rate: float = dataframe[i].isnull().sum() / len(dataframe) * 100\n",
    "        if null_rate > 0:\n",
    "            print(f\"{i} процент пустых значений: %{null_rate:.2f}\")\n",
    "            \n",
    "\n",
    "# Проверка пропущенных данных\n",
    "check_null_columns(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Проблема зашумленности данных:\n",
    "\n",
    "**Зашумленность** – это наличие случайных ошибок или вариаций в данных, которые могут затруднить выявление истинных закономерностей. Шум может возникать из-за ошибок измерений, неправильных записей или других факторов.\n",
    "\n",
    "**Выбросы** – это значения, которые значительно отличаются от остальных наблюдений в наборе данных. Выбросы могут указывать на ошибки в данных или на редкие, но важные события. Их наличие может повлиять на статистические методы анализа.\n",
    "\n",
    "Представленный ниже код помогает определить наличие выбросов в наборе данных и устранить их (при наличии), заменив значения ниже нижней границы (рассматриваемого минимума) на значения нижней границы, а значения выше верхней границы (рассматриваемого максимума) – на значения верхней границы."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Колонка est_diameter_min:\n",
      "\tЕсть выбросы: Да\n",
      "\tКоличество выбросов: 8306\n",
      "\tМинимальное значение: 0.0006089126\n",
      "\tМаксимальное значение: 37.8926498379\n",
      "\t1-й квартиль (Q1): 0.0192555078\n",
      "\t3-й квартиль (Q3): 0.1434019235\n",
      "\n",
      "Колонка est_diameter_max:\n",
      "\tЕсть выбросы: Да\n",
      "\tКоличество выбросов: 8306\n",
      "\tМинимальное значение: 0.00136157\n",
      "\tМаксимальное значение: 84.7305408852\n",
      "\t1-й квартиль (Q1): 0.0430566244\n",
      "\t3-й квартиль (Q3): 0.320656449\n",
      "\n",
      "Колонка relative_velocity:\n",
      "\tЕсть выбросы: Да\n",
      "\tКоличество выбросов: 1574\n",
      "\tМинимальное значение: 203.34643253\n",
      "\tМаксимальное значение: 236990.1280878666\n",
      "\t1-й квартиль (Q1): 28619.02064490995\n",
      "\t3-й квартиль (Q3): 62923.60463276395\n",
      "\n",
      "Колонка miss_distance:\n",
      "\tЕсть выбросы: Нет\n",
      "\tКоличество выбросов: 0\n",
      "\tМинимальное значение: 6745.532515957\n",
      "\tМаксимальное значение: 74798651.4521972\n",
      "\t1-й квартиль (Q1): 17210820.23576468\n",
      "\t3-й квартиль (Q3): 56548996.45139917\n",
      "\n",
      "Колонка absolute_magnitude:\n",
      "\tЕсть выбросы: Да\n",
      "\tКоличество выбросов: 101\n",
      "\tМинимальное значение: 9.23\n",
      "\tМаксимальное значение: 33.2\n",
      "\t1-й квартиль (Q1): 21.34\n",
      "\t3-й квартиль (Q3): 25.7\n",
      "\n"
     ]
    },
    {
     "data": {
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6da1fv16TJ0/Wr7/+mi80xLWNMZgxmDG4eIzBL730kjp37qzevXsrOztb7733nu68804tXLhQHTp0cOq7fPlyvf/++xo6dKjsdrumTp2qdu3a6aeffnJ81kFhxtdZs2ZpwIABuummm5ScnKz9+/frpZde0vfff69ffvnlHy+/crEx9Omnn9bjjz+unj176u6779bBgwf18ssvq0WLFpd0/gcffFBly5bVuHHjtHLlSr322msqVaqUfvjhB8XExOiZZ57R559/rueee061atVSv379HM8t7P3v37+/PvjgA/Xt21eNGzfW8uXL8319ztWzZ09VqlRJycnJ+vnnnzVz5kxFRERowoQJBfZv0aKFhg4dqv/+979Oy3CduxxXYRTltdHbb7+thIQExcfHa8KECTp58qSmTZumW265Rb/88gtLygAAgH/OoFBefPFFI8msXbvWqT0uLs7UrFnTqS0lJcVIMunp6cYYY06fPm1iYmJM+/btjSSTkpLi6Hvy5Ml853r33XeNJLNixQpH25gxY4wkc/DgQcsa4+LijCTzwgsvONqysrLMjTfeaCIiIkx2drYxxpizZ8+arKwsp+ceOXLEREZGmoEDBzra0tPTjSTTq1cv4+PjY/bt2+fY16ZNG3PXXXcZSea5557LV2evXr1Mx44dHe07d+40Xl5eplevXvmuo6B7cN9995mAgABz+vRpy+s993ydO3d2ah88eHC+r1dB54mPjzeVK1e+4Dk++OADI8msXr3a0SbJ9OjRw/j4+Di1Dxo0yHFfEhMTHe2nT582OTk5TsdNT083drvdPPnkk07tS5YsMZLMU089ZX7//XcTGBhounbt6tRn2LBhRpL59ttvHW3Hjh0zlSpVMhUrVnSca+nSpUaSWbp0qVMtXl5eZvDgwRe87jz/+c9/jCRz6NAhR1vNmjVNXFxcvr6//fabkWTefPNNR1ve1yjPqVOnjJeXl7nvvvucnpuQkGBKlixZqJqs5H3PhoeHm6NHjzrak5KSjCRTt25dc+bMGUd7r169jK+vr9P3WVxcnNO1denSJd/P+PlCQkKcvt6XIu9r+uOPPzraDhw4YEJCQpz+PbnUGp977rl8x8lT2J+NChUq5Pu36cCBA8Zut5uHH37Y0fbEE08YSWbevHn5jpubm2uMMebtt982Xl5eTt/Dxhjz6quvGknm+++/v+D14NrCGMwYzBh85Y/BxuT/OmdnZ5tatWqZ1q1bO7VLyvd13blzp/Hz8zPdunVztF1sfM3OzjYRERGmVq1a5tSpU472hQsXGknmiSeecLSdfy/yfsbO/Tfh3PrGjBnj2LYaQ3fs2GG8vb3N008/7dS+fv164+Pjk6/9QvL+7YqPj3eMlcYY06RJE2Oz2cz999/vaDt79qwpX758vu+Dwtz/tLQ0I8kMGzbMqW///v3zXXfePTv33yZjjOnWrZsJCwtzaqtQoYJJSEhwbH/44Yf5vv/znH8eq2MU9rXRsWPHTKlSpcw999zjdLx9+/aZkJCQfO0AAACXguVcCinvrd15s06KYsqUKTp8+LDGjBmTb9+5MylOnz6tQ4cOqXHjxpJ0STOCfHx8dN999zm2fX19dd999+nAgQNKS0uTJHl7ezvWVM7NzdVff/2ls2fPqmHDhgWes379+qpZs6befvttSdLOnTu1dOnSfG+3PNfAgQO1ePFi7du3T5L05ptvqkmTJrr++uvz9T33Hhw7dkyHDh1S8+bNdfLkSW3ZsqVQ133+LLYHH3xQkvT5558XeJ6MjAwdOnRIcXFx+v3335WRkeH0/JMnT+rQoUNas2aNZsyYocjIyHy1R0ZGqkOHDkpJSXE854MPPtCAAQPy1We32x0zFnNycnT48GEFBgbqhhtuyHfP27Ztq/vuu09PPvmk7rjjDvn5+Wn69OlOfT7//HPdfPPNuuWWWxxtgYGBuvfee7Vjxw5t2rTJqX/e9e7atUsTJ05Ubm6uWrduXcCdzC9v1qKfn99F+xZmtuiJEyeUm5ursLCwQp3/Utx5550KCQlxbDdq1EiS1KdPH6flFxo1aqTs7OwLvuW7VKlS+uOPP7Rq1aoL9vnxxx+d3upfVJ9//rkaN26sm2++2dEWHh6u3r17X/S5hanxQorys1GjRg3HDMe8Gm+44Qb9/vvvjra5c+eqbt26+WZjSnLM7P3www9VvXp1xcbG6tChQ45H3vfl+Utb4NrGGMwYzBhcPMbgc7/OR44cUUZGhpo3b17g93aTJk3UoEEDx3ZMTIy6dOmiJUuWOJYTutj4unr1ah04cECDBw92ukcdOnRQbGxsviV2XG3evHnKzc1Vz549ncaysmXLqlq1apc0lg0aNMjpXTCNGjWSMcbpnQve3t5q2LCh09grFe7+L168WJI0ePBgp+fm/dwW5P7773fabt68uQ4fPqzMzMwiXFnRFfa10ZdffqmjR4+qV69eTl8Hb29vNWrUiNcUAADAJa7oEH3FihXq1KmToqKiClwT9GKs1pEsWbJkkWvZuXOnfHx8ivwLfEZGhp555hmNGDFCkZGR+fb/9ddfeuihhxQZGSl/f3+Fh4erUqVKjucWVVRUVL7ry/vF89y1G998803VqVNHfn5+CgsLU3h4uD777DPLcw4YMMDxi+qsWbPUtGlTVatWzbKOG2+8UbVq1dJbb70lY4zjrbYF2bhxo7p166aQkBAFBwcrPDxcffr0kVT4e3B+LVWqVJGXl5fTNX///fe69dZbVbJkSZUqVUrh4eH6z3/+U+B5nnzySYWHh6tevXrasWOHli1bpqCgoHznHTBggObMmaOsrCx9+OGHKl26dIG/GOfm5mry5MmqVq2a7Ha7ypQpo/DwcK1bt67Aa3z++ecVGhqqNWvW6L///a8iIiKc9u/cuVM33HBDvuflvU32/Lezd+3aVeHh4apQoYLGjh2rxx57TN27d8/3/IIcOnRIJUqUcHobvZWjR49K0gXXVQ0LC1O1atU0c+ZMffHFFzpw4IAOHTrk0nWwY2JinLbzAvXo6OgC248cOWJ5rEceeUSBgYG6+eabVa1aNSUmJur777936jNx4kRt2LBB0dHRuvnmmzV27Nh8v9hezM6dOwv8mSro63wpNV5IUX42zr+3klS6dGmne7h9+3bHW/Gt/Pbbb9q4caPCw8OdHnn/Xh04cKDQ9ePqxxjMGMwYXDzG4IULF6px48by8/NTaGiowsPDNW3atALvc0Hfw9dff71OnjypgwcPSrr4+Jp3rwv6esTGxha4vI8r/fbbbzLGqFq1avnGs82bN1/SWFaU1zDnv34pzP3fuXOnvLy8HP/W5alatWqhaypdurSkC79+coXCvjb67bffJEmtW7fO93XI+z4HAAD4p67oNdFPnDihunXrauDAgbrjjjuK/PyRI0fmmznRpk0b3XTTTUU+1tatW1W5cmWnWayFMWHCBHl5eWnUqFH5PqhM+nuNwR9++EGjRo3SjTfeqMDAQOXm5qpdu3aWH5rzT73zzjvq37+/unbtqlGjRikiIkLe3t5KTk7W9u3bC3xOnz59NHr0aK1cuVJvvvmmHnvssYueZ+DAgZo6dapuvvlm7du3Tz179tQLL7zg1Ofo0aOKi4tTcHCwnnzySVWpUkV+fn76+eef9cgjj1zyPTh/Hdvt27erTZs2io2N1aRJkxQdHS1fX199/vnnmjx5cr7z3H333WrTpo3++OMPTZ48Wd27d9cPP/zgNLtZ+numk6+vrxYsWKCUlBQlJCQU+MF3zzzzjB5//HENHDhQ48ePV2hoqLy8vDRs2LACr/GXX35xvOBfv369evXqdUn3Ic/zzz+vunXr6syZM1q1apWeeuop+fj4FDgz83w7duxQTExMvntakLxZj2XLlr1gv/fff1+9e/dWfHy8U/ul/AdXQby9vYvUbi7wAW/Vq1fX1q1btXDhQi1evFhz587V1KlT9cQTT2jcuHGS/v45bt68uebPn68vvvhCzz33nCZMmKB58+apffv2//yCLqIwNVop6s/GpdzDguTm5qp27dqaNGlSgfvPDwtwbWMMZgxmDL7yx+Bvv/1WnTt3VosWLTR16lSVK1dOJUqUUEpKitOHcReFO8dXq3tamA/VzZObmyubzaZFixYVOD5eyoe1FuU1zLljrzvu/8VqKurYfzFFuffnyvs5fvvttwv8/i/q2AEAAFCQK/oVRfv27S/4AjkrK0v/93//p3fffVdHjx5VrVq1NGHCBLVs2VLS3y9cz33xunbtWm3atEmvvvpqkerIysrSmjVrnD7UqzD27Nmjl156ScnJyQoKCsr3C/yRI0f09ddfa9y4cXriiScc7XmzKS7Fnj17dOLECadfhH799VdJcnygzkcffaTKlStr3rx5Tr9AXOiXubCwMHXu3NnxtvS8t61eSO/evTVq1Cg99NBD6tGjR4GzyJYtW6bDhw9r3rx5atGihaM9PT29UNeb57fffnOaUbNt2zbl5uY6rvnTTz9VVlaWPvnkE6fZNFZv76xatapjRs6tt96qmJgYzZkzRw888IBTPx8fH/Xt21dPP/20Nm7caPkBlR999JFatWql119/3an96NGjjg+oynPixAkNGDBANWrUUNOmTTVx4kR169bN6T9/KlSooK1bt+Y7T95b7ytUqODU3qBBA8fPRfv27fXnn39qwoQJevzxxwsMHPKcPXtWa9euVbt27Sz7nGvTpk2y2WwXnT1dr149zZgxQ82bN9eTTz6pxo0b67nnnivS7OnLqWTJkvrXv/6lf/3rX8rOztYdd9yhp59+WklJSY63j5crV06DBw/W4MGDdeDAAdWvX19PP/10oX/Jr1ChQoE/+wV9nS+lRquwoKg/G4VRpUoVbdiw4aJ91q5dqzZt2hQqHMK1izGYMZgxuHiMwXPnzpWfn5+WLFnitKRM3rsozlfQz9qvv/6qgIAAp3edXGh8zbvXW7duzfcuhK1bt+b7WpwrbzZ13gz+PAXNXrcap6pUqSJjjCp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      "text/plain": [
       "<Figure size 1500x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Числовые столбцы DataFrame\n",
    "numeric_columns: list[str] = [\n",
    "    'est_diameter_min',\n",
    "    'est_diameter_max', \n",
    "    'relative_velocity', \n",
    "    'miss_distance', \n",
    "    'absolute_magnitude'\n",
    "]\n",
    "\n",
    "# Проверка выбросов в DataFrame\n",
    "def check_outliers(dataframe: DataFrame, columns: list[str]) -> None:\n",
    "    for column in columns:\n",
    "        if not pd.api.types.is_numeric_dtype(dataframe[column]): # Проверяем, является ли колонка числовой\n",
    "            continue\n",
    "        \n",
    "        Q1: float = dataframe[column].quantile(0.25) # 1-й квартиль (25%)\n",
    "        Q3: float = dataframe[column].quantile(0.75) # 3-й квартиль (75%)\n",
    "        IQR: float = Q3 - Q1 # Вычисляем межквартильный размах\n",
    "\n",
    "        # Определяем границы для выбросов\n",
    "        lower_bound: float = Q1 - 1.5 * IQR # Нижняя граница\n",
    "        upper_bound: float = Q3 + 1.5 * IQR # Верхняя граница\n",
    "\n",
    "        # Подсчитываем количество выбросов\n",
    "        outliers: DataFrame = dataframe[(dataframe[column] < lower_bound) | (dataframe[column] > upper_bound)]\n",
    "        outlier_count: int = outliers.shape[0]\n",
    "\n",
    "        print(f\"Колонка {column}:\")\n",
    "        print(f\"\\tЕсть выбросы: {'Да' if outlier_count > 0 else 'Нет'}\")\n",
    "        print(f\"\\tКоличество выбросов: {outlier_count}\")\n",
    "        print(f\"\\tМинимальное значение: {dataframe[column].min()}\")\n",
    "        print(f\"\\tМаксимальное значение: {dataframe[column].max()}\")\n",
    "        print(f\"\\t1-й квартиль (Q1): {Q1}\")\n",
    "        print(f\"\\t3-й квартиль (Q3): {Q3}\\n\")\n",
    "\n",
    "# Визуализация выбросов\n",
    "def visualize_outliers(dataframe: DataFrame, columns: list[str]) -> None:\n",
    "    # Диаграммы размахов\n",
    "    plt.figure(figsize=(15, 10))\n",
    "    rows: int = ceil(len(columns) / 3)\n",
    "    for index, column in enumerate(columns, 1):\n",
    "        plt.subplot(rows, 3, index)\n",
    "        plt.boxplot(dataframe[column], vert=True, patch_artist=True)\n",
    "        plt.title(f\"Диаграмма размахов для \\\"{column}\\\"\")\n",
    "        plt.xlabel(column)\n",
    "            \n",
    "    # Отображение графиков\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# Проверка выбросов\n",
    "check_outliers(df, numeric_columns)\n",
    "visualize_outliers(df, numeric_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Колонка est_diameter_min:\n",
      "\tЕсть выбросы: Нет\n",
      "\tКоличество выбросов: 0\n",
      "\tМинимальное значение: 0.0006089126\n",
      "\tМаксимальное значение: 0.32962154705\n",
      "\t1-й квартиль (Q1): 0.0192555078\n",
      "\t3-й квартиль (Q3): 0.1434019235\n",
      "\n",
      "Колонка est_diameter_max:\n",
      "\tЕсть выбросы: Нет\n",
      "\tКоличество выбросов: 0\n",
      "\tМинимальное значение: 0.00136157\n",
      "\tМаксимальное значение: 0.7370561859\n",
      "\t1-й квартиль (Q1): 0.0430566244\n",
      "\t3-й квартиль (Q3): 0.320656449\n",
      "\n",
      "Колонка relative_velocity:\n",
      "\tЕсть выбросы: Нет\n",
      "\tКоличество выбросов: 0\n",
      "\tМинимальное значение: 203.34643253\n",
      "\tМаксимальное значение: 114380.48061454494\n",
      "\t1-й квартиль (Q1): 28619.02064490995\n",
      "\t3-й квартиль (Q3): 62923.60463276395\n",
      "\n",
      "Колонка miss_distance:\n",
      "\tЕсть выбросы: Нет\n",
      "\tКоличество выбросов: 0\n",
      "\tМинимальное значение: 6745.532515957\n",
      "\tМаксимальное значение: 74798651.4521972\n",
      "\t1-й квартиль (Q1): 17210820.23576468\n",
      "\t3-й квартиль (Q3): 56548996.45139917\n",
      "\n",
      "Колонка absolute_magnitude:\n",
      "\tЕсть выбросы: Нет\n",
      "\tКоличество выбросов: 0\n",
      "\tМинимальное значение: 14.8\n",
      "\tМаксимальное значение: 32.239999999999995\n",
      "\t1-й квартиль (Q1): 21.34\n",
      "\t3-й квартиль (Q3): 25.7\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Устранить выборсы в DataFrame\n",
    "def remove_outliers(dataframe: DataFrame, columns: list[str]) -> DataFrame:\n",
    "    for column in columns:\n",
    "        if not pd.api.types.is_numeric_dtype(dataframe[column]): # Проверяем, является ли колонка числовой\n",
    "            continue\n",
    "        \n",
    "        Q1: float = dataframe[column].quantile(0.25) # 1-й квартиль (25%)\n",
    "        Q3: float = dataframe[column].quantile(0.75) # 3-й квартиль (75%)\n",
    "        IQR: float = Q3 - Q1 # Вычисляем межквартильный размах\n",
    "\n",
    "        # Определяем границы для выбросов\n",
    "        lower_bound: float = Q1 - 1.5 * IQR # Нижняя граница\n",
    "        upper_bound: float = Q3 + 1.5 * IQR # Верхняя граница\n",
    "\n",
    "        # Устраняем выбросы:\n",
    "        # Заменяем значения ниже нижней границы на нижнюю границу\n",
    "        # А значения выше верхней границы – на верхнюю\n",
    "        dataframe[column] = dataframe[column].apply(lambda x: lower_bound if x < lower_bound else upper_bound if x > upper_bound else x)\n",
    "    \n",
    "    return dataframe\n",
    "\n",
    "\n",
    "# Устраняем выборсы\n",
    "df: DataFrame = remove_outliers(df, numeric_columns)\n",
    "\n",
    "# Проверка выбросов\n",
    "check_outliers(df, numeric_columns)\n",
    "visualize_outliers(df, numeric_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Разбиение набора данных на выборки:\n",
    "\n",
    "**Групповое разбиение данных** – это метод разделения данных на несколько групп или подмножеств на основе определенного признака или характеристики. При этом наблюдения для одного объекта должны попасть только в одну выборку.\n",
    "\n",
    "**Основные виды выборки данных**:\n",
    "1. Обучающая выборка (60-80%). Обучение модели (подбор коэффициентов некоторой математической функции для аппроксимации).\n",
    "2. Контрольная выборка (10-20%). Выбор метода обучения, настройка гиперпараметров.\n",
    "3. Тестовая выборка (10-20% или 20-30%). Оценка качества модели перед передачей заказчику.\n",
    "\n",
    "Разделим выборку данных на 3 группы и проанализируем качество распределения данных.\n",
    "\n",
    "Весь набор данных состоит из 90836 объектов, из которых 81996 (около 90.3%) неопасны (False), а 8840 (около 9.7%) опасны (True). Это говорит о том, что класс \"неопасные\" значительно преобладает.\n",
    "\n",
    "Все выборки показывают одинаковое распределение классов, что свидетельствует о том, что данные были отобраны случайным образом и не содержат явного смещения.\n",
    "\n",
    "Однако, несмотря на сбалансированность при разбиении данных, в целом данные обладают значительным дисбалансом между классами. Это может быть проблемой при обучении модели, так как она может иметь тенденцию игнорировать опасные объекты (True), что следует учитывать при дальнейшем анализе и выборе методов обработки данных.\n",
    "\n",
    "Для получения более сбалансированных выборок данных необходимо воспользоваться методами приращения (аугментации) данных, а именно методами oversampling и undersampling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Функция для создания выборок\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",
    ") -> tuple[Any, Any, Any]:\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: DataFrame = df_input # Contains all columns.\n",
    "    y: DataFrame = 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, \n",
    "        stratify=y, \n",
    "        test_size=(1.0 - frac_train), \n",
    "        random_state=random_state\n",
    "    )\n",
    "\n",
    "    # Split the temp dataframe into val and test dataframes.\n",
    "    relative_frac_test: float = 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": 7,
   "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",
      "Распределение выборки данных по классам \"hazardous\":\n",
      " hazardous\n",
      "False    49197\n",
      "True      5304\n",
      "Name: count, dtype: int64\n",
      "Процент объектов класса \"False\": 90.27%\n",
      "Процент объектов класса \"True\": 9.73%\n",
      "\n",
      "Контрольная выборка:  (18167, 6)\n",
      "Распределение выборки данных по классам \"hazardous\":\n",
      " hazardous\n",
      "False    16399\n",
      "True      1768\n",
      "Name: count, dtype: int64\n",
      "Процент объектов класса \"False\": 90.27%\n",
      "Процент объектов класса \"True\": 9.73%\n",
      "\n",
      "Тестовая выборка:  (18168, 6)\n",
      "Распределение выборки данных по классам \"hazardous\":\n",
      " hazardous\n",
      "False    16400\n",
      "True      1768\n",
      "Name: count, dtype: int64\n",
      "Процент объектов класса \"False\": 90.27%\n",
      "Процент объектов класса \"True\": 9.73%\n",
      "\n",
      "Для обучающей выборки аугментация данных требуется\n",
      "Для контрольной выборки аугментация данных требуется\n",
      "Для тестовой выборки аугментация данных требуется\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Вывод распределения количества наблюдений по меткам (классам)\n",
    "print(df.hazardous.value_counts(), '\\n')\n",
    "\n",
    "data: DataFrame = df[[\n",
    "   'est_diameter_min', \n",
    "   'est_diameter_max', \n",
    "   'relative_velocity', \n",
    "   'miss_distance', \n",
    "   'absolute_magnitude', \n",
    "   'hazardous'\n",
    "]].copy()\n",
    "\n",
    "df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
    "   data, \n",
    "   stratify_colname=\"hazardous\", \n",
    "   frac_train=0.60, \n",
    "   frac_val=0.20, \n",
    "   frac_test=0.20\n",
    ")\n",
    "\n",
    "# Оценка сбалансированности\n",
    "def check_balance(dataframe: DataFrame, dataframe_name: str, column: str) -> None:\n",
    "    counts: Series[int] = dataframe[column].value_counts()\n",
    "    print(dataframe_name + \": \", dataframe.shape)\n",
    "    print(f\"Распределение выборки данных по классам \\\"{column}\\\":\\n\", counts)\n",
    "    total_count: int = len(dataframe)\n",
    "    for value in counts.index:\n",
    "        percentage: float = counts[value] / total_count * 100\n",
    "        print(f\"Процент объектов класса \\\"{value}\\\": {percentage:.2f}%\")\n",
    "    print()\n",
    "   \n",
    "# Определение необходимости аугментации данных\n",
    "def need_augmentation(dataframe: DataFrame,\n",
    "                      column: str, \n",
    "                      first_value: Any, second_value: Any) -> bool:\n",
    "    counts: Series[int] = dataframe[column].value_counts()\n",
    "    ratio: float = counts[first_value] / counts[second_value]\n",
    "    return ratio > 1.5 or ratio < 0.67\n",
    " \n",
    " # Визуализация сбалансированности классов\n",
    "def visualize_balance(dataframe_train: DataFrame,\n",
    "                      dataframe_val: DataFrame,\n",
    "                      dataframe_test: DataFrame, \n",
    "                      column: str) -> None:\n",
    "   fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
    "\n",
    "   # Обучающая выборка\n",
    "   counts_train: Series[int] = dataframe_train[column].value_counts()\n",
    "   axes[0].pie(counts_train, labels=counts_train.index, autopct='%1.1f%%', startangle=90)\n",
    "   axes[0].set_title(f\"Распределение классов \\\"{column}\\\" в обучающей выборке\")\n",
    "\n",
    "   # Контрольная выборка\n",
    "   counts_val: Series[int] = dataframe_val[column].value_counts()\n",
    "   axes[1].pie(counts_val, labels=counts_val.index, autopct='%1.1f%%', startangle=90)\n",
    "   axes[1].set_title(f\"Распределение классов \\\"{column}\\\" в контрольной выборке\")\n",
    "\n",
    "   # Тестовая выборка\n",
    "   counts_test: Series[int] = dataframe_test[column].value_counts()\n",
    "   axes[2].pie(counts_test, labels=counts_test.index, autopct='%1.1f%%', startangle=90)\n",
    "   axes[2].set_title(f\"Распределение классов \\\"{column}\\\" в тренировочной выборке\")\n",
    "\n",
    "   # Отображение графиков\n",
    "   plt.tight_layout()\n",
    "   plt.show()\n",
    " \n",
    "\n",
    "# Проверка сбалансированности\n",
    "check_balance(df_train, 'Обучающая выборка', 'hazardous')\n",
    "check_balance(df_val, 'Контрольная выборка', 'hazardous')\n",
    "check_balance(df_test, 'Тестовая выборка', 'hazardous')\n",
    "\n",
    "# Проверка необходимости аугментации\n",
    "print(f\"Для обучающей выборки аугментация данных {'не ' if not need_augmentation(df_train, 'hazardous', True, False) else ''}требуется\")\n",
    "print(f\"Для контрольной выборки аугментация данных {'не ' if not need_augmentation(df_val, 'hazardous', True, False) else ''}требуется\")\n",
    "print(f\"Для тестовой выборки аугментация данных {'не ' if not need_augmentation(df_test, 'hazardous', True, False) else ''}требуется\")\n",
    "   \n",
    "# Визуализация сбалансированности классов\n",
    "visualize_balance(df_train, df_val, df_test, 'hazardous')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Приращение данных:\n",
    "\n",
    "**Аугментация данных** может быть полезна в том случае, когда имеется недостаточное количество данных и мы хотим сгенерировать новые данные на основе имеющихся, слегка модифицировав их.\n",
    "\n",
    "**Методы решения:**\n",
    "1. **Выборка с избытком (oversampling).** Копирование наблюдений или генерация новых наблюдений на основе существующих с помощью алгоритмов SMOTE и ADASYN (нахождение k-ближайших соседей).\n",
    "2. **Выборка с недостатком (undersampling).** Исключение некоторых наблюдений для меток с большим количеством наблюдений. Наблюдения можно исключать случайным образом или на основе определения связей Томека для наблюдений разных меток."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "После применения метода oversampling:\n",
      "Обучающая выборка:  (100699, 6)\n",
      "Распределение выборки данных по классам \"hazardous\":\n",
      " hazardous\n",
      "True     51502\n",
      "False    49197\n",
      "Name: count, dtype: int64\n",
      "Процент объектов класса \"True\": 51.14%\n",
      "Процент объектов класса \"False\": 48.86%\n",
      "\n",
      "Контрольная выборка:  (32759, 6)\n",
      "Распределение выборки данных по классам \"hazardous\":\n",
      " hazardous\n",
      "False    16399\n",
      "True     16360\n",
      "Name: count, dtype: int64\n",
      "Процент объектов класса \"False\": 50.06%\n",
      "Процент объектов класса \"True\": 49.94%\n",
      "\n",
      "Тестовая выборка:  (33556, 6)\n",
      "Распределение выборки данных по классам \"hazardous\":\n",
      " hazardous\n",
      "True     17156\n",
      "False    16400\n",
      "Name: count, dtype: int64\n",
      "Процент объектов класса \"True\": 51.13%\n",
      "Процент объектов класса \"False\": 48.87%\n",
      "\n",
      "Для обучающей выборки аугментация данных не требуется\n",
      "Для контрольной выборки аугментация данных не требуется\n",
      "Для тестовой выборки аугментация данных не требуется\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Метод приращения с избытком (oversampling)\n",
    "def oversample(df: DataFrame, column: str) -> DataFrame:\n",
    "    X: DataFrame = df.drop(column, axis=1)\n",
    "    y: DataFrame = df[column] # type: ignore\n",
    "    \n",
    "    adasyn = ADASYN()\n",
    "    X_resampled, y_resampled = adasyn.fit_resample(X, y) # type: ignore\n",
    "    \n",
    "    df_resampled: DataFrame = pd.concat([X_resampled, y_resampled], axis=1)\n",
    "    return df_resampled\n",
    "\n",
    "\n",
    "# Приращение данных (oversampling)\n",
    "df_train_oversampled: DataFrame = oversample(df_train, 'hazardous')\n",
    "df_val_oversampled: DataFrame = oversample(df_val, 'hazardous')\n",
    "df_test_oversampled: DataFrame = oversample(df_test, 'hazardous')\n",
    "\n",
    "# Проверка сбалансированности\n",
    "print('После применения метода oversampling:')\n",
    "check_balance(df_train_oversampled, 'Обучающая выборка', 'hazardous')\n",
    "check_balance(df_val_oversampled, 'Контрольная выборка', 'hazardous')\n",
    "check_balance(df_test_oversampled, 'Тестовая выборка', 'hazardous')\n",
    "\n",
    "# Проверка необходимости аугментации\n",
    "print(f\"Для обучающей выборки аугментация данных {'не ' if not need_augmentation(df_train_oversampled, 'hazardous', True, False) else ''}требуется\")\n",
    "print(f\"Для контрольной выборки аугментация данных {'не ' if not need_augmentation(df_val_oversampled, 'hazardous', True, False) else ''}требуется\")\n",
    "print(f\"Для тестовой выборки аугментация данных {'не ' if not need_augmentation(df_test_oversampled, 'hazardous', True, False) else ''}требуется\")\n",
    "   \n",
    "# Визуализация сбалансированности классов\n",
    "visualize_balance(df_train_oversampled, df_val_oversampled, df_test_oversampled, 'hazardous')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "После применения метода undersampling:\n",
      "Обучающая выборка:  (10608, 6)\n",
      "Распределение выборки данных по классам \"hazardous\":\n",
      " hazardous\n",
      "False    5304\n",
      "True     5304\n",
      "Name: count, dtype: int64\n",
      "Процент объектов класса \"False\": 50.00%\n",
      "Процент объектов класса \"True\": 50.00%\n",
      "\n",
      "Контрольная выборка:  (3536, 6)\n",
      "Распределение выборки данных по классам \"hazardous\":\n",
      " hazardous\n",
      "False    1768\n",
      "True     1768\n",
      "Name: count, dtype: int64\n",
      "Процент объектов класса \"False\": 50.00%\n",
      "Процент объектов класса \"True\": 50.00%\n",
      "\n",
      "Тестовая выборка:  (3536, 6)\n",
      "Распределение выборки данных по классам \"hazardous\":\n",
      " hazardous\n",
      "False    1768\n",
      "True     1768\n",
      "Name: count, dtype: int64\n",
      "Процент объектов класса \"False\": 50.00%\n",
      "Процент объектов класса \"True\": 50.00%\n",
      "\n",
      "Для обучающей выборки аугментация данных не требуется\n",
      "Для контрольной выборки аугментация данных не требуется\n",
      "Для тестовой выборки аугментация данных не требуется\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Метод приращения с недостатком (undersampling)\n",
    "def undersample(df: DataFrame, column: str) -> DataFrame:\n",
    "    X: DataFrame = df.drop(column, axis=1)\n",
    "    y: DataFrame = df[column] # type: ignore\n",
    "    \n",
    "    undersampler = RandomUnderSampler()\n",
    "    X_resampled, y_resampled = undersampler.fit_resample(X, y) # type: ignore\n",
    "    \n",
    "    df_resampled: DataFrame = pd.concat([X_resampled, y_resampled], axis=1)\n",
    "    return df_resampled\n",
    "\n",
    "\n",
    "# Приращение данных (undersampling)\n",
    "df_train_undersampled: DataFrame = undersample(df_train, 'hazardous')\n",
    "df_val_undersampled: DataFrame = undersample(df_val, 'hazardous')\n",
    "df_test_undersampled: DataFrame = undersample(df_test, 'hazardous')\n",
    "\n",
    "# Проверка сбалансированности\n",
    "print('После применения метода undersampling:')\n",
    "check_balance(df_train_undersampled, 'Обучающая выборка', 'hazardous')\n",
    "check_balance(df_val_undersampled, 'Контрольная выборка', 'hazardous')\n",
    "check_balance(df_test_undersampled, 'Тестовая выборка', 'hazardous')\n",
    "\n",
    "# Проверка необходимости аугментации\n",
    "print(f\"Для обучающей выборки аугментация данных {'не ' if not need_augmentation(df_train_undersampled, 'hazardous', True, False) else ''}требуется\")\n",
    "print(f\"Для контрольной выборки аугментация данных {'не ' if not need_augmentation(df_val_undersampled, 'hazardous', True, False) else ''}требуется\")\n",
    "print(f\"Для тестовой выборки аугментация данных {'не ' if not need_augmentation(df_test_undersampled, 'hazardous', True, False) else ''}требуется\")\n",
    "   \n",
    "# Визуализация сбалансированности классов\n",
    "visualize_balance(df_train_undersampled, df_val_undersampled, df_test_undersampled, 'hazardous')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "aimenv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}