From 07039cc261f315adf06bbaf3d68010e56dc4180c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=95=D0=BB=D0=B5=D0=BD=D0=B0=20=D0=91=D0=B0=D0=BA=D0=B0?= =?UTF-8?q?=D0=BB=D1=8C=D1=81=D0=BA=D0=B0=D1=8F?= Date: Tue, 19 Nov 2024 02:19:09 +0400 Subject: [PATCH] =?UTF-8?q?=D0=B4=D0=B0=20=D0=BA=D0=BE=D1=80=D0=BE=D1=87?= =?UTF-8?q?=D0=B5=20=D0=BF=D0=BE=D1=81=D0=BB=D0=B0=D0=BB=D0=B0=20=D1=8F=20?= =?UTF-8?q?=D0=B2=D1=81=D0=B5=20=D1=8D=D1=82=D0=BE=20=D0=B8=20=D0=B2=D0=BE?= =?UTF-8?q?=D1=82=20=D0=BB=D0=B0=D0=B1=D0=B0...?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- lab_2/lab2.ipynb | 1125 ++++++++++++++++++++++------------------------ 1 file changed, 526 insertions(+), 599 deletions(-) diff --git a/lab_2/lab2.ipynb b/lab_2/lab2.ipynb index 20ba785..9096c9f 100644 --- a/lab_2/lab2.ipynb +++ b/lab_2/lab2.ipynb @@ -4,102 +4,48 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "

Уф.. начинаем длинную тяжелую лабу...

" + "## Лабораторная работа №2\n", + "\n", + "## Общие данные\n", + "\n", + "Типы пропущенных данных:\n", + "\n", + "None - представление пустых данных в Python \n", + "NaN - представление пустых данных в Pandas \n", + "' ' - пустая строка\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "3 набора данных, с которыми будет проводиться работа:\n", - "
    \n", - "
  1. \n", - "

    Объекты вокруг Земли

    \n", - " Ссылка \n", - "
    2. \n", - "
  2. \n", - "

    Оценки студентов на экзамене

    \n", - " Ссылка\n", - "
    3. \n", - "
  3. \n", - "

    Прогноз цены мобильного телефона

    \n", - " Ссылка\n", - "
    4. \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "

Информация о первом датасете:

\n", + "## 1 Датасет: NASA - Nearest Earth Objects \n", + "https://www.kaggle.com/datasets/sameepvani/nasa-nearest-earth-objects\n", "\n", - "

О наборе данных
\n", - "Контекст
\n", - "В космическом пространстве существует бесконечное количество объектов. Некоторые из них находятся ближе, чем мы думаем. Хотя нам может казаться, что расстояние в 70 000 км не может причинить нам вред, в астрономическом масштабе это очень маленькое расстояние, которое может нарушить многие природные явления. Таким образом, эти объекты/астероиды могут причинить вред. Поэтому разумно знать, что нас окружает и что может причинить нам вред. Таким образом, этот набор данных содержит список сертифицированных НАСА астероидов, которые классифицируются как ближайшие к Земле объекты.

\n", + "Перевод контекста со страницы на Kaggle: \n", + " В космическом пространстве существует бесконечное количество объектов. Некоторые из них находятся ближе, чем мы думаем. Несмотря на то, что мы можем думать, что расстояние в 70 000 км потенциально не может причинить нам вреда, по астрономическим меркам это очень небольшое расстояние, которое может нарушить многие природные явления. Таким образом, эти объекты/астероиды могут оказаться опасными. Следовательно, разумно знать, что нас окружает и что из этого может причинить нам вред. Таким образом, этот набор данных составляет список сертифицированных НАСА астероидов, которые классифицируются как ближайшие к Земле объекты.\n", "\n", - "
\n", - "

Информация о втором датасете:

\n", - "

О наборе данных
\n", - "Контекст
\n", - "Оценки, полученные студентами
\n", - "Содержание
\n", - "Этот набор данных состоит из оценок, полученных учениками по различным предметам.
\n", - "Благодарности
\n", - "http://roycekimmons.com/tools/generated_data/exams
\n", - "Вдохновение
\n", - "Понять влияние предыстории родителей, подготовки к тестированию и т.д. На успеваемость учащихся

\n", - "
\n", + "- По описаню можно понять, что объектами исследования являютя объекты, которые находятся в близи Земли\n", + "- Атрибуты обьекта: id, name, est_diameter_min, est_diameter_max, relative_velocity, miss_distance, orbiting_body, sentry_object, absolute_magnitude, hazardous\n", + "- В описании говорится о возможной опасности объектов, поэтому можно сделать вывод, что цель данного датасета научится определять опасность околоземных объектов\n", "\n", - "

Информация о третьем датасете:

\n", - "

О наборе данных
\n", - "Этот набор данных был собран путём сбора данных с онлайн-сайтов.\n", - "Столбцы выглядят следующим образом.\n", + "## 2 Датасет: Indicators of Heart Disease \n", + "https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease\n", "\n", - "Название: в этом столбце содержится название мобильного телефона.\n", + "Перевод контекста со страницы на Kaggle: \n", + " По данным Всемирной организации здравоохранения (ВОЗ), инсульт является второй по значимости причиной смертности во всем мире, на его долю приходится примерно 11% от общего числа смертей. Этот набор данных используется для прогнозирования вероятности инсульта у пациента на основе таких входных параметров, как пол, возраст, различные заболевания и статус курильщика. Каждая строка данных содержит соответствующую информацию о пациенте.\n", + "- Из этого описания очевидно что объектами иследования являются реальные пациенты.\n", + "- Атрибуты объектов: id, gender, age, hypertension, heart_disease, ever_married, work_type, Residence_type, avg_glucose_level, bmi, smoking_status, stroke\n", + "- Очевидная цель этого датасета - это научиться определять будет у человека сердечный приступ или нет.\n", "\n", - "Рейтинг: в этом столбце указаны оценки, выставленные телефону. Минимальная оценка — 0, максимальная — 5.\n", + "## 3 Датасет: Pima Indians Diabetes Database\n", + "https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database\n", "\n", - "Spec_score: в этом столбце указана оценка телефона на основе его характеристик. Минимальное значение — 0, максимальное — 100.\n", - "\n", - "No_of_sim: в этом столбце указано, поддерживает ли телефон две SIM-карты, 3G, 4G, 5G, LTE.\n", - "\n", - "Оперативная память: В этом столбце содержится информация о оперативной памяти телефона\n", - "\n", - "Аккумулятор: В этой колонке представлена информация о характеристиках аккумулятора телефона.\n", - "\n", - "Дисплей: В этом столбце содержится информация о размере экрана телефона.\n", - "\n", - "Камера: В этой колонке представлена информация о камере, задней и фронтальной.\n", - "\n", - "Внешняя_память: этот столбец содержит информацию о том, поддерживает ли устройство внешнюю память и\n", - "какой объём памяти.\n", - "\n", - "Android_version: этот столбец сообщает нам о версии Android на телефоне.\n", - "\n", - "Цена: Цена телефона.\n", - "\n", - "Компания: Компания, которой принадлежит телефон.\n", - "\n", - "Встроенная_память: в этом столбце представлена информация о встроенной памяти телефона.\n", - "\n", - "быстрая_зарядка: показывает, поддерживает ли устройство быструю зарядку. Если да, то насколько.\n", - "\n", - "Screen_resolution: Это описывает разрешение экрана телефона.\n", - "\n", - "Процессор: В этом столбце приведена информация о процессоре телефона.\n", - "\n", - "Имя_процессора: в этом столбце описывается название процессора.\n", - "
\n", - "

\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

начинаем...
первое...
Проблемная область: Это данные о ближайших к Земле объектах (астероиды и кометы), которые могут угрожать нашей планете. Важно анализировать их траектории, размеры и скорость для предотвращения потенциальных катастроф.
Объекты наблюдения: Астероиды, кометы и другие объекты.
Атрибуты: 'id', 'name', 'est_diameter_min', 'est_diameter_max', 'relative_velocity', 'miss_distance', 'orbiting_body', 'sentry_object', 'absolute_magnitude', 'hazardous'
Связи между объектами: Нет явных связей между объектами, но можно изучать корреляции между размером, скоростью и расстоянием объекта.

" + "Перевод контекста со страницы на Kaggle: \n", + " Этот набор данных изначально был получен из Национального института диабета, заболеваний пищеварительной системы и почек. Целью набора данных является диагностическое прогнозирование наличия или отсутствия у пациента диабета на основе определенных диагностических измерений, включенных в набор данных. На выбор этих случаев из более крупной базы данных налагалось несколько ограничений. В частности, все пациенты здесь — женщины в возрасте не менее 21 года индейского происхождения пима.\n", + "- объект иследования - женьщины индейци пима\n", + "- очевидно цель датасета это предсказание диабета.\n", + "- атрибуты: Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age, Outcome" ] }, { @@ -111,343 +57,29 @@ "name": "stdout", "output_type": "stream", "text": [ - "вот столько колонОчек 10\n", - "вот такие колонОчки: ['id', 'name', 'est_diameter_min', 'est_diameter_max', 'relative_velocity', 'miss_distance', 'orbiting_body', 'sentry_object', 'absolute_magnitude', 'hazardous']\n" + "колонки: id, name, est_diameter_min, est_diameter_max, relative_velocity, miss_distance, orbiting_body, sentry_object, absolute_magnitude, hazardous\n", + "колонки: id, gender, age, hypertension, heart_disease, ever_married, work_type, Residence_type, avg_glucose_level, bmi, smoking_status, stroke\n", + "колонки: Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age, Outcome\n" ] } ], "source": [ "import pandas as pd\n", "\n", - "data = pd.read_csv(\"./csv/1.csv\", sep=\",\")\n", - "print(\"вот столько колонОчек\", data.columns.size)\n", - "print(\"вот такие колонОчки:\", data.columns.tolist()) " + "neo = pd.read_csv(\"..//static//csv//neo_v2.csv\", sep=\",\")\n", + "healthcare = pd.read_csv(\"..//static//csv//healthcare-dataset-stroke-data.csv\", sep=\",\")\n", + "diabetes = pd.read_csv(\"..//static//csv//diabetes.csv\", sep=\",\")\n", + "\n", + "print('колонки околоземных обьектов: ' + ', '.join(neo.columns))\n", + "print('колонки пациентов: ' + ', '.join(healthcare.columns))\n", + "print('колонки индейцев: ' + ', '.join(diabetes.columns))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "

\n", - "Получение сведений о пропущенных данных
Типы пропущенных данных:
None - представление пустых данных в Python
NaN - представление пустых данных в Pandas
'' - пустая строка\n", - "

" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "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": [ - "# Проверим, есть ли пропущенные значения\n", - "print(data.isnull().sum(), \"\\n\")\n", - "\n", - "# Есть ли пустые значения признаков\n", - "print(data.isnull().any(), \"\\n\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Тут понятно, что пропущенных значений нет, поэтому заполнять пустые места не нужно
И еще на сайте видно, что колонки \"orbiting_body\" и \"sentry_object\" не имеют никаких значений кроме \"Земля\" и \"false\" соответственно. Значит удалим их

" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Index(['id', 'name', 'est_diameter_min', 'est_diameter_max',\n", - " 'relative_velocity', 'miss_distance', 'absolute_magnitude',\n", - " 'hazardous'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "data = data.drop(columns=['sentry_object'])\n", - "data = data.drop(columns=['orbiting_body'])\n", - "print(data.columns)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

при просмотре типа данных на сайте kaggle выяснилось, что числовые колонки - это 3-7. По ним и будем просматриватьвыбросы и усреднять значения

" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "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: 0.32962154705\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: 0.7370561859\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: 114380.48061454494\n", - " 1-й квартиль (Q1): 28619.02064490995\n", - " 3-й квартиль (Q3): 62923.60463276395\n", - "\n", - "Колонка miss_distance:\n", - " Есть выбросы: Нет\n", - "Колонка absolute_magnitude:\n", - " Есть выбросы: Да\n", - " Количество выбросов: 101\n", - " Минимальное значение: 14.8\n", - " Максимальное значение: 32.239999999999995\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(data[column]): # Проверяем, является ли колонка числовой\n", - " q1 = data[column].quantile(0.25) # Находим 1-й квартиль (Q1)\n", - " q3 = data[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 = data[(data[column] < lower_bound) | (data[column] > upper_bound)]\n", - " outlier_count = outliers.shape[0]\n", - "\n", - " # Устраняем выбросы: заменяем значения ниже нижней границы на саму нижнюю границу, а выше верхней — на верхнюю\n", - " data[column] = data[column].apply(lambda x: lower_bound if x < lower_bound else upper_bound if x > upper_bound else x)\n", - "\n", - " print(f\"Колонка {column}:\")\n", - " print(f\" Есть выбросы: {'Да' if outlier_count > 0 else 'Нет'}\")\n", - " if(outlier_count > 0) :\n", - " print(f\" Количество выбросов: {outlier_count}\")\n", - " print(f\" Минимальное значение: {data[column].min()}\")\n", - " print(f\" Максимальное значение: {data[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": [ - "

так, теперь мы построим диаграммы, чтобы найти зависимость значения \"опасен ли объект\" от других колонок

" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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", 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", 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" - ] - }, - "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(data['hazardous'], data[column], alpha=0.5) # Создаем диаграмму рассеяния\n", - " plt.title(f'Зависимость {column} от hazardous')\n", - " plt.xlabel('hazardous (0 = не опасно, 1 = опасно)')\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": "code", - "execution_count": null, - "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", - " data_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", - " data_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", - " data_train, data_val, data_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 data_input.columns:\n", - " raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n", - "\n", - " X = data_input # Contains all columns.\n", - " y = data_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", - " data_train, data_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", - " data_val, data_test, y_val, y_test = train_test_split(\n", - " data_temp,\n", - " y_temp,\n", - " stratify=y_temp,\n", - " test_size=relative_frac_test,\n", - " random_state=random_state,\n", - " )\n", - "\n", - " assert len(data_input) == len(data_train) + len(data_val) + len(data_test)\n", - "\n", - " return data_train, data_val, data_test" + "#Проверим пустые занчения" ] }, { @@ -459,248 +91,543 @@ "name": "stdout", "output_type": "stream", "text": [ - "hazardous\n", - "False 81996\n", - "True 8840\n", - "Name: count, dtype: int64\n", + "Околоземные обьекты\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", "\n", - "Обучающая выборка: (54501, 6)\n", - "hazardous\n", - "False 49197\n", - "True 5304\n", - "Name: count, dtype: int64\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", + "Пациенты\n", + "id 0\n", + "gender 0\n", + "age 0\n", + "hypertension 0\n", + "heart_disease 0\n", + "ever_married 0\n", + "work_type 0\n", + "Residence_type 0\n", + "avg_glucose_level 0\n", + "bmi 201\n", + "smoking_status 0\n", + "stroke 0\n", + "dtype: int64\n", + "\n", + "id False\n", + "gender False\n", + "age False\n", + "hypertension False\n", + "heart_disease False\n", + "ever_married False\n", + "work_type False\n", + "Residence_type False\n", + "avg_glucose_level False\n", + "bmi True\n", + "smoking_status False\n", + "stroke False\n", + "dtype: bool\n", + "\n", + "bmi процент пустых значений: %3.93\n", + "\n", + "Индейцы\n", + "Pregnancies 0\n", + "Glucose 0\n", + "BloodPressure 0\n", + "SkinThickness 0\n", + "Insulin 0\n", + "BMI 0\n", + "DiabetesPedigreeFunction 0\n", + "Age 0\n", + "Outcome 0\n", + "dtype: int64\n", + "\n", + "Pregnancies False\n", + "Glucose False\n", + "BloodPressure False\n", + "SkinThickness False\n", + "Insulin False\n", + "BMI False\n", + "DiabetesPedigreeFunction False\n", + "Age False\n", + "Outcome False\n", + "dtype: bool\n", + "\n" ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "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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", - "text/plain": [ - "
" - ] - }, - "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": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ - "# Вывод распределения количества наблюдений по меткам (классам)\n", - "print(data.hazardous.value_counts())\n", + "# Околоземные обьекты\n", + "print(\"Околоземные обьекты\")\n", + "# Количество пустых значений признаков\n", + "print(neo.isnull().sum())\n", + "\n", "print()\n", "\n", + "# Есть ли пустые значения признаков\n", + "print(neo.isnull().any())\n", "\n", - "data = data[['est_diameter_min', 'est_diameter_max', 'relative_velocity', 'miss_distance', 'absolute_magnitude', 'hazardous']].copy()\n", + "print()\n", "\n", - "data_train, data_val, data_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", + "# Процент пустых значений признаков\n", + "for i in neo.columns:\n", + " null_rate = neo[i].isnull().sum() / len(neo) * 100\n", + " if null_rate > 0:\n", + " print(f\"{i} процент пустых значений: %{null_rate:.2f}\\n\")\n", "\n", - "print(\"Обучающая выборка: \", data_train.shape)\n", - "print(data_train.hazardous.value_counts())\n", - "hazardous_counts = data_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", + "# Пациенты\n", + "print(\"Пациенты\")\n", + "# Количество пустых значений признаков\n", + "print(healthcare.isnull().sum())\n", "\n", - "print(\"Контрольная выборка: \", data_val.shape)\n", - "print(data_val.hazardous.value_counts())\n", - "hazardous_counts = data_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", + "print()\n", "\n", - "print(\"Тестовая выборка: \", data_test.shape)\n", - "print(data_test.hazardous.value_counts())\n", - "hazardous_counts = data_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": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Обучающая выборка после oversampling: (100249, 6)\n", - "hazardous\n", - "True 51052\n", - "False 49197\n", - "Name: count, dtype: int64\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from imblearn.over_sampling import ADASYN\n", + "# Есть ли пустые значения признаков\n", + "print(healthcare.isnull().any())\n", "\n", - "# Создание экземпляра ADASYN\n", - "ada = ADASYN()\n", + "print()\n", "\n", - "# Применение ADASYN\n", - "X_resampled, y_resampled = ada.fit_resample(data_train.drop(columns=['hazardous']), data_train['hazardous'])\n", + "# Процент пустых значений признаков\n", + "for i in healthcare.columns:\n", + " null_rate = healthcare[i].isnull().sum() / len(healthcare) * 100\n", + " if null_rate > 0:\n", + " print(f\"{i} процент пустых значений: %{null_rate:.2f}\\n\")\n", "\n", - "# Создание нового DataFrame\n", - "data_train_adasyn = pd.DataFrame(X_resampled)\n", - "data_train_adasyn['hazardous'] = y_resampled # Добавление целевой переменной\n", "\n", - "# Вывод информации о новой выборке\n", - "print(\"Обучающая выборка после oversampling: \", data_train_adasyn.shape)\n", - "print(data_train_adasyn['hazardous'].value_counts())\n", - "hazardous_counts = data_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()" + "# Индейцы\n", + "print(\"Индейцы\")\n", + "# Количество пустых значений признаков\n", + "print(diabetes.isnull().sum())\n", + "\n", + "print()\n", + "\n", + "# Есть ли пустые значения признаков\n", + "print(diabetes.isnull().any())\n", + "\n", + "print()\n", + "\n", + "# Процент пустых значений признаков\n", + "for i in diabetes.columns:\n", + " null_rate = diabetes[i].isnull().sum() / len(diabetes) * 100\n", + " if null_rate > 0:\n", + " print(f\"{i} процент пустых значений: %{null_rate:.2f}\\n\")" ] }, { "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.

" + "После этого видно, что в атрибуде bmi датасета Indicators of Heart Disease есть пустые значения, заполним значением Unknown\n" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "healthcare['bmi'] = healthcare['bmi'].fillna('Unknown')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Проверим датасет по числовым данным, для выявления аномальных распределений" + ] + }, + { + "cell_type": "code", + "execution_count": 12, "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" + " 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 \n", + " id age hypertension heart_disease \\\n", + "count 5110.000000 5110.000000 5110.000000 5110.000000 \n", + "mean 36517.829354 43.226614 0.097456 0.054012 \n", + "std 21161.721625 22.612647 0.296607 0.226063 \n", + "min 67.000000 0.080000 0.000000 0.000000 \n", + "25% 17741.250000 25.000000 0.000000 0.000000 \n", + "50% 36932.000000 45.000000 0.000000 0.000000 \n", + "75% 54682.000000 61.000000 0.000000 0.000000 \n", + "max 72940.000000 82.000000 1.000000 1.000000 \n", + "\n", + " avg_glucose_level bmi stroke \n", + "count 5110.000000 5110.000000 5110.000000 \n", + "mean 106.147677 28.893237 0.048728 \n", + "std 45.283560 7.698018 0.215320 \n", + "min 55.120000 10.300000 0.000000 \n", + "25% 77.245000 23.800000 0.000000 \n", + "50% 91.885000 28.400000 0.000000 \n", + "75% 114.090000 32.800000 0.000000 \n", + "max 271.740000 97.600000 1.000000 \n", + " Pregnancies Glucose BloodPressure SkinThickness Insulin \\\n", + "count 768.000000 768.000000 768.000000 768.000000 768.000000 \n", + "mean 3.845052 120.894531 69.105469 20.536458 79.799479 \n", + "std 3.369578 31.972618 19.355807 15.952218 115.244002 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 1.000000 99.000000 62.000000 0.000000 0.000000 \n", + "50% 3.000000 117.000000 72.000000 23.000000 30.500000 \n", + "75% 6.000000 140.250000 80.000000 32.000000 127.250000 \n", + "max 17.000000 199.000000 122.000000 99.000000 846.000000 \n", + "\n", + " BMI DiabetesPedigreeFunction Age Outcome \n", + "count 768.000000 768.000000 768.000000 768.000000 \n", + "mean 31.992578 0.471876 33.240885 0.348958 \n", + "std 7.884160 0.331329 11.760232 0.476951 \n", + "min 0.000000 0.078000 21.000000 0.000000 \n", + "25% 27.300000 0.243750 24.000000 0.000000 \n", + "50% 32.000000 0.372500 29.000000 0.000000 \n", + "75% 36.600000 0.626250 41.000000 1.000000 \n", + "max 67.100000 2.420000 81.000000 1.000000 \n" ] - }, - { - "data": { - "image/png": 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t21dYWVkJlUplFOeWLVv00xISEoSHh4eoV6+eflr2/SmE8bmrIHVeq1YtUbFiRZGUlKSfduTIEaM6z49CdWVp27YtXF1dUalSJbzzzjuwt7fHtm3b9N8MlEol5PLMojUaDZ49e6b/eePvv//Wl7Nlyxa4uLjgo48+MlrHywy1NmDAADg4OOjfv/322/Dw8MDvv/8OALh48SIiIiLw7rvv4tmzZ4iNjUVsbCySk5PRpk0b/Pnnn0YPAKhUKlhbW+e63q1bt0Kr1aJ37976MmNjY+Hu7o5q1aoZ/WyTnp4OAPo7G6Zs2rQJZcqUQbt27QzKDAwMhL29vVGZGRkZBvPFxsZCpVLlGvejR4+wcOFCTJo0yWjIvk2bNqFGjRrw8/MzKFPXfSn7+nPy119/oXfv3ujZsydmz55tcp781AcAgz5tz58/R0JCApo3b27QtrZv3w6tVouvv/5a3xZ1dG3rwIEDSEpKwpdffmm0b3XznDt3DtHR0Rg1apTBPCEhIfDz88Pu3bsNltONJRwbG4uLFy9izZo18PDwQI0aNXLdJp2RI0cavG/evLnRuMT52f6skpOT0b17d5QrVw4bNmzQ949VKBT6PuJarRZxcXFQq9Vo0KCBybJ69uwJV1dX/XuNRoN9+/ahW7dueOONN/TTa9SogeDgYINldcfeJ598YjD9008/BQCjesyPsmXL4urVq4iIiCjwskBm3enGfZ47dy4UCgWCgoIKVVZORo8erf+/TCbD6NGjkZ6ejoMHDwKQ/vjWyeucae7j4ODBg0hPT8eYMWMMjs9hw4bB0dHRoLzy5cvj4cOHuW5jQeqnsG0xJSUF06ZNw+jRow3ae17S0tIQGxuL6OhoHDhwAIcOHUKbNm1Mlh8bG4vnz58bdXfKySeffGLQ3/29996Dm5ubfhsKut+AvNtsVp999hnWr1+P3377zehX5oK27cLK7zbGxMTgzz//xJAhQ4z2n6l8Iz09Pddr0YEDBxAfH4++ffsabJ9CoUDjxo3Ntn3Z9enTB+XKldO/1/26rLtOPHnyBBcvXsTAgQNRpkwZ/Xzt2rWDv7+/QVkF3UdeXl5G53bdrxE7duzI8cHJgl5revXqZRB748aNAQD9+/eHhYWFwfT09HSjLkuenp7o3r27/r2joyMGDBiACxcu4OnTpyZjzE1edf748WOEhYVhwIABBufYoKAg1KpVq8DrK1RXlsWLF6N69eqwsLCAm5sbfH19DU6uWq0W8+fPx5IlS3Dnzh2DPrK67i5AZhcYX19fg4o2h2rVqhm8l8lk8PHx0fdH1F3IBw4cmGMZCQkJBjsiNjbWqNzsIiIiIITIcb7sP9vp+lLmNn51REQEEhIS9N0PsouOjjZ4v3//foPkKT8mT54MT09PjBgxwqivckREBK5fv55jmdnXb8qjR48QEhKC5ORkPHv2LMcvXfmpDwDYtWsXpk+fjosXLxr0Cc1abmRkJORyudGJKCtdF6yAgIAc57l37x4AwNfX1+gzPz8/HD9+3GDagwcPDOrKw8MDW7ZsydcY5dbW1kb1XK5cOaO+fvnZ/qyGDRuGyMhInDx50uD4A4DVq1fj+++/x40bN5CRkaGf7uXlZVRO9mkxMTFITU012d59fX31CRCQWY9yuRw+Pj4G87m7u6Ns2bL6ei6IadOmoWvXrqhevToCAgLQoUMHvPfee/rRM/JSv359/f+VSiUWLVpksgtbYcnlclStWtVgWvXq1QHA4Fwk5fGtk9c509zHQU7lWVlZoWrVqgbtoUmTJti8eTN69+6N+vXrQyaT4cWLFya3Iz/1U9i2OGfOHKhUKkyYMMEoqc/Nxo0bsXHjRv37hg0bmnyeYfLkyZg8eTKAzHNB69atMW/ePJPHl+5Y9/PzM5iuUChQrVq1Qu+3/LRZnR9++AGnT58GAJN9xgvatgsrv9uoS6JyO99nlZCQkOe1Gfj3GbvsHB0d87We3Jg6p2f/UqHLU3T7QFcfOZ2XsybCBd1Hpq4Lffr0wfLlyzF06FB8+eWXaNOmDXr06IG3337bIC8syLUm+zbqkvRKlSqZnJ69/fn4+BjVXdZ27O7ubnJ7c5LfOs9+TtFNy+mmWU4KlRE3atRIPyqLKTNnzsSkSZMwZMgQfPPNN3BycoJcLseYMWOKxVA0uhhmz55t1LdJJ+sBmZ6ejidPnuTZN0mr1UImk2HPnj0mn9rPfpDrvrnl1ki0Wi3Kly+P9evXm/w8+0WocePGmD59usG0RYsWYceOHSaXv379OlatWoV169aZ7O+n1WpRq1YtzJkzx+Ty2Q8UU27duoX69etj7ty5eO+997B69WqTX4ryUx/Hjh1Dly5d0KJFCyxZsgQeHh6wtLTEypUrjR7YlIKbmxvWrVsHIPPEvmLFCnTo0AHHjx/P85tzfkZ6KOj2z58/Hxs2bMC6deuM2vq6deswaNAgdOvWDZ999hnKly8PhUKB0NBQ/ZeWrMzx9P3L/BKW/SHYFi1aIDIyEjt27MD+/fuxfPlyzJ07F8uWLcPQoUPzLG/dunVwc3ODSqXCoUOH8OGHH8La2trgAdOiJvXxXVRe5jjI7ttvv0WnTp3QoUOHPOctSP0UpC3GxsZi9uzZGD9+PJycnPK9HAC0b98en332GYDM/vazZs1Cq1atcO7cOYNjavjw4ejVqxc0Gg2uX7+OKVOmoFu3biYfJCwOI2GcPn0aM2bMwNmzZzF27Fh06NABLi4u+s8L2raLk7i4OKSnp+d5bQYy+5mbmi+vG466u/upqakmP09JSTH5K31O14n8/sqSVUH3kal2Z2Njgz///BOHDx/G7t27sXfvXvz6669o3bo19u/fD4VCUeBrTU7baM5tL4hXvV7z3qr+x+bNm9GqVSv8/PPPBtPj4+MNDlxvb2+cOXMGGRkZZr1oZP9pWwiBW7du6e+k6R4qdXR0zPFBr6wuXbqEjIyMXL+M6MoVQsDLy0v/7Sw3165dg0wmM/lNP2uZBw8eRNOmTfN1MnZxcTHaptwe0Bw/fjzq1q2LPn365Lj+S5cuoU2bNoVOqnQ/ibu5uWHHjh349NNP0alTJ6ODPj/1sWXLFlhbW2Pfvn0GPzOuXLnSKG6tVotr167l+OVL1w6uXLli8psuAFSuXBlA5kMv2e+MhIeH6z/Xsba2Nqj/Ll26wMnJCYsWLcpxtJKCyO/2A5lJ/Lhx4zBmzBj069fP6PPNmzejatWq+lEmdHR37fKiG9XAVFeS8PBwg/eVK1eGVqtFRESEQbeeqKgoxMfHG9RjuXLljEbm0H05zs7JyQmDBw/G4MGD8eLFC7Ro0QJTpkzJV2LetGlT/cNZnTt3xtWrVxEaGmq2xFyr1eL27dsG54KbN28CgH69Uh/fOnmdM819HGQtL+sd2vT0dNy5c8dgWR8fH1y9ehVhYWH6ERj2799vsktcfuqnIG1RZ/r06XBwcMB//vMfo8/y4uHhYRCTr68vmjRpgu3btxsML1ytWjX9fMHBwUhJScHEiRMNRnDR0d1lzF5/uu2qV6+eflt18+Vnv+WnzeoMGTIEEyZMwOPHj+Hv74+xY8caPBxd0LZdWPndRl096Ya9zI1uxKncuiDqrh/ly5fPVx6RW9y6rhFZ3bx5M993902Vm5/zsrn2kVwuR5s2bdCmTRvMmTMHM2fOxMSJE3H48GG0bdv2pa81BXXr1i0IIQzWlVM7Ngddnd+6dctkLAVlluESs1MoFEbfJDZt2mTUD6hnz56IjY3FokWLjMp4mW8ia9asQVJSkv795s2b8eTJE3Ts2BEAEBgYCG9vb3z33XcmfxKNiYkxil2hUJgciiurHj16QKFQYOrUqUbxCyHw7Nkz/Xu1Wo0tW7agUaNGuf5c1rt3b2g0GnzzzTdGn6nVaqMEpiBOnTqFHTt24L///W+OSXfv3r3x6NEj/PTTT0afpaamIjk5Oc/1VK9eXT9Kx8KFC6HVao0ucPmtD4VCAZlMZnD39O7du0YX327dukEul2PatGlGv9Lo9k379u3h4OCA0NBQo36ounkaNGiA8uXLY9myZQbdRvbs2YPr168bjAJjSnp6OtRqdb6GlsyP/G7/kydP0Lt3bzRr1izHPv26uwBZ2+qZM2dw6tSpfMcSHByM7du3GyQP169fx759+wzm1Y2oNG/ePIPpul9istajt7c3/vzzT4P5fvzxR6M75lmPJyDzFykfH59C13VqaqrZ9pNO1nObEAKLFi2CpaWlvo+x1Me3Tl7nTHMfB23btoWVlRUWLFhg0P5+/vlnJCQkGJVnaWmJ+vXro23btmjbtm2uXdTyUpC2CGQeX0uXLsWUKVPMkmDq7pDm1dZ05y1Td+vatGkDpVKJBQsWGJzf1q9fj6ioKP21qjD7La82q6NLJj09PTFr1iysW7fOYCjaomzbWeV3G11dXdGiRQusWLHC6MtO9uv1xo0bYWVlhWbNmuW43uDgYDg6OmLmzJkGXTN0sucR2QUGBqJ8+fJYvny5UVvYvn07Hj16pD/+CsLDwwN169bF6tWrkZCQoJ9+4MAB/RcOHXPsI1PDc+puhum262WvNQX1+PFjg9GVEhMTsWbNGtStW7fA3Vjyw9PTEwEBAVizZo1BTnn06FGEhYUVuLwiuWPeuXNnTJs2DYMHD0aTJk0QFhaG9evXG/VdGzBgANasWYNPPvkEf/31F5o3b47k5GQcPHgQo0aNQteuXQu1ficnJzRr1gyDBw9GVFQU5s2bBx8fHwwbNgxA5re75cuXo2PHjqhZsyYGDx6MChUq4NGjRzh8+DAcHR2xc+dOJCcnY/HixViwYAGqV69uMH6prvIvX76MU6dO4a233oK3tzemT5+O8ePH4+7du+jWrRscHBxw584dbNu2DcOHD8e4ceNw8OBBTJo0CZcvX8bOnTtz3ZagoCCMGDECoaGhuHjxItq3bw9LS0tERERg06ZNmD9/Pt5+++1C1dP+/fvRrl27XL/tv/fee/jtt98wcuRIHD58GE2bNoVGo8GNGzfw22+/6cc0zS93d3fMnj0bQ4cORf/+/dGpU6cC1UdISAjmzJmDDh064N1330V0dDQWL14MHx8fXL58WT+fj48PJk6ciG+++QbNmzdHjx49oFQqcfbsWXh6eiI0NBSOjo6YO3cuhg4dioYNG+Ldd99FuXLlcOnSJaSkpGD16tWwtLTErFmzMHjwYAQFBaFv3776obiqVKmCsWPHGsSXnJxs8BP+2rVroVKpDB5EeRn53f6PP/4YMTEx+Pzzzw36twJA7dq1Ubt2bXTu3Blbt25F9+7dERISgjt37mDZsmXw9/fPsQ9vdlOnTsXevXvRvHlzjBo1Cmq1GgsXLkTNmjUN4qlTpw4GDhyIH3/8EfHx8QgKCsJff/2F1atXo1u3bmjVqpV+3qFDh2LkyJHo2bMn2rVrh0uXLmHfvn0Gv7YBgL+/P1q2bInAwEA4OTnh3Llz2Lx5s8HDa7nZvn07XFxc9F1Zjh07hjFjxuRr2fywtrbG3r17MXDgQDRu3Bh79uzB7t27MWHCBP2vRVIf3zp5nTPNfRy4urpi/PjxmDp1Kjp06IAuXbogPDwcS5YsQcOGDdG/f/9CbXN+FKQtApkX1xo1amDw4MGFWt/t27f1dfHo0SMsWrQIjo6ORolueHg49u7dq/+lb/bs2WjYsKHJ4dacnJzw1VdfYdKkSQgODkbXrl1x+/ZtLFq0CHXq1NH/YlTQ/ZafNmvK8OHD8csvv2DkyJH6vwBelG07q4Js44IFC9CsWTPUr18fw4cPh5eXF+7evYvdu3frB4WYPHkyNmzYgC+//DLXfuKOjo5YunQp3nvvPdSvXx/vvPMOXF1dcf/+fezevRtNmzY1edNRx8rKCt999x0GDhyIhg0bok+fPnB2dsaFCxewYsUK1K5dWz9UY0GFhoYiJCQEzZo1w5AhQxAXF6c/L2c9t5tjH02bNg1//vknQkJCULlyZURHR2PJkiWoWLGi/ouNOa41BVG9enW8//77OHv2LNzc3LBixQpERUWZ/GXZXGbOnImuXbuiadOmGDx4MJ4/f45FixYhICCg4NtYkCFc8hreR0elUolPP/1UeHh4CBsbG9G0aVNx6tQpk8OLpaSkiIkTJwovLy9haWkp3N3dxdtvvy0iIyOFEIUbLnHDhg1i/Pjxonz58sLGxkaEhIQYDY8khBAXLlwQPXr0EM7OzkKpVIrKlSuL3r17iz/++MNg3Xm9sg+HtWXLFtGsWTNhZ2cn7OzshJ+fn/jwww9FeHi4EEKIjz76SLRo0cJo2CEhTA8PKETmcGOBgYHCxsZGODg4iFq1aonPP/9cPH78WD9PQYdTk8lk4vz58wbTTe2j9PR0MWvWLFGzZk2hVCpFuXLlRGBgoJg6dapISEgwWl9e5QkhROvWrcUbb7whkpKSClwfP//8s6hWrZpQKpXCz89PrFy5Msd6W7FihahXr54+7qCgIHHgwAGDef73v/+JJk2aCBsbG+Ho6CgaNWokNmzYYDDPr7/+qi/HyclJ9OvXTz88qI5uuDHdy97eXtSvX1+sXbs21zrSLWtqaLDCbn9QUFCO7VU3jJ5WqxUzZ84UlStXFkqlUtSrV0/s2rUrxyGjchrO7ejRoyIwMFBYWVmJqlWrimXLlpmMOyMjQ0ydOlV/rFeqVEmMHz/eYKgrIYTQaDTiiy++EC4uLsLW1lYEBweLW7duGQ09N336dNGoUSNRtmxZYWNjI/z8/MSMGTNEenp6rnWtO4/pXlZWVsLHx0d8/fXXRrHkJq/hEu3s7ERkZKRo3769sLW1FW5ubmLy5MlGw3cKId3xXdBzprmPg0WLFgk/Pz9haWkp3NzcxAcffCCeP39usk6zepnhEoXIf1vUDb22bds2o23M73CJWevCxcVFtG/fXpw6dUo/T/brjFwuFxUrVhQDBw7U121O57fFixcb1N+IESPEs2fPjObL737LT5vNOlxiVuHh4cLa2lqMHTvWYHp+2rYp+R0usSDbKIQQV65cEd27dxdly5YV1tbWwtfXV0yaNEkIIcSGDRtEQECAmD9/vtEQiqaG19NNDw4OFmXKlBHW1tbC29tbDBo0SJw7dy7X7dPZs2ePaNWqlXB0dBSWlpbCy8tLfPLJJ0bHQW7n4azndZ0tW7aIGjVqCKVSKfz9/cXWrVtzbLcvc/75448/RNeuXYWnp6ewsrISnp6eom/fvuLmzZv6eV72WqOr+02bNhlMN5WT6uLct2+fqF27tv46mX3ZggyXmN8637hxo/Dz8xNKpVIEBASI//3vf6Jnz57Cz8/PaPncyP5ZQYlw5MgRtGrVCps2bTLLN/G7d+/Cy8sLd+7cybFf0pQpU3D37l2Dv4JGRKXboEGDsHnz5iK5G2RO5j5n0uvrdWmzRLmpUqUKAgICsGvXLqlDAZDZrcfV1TXPv0idVZH0MSciIiIiKg0yMjKgVqsNph05cgSXLl1Cy5YtC1RWkfQxLyns7e3Rr1+/XB9GrF27Njw9PV9hVERERERUXDx69Aht27ZF//794enpiRs3bmDZsmVwd3c3+sOBeWFingsXFxf9Qzs56dGjxyuKhoiIiIiKm3LlyiEwMBDLly9HTEwM7OzsEBISgv/+979Gf9gvLyWqjzkRERER0euKfcyJiIiIiIoBJuZERERERMUAE3MiIiIiomKAiTkRERERUTHAxJyIiIiIqBhgYk5EREREVAwwMSciIiIiKgaYmBMRERERFQNMzImIiIiIigEm5kRERERExQATcyIiIiKiYoCJORERERFRMcDEnIiIiIioGGBiTkRERERUDDAxJyIiIiIqBpiYExEREREVA0zMiYiIiIiKASbmRERERETFgIXUARARUe60WoFnyemISlQhJikNUYkqRCelISE1A2qNFmqtwJd2u+CQEQvILQBLW8DBHbB3Axw8AId//rVQSr0pRESUC5kQQkgdBBERZUpJV+Pa40RcfpiAK48SEPYoAXdik6HW5n6qvl5hOmyeXcu9cHs3wKMO4FEX8KwLeNYDHD3NFjsREb0c3jEnIpKQWqPFmTtxOHg9CiduxeJW9AvkkYMX3osoIGJ/5kvHrjzwxptA9Q6ZLzvnIlo5ERHlhXfMiYhesYTUDBwJj8bB69E4Gh6NRJX6pcvM1x3zvMjkQMVGgG9HwLcT4Fr9peMiIqL8Y2JORPSKnL8Xh7Wn7uH3K0+RrtaatWyzJObZedYDGg4FAnoCljbmLZuIiIwwMSciKkIp6Wpsv/AY607fw7UniUW2niJJzHVsygF1+wENhgDO3kWzDiIiYmJORFQUElIz8MPRSKw9fQ9JZuiqkpciTcz1ZEC1dkCriZkPjxIRkVnx4U8iIjNSZWiw6uRdLD0SiYTUDKnDMTPxz8OjB4Ca3YHWX/EOOhGRGTExJyIyA41WYNO5B5h3MAJPE1VSh1PEBHB1K3D9f0D9AUDQF5njphMR0UthVxYiopcU9jABn22+hBtPkySL4dV0ZcmBpS3Q5mug8UhAJpMmBiKiEkAudQBERK+rdLUWs/fdQPclJyRNyiWXkQLs/RJY2QmIuy11NEREry0m5kREhRD2MAH/t/A4Fh+OzPOvcpYa908CS5sCp5cC/DGWiKjAmJgTERWAEALzDt5E9yUnEB5Viu+S50R393xVCJD4ROpoiIheK0zMiYjyKTlNjRFrz2PewQjeJc/LvRPAT62Ah+eljoSI6LXBxJyIKB8exKWgx5KT2H8tSupQXh9JT4BVnYBLv0odCRHRa4GJORFRHk5GxqLLouPsulIYahWwbTiwfxKg1UodDRFRscbEnIgoF5vPP8SAn//C85SS9seCXrGTC4CN7wLqNKkjISIqtpiYExHlYN3pe/hs8yX2JzeXm3uAX3oD6SlSR0JEVCwxMSciMmHViTv4avsVjvpnbrePAOt7MTknIjKBiTkRUTa/nLmPKTsl+iuapcG948CGd4AMldSREBEVK0zMiYiy2HbhISZuD5M6jJLvzlHgt/cAjVrqSIiIig0m5kRE/zh/Lw5fbA5j95VXJWI/sG+81FEQERUbTMyJiAA8jk/FiLV/I13DIf1eqb9+BM6vljoKIqJigYk5EZV6qgwNhq89h9gXHMpPEr+PA+6dkjoKIiLJMTEnolJv3KZLuPIoUeowSi9NemZ/8/gHUkdCRCQpJuZEVKotP3Ybuy4/kToMSo4Bfu3Ph0GJqFRjYk5Epdat6BeYvS9c6jBI58lF4Nj3UkdBRCQZJuZEVCpptAKfbb6ENDUf9ixW/pwNPOVwlURUOjExJ6JSafmx27hwP17qMCg7bQaw/QN2aSGiUomJORGVOreiX2DOgZtSh0E5eRrGLi1EVCoxMSeiUmfC1jB2YSnu/pwNxPDLExGVLkzMiahUOXAtCn/djZM6DMqLNgM4NE3qKIiIXikm5kRUami1ArP33ZA6DMqv6zuBh+eljoKI6JVhYk5EpcbWC49wM+qF1GFQQRycLHUERESvDBNzIioV0tQazOUDn6+fu8eAWweljoKI6JVgYk5EpcIvZ+7jUXyq1GFQYfzBvuZEVDowMSeiEk+rFVh54q7UYVBhPbkE3D0udRREREWOiTkRlXhHb8bgflyK1GHQyzi7XOoIiIiKHBNzIirx1p6+J3UI9LKu7wKSoqSOgoioSDExJ6IS7UFcCo6ER0sdBr0sbQbw92qpoyAiKlJMzImoRFt/5j60QuooyCzOrwK0GqmjICIqMkzMiajEEkJgy98PpQ6DzCXxERB5WOooiIiKDBNzIiqxLjyIR0xSmtRhkDmF75Y6AiKiIsPEnIhKrIPX+LBgiRO+V+oIiIiKDBNzIiqx/rjOhz5LnKTHwOOLUkdBRFQkmJgTUYn0IC4F4VFJUodBRSF8j9QREBEVCSbmRFQiHbzObiwlVvjvUkdARFQkmJgTUYl0KvKZ1CFQUXkaBqgSpI6CiMjsmJgTUYl05RETt5JLAE8uSR0EEZHZMTEnohLn2Ys0PE5QSR0GFSU+AEpEJRATcyIqccJ4t7zke3JR6giIiMyOiTkRlTjsxlIK8I45EZVATMyJqMThHfNSIO42HwAlohKHiTkRlTj3nqVIHQIVOQE8vyt1EEREZsXEnIhKnKhEPvhZKiQ9lToCIiKzYmJORCVKulqL5ykZUodBrwITcyIqYZiYE9Ers2rVKpQtW7ZI1xGdxLvlpQYTcyIqYSykDoCIXj+DBg3C6tWrjaZHRETAx8dHgoj+FZ2UZvA+/vh6JJzYYDDNwqkiKgxbBgAQ6nTEHfoZKdf/hNBkwMarPpzafwCFXbkc1yGEQMLx9XhxaR+0aclQVqgBp/ajYOlU4Z8yM/Bs7wKkRJyGwq4cnNqPgk2VuvrlE85sgSYxBk7tRpppq1/elCMqTD2abjDN11mOG6PtAQAqtcCn+1TYeFWNNLVAsI8FlnSyhpt9zvd3hBCYfCQNP/2dgXiVQNNKCiwNsUY1ZwUAIE0tMHSnCjtuZMDdXo4lIdZoW/Xfy9LsE2m4n6DFwk42plfwIufEXCaT5bq9kydPxpQpU3Kdh4joVWNiTkSF0qFDB6xcudJgmqurq0TR/Cs6Mc1omqXLG3DrM+PfCfJ/k8m4P35CauQ5uHT7EnKlHeIOLEXMtplw7z87x3UkntmCxPM74RIyFhZl3BB/bB2if/sankOXQmZhhaRLe5H+9Bbc+3+H1NvnEbtzNiqOXgeZTIaM+Kd4cWkfPAbOM+dmm0VNVzkODrDVv7fIknOP3avC7gg1NvWyQRmlDKP3qNDjt1ScGGKXY3nfnkjHgjPpWN3NBl7l5Jh0OA3B61Jw7UN7WFvI8OP5DJx/rMGp9+2w55Ya725JRdQ4e8hkMtx5rsVPf2fg3PCcy8/tjvmTJ0/0///111/x9ddfIzw8XD/N3t5e/38hBDQaDSwseEkkImmxKwsRFYpSqYS7u7vBa/78+ahVqxbs7OxQqVIljBo1Ci9evMixjEuXLqFVq1ZwcHCAo6MjAgMDce7cOf3nx48fR/PmzWFjY4NKlSrh448/RnJycq5xJalM9C+XK6CwL/fvy7YMAECblowXlw+gXOv3YVO5DpTuPnDpNAZpj64j7dENk+ULIZB0bgfKvNUHttXehFV5L7h0/gTqF3FIuXkKAJDx7AFsfBrDyrUyHOqHQJuSAG1qIgAgbv8SlGs5CHKlrcnypWQhB9zt5fqXi23mJSJBJfDzhQzMCbZGay8LBHoqsLKrNU4+0OD0Q7XJsoQQmHcmHV+1UKKrnyVquymwppsNHicJbL+Rucz1WA26+FqgZnkFPmxohZgUgdgUAQD4YHcqZrVVwlGZy53vtKQcP8raLsuUKQOZTKZ/f+PGDTg4OGDPnj0IDAyEUqnE8ePHMWjQIHTr1s2gnDFjxqBly5b691qtFqGhofDy8oKNjQ3q1KmDzZs356N2iYjyxsSciMxGLpdjwYIFuHr1KlavXo1Dhw7h888/z3H+fv36oWLFijh79izOnz+PL7/8EpaWlgCAyMhIdOjQAT179sTly5fx66+/4vjx4xg9enSuMWi0wmia+vljPFw8AI+WvY+YnbOhTowGAKQ9vQVo1QbdTCydK0Hh6Iq0x6YTc3VCFDTJzw2WkSvtoPT01S9jVd4LaQ+vQZuRBtWdv6Gwd4LcxhEvrh6GzMIKttWb5LoNUomI08Lz+yRUnZ+EfltTcD9BCwA4/0SDDC0Mupn4uSjwRhkZTj3QmCzrTrzA0xfCYJky1jI0rqjQL1PHTYHj9zVIzRDYF6mGh70MLrYyrL+cAWsLGbrXsMw9YK3pLwX59eWXX+K///0vrl+/jtq1a+drmdDQUKxZswbLli3D1atXMXbsWPTv3x9Hjx59qViIiAB2ZSGiQtq1a5dBd4COHTti06ZN+vdVqlTB9OnTMXLkSCxZssRkGffv38dnn30GPz8/AEC1atX0n4WGhqJfv34YM2aM/rMFCxYgKCgIS5cuhbW1tcky1dkSc6WHL5w7jYWlUwVoXsQh4cQGPF3/BTyHLIY2+TmgsIDc2t5gGYVdWWiSn5ssX/Mic7rcrqzhMrZloUmOBwDY12qH9Oi7ePzzKChsHOHS9QtoVS+QcHw93PqG4vmfa5Fy/U9YlHWHc6f/wMLBxeS6XqXGFRRY1dUGvi5yPEkSmHo0Dc1XJuPKB/Z4+kLASgGUtTa8e+1mJ8PTF8ZfhADg6Qutfh6jZZIzPxtSzxKXozTwX/ICLrYy/NbLBs9VwNdHVDgy0A5fHVJh45UMeDvJsaKLDSo4ZruX9JKJ+bRp09CuXbt8z5+WloaZM2fi4MGDeOuttwAAVatWxfHjx/HDDz8gKCjopeIhImJiTkSF0qpVKyxdulT/3s7ODgcPHkRoaChu3LiBxMREqNVqqFQqpKSkwNbWuOvGJ598gqFDh2Lt2rVo27YtevXqBW9vbwCZ3VwuX76M9evX6+cXQkCr1eLOnTuoUaNGvuK08W7w75vyXlB6+uLh0iFIvnEcckurQm597mQKCzi3/8BgWuzueXAI/D+kR91GasQpeAxeiMQzW/D84I9w7T6hSOIoiI7V/r07XdsNaFxRgcrzkvDb1QzYWOb+IGVhWSpkWBxi+GDn4B2p+LiRFS481WD7DTUujbTHtyfS8PFeFbb0Nm/3nwYNGuQ9Uxa3bt1CSkqKUTKfnp6OevXqmTM0Iiql2JWFiArFzs4OPj4++ldaWho6d+6M2rVrY8uWLTh//jwWL14MIDNxMWXKlCm4evUqQkJCcOjQIfj7+2Pbtm0AgBcvXmDEiBG4ePGi/nXp0iVERETok3dTLOS5J5Fya3tYOlWAOv4x5HblAI0aWpVhP3hNcnyOo7Io7DOna/+5O65fJiUeimx30XVU9y4j49k9ONTvDNX9y7Cp2gByK2vY+jWD6n5YrvFKpay1DNWd5bgVp4W7vQzpGiBeZXh3PCpZwN3edH27/zNaS1SyiWXsTF96Dt9R42q0BqMbWeHIXQ06VbOAnZUMvWta4shdE11m5C93b8nOzvDBUrlcDiEM483I+PeZBd3zErt37zZol9euXWM/cyIyCybmRGQW58+fh1arxffff48333wT1atXx+PHj/Ncrnr16hg7diz279+PHj166Ed6qV+/Pq5du2aQ/OteVlY53+lW5JGYa9NToY5/AoWdE5TuPoDcAqn3Luk/z3j2EJrEGCg9/Uwub1HGDQq7clDdu/hvmWkpSHscbnIZoU5H3IGlcA4eDZlcAQgthPafJFOrgRDaXOOVyot0gcg4LTwcZAj0UMBSDvxx+9+uI+GxGtxPEHirksLk8l5lZXC3lxksk5gmcOahxuQyKrXAh7+r8ENnGyjkMmi0QMY/1ZShNf3swMsm5tm5uroajOYCABcvXtT/39/fH0qlEvfv3zdqk5UqVTJrLERUOjExJyKz8PHxQUZGBhYuXIjbt29j7dq1WLZsWY7zp6amYvTo0Thy5Aju3buHEydO4OzZs/ouKl988QVOnjyJ0aNH4+LFi4iIiMCOHTvyfPjTwdrwgcHnh36G6n4Y1AlRUD28jpitMwCZHHb+QZAr7WBfux2eH1oO1b3LSHt6C89+nwelpx+UFf5Nsh/9NBIpN08CyBwf26FBVySc/BUpEWeQHnMXsbvnwMLeCbbV3zKKJ/7kRthUbQArt8y7/MoK/ki5eRLp0XeQ9PcuWFfIX5ecojZuvwpH76pxN16Lkw/U6P5rChRyGfoGWKKMtQzv17PEJ/tVOHxHjfOPNRi8Q4W3KirwZsUsD4QueoFt1zPvMMtkMoxpbIXpx9Lwv/AMhEVpMGBbKjwdZOjmZ5xQf3M0DZ2qWaCeR2bS3vQNBbbeyMDlKA0W/ZWOpm+YSMKVDmatg9atW+PcuXNYs2YNIiIiMHnyZFy5ckX/uYODA8aNG4exY8di9erViIyMxN9//42FCxeaHNefiKig2MeciMyiTp06mDNnDmbNmoXx48ejRYsWCA0NxYABA0zOr1Ao8OzZMwwYMABRUVFwcXFBjx49MHXqVABA7dq1cfToUUycOBHNmzeHEALe3t7o06dPrnGUd1QavFcnxSJ252xoUhOhsCkDZUV/uL/3vX7IRKc2wxAnkyNm+0wITQasverDud0owzLiHkKblqJ/79i4J0SGCs/2LYRWlQzriv4o33saZBaGd/LTY+4i5cYxeAxaqJ9m69cUqgdheLr+C1g6V4DL/32WR82+Gg8Ttei7JRXPUgVcbWVo9oYCp9+3g+s/3U7mdrCGfJ8KPX9LQZoGCPa2wJIQwwdww59pkZD2753tz5taITlDYPhOFeJVAs3eUGBvf1tYWxj+qnElWoPfrqlxccS/XUve9rfAkbsWaL4yGb7OcvzS00T/cns3M9YAEBwcjEmTJuHzzz+HSqXCkCFDMGDAAISF/dvd6JtvvoGrqytCQ0Nx+/ZtlC1bFvXr18eECdI/J0BErz+ZyN6hjojoNfbweQqazTosdRiv3PUK02Hz7JrUYbxaLScALb+QOgoiIrNhVxYiKlHKO5geRpFKIAfz3jEnIpIaE3MiKlGsLOQoZ5vHH6ahksHBQ+oIiIjMiok5EZU4bo68a14qmLmPORGR1JiYE1GJU9nZvH+IhoojGeDkJXUQRERmxcSciEqcWhXKSB0CFTUnL8Ca+5mIShYm5kRU4gQwMS/5POtJHQERkdkxMSeiEod3zEsBj7pSR0BEZHZMzImoxHG2V8KzDB8ALdE860odARGR2TExJ6ISid1ZSjIZ4FFH6iCIiMyOiTkRlUhveTtLHQIVFfdafPCTiEokJuZEVCK1rcExrkss305SR0BEVCSYmBNRiVTJyRa+bg5Sh0FFwbej1BEQERUJJuZEVGK1qVFe6hDI3Bw8+eAnEZVYTMyJqMRq68/uLCWObwepIyAiKjJMzImoxKpXqSxcHZRSh0Hm5BsidQREREWGiTkRlVgymQw961eUOgwyF8cKgHcrqaMgIioyTMyJqETr1/gNyGVSR0FmETgIkCukjoKIqMgwMSeiEq2Sky1a+vIh0Nee3BKoP1DqKIiIihQTcyIq8d57s7LUIdDLqtEZcODDvERUsjExJ6ISL6i6K95wspU6DHoZDYdKHQERUZFjYk5EJZ5cLsPgplWkDoMKy6MOUKWZ1FEQERU5JuZEVCq82/gNVChrI3UYVBhtvpY6AiKiV4KJORGVCkoLBca2qy51GFRQVZoDPm2ljoKI6JVgYk5EpUaPehVQ3c1e6jCoINpOlToCIqJXhok5EZUacrkMnwX7SR0G5VeN/wMqBkodBRHRK8PEnIhKlXb+bmhUxUnqMCgvckugNfuWE1HpwsSciEqd0J61oLTg6a9Ya/EZ4MpnAoiodOGViYhKHW9Xe3zCB0GLL/faQPNPpY6CiOiVY2JORKXSsOZVUe+NslKHQdnJLYFuSwCFhdSREBG9ckzMiahUkstl+K5XHXZpKW5afAa415I6CiIiSfCKRESllrerPT4L9pU6DNLxqMsuLERUqjExJ6JSbWjzqvi/Op5Sh0F2rkCfdezCQkSlGhNzIir1Zr9dGwEVHKUOo/RSWGUm5WUrSR0JEZGkmJgTUalnbanATwMawMVeKXUopVOn74A33pQ6CiIiyTExJyIC4FHGBj+8Vx9WCp4WX6lGw4HAgVJHQURULPAKRET0j8DKTvj27dqQyaSOpJSo1h4IDpU6CiKiYoOJORFRFt3qVcCMbhyur8h5BQG91/JhTyKiLJiYExFl827jNzC1S02pwyi5KjcD+m4ELK2ljoSIqFhhYk5EZMLAJlUwvVsAu7WYW9VWQL9NgJWt1JEQERU7TMyJiHLQ/83KmP12HVjImZ2bRfWOwLu/MiknIsoBE3Mioly8HVgRa95vhHK2llKH8npr+h/gnV8ACw5JSUSUEybmRER5aOLtgv+NbgY/dwepQ3n9WFgDPX4C2k0D5LzkEBHlhmdJIqJ8qORkiy0fNEF7fzepQ3l9OHgAg38HaveWOhIiotcCE3MionyyU1rgh/cCMbZtdfY7z0vlpsDwI0CFQKkjISJ6bTAxJyIqAJlMhv+0rYZto5rC141dW4xY2gIdZgGDdgMO7lJHQ0T0WmFiTkRUCLUqlsHOj5phdCsf3j3XqdwU+OAE8OZIcJxJIqKCY2JORFRIVhZyjAv2xbZRTUv3g6GWdv/eJXeqKnU0RESvLZkQQkgdBBHR606jFdh07gHm/xGBJwmqV77+6xWmw+bZtVe7UrkFUH8gEPQF4MCHYomIXhYTcyIiM1JlaLD65F0sPRqJ+JSMV7beV5uYy4CAHkCriYCz9ytaJxFRycfEnIioCCSkZuCHo5FYe/oeklTqIl/fq0nMZUC1dpkJuWfdIl4XEVHpw8SciKgIpaZrsP3iI6w9dQ/XniQW2XqKNDG3KQfU7Qc0GMI75ERERYiJORHRK3L+3nOsO30Pu8OeIF2tNWvZRZKYe9YHGg4FAnoCltbmLZuIiIwwMSciesUSVRk4Eh6Dg9eicCQ8Golm6OpilsRcpgAqNQJ8OwK+nQCXai8dFxER5R8TcyIiCak1Wvx1Jw4Hr0fj+K0YRMYkQ6Mt+Gm50Im5vRtQqXFmMl4tGLBzLngZRERkFhZSB0BEVJpZKORo4uOCJj4uADL7pF97koDLDxMQ9igBVx4l4HZMMtSFSNaN2LsDHnUyH9z0qAt41gMcPV6+XCIiMgveMSciKua0WoG4lHREJaoQnZSG6EQVohPTEJ+aAY1WQK3V4gvbXXDIeJY5triVbWYS7uAGOHhk3hV3cAcslFJvChER5YKJORERERFRMSCXOgAiIiIiImJiTkRERERULDAxJyIiIiIqBpiYExEREREVA0zMiYiIiIiKASbmRERERETFABNzIiIiIqJigIk5EREREVExwMSciIiIiKgYYGJORERERFQMMDEnIiIiIioGmJgTERERERUDTMyJiIiIiIoBJuZERERERMUAE3MiIiIiomKAiTkRERERUTHAxJyIiIiIqBhgYk5EREREVAwwMSciIiIiKgaYmBMRERERFQNMzImIiIiIigEm5kRERERExQATcyIiIiKiYoCJORERERFRMfD/51h8ezkKavEAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "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(data_train.drop(columns=['hazardous']), data_train['hazardous'])\n", - "\n", - "# Создание нового DataFrame\n", - "data_train_undersampled = pd.DataFrame(X_resampled)\n", - "data_train_undersampled['hazardous'] = y_resampled # Добавление целевой переменной\n", - "\n", - "# Вывод информации о новой выборке\n", - "print(\"Обучающая выборка после undersampling: \", data_train_undersampled.shape)\n", - "print(data_train_undersampled['hazardous'].value_counts())\n", - "\n", - "# Визуализация распределения классов\n", - "hazardous_counts = data_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()" + "print(neo.describe())\n", + "print(healthcare.describe())\n", + "print(diabetes.describe())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Аномальное рапределение будем искать по z-индексую. Z-индекс показывает, насколько далеко значение находится от среднего в стандартных отклонениях. Значения Z-индекса больше 3 или меньше -3 обычно считаются аномальными." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Аномалии в наборе данных Neo:\n", + "В атрибуте 'est_diameter_min' обнаружены аномалии: [1.1982708007, 1.0293308202, 1.5085335612, 1.0246014747, 1.4078454339, 1.5507970872, 1.5224918504, 2.8350124902, 1.4208720673, 1.1818299089, 2.2727673228, 1.8053232555, 1.6693791177, 1.7240703244, 1.1709948272, 1.4809997207, 1.332155667, 1.0878148336, 4.5767266723, 1.2780709882, 1.2093582639, 1.5651464359, 1.1982708007, 2.2727673228, 1.4208720673, 1.332155667, 1.0340819954, 1.1080388213, 4.5767266723, 1.4208720673, 2.2832579402, 2.3689449936, 1.1390819672, 2.8090209395, 1.214940408, 2.0443487103, 1.0581688593, 2.9549829311, 1.5507970872, 1.5224918504, 1.0828167784, 2.6825941712, 1.2839702958, 1.133848361, 3.4084346887, 1.1818299089, 2.091967709, 1.0878148336, 1.6089960451, 1.0679599752, 2.3689449936, 1.9344387205, 1.3018321019, 1.6389095149, 1.6464743776, 1.4208720673, 2.1016237932, 1.7805532918, 3.1956188672, 1.0340819954, 1.5651464359, 1.1872849879, 1.2261821132, 1.1080388213, 4.1740243339, 1.0878148336, 1.7642290811, 1.2721987854, 1.2780709882, 1.85590173, 3.0658787593, 1.9344387205, 1.6617090174, 1.7161489408, 2.9549829311, 1.2663535629, 3.4399725466, 1.4013769717, 1.1818299089, 1.2318419127, 1.3694777373, 1.2958507267, 4.1740243339, 1.8904055193, 1.6848256925, 1.5224918504, 1.4208720673, 1.038855101, 1.128638801, 1.1982708007, 1.5016024791, 1.2547435637, 2.091967709, 1.0878148336, 1.1709948272, 1.3885290704, 1.1496217629, 1.4208720673, 1.3694777373, 1.1818299089, 2.0349557812, 1.128638801, 1.1496217629, 1.8991312347, 1.1709948272, 2.9549829311, 1.4208720673, 1.0878148336, 1.4208720673, 1.2721987854, 1.2780709882, 1.3260349677, 1.5651464359, 1.1602590821, 1.2093582639, 1.7723723926, 2.9549829311, 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159394.0656018125, 127980.0832573206, 133186.0885066922, 149896.3413337513, 127871.6081083384, 133867.9739344205, 156368.9685988652, 125875.9150517884, 157299.5000735874, 132301.2341032402, 134999.2888555982, 129849.1579730952, 136525.8839184256, 135448.9749945058, 129340.0296903233, 132793.7292919145, 134083.9394586238, 125194.8057624533, 135428.0562364788, 149010.5204755798, 145022.928741248, 127075.4706858768, 164471.1998394899, 124304.6385545864, 157312.1436108912, 127045.8992067592, 126952.8578109504, 150267.6666728478, 124566.708732502, 143872.1567574239, 139182.1112221128, 133886.486067071, 125390.752691566, 177510.7295602038, 151528.2959986157, 138835.9894640767, 130770.4822482604, 131516.2602845568, 129271.3092083959, 144794.5707388007, 124135.322340596, 147052.5910521239, 140115.0078285808, 125072.7373260004, 132909.3451896278, 124047.4215818884, 128562.8668511396, 128835.4325307538, 151102.4743999796, 126515.2408994419, 126189.4105726916, 140347.3532916719, 147117.3950382462, 126313.541578967, 131738.4262679008, 131537.8513943586, 127227.216994286, 149843.7442418908, 127874.948421361, 139317.8394963845, 157742.4051444262, 132417.1136575519, 141452.9943771182, 132308.7361709476, 124647.2806434275, 129142.4219427542, 130740.2847594194, 154581.8076981536, 187221.0733344144, 127015.9871583006, 149160.6452428978, 129256.6666398854, 138084.7165128119, 154243.0836316415, 126058.6942420756, 128214.7144037726, 139606.3015661095, 125252.2987777217]\n", + "В атрибуте 'absolute_magnitude' обнаружены аномалии: [13.82, 13.82, 14.77, 14.46, 14.6, 14.02, 14.69, 14.77, 14.44, 14.02, 14.77, 14.77, 14.46, 14.34, 14.77, 14.6, 10.31, 14.7, 14.46, 14.6, 14.77, 12.44, 13.8, 14.13, 13.53, 12.58, 14.4, 14.34, 14.6, 14.81, 32.3, 14.6, 14.35, 33.2, 14.77, 14.44, 14.46, 32.3, 32.56, 14.77, 14.46, 14.04, 32.56, 14.46, 14.77, 14.6, 32.3, 14.27, 9.23, 14.13, 10.31, 14.13, 14.34, 14.77, 14.46, 14.02, 14.82, 14.4, 14.13, 32.95, 14.77, 14.46, 14.6, 32.56, 14.04, 32.56, 14.56, 14.46, 14.4, 32.95, 13.76, 12.76, 32.95, 32.3, 10.31, 14.6, 12.76, 13.53, 14.69, 14.77, 14.6, 13.76, 14.13, 14.77, 14.69, 14.31, 14.74, 14.84, 14.46, 14.77, 14.34, 13.84, 14.6, 12.44, 14.46, 32.95, 32.56, 14.84, 14.77, 14.34, 14.74, 32.95, 14.13, 32.95, 14.6]\n", + "\n", + "Аномалии в наборе данных Healthcare:\n", + "В атрибуте 'hypertension' обнаружены аномалии: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n", + "В атрибуте 'heart_disease' обнаружены аномалии: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n", + "В атрибуте 'avg_glucose_level' обнаружены аномалии: [252.72, 243.58, 259.63, 249.31, 263.32, 271.74, 242.52, 250.89, 247.51, 243.53, 242.3, 243.5, 251.6, 247.69, 250.2, 254.6, 254.63, 246.34, 251.46, 267.76, 246.53, 244.28, 251.99, 253.16, 242.84, 249.29, 242.94, 247.48, 266.59, 243.73, 243.59, 250.8, 255.17, 267.61, 260.85, 248.37, 263.56, 247.97, 248.24, 253.93, 254.95, 247.87, 261.67, 256.74, 244.3, 242.62, 243.52, 267.6, 253.86]\n", + "В атрибуте 'bmi' обнаружены аномалии: [56.6, 54.6, 60.9, 54.7, 64.8, 54.7, 60.2, 71.9, 54.6, 55.7, 55.7, 57.5, 54.2, 52.3, 78.0, 53.4, 55.2, 55.0, 54.8, 52.8, 66.8, 55.1, 55.9, 57.3, 56.0, 57.7, 54.0, 56.1, 97.6, 53.9, 53.8, 52.7, 52.8, 55.7, 53.5, 63.3, 52.8, 61.2, 58.1, 52.7, 53.4, 59.7, 52.5, 52.9, 54.7, 61.6, 53.8, 54.3, 55.0, 57.2, 64.4, 92.0, 55.9, 57.9, 55.7, 57.2, 60.9, 54.1, 56.6]\n", + "В атрибуте 'stroke' обнаружены аномалии: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n", + "\n", + "Аномалии в наборе данных Diabetes:\n", + "В атрибуте 'Pregnancies' обнаружены аномалии: [15, 17, 14, 14]\n", + "В атрибуте 'Glucose' обнаружены аномалии: [0, 0, 0, 0, 0]\n", + "В атрибуте 'BloodPressure' обнаружены аномалии: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n", + "В атрибуте 'SkinThickness' обнаружены аномалии: [99]\n", + "В атрибуте 'Insulin' обнаружены аномалии: [543, 846, 495, 485, 495, 478, 744, 680, 545, 465, 579, 474, 480, 600, 440, 540, 480, 510]\n", + "В атрибуте 'BMI' обнаружены аномалии: [0.0, 0.0, 0.0, 0.0, 0.0, 67.1, 0.0, 0.0, 59.4, 0.0, 0.0, 57.3, 0.0, 0.0]\n", + "В атрибуте 'DiabetesPedigreeFunction' обнаружены аномалии: [2.288, 1.893, 1.781, 2.329, 1.476, 2.137, 1.731, 1.6, 2.42, 1.699, 1.698]\n", + "В атрибуте 'Age' обнаружены аномалии: [69, 72, 81, 70, 69]\n" + ] + } + ], + "source": [ + "from scipy import stats\n", + "# Вычисляем Z-индексы только для числовых столбцов\n", + "neo_zscores = neo.select_dtypes(include=['float64', 'int64']).apply(stats.zscore, nan_policy='omit')\n", + "healthcare_zscores = healthcare.select_dtypes(include=['float64', 'int64']).apply(stats.zscore, nan_policy='omit')\n", + "diabetes_zscores = diabetes.select_dtypes(include=['float64', 'int64']).apply(stats.zscore, nan_policy='omit')\n", + "\n", + "# Устанавливаем порог для поиска аномалий\n", + "threshold = 3\n", + "\n", + "# Функция для нахождения аномалий и вывода сообщения\n", + "def find_anomalies(zscores, df):\n", + " for column in zscores.columns:\n", + " # Проверяем, есть ли аномалии в Z-индексах\n", + " anomalies = df[column][(zscores[column].abs() > threshold)]\n", + " if not anomalies.empty:\n", + " print(f\"В атрибуте '{column}' обнаружены аномалии: {anomalies.tolist()}\")\n", + "\n", + "# Находим аномалии\n", + "try:\n", + " print(\"Аномалии в наборе данных Neo:\")\n", + " find_anomalies(neo_zscores, neo)\n", + "\n", + " print(\"\\nАномалии в наборе данных Healthcare:\")\n", + " find_anomalies(healthcare_zscores, healthcare)\n", + "\n", + " print(\"\\nАномалии в наборе данных Diabetes:\")\n", + " find_anomalies(diabetes_zscores, diabetes)\n", + "\n", + "except Exception as e:\n", + " print(f\"Произошла ошибка: {e}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Теперь выполним 10 пункт, разобьем данные на выборки" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Набор данных Neo:\n", + "Обучающая выборка:\n", + "hazardous\n", + "False 0.902681\n", + "True 0.097319\n", + "Name: proportion, dtype: float64\n", + "\n", + "Контрольная выборка:\n", + "hazardous\n", + "False 0.902686\n", + "True 0.097314\n", + "Name: proportion, dtype: float64\n", + "\n", + "Тестовая выборка:\n", + "hazardous\n", + "False 0.902686\n", + "True 0.097314\n", + "Name: proportion, dtype: float64\n", + "\n", + "Набор данных Healthcare:\n", + "Обучающая выборка:\n", + "stroke\n", + "0 0.951321\n", + "1 0.048679\n", + "Name: proportion, dtype: float64\n", + "\n", + "Контрольная выборка:\n", + "stroke\n", + "0 0.951076\n", + "1 0.048924\n", + "Name: proportion, dtype: float64\n", + "\n", + "Тестовая выборка:\n", + "stroke\n", + "0 0.951076\n", + "1 0.048924\n", + "Name: proportion, dtype: float64\n", + "\n", + "Набор данных Diabetes:\n", + "Обучающая выборка:\n", + "Outcome\n", + "0 0.651466\n", + "1 0.348534\n", + "Name: proportion, dtype: float64\n", + "\n", + "Контрольная выборка:\n", + "Outcome\n", + "0 0.649351\n", + "1 0.350649\n", + "Name: proportion, dtype: float64\n", + "\n", + "Тестовая выборка:\n", + "Outcome\n", + "0 0.649351\n", + "1 0.350649\n", + "Name: proportion, dtype: float64\n", + "Набор данных Neo:\n", + "Аугментация данных не требуется.\n", + "\n", + "Набор данных Healthcare:\n", + "Необходима аугментация данных.\n", + "\n", + "Набор данных Diabetes:\n", + "Аугментация данных не требуется.\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "def split_data(df, target_column, test_size=0.2, random_state=42):\n", + " # Разделяем данные на обучающую и временную выборки\n", + " X_train, X_temp, y_train, y_temp = train_test_split(df.drop(columns=[target_column]), \n", + " df[target_column], \n", + " test_size=test_size, \n", + " random_state=random_state, \n", + " stratify=df[target_column])\n", + " # Делим временную выборку на контрольную и тестовую\n", + " X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, \n", + " test_size=0.5, \n", + " random_state=random_state, \n", + " stratify=y_temp)\n", + " \n", + " return X_train, X_val, X_test, y_train, y_val, y_test\n", + "\n", + "# Для набора данных neo\n", + "neo_train, neo_val, neo_test, neo_train_labels, neo_val_labels, neo_test_labels = split_data(neo, 'hazardous')\n", + "\n", + "# Для набора данных healthcare\n", + "healthcare_train, healthcare_val, healthcare_test, healthcare_train_labels, healthcare_val_labels, healthcare_test_labels = split_data(healthcare, 'stroke')\n", + "\n", + "# Для набора данных diabetes\n", + "diabetes_train, diabetes_val, diabetes_test, diabetes_train_labels, diabetes_val_labels, diabetes_test_labels = split_data(diabetes, 'Outcome')\n", + "def check_balance(y_train, y_val, y_test):\n", + " print(\"Обучающая выборка:\")\n", + " print(y_train.value_counts(normalize=True))\n", + " print(\"\\nКонтрольная выборка:\")\n", + " print(y_val.value_counts(normalize=True))\n", + " print(\"\\nТестовая выборка:\")\n", + " print(y_test.value_counts(normalize=True))\n", + "\n", + "print(\"Набор данных Neo:\")\n", + "check_balance(neo_train_labels, neo_val_labels, neo_test_labels)\n", + "\n", + "print(\"\\nНабор данных Healthcare:\")\n", + "check_balance(healthcare_train_labels, healthcare_val_labels, healthcare_test_labels)\n", + "\n", + "print(\"\\nНабор данных Diabetes:\")\n", + "check_balance(diabetes_train_labels, diabetes_val_labels, diabetes_test_labels)\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Для набота Neo \n", + "- Пропорция классов сильно несбалансирована: только 9.73% объектов относятся к классу True (опасные), а 90.27% — к классу False (неопасные).\n", + "- В данном случае, если модель будет обучаться только на этих данных, она может иметь высокую точность, просто предсказывая, что все объекты неопасные. Это приведет к тому, что модель будет плохо определять опасные объекты.\n", + "\n", + "# Набор данных Healthcare\n", + "- Пропорция классов также сильно несбалансирована: только 4.87% объектов относятся к классу 1 (с инсультом).\n", + "- Как и в предыдущем случае, если модель будет обучаться на этих данных, она может показывать высокую точность, просто предсказывая, что все объекты без инсульта.\n", + "\n", + "# Набор данных Diabetes\n", + "- Здесь классы более сбалансированы, чем в предыдущих примерах, хотя класс 0 все еще составляет 65.15% и класс 1 34.85%.\n", + "- Модель может научиться определять оба класса, но если точность по классу 1 будет низкой, можно рассмотреть методы аугментации.\n", + "\n", + "1. Oversampling (приращение данных): Увеличение числа примеров для меньшинства классов.\n", + "2. Undersampling (уменьшение данных): Уменьшение числа примеров для большинства классов." + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Oversampling для Neo:\n", + "hazardous\n", + "False 81996\n", + "True 81996\n", + "Name: count, dtype: int64\n", + "\n", + "Undersampling для Healthcare:\n", + "stroke\n", + "0 249\n", + "1 249\n", + "Name: count, dtype: int64\n", + "\n", + "Oversampling для Diabetes:\n", + "Outcome\n", + "1 500\n", + "0 500\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "from imblearn.over_sampling import RandomOverSampler\n", + "from imblearn.under_sampling import RandomUnderSampler\n", + "\n", + "# Пример Oversampling для Neo\n", + "X_neo = neo.drop('hazardous', axis=1) \n", + "y_neo = neo['hazardous'] \n", + "\n", + "# Oversampling\n", + "ros_neo = RandomOverSampler(random_state=42)\n", + "X_neo_resampled, y_neo_resampled = ros_neo.fit_resample(X_neo, y_neo)\n", + "neo_resampled = pd.DataFrame(X_neo_resampled, columns=X_neo.columns)\n", + "neo_resampled['hazardous'] = y_neo_resampled\n", + "\n", + "print(\"Oversampling для Neo:\")\n", + "print(neo_resampled['hazardous'].value_counts())\n", + "\n", + "\n", + "X_healthcare = healthcare.drop('stroke', axis=1)\n", + "y_healthcare = healthcare['stroke']\n", + "\n", + "# Пример Undersampling для Healthcare\n", + "rus_healthcare = RandomUnderSampler(random_state=42)\n", + "X_healthcare_resampled_under, y_healthcare_resampled_under = rus_healthcare.fit_resample(X_healthcare, y_healthcare)\n", + "healthcare_resampled_under = pd.DataFrame(X_healthcare_resampled_under, columns=X_healthcare.columns)\n", + "healthcare_resampled_under['stroke'] = y_healthcare_resampled_under\n", + "\n", + "print(\"\\nUndersampling для Healthcare:\")\n", + "print(healthcare_resampled_under['stroke'].value_counts())\n", + "\n", + "# Пример Oversampling для Diabetes\n", + "X_diabetes = diabetes.drop('Outcome', axis=1)\n", + "y_diabetes = diabetes['Outcome']\n", + "\n", + "# Oversampling\n", + "ros_diabetes = RandomOverSampler(random_state=42)\n", + "X_diabetes_resampled, y_diabetes_resampled = ros_diabetes.fit_resample(X_diabetes, y_diabetes)\n", + "diabetes_resampled = pd.DataFrame(X_diabetes_resampled, columns=X_diabetes.columns)\n", + "diabetes_resampled['Outcome'] = y_diabetes_resampled\n", + "\n", + "print(\"\\nOversampling для Diabetes:\")\n", + "print(diabetes_resampled['Outcome'].value_counts())" + ] } ], "metadata": { "kernelspec": { - "display_name": "venv", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -714,7 +641,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.6" + "version": "3.12.5" } }, "nbformat": 4,