diff --git a/lab_2/lab2.ipynb b/lab_2/lab2.ipynb new file mode 100644 index 0000000..99a0768 --- /dev/null +++ b/lab_2/lab2.ipynb @@ -0,0 +1,1507 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "https://www.kaggle.com/datasets/harishkumardatalab/medical-insurance-price-prediction Набор представляет собой данные о мед страховке.\n", + "Пример цели: Рассчитать стоимость будущей страховки\n", + "Входные данные: возраст, пол, индекс массы тела, дети, курит ли, регион, стоимость" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['age', 'sex', 'bmi', 'children', 'smoker', 'region', 'charges'], dtype='object')\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "df = pd.read_csv(\".//static//csv//Medical_insurance.csv\")\n", + "print(df.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Визуализация данных - ящик с усами. Как видим - у выборки есть смещение в меньшую сторону\n", + "plt.figure(figsize=(10, 6))\n", + "sns.boxplot(x=df[\"bmi\"])\n", + "plt.title(\"Box Plot для bmi\")\n", + "plt.xlabel(\"bmi\")\n", + "plt.show()\n", + "\n", + "#Визуализируем отношение стоимости мед счетов и возраста\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(df[\"age\"], df[\"charges\"])\n", + "plt.xlabel('Age')\n", + "plt.ylabel(\"Charge\")\n", + "plt.title('Scatter Plot of Age vs Charge')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Выбросы:\n", + " age sex bmi children smoker region charges\n", + "14 27 male 42.13 0 yes southeast 39611.7577\n", + "19 30 male 35.30 0 yes southwest 36837.4670\n", + "23 34 female 31.92 1 yes northeast 37701.8768\n", + "29 31 male 36.30 2 yes southwest 38711.0000\n", + "30 22 male 35.60 0 yes southwest 35585.5760\n", + "... ... ... ... ... ... ... ...\n", + "2735 52 male 41.80 2 yes southeast 47269.8540\n", + "2736 64 male 36.96 2 yes southeast 49577.6624\n", + "2744 32 male 33.63 1 yes northeast 37607.5277\n", + "2764 22 female 31.02 3 yes southeast 35595.5898\n", + "2765 47 male 36.08 1 yes southeast 42211.1382\n", + "\n", + "[296 rows x 7 columns]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# Статистический анализ для определения выбросов\n", + "Q1 = df[\"charges\"].quantile(0.25)\n", + "Q3 = df[\"charges\"].quantile(0.75)\n", + "IQR = Q3 - Q1\n", + "\n", + "# Определение порога для выбросов\n", + "threshold = 1.5 * IQR\n", + "outliers = (df[\"charges\"] < (Q1 - threshold)) | (df[\"charges\"] > (Q3 + threshold))\n", + "\n", + "# Вывод выбросов\n", + "print(\"Выбросы:\")\n", + "print(df[outliers])\n", + "\n", + "# Обработка выбросов\n", + "# В данном случае мы обнулим выбросы\n", + "median_charge = df[\"charges\"].median()\n", + "df.loc[outliers, \"charges\"] = 0\n", + "df = df[df.charges != 0]\n", + "\n", + "# Визуализация данных после обработки\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(df[\"age\"], df[\"charges\"])\n", + "plt.xlabel(\"Age\")\n", + "plt.ylabel(\"Charge\")\n", + "plt.title(\"Scatter Plot of Age vs Charge\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Теперь создадим выборки." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Размер обучающей выборки: 1485\n", + "Размер контрольной выборки: 495\n", + "Размер тестовой выборки: 496\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "train_df, temp_df = train_test_split(df, test_size=0.4, random_state=42)\n", + "\n", + "# Разделение остатка на контрольную и тестовую выборки\n", + "val_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=42)\n", + "\n", + "# Проверка размеров выборок\n", + "print(\"Размер обучающей выборки:\", len(train_df))\n", + "print(\"Размер контрольной выборки:\", len(val_df))\n", + "print(\"Размер тестовой выборки:\", len(test_df))\n", + "\n", + "# Сохранение выборок в файлы\n", + "train_df.to_csv(\".//static//csv//train_data.csv\", index=False)\n", + "val_df.to_csv(\".//static//csv//val_data.csv\", index=False)\n", + "test_df.to_csv(\".//static//csv//test_data.csv\", index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Проанализируем сбалансированность выборок" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Распределение Review_type в обучающей выборке:\n", + "sex\n", + "female 765\n", + "male 720\n", + "Name: count, dtype: int64\n", + "Процент мужчин: 48.48%\n", + "Процент женщин: 51.52%\n", + "\n", + "Распределение Review_type в контрольной выборке:\n", + "sex\n", + "male 257\n", + "female 238\n", + "Name: count, dtype: int64\n", + "Процент мужчин: 51.92%\n", + "Процент женщин: 48.08%\n", + "\n", + "Распределение Review_type в тестовой выборке:\n", + "sex\n", + "female 259\n", + "male 237\n", + "Name: count, dtype: int64\n", + "Процент мужчин: 47.78%\n", + "Процент женщин: 52.22%\n", + "\n", + "Аугментация данных не требуется.\n", + "Аугментация данных не требуется.\n", + "Аугментация данных не требуется.\n" + ] + } + ], + "source": [ + "train_df = pd.read_csv(\".//static//csv//train_data.csv\")\n", + "val_df = pd.read_csv(\".//static//csv//val_data.csv\")\n", + "test_df = pd.read_csv(\".//static//csv//test_data.csv\")\n", + "\n", + "# Оценка сбалансированности\n", + "def check_balance(df, name):\n", + " counts = df['sex'].value_counts()\n", + " print(f\"Распределение Review_type в {name}:\")\n", + " print(counts)\n", + " print(f\"Процент мужчин: {counts['male'] / len(df) * 100:.2f}%\")\n", + " print(f\"Процент женщин: {counts['female'] / len(df) * 100:.2f}%\")\n", + " print()\n", + "\n", + "# Определение необходимости аугментации данных\n", + "def need_augmentation(df):\n", + " counts = df['sex'].value_counts()\n", + " ratio = counts['male'] / counts['female']\n", + " if ratio > 1.5 or ratio < 0.67:\n", + " print(\"Необходима аугментация данных для балансировки классов.\")\n", + " else:\n", + " print(\"Аугментация данных не требуется.\")\n", + " \n", + "check_balance(train_df, \"обучающей выборке\")\n", + "check_balance(val_df, \"контрольной выборке\")\n", + "check_balance(test_df, \"тестовой выборке\")\n", + "\n", + "need_augmentation(train_df)\n", + "need_augmentation(val_df)\n", + "need_augmentation(test_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "По результатам анализа требуется приращение, соотношения вне допустимого диапазона" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Оверсэмплинг:\n", + "Распределение sex в обучающей выборке:\n", + "sex\n", + "male 765\n", + "female 765\n", + "Name: count, dtype: int64\n", + "Процент мужчин: 50.00%\n", + "Процент женщин: 50.00%\n", + "\n", + "Распределение sex в контрольной выборке:\n", + "sex\n", + "male 257\n", + "female 257\n", + "Name: count, dtype: int64\n", + "Процент мужчин: 50.00%\n", + "Процент женщин: 50.00%\n", + "\n", + "Распределение sex в тестовой выборке:\n", + "sex\n", + "female 259\n", + "male 259\n", + "Name: count, dtype: int64\n", + "Процент мужчин: 50.00%\n", + "Процент женщин: 50.00%\n", + "\n", + "Андерсэмплинг:\n", + "Распределение sex в обучающей выборке:\n", + "sex\n", + "female 720\n", + "male 720\n", + "Name: count, dtype: int64\n", + "Процент мужчин: 50.00%\n", + "Процент женщин: 50.00%\n", + "\n", + "Распределение sex в контрольной выборке:\n", + "sex\n", + "female 238\n", + "male 238\n", + "Name: count, dtype: int64\n", + "Процент мужчин: 50.00%\n", + "Процент женщин: 50.00%\n", + "\n", + "Распределение sex в тестовой выборке:\n", + "sex\n", + "female 237\n", + "male 237\n", + "Name: count, dtype: int64\n", + "Процент мужчин: 50.00%\n", + "Процент женщин: 50.00%\n", + "\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from imblearn.over_sampling import RandomOverSampler\n", + "from imblearn.under_sampling import RandomUnderSampler\n", + "from sklearn.preprocessing import LabelEncoder\n", + "\n", + "# Загрузка данных\n", + "train_df = pd.read_csv(\".//static//csv//train_data.csv\")\n", + "val_df = pd.read_csv(\".//static//csv//val_data.csv\")\n", + "test_df = pd.read_csv(\".//static//csv//test_data.csv\")\n", + "\n", + "# Преобразование категориальных признаков в числовые\n", + "def encode(df):\n", + " label_encoders = {}\n", + " for column in df.select_dtypes(include=['object']).columns:\n", + " if column != 'sex': # Пропускаем целевую переменную\n", + " le = LabelEncoder()\n", + " df[column] = le.fit_transform(df[column])\n", + " label_encoders[column] = le\n", + " return label_encoders\n", + "\n", + "# Преобразование целевой переменной в числовые значения\n", + "def encode_target(df):\n", + " le = LabelEncoder()\n", + " df['sex'] = le.fit_transform(df['sex'])\n", + " return le\n", + "\n", + "# Применение кодирования\n", + "label_encoders = encode(train_df)\n", + "encode(val_df)\n", + "encode(test_df)\n", + "\n", + "# Кодирование целевой переменной\n", + "le_target = encode_target(train_df)\n", + "encode_target(val_df)\n", + "encode_target(test_df)\n", + "\n", + "# Проверка типов данных\n", + "def check_data_types(df):\n", + " for column in df.columns:\n", + " if df[column].dtype == 'object':\n", + " print(f\"Столбец '{column}' содержит строковые данные.\")\n", + "\n", + "check_data_types(train_df)\n", + "check_data_types(val_df)\n", + "check_data_types(test_df)\n", + "\n", + "# Функция для выполнения oversampling\n", + "def oversample(df):\n", + " if 'sex' not in df.columns:\n", + " print(\"Столбец 'sex' отсутствует.\")\n", + " return df\n", + " \n", + " X = df.drop('sex', axis=1)\n", + " y = df['sex']\n", + " \n", + " oversampler = RandomOverSampler(random_state=42)\n", + " X_resampled, y_resampled = oversampler.fit_resample(X, y) # type: ignore\n", + " \n", + " resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n", + " return resampled_df\n", + "\n", + "# Функция для выполнения undersampling\n", + "def undersample(df):\n", + " if 'sex' not in df.columns:\n", + " print(\"Столбец 'sex' отсутствует.\")\n", + " return df\n", + " \n", + " X = df.drop('sex', axis=1)\n", + " y = df['sex']\n", + " \n", + " undersampler = RandomUnderSampler(random_state=42)\n", + " X_resampled, y_resampled = undersampler.fit_resample(X, y) # type: ignore\n", + " \n", + " resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n", + " return resampled_df\n", + "\n", + "# Применение oversampling и undersampling к каждой выборке\n", + "train_df_oversampled = oversample(train_df)\n", + "val_df_oversampled = oversample(val_df)\n", + "test_df_oversampled = oversample(test_df)\n", + "\n", + "train_df_undersampled = undersample(train_df)\n", + "val_df_undersampled = undersample(val_df)\n", + "test_df_undersampled = undersample(test_df)\n", + "\n", + "# Обратное преобразование целевой переменной в строковые метки\n", + "def decode_target(df, le_target):\n", + " df['sex'] = le_target.inverse_transform(df['sex'])\n", + "\n", + "decode_target(train_df_oversampled, le_target)\n", + "decode_target(val_df_oversampled, le_target)\n", + "decode_target(test_df_oversampled, le_target)\n", + "\n", + "decode_target(train_df_undersampled, le_target)\n", + "decode_target(val_df_undersampled, le_target)\n", + "decode_target(test_df_undersampled, le_target)\n", + "\n", + "# Проверка результатов\n", + "def check_balance(df, name):\n", + " if 'sex' not in df.columns:\n", + " print(f\"Столбец 'sex' отсутствует в {name}.\")\n", + " return\n", + " \n", + " counts = df['sex'].value_counts()\n", + " print(f\"Распределение sex в {name}:\")\n", + " print(counts)\n", + " \n", + " if 'male' in counts and 'female' in counts:\n", + " print(f\"Процент мужчин: {counts['male'] / len(df) * 100:.2f}%\")\n", + " print(f\"Процент женщин: {counts['female'] / len(df) * 100:.2f}%\")\n", + " else:\n", + " print(\"Отсутствуют один или оба класса (male/female.\")\n", + " print()\n", + "\n", + "# Проверка сбалансированности после oversampling\n", + "print(\"Оверсэмплинг:\")\n", + "check_balance(train_df_oversampled, \"обучающей выборке\")\n", + "check_balance(val_df_oversampled, \"контрольной выборке\")\n", + "check_balance(test_df_oversampled, \"тестовой выборке\")\n", + "\n", + "# Проверка сбалансированности после undersampling\n", + "print(\"Андерсэмплинг:\")\n", + "check_balance(train_df_undersampled, \"обучающей выборке\")\n", + "check_balance(val_df_undersampled, \"контрольной выборке\")\n", + "check_balance(test_df_undersampled, \"тестовой выборке\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stroke Prediction Dataset\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset Датасет инсультов\n", + "Цель: выжить\n", + "Входные данные: пол, возраст, женат ли, есть ли заболевания сердца" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " id gender age hypertension heart_disease ever_married \\\n", + "0 9046 Male 67 0 1 Yes \n", + "1 51676 Female 61 0 0 Yes \n", + "2 31112 Male 80 0 1 Yes \n", + "3 60182 Female 49 0 0 Yes \n", + "4 1665 Female 79 1 0 Yes \n", + "... ... ... ... ... ... ... \n", + "5105 18234 Female 80 1 0 Yes \n", + "5106 44873 Female 81 0 0 Yes \n", + "5107 19723 Female 35 0 0 Yes \n", + "5108 37544 Male 51 0 0 Yes \n", + "5109 44679 Female 44 0 0 Yes \n", + "\n", + " work_type Residence_type avg_glucose_level bmi smoking_status \\\n", + "0 Private Urban 228.69 36.6 formerly smoked \n", + "1 Self-employed Rural 202.21 NaN never smoked \n", + "2 Private Rural 105.92 32.5 never smoked \n", + "3 Private Urban 171.23 34.4 smokes \n", + "4 Self-employed Rural 174.12 24.0 never smoked \n", + "... ... ... ... ... ... \n", + "5105 Private Urban 83.75 NaN never smoked \n", + "5106 Self-employed Urban 125.20 40.0 never smoked \n", + "5107 Self-employed Rural 82.99 30.6 never smoked \n", + "5108 Private Rural 166.29 25.6 formerly smoked \n", + "5109 Govt_job Urban 85.28 26.2 Unknown \n", + "\n", + " stroke \n", + "0 1 \n", + "1 1 \n", + "2 1 \n", + "3 1 \n", + "4 1 \n", + "... ... \n", + "5105 0 \n", + "5106 0 \n", + "5107 0 \n", + "5108 0 \n", + "5109 0 \n", + "\n", + "[5110 rows x 12 columns]\n", + "Index(['id', 'gender', 'age', 'hypertension', 'heart_disease', 'ever_married',\n", + " 'work_type', 'Residence_type', 'avg_glucose_level', 'bmi',\n", + " 'smoking_status', 'stroke'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "df = pd.read_csv(\".//static//csv//healthcare-dataset-stroke-data.csv\", sep=\",\")\n", + "\n", + "df[\"age\"] = df[\"age\"].astype(int)\n", + "print(df)\n", + "df[\"age\"].dtype\n", + "\n", + "print(df.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n", + " 'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt \n", + "import seaborn as sns\n", + "df = pd.read_csv(\".//static//csv//diabetes.csv\")\n", + "print(df.columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Анализируем датафрейм при помощи \"ящика с усами\". Естьсмещение в сторону меньших значений, это можно исправить при помощи oversampling и undersampling." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Box plot для столбца возраст\n", + "plt.figure(figsize=(10, 6))\n", + "sns.boxplot(x=df['Age'])\n", + "plt.title('Box Plot для Age')\n", + "plt.xlabel('Age')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Выбросы:\n", + " Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n", + "4 0 137 40 35 168 43.1 \n", + "12 10 139 80 0 0 27.1 \n", + "39 4 111 72 47 207 37.1 \n", + "45 0 180 66 39 0 42.0 \n", + "58 0 146 82 0 0 40.5 \n", + "100 1 163 72 0 0 39.0 \n", + "147 2 106 64 35 119 30.5 \n", + "187 1 128 98 41 58 32.0 \n", + "218 5 85 74 22 0 29.0 \n", + "228 4 197 70 39 744 36.7 \n", + "243 6 119 50 22 176 27.1 \n", + "245 9 184 85 15 0 30.0 \n", + "259 11 155 76 28 150 33.3 \n", + "292 2 128 78 37 182 43.3 \n", + "308 0 128 68 19 180 30.5 \n", + "330 8 118 72 19 0 23.1 \n", + "370 3 173 82 48 465 38.4 \n", + "371 0 118 64 23 89 0.0 \n", + "383 1 90 62 18 59 25.1 \n", + "395 2 127 58 24 275 27.7 \n", + "445 0 180 78 63 14 59.4 \n", + "534 1 77 56 30 56 33.3 \n", + "593 2 82 52 22 115 28.5 \n", + "606 1 181 78 42 293 40.0 \n", + "618 9 112 82 24 0 28.2 \n", + "621 2 92 76 20 0 24.2 \n", + "622 6 183 94 0 0 40.8 \n", + "659 3 80 82 31 70 34.2 \n", + "661 1 199 76 43 0 42.9 \n", + "\n", + " DiabetesPedigreeFunction Age Outcome \n", + "4 2.288 33 1 \n", + "12 1.441 57 0 \n", + "39 1.390 56 1 \n", + "45 1.893 25 1 \n", + "58 1.781 44 0 \n", + "100 1.222 33 1 \n", + "147 1.400 34 0 \n", + "187 1.321 33 1 \n", + "218 1.224 32 1 \n", + "228 2.329 31 0 \n", + "243 1.318 33 1 \n", + "245 1.213 49 1 \n", + "259 1.353 51 1 \n", + "292 1.224 31 1 \n", + "308 1.391 25 1 \n", + "330 1.476 46 0 \n", + "370 2.137 25 1 \n", + "371 1.731 21 0 \n", + "383 1.268 25 0 \n", + "395 1.600 25 0 \n", + "445 2.420 25 1 \n", + "534 1.251 24 0 \n", + "593 1.699 25 0 \n", + "606 1.258 22 1 \n", + "618 1.282 50 1 \n", + "621 1.698 28 0 \n", + "622 1.461 45 0 \n", + "659 1.292 27 1 \n", + "661 1.394 22 1 \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Имеется смещение в меньшую сторону\n", + "df_cleaned = df.dropna()\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(df[\"Age\"], df[\"DiabetesPedigreeFunction\"])\n", + "plt.xlabel(\"Age\")\n", + "plt.ylabel(\"DiabetesPedigreeFunction\")\n", + "plt.title(\"Scatter Plot of Age vs DiabetesPedigreeFunction\")\n", + "plt.show()\n", + "\n", + "# Статистический анализ для определения выбросов\n", + "Q1 = df[\"DiabetesPedigreeFunction\"].quantile(0.25)\n", + "Q3 = df[\"DiabetesPedigreeFunction\"].quantile(0.75)\n", + "IQR = Q3 - Q1\n", + "\n", + "# Определение порога для выбросов\n", + "threshold = 1.5 * IQR\n", + "outliers = (df[\"DiabetesPedigreeFunction\"] < (Q1 - threshold)) | (\n", + " df[\"DiabetesPedigreeFunction\"] > (Q3 + threshold)\n", + ")\n", + "\n", + "# Вывод выбросов\n", + "print(\"Выбросы:\")\n", + "print(df[outliers])\n", + "\n", + "# Обработка выбросов\n", + "# В данном случае мы уберем выбросы\n", + "median_charge = df[\"DiabetesPedigreeFunction\"].median()\n", + "df.loc[outliers, \"DiabetesPedigreeFunction\"] = 0\n", + "df = df[df.DiabetesPedigreeFunction != 0]\n", + "\n", + "# Визуализация данных после обработки\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(df[\"Age\"], df[\"DiabetesPedigreeFunction\"])\n", + "plt.xlabel(\"Age\")\n", + "plt.ylabel(\"DiabetesPedigreeFunction\")\n", + "plt.title(\"Scatter Plot of Age vs DiabetesPedigreeFunction\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Разбиение набора данных на обучающую, контрольную и тестовую выборки" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Размер обучающей выборки: 460\n", + "Размер контрольной выборки: 154\n", + "Размер тестовой выборки: 154\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Разделение на обучающую и тестовую выборки\n", + "train_df, test_df = train_test_split(df_cleaned, test_size=0.2, random_state=42)\n", + "\n", + "# Разделение обучающей выборки на обучающую и контрольную\n", + "train_df, val_df = train_test_split(train_df, test_size=0.25, random_state=42)\n", + "\n", + "print(\"Размер обучающей выборки:\", len(train_df))\n", + "print(\"Размер контрольной выборки:\", len(val_df))\n", + "print(\"Размер тестовой выборки:\", len(test_df))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Видим недостаток баланса:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Распределение Age в обучающей выборке:\n", + "Age\n", + "22 39\n", + "21 36\n", + "25 31\n", + "24 25\n", + "26 23\n", + "27 22\n", + "28 21\n", + "23 21\n", + "41 17\n", + "31 15\n", + "29 15\n", + "37 14\n", + "30 12\n", + "45 11\n", + "46 11\n", + "36 11\n", + "42 10\n", + "34 10\n", + "38 10\n", + "32 9\n", + "35 8\n", + "52 8\n", + "33 7\n", + "43 7\n", + "40 7\n", + "47 5\n", + "50 5\n", + "51 5\n", + "39 4\n", + "48 4\n", + "57 4\n", + "66 3\n", + "49 3\n", + "44 3\n", + "58 2\n", + "55 2\n", + "53 2\n", + "63 2\n", + "67 2\n", + "54 2\n", + "56 2\n", + "60 2\n", + "59 2\n", + "61 1\n", + "65 1\n", + "81 1\n", + "69 1\n", + "68 1\n", + "70 1\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение Age в контрольной выборке:\n", + "Age\n", + "22 20\n", + "21 15\n", + "24 11\n", + "23 10\n", + "25 9\n", + "33 8\n", + "26 7\n", + "31 6\n", + "30 5\n", + "28 5\n", + "27 5\n", + "39 4\n", + "29 4\n", + "42 4\n", + "40 4\n", + "32 3\n", + "37 2\n", + "46 2\n", + "51 2\n", + "44 2\n", + "34 2\n", + "43 2\n", + "41 2\n", + "45 2\n", + "54 2\n", + "35 2\n", + "58 1\n", + "53 1\n", + "61 1\n", + "59 1\n", + "64 1\n", + "36 1\n", + "50 1\n", + "72 1\n", + "49 1\n", + "47 1\n", + "66 1\n", + "62 1\n", + "69 1\n", + "55 1\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение Age в тестовой выборке:\n", + "Age\n", + "22 13\n", + "21 12\n", + "24 10\n", + "29 10\n", + "28 9\n", + "25 8\n", + "23 7\n", + "38 6\n", + "27 5\n", + "39 4\n", + "30 4\n", + "43 4\n", + "42 4\n", + "32 4\n", + "36 4\n", + "58 4\n", + "31 3\n", + "62 3\n", + "44 3\n", + "26 3\n", + "41 3\n", + "37 3\n", + "60 3\n", + "54 2\n", + "50 2\n", + "34 2\n", + "65 2\n", + "63 2\n", + "45 2\n", + "40 2\n", + "33 2\n", + "53 2\n", + "55 1\n", + "48 1\n", + "67 1\n", + "56 1\n", + "51 1\n", + "49 1\n", + "57 1\n", + "Name: count, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "def check_balance(df, name):\n", + " counts = df['Age'].value_counts()\n", + " print(f\"Распределение Age в {name}:\")\n", + " print(counts)\n", + " print()\n", + "\n", + "check_balance(train_df, \"обучающей выборке\")\n", + "check_balance(val_df, \"контрольной выборке\")\n", + "check_balance(test_df, \"тестовой выборке\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Используем oversample" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Распределение Age в обучающей выборке после oversampling:\n", + "Age\n", + "26 39\n", + "25 39\n", + "33 39\n", + "23 39\n", + "35 39\n", + "39 39\n", + "29 39\n", + "27 39\n", + "21 39\n", + "28 39\n", + "41 39\n", + "31 39\n", + "24 39\n", + "22 39\n", + "42 39\n", + "32 39\n", + "36 39\n", + "51 39\n", + "34 39\n", + "30 39\n", + "45 39\n", + "49 39\n", + "57 39\n", + "53 39\n", + "48 39\n", + "55 39\n", + "46 39\n", + "47 39\n", + "52 39\n", + "50 39\n", + "37 39\n", + "58 39\n", + "61 39\n", + "40 39\n", + "65 39\n", + "67 39\n", + "38 39\n", + "66 39\n", + "81 39\n", + "44 39\n", + "43 39\n", + "56 39\n", + "54 39\n", + "60 39\n", + "69 39\n", + "70 39\n", + "68 39\n", + "63 39\n", + "59 39\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение Age в контрольной выборке после oversampling:\n", + "Age\n", + "25 20\n", + "58 20\n", + "27 20\n", + "26 20\n", + "31 20\n", + "22 20\n", + "28 20\n", + "39 20\n", + "21 20\n", + "45 20\n", + "41 20\n", + "61 20\n", + "51 20\n", + "64 20\n", + "36 20\n", + "30 20\n", + "53 20\n", + "40 20\n", + "23 20\n", + "59 20\n", + "29 20\n", + "42 20\n", + "46 20\n", + "43 20\n", + "44 20\n", + "34 20\n", + "24 20\n", + "32 20\n", + "33 20\n", + "72 20\n", + "35 20\n", + "50 20\n", + "54 20\n", + "47 20\n", + "49 20\n", + "66 20\n", + "62 20\n", + "69 20\n", + "55 20\n", + "37 20\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение Age в тестовой выборке после oversampling:\n", + "Age\n", + "43 13\n", + "21 13\n", + "34 13\n", + "50 13\n", + "55 13\n", + "22 13\n", + "44 13\n", + "37 13\n", + "65 13\n", + "40 13\n", + "60 13\n", + "29 13\n", + "28 13\n", + "24 13\n", + "36 13\n", + "48 13\n", + "45 13\n", + "39 13\n", + "41 13\n", + "27 13\n", + "23 13\n", + "38 13\n", + "25 13\n", + "67 13\n", + "31 13\n", + "30 13\n", + "54 13\n", + "33 13\n", + "62 13\n", + "26 13\n", + "56 13\n", + "58 13\n", + "53 13\n", + "63 13\n", + "42 13\n", + "32 13\n", + "51 13\n", + "49 13\n", + "57 13\n", + "Name: count, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "from imblearn.over_sampling import RandomOverSampler\n", + "\n", + "def oversample(df):\n", + " X = df.drop('Age', axis=1)\n", + " y = df['Age']\n", + " \n", + " oversampler = RandomOverSampler(random_state=42)\n", + " X_resampled, y_resampled = oversampler.fit_resample(X, y) # type: ignore\n", + " \n", + " resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n", + " return resampled_df\n", + "\n", + "train_df_oversampled = oversample(train_df)\n", + "val_df_oversampled = oversample(val_df)\n", + "test_df_oversampled = oversample(test_df)\n", + "\n", + "check_balance(train_df_oversampled, \"обучающей выборке после oversampling\")\n", + "check_balance(val_df_oversampled, \"контрольной выборке после oversampling\")\n", + "check_balance(test_df_oversampled, \"тестовой выборке после oversampling\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Diamonds Prices2022" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "В данном наборе данных представлена цена на алмазы. Входные данные: цвет, караты, цена, чистота, размеры\n", + "Цель: узнать, какие алмазы ценятся больше всего" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['Unnamed: 0', 'carat', 'cut', 'color', 'clarity', 'depth', 'table',\n", + " 'price', 'x', 'y', 'z'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "df = pd.read_csv(\".//static//csv//Diamonds Prices2022.csv\")\n", + "print(df.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "sns.boxplot(x=df['carat'])\n", + "plt.title('Box Plot для carat')\n", + "plt.xlabel('carat')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "\n", + "# Визуализация данных после обработки\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(df[\"carat\"], df[\"price\"])\n", + "plt.xlabel(\"carat\")\n", + "plt.ylabel(\"price\")\n", + "plt.title(\"Scatter Plot of Price vs Carat\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Удаление строк с пустыми значениями" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "df_cleaned = df.dropna()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Разбиение набора данных на обучающую, контрольную и тестовую выборки" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Применение методов приращения данных (аугментации)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Размер обучающей выборки: 32365\n", + "Размер контрольной выборки: 10789\n", + "Размер тестовой выборки: 10789\n", + "Распределение price в обучающей выборке:\n", + "price\n", + "789 80\n", + "605 79\n", + "544 72\n", + "552 72\n", + "828 71\n", + " ..\n", + "9942 1\n", + "7787 1\n", + "18663 1\n", + "7979 1\n", + "8164 1\n", + "Name: count, Length: 9496, dtype: int64\n", + "\n", + "Распределение price в контрольной выборке:\n", + "price\n", + "625 34\n", + "828 31\n", + "605 30\n", + "789 26\n", + "544 26\n", + " ..\n", + "4188 1\n", + "7541 1\n", + "3498 1\n", + "3314 1\n", + "12196 1\n", + "Name: count, Length: 5383, dtype: int64\n", + "\n", + "Распределение price в тестовой выборке:\n", + "price\n", + "802 33\n", + "844 29\n", + "776 29\n", + "675 26\n", + "645 25\n", + " ..\n", + "1567 1\n", + "5529 1\n", + "2031 1\n", + "417 1\n", + "5431 1\n", + "Name: count, Length: 5338, dtype: int64\n", + "\n", + "Распределение price в обучающей выборке после oversampling:\n", + "price\n", + "5076 80\n", + "1789 80\n", + "3931 80\n", + "1263 80\n", + "2026 80\n", + " ..\n", + "3678 80\n", + "4592 80\n", + "516 80\n", + "7152 80\n", + "2353 80\n", + "Name: count, Length: 9496, dtype: int64\n", + "\n", + "Распределение price в контрольной выборке после oversampling:\n", + "price\n", + "966 34\n", + "13638 34\n", + "3669 34\n", + "1052 34\n", + "2818 34\n", + " ..\n", + "4032 34\n", + "544 34\n", + "3362 34\n", + "6559 34\n", + "792 34\n", + "Name: count, Length: 5383, dtype: int64\n", + "\n", + "Распределение price в тестовой выборке после oversampling:\n", + "price\n", + "3742 33\n", + "559 33\n", + "8403 33\n", + "1238 33\n", + "1243 33\n", + " ..\n", + "1149 33\n", + "2401 33\n", + "958 33\n", + "702 33\n", + "14618 33\n", + "Name: count, Length: 5338, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "from imblearn.over_sampling import RandomOverSampler\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Разделение на обучающую и тестовую выборки\n", + "train_df, test_df = train_test_split(df_cleaned, test_size=0.2, random_state=42)\n", + "\n", + "# Разделение обучающей выборки на обучающую и контрольную\n", + "train_df, val_df = train_test_split(train_df, test_size=0.25, random_state=42)\n", + "\n", + "print(\"Размер обучающей выборки:\", len(train_df))\n", + "print(\"Размер контрольной выборки:\", len(val_df))\n", + "print(\"Размер тестовой выборки:\", len(test_df))\n", + "\n", + "def check_balance(df, name):\n", + " counts = df[\"price\"].value_counts()\n", + " print(f\"Распределение price в {name}:\")\n", + " print(counts)\n", + " print()\n", + "\n", + "\n", + "check_balance(train_df, \"обучающей выборке\")\n", + "check_balance(val_df, \"контрольной выборке\")\n", + "check_balance(test_df, \"тестовой выборке\")\n", + "\n", + "def oversample(df):\n", + " X = df.drop(\"price\", axis=1)\n", + " y = df[\"price\"]\n", + "\n", + " oversampler = RandomOverSampler(random_state=42)\n", + " X_resampled, y_resampled = oversampler.fit_resample(X, y) # type: ignore\n", + "\n", + " resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n", + " return resampled_df\n", + "\n", + "\n", + "train_df_oversampled = oversample(train_df)\n", + "val_df_oversampled = oversample(val_df)\n", + "test_df_oversampled = oversample(test_df)\n", + "\n", + "check_balance(train_df_oversampled, \"обучающей выборке после oversampling\")\n", + "check_balance(val_df_oversampled, \"контрольной выборке после oversampling\")\n", + "check_balance(test_df_oversampled, \"тестовой выборке после oversampling\")" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Распределение price в обучающей выборке после oversampling:\n", + "price\n", + "5076 80\n", + "1789 80\n", + "3931 80\n", + "1263 80\n", + "2026 80\n", + " ..\n", + "3678 80\n", + "4592 80\n", + "516 80\n", + "7152 80\n", + "2353 80\n", + "Name: count, Length: 9496, dtype: int64\n", + "\n", + "Распределение price в контрольной выборке после oversampling:\n", + "price\n", + "966 34\n", + "13638 34\n", + "3669 34\n", + "1052 34\n", + "2818 34\n", + " ..\n", + "4032 34\n", + "544 34\n", + "3362 34\n", + "6559 34\n", + "792 34\n", + "Name: count, Length: 5383, dtype: int64\n", + "\n", + "Распределение price в тестовой выборке после oversampling:\n", + "price\n", + "3742 33\n", + "559 33\n", + "8403 33\n", + "1238 33\n", + "1243 33\n", + " ..\n", + "1149 33\n", + "2401 33\n", + "958 33\n", + "702 33\n", + "14618 33\n", + "Name: count, Length: 5338, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "from imblearn.over_sampling import RandomOverSampler\n", + "\n", + "\n", + "def oversample(df):\n", + " X = df.drop(\"price\", axis=1)\n", + " y = df[\"price\"]\n", + "\n", + " oversampler = RandomOverSampler(random_state=42)\n", + " X_resampled, y_resampled = oversampler.fit_resample(X, y) # type: ignore\n", + "\n", + " resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n", + " return resampled_df\n", + "\n", + "\n", + "train_df_oversampled = oversample(train_df)\n", + "val_df_oversampled = oversample(val_df)\n", + "test_df_oversampled = oversample(test_df)\n", + "\n", + "check_balance(train_df_oversampled, \"обучающей выборке после oversampling\")\n", + "check_balance(val_df_oversampled, \"контрольной выборке после oversampling\")\n", + "check_balance(test_df_oversampled, \"тестовой выборке после oversampling\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "aisenv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}