From c11d2ba4b1cb875d060cc3ef1a0753086ad12677 Mon Sep 17 00:00:00 2001 From: kamilia Date: Fri, 11 Oct 2024 19:31:37 +0400 Subject: [PATCH] lab 2 is done --- lab_2/lab_2.ipynb | 1240 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1240 insertions(+) create mode 100644 lab_2/lab_2.ipynb diff --git a/lab_2/lab_2.ipynb b/lab_2/lab_2.ipynb new file mode 100644 index 0000000..ee9102c --- /dev/null +++ b/lab_2/lab_2.ipynb @@ -0,0 +1,1240 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from imblearn.over_sampling import RandomOverSampler\n", + "from imblearn.under_sampling import RandomUnderSampler" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Прогнозирование стоимости страховки\thttps://www.kaggle.com/datasets/harishkumardatalab/medical-insurance-price-prediction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Набор данных по медицинскому страхованию содержит информацию о ряде факторов, которые могут повлиять на медицинские расходы, включая возраст, пол, ИМТ, статус курящего, количество детей и регион. Этот набор данных может быть использован для обучения модели машинного обучения, которая может прогнозировать медицинские расходы для новых клиентов." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['age', 'sex', 'bmi', 'children', 'smoker', 'region', 'charges'], dtype='object')\n" + ] + } + ], + "source": [ + "df1 = pd.read_csv(\"..//static//csv//Medical_insurance.csv\")\n", + "\n", + "print(df1.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "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. Выборка относительно сбалансированна, пускай и смещена в меньшую сторону. Пустых значений нет.\n", + "plt.figure(figsize=(10, 6))\n", + "sns.boxplot(x=df1[\"age\"])\n", + "plt.title(\"Box Plot для age\")\n", + "plt.xlabel(\"Age\")\n", + "plt.show()\n", + "\n", + "#Визуализируем отношение стоимости и возраста\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(df1[\"age\"], df1[\"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": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Выбросы:\n", + "Empty DataFrame\n", + "Columns: [age, sex, bmi, children, smoker, region, charges]\n", + "Index: []\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_8452\\3242078583.py:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " print(df[outliers])\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Есть шумы, убираем\n", + "\n", + "# Статистический анализ для определения выбросов\n", + "Q1 = df1[\"charges\"].quantile(0.25)\n", + "Q3 = df1[\"charges\"].quantile(0.75)\n", + "IQR = Q3 - Q1\n", + "\n", + "# Определение порога для выбросов\n", + "threshold = 1.5 * IQR\n", + "outliers = (df1[\"charges\"] < (Q1 - threshold)) | (df1[\"charges\"] > (Q3 + threshold))\n", + "\n", + "# Вывод выбросов\n", + "print(\"Выбросы:\")\n", + "print(df[outliers])\n", + "\n", + "# Обработка выбросов\n", + "# В данном случае мы заменим выбросы на медианное значение\n", + "median_charge = df1[\"charges\"].median()\n", + "df1.loc[outliers, \"charges\"] = 0\n", + "df1 = df1[df1.charges != 0]\n", + "\n", + "# Визуализация данных после обработки\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(df1[\"age\"], df1[\"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": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Размер обучающей выборки: 1485\n", + "Размер контрольной выборки: 495\n", + "Размер тестовой выборки: 496\n" + ] + } + ], + "source": [ + "#Разбиение набора данных на обучающую, контрольную и тестовую выборки\n", + "\n", + "train_df1, temp_df1 = train_test_split(df1, test_size=0.4, random_state=42)\n", + "\n", + "# Разделение остатка на контрольную и тестовую выборки\n", + "val_df1, test_df1 = train_test_split(temp_df1, test_size=0.5, random_state=42)\n", + "\n", + "# Проверка размеров выборок\n", + "print(\"Размер обучающей выборки:\", len(train_df1))\n", + "print(\"Размер контрольной выборки:\", len(val_df1))\n", + "print(\"Размер тестовой выборки:\", len(test_df1))\n", + "\n", + "# Сохранение выборок в файлы\n", + "train_df1.to_csv(\"..//static//csv//train_data.csv\", index=False)\n", + "val_df1.to_csv(\"..//static//csv//val_data.csv\", index=False)\n", + "test_df1.to_csv(\"..//static//csv//test_data.csv\", index=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "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": [ + "#Проанализируем сбалансированность выборок\n", + "train_df1 = pd.read_csv(\"..//static//csv//train_data.csv\")\n", + "val_df1 = pd.read_csv(\"..//static//csv//val_data.csv\")\n", + "test_df1 = pd.read_csv(\"..//static//csv//test_data.csv\")\n", + "\n", + "# Оценка сбалансированности\n", + "def check_balance(df1, name):\n", + " counts = df1['sex'].value_counts()\n", + " print(f\"Распределение Review_type в {name}:\")\n", + " print(counts)\n", + " print(f\"Процент мужчин: {counts['male'] / len(df1) * 100:.2f}%\")\n", + " print(f\"Процент женщин: {counts['female'] / len(df1) * 100:.2f}%\")\n", + " print()\n", + "\n", + "# Определение необходимости аугментации данных\n", + "def need_augmentation(df1):\n", + " counts = df1['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_df1, \"обучающей выборке\")\n", + "check_balance(val_df1, \"контрольной выборке\")\n", + "check_balance(test_df1, \"тестовой выборке\")\n", + "\n", + "\n", + "\n", + "need_augmentation(train_df1)\n", + "need_augmentation(val_df1)\n", + "need_augmentation(test_df1)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "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 sklearn.preprocessing import LabelEncoder\n", + "from imblearn.over_sampling import RandomOverSampler\n", + "from imblearn.under_sampling import RandomUnderSampler\n", + "\n", + "# Загрузка данных\n", + "train_df1 = pd.read_csv(\"..//static//csv//train_data.csv\")\n", + "val_df1 = pd.read_csv(\"..//static//csv//val_data.csv\")\n", + "test_df1 = pd.read_csv(\"..//static//csv//test_data.csv\")\n", + "\n", + "# Преобразование категориальных признаков в числовые\n", + "def encode(df1):\n", + " label_encoders = {}\n", + " for column in df1.select_dtypes(include=['object']).columns:\n", + " if column != 'sex': # Пропускаем целевую переменную\n", + " le = LabelEncoder()\n", + " df1[column] = le.fit_transform(df1[column])\n", + " label_encoders[column] = le\n", + " return label_encoders\n", + "\n", + "# Преобразование целевой переменной в числовые значения\n", + "def encode_target(df1):\n", + " le = LabelEncoder()\n", + " df1['sex'] = le.fit_transform(df1['sex'])\n", + " return le\n", + "\n", + "# Применение кодирования\n", + "label_encoders = encode(train_df1)\n", + "encode(val_df1)\n", + "encode(test_df1)\n", + "\n", + "# Кодирование целевой переменной\n", + "le_target = encode_target(train_df1)\n", + "encode_target(val_df1)\n", + "encode_target(test_df1)\n", + "\n", + "# Проверка типов данных\n", + "def check_data_types(df1):\n", + " for column in df1.columns:\n", + " if df1[column].dtype == 'object':\n", + " print(f\"Столбец '{column}' содержит строковые данные.\")\n", + "\n", + "check_data_types(train_df1)\n", + "check_data_types(val_df1)\n", + "check_data_types(test_df1)\n", + "\n", + "# Функция для выполнения oversampling\n", + "def oversample(df1):\n", + " if 'sex' not in df1.columns:\n", + " print(\"Столбец 'sex' отсутствует.\")\n", + " return df1\n", + " \n", + " X = df1.drop('sex', axis=1)\n", + " y = df1['sex']\n", + " \n", + " oversampler = RandomOverSampler(random_state=42)\n", + " X_resampled, y_resampled = oversampler.fit_resample(X, y) # type: ignore\n", + " \n", + " resampled_df1 = pd.concat([X_resampled, y_resampled], axis=1)\n", + " return resampled_df1\n", + "\n", + "# Функция для выполнения undersampling\n", + "def undersample(df1):\n", + " if 'sex' not in df1.columns:\n", + " print(\"Столбец 'sex' отсутствует.\")\n", + " return df1\n", + " \n", + " X = df1.drop('sex', axis=1)\n", + " y = df1['sex']\n", + " \n", + " undersampler = RandomUnderSampler(random_state=42)\n", + " X_resampled, y_resampled = undersampler.fit_resample(X, y) # type: ignore\n", + " \n", + " resampled_df1 = pd.concat([X_resampled, y_resampled], axis=1)\n", + " return resampled_df1\n", + "\n", + "# Применение oversampling и undersampling к каждой выборке\n", + "train_df1_oversampled = oversample(train_df1)\n", + "val_df1_oversampled = oversample(val_df1)\n", + "test_df1_oversampled = oversample(test_df1)\n", + "\n", + "train_df1_undersampled = undersample(train_df1)\n", + "val_df1_undersampled = undersample(val_df1)\n", + "test_df1_undersampled = undersample(test_df1)\n", + "\n", + "# Обратное преобразование целевой переменной в строковые метки\n", + "def decode_target(df1, le_target):\n", + " df1['sex'] = le_target.inverse_transform(df1['sex'])\n", + "\n", + "decode_target(train_df1_oversampled, le_target)\n", + "decode_target(val_df1_oversampled, le_target)\n", + "decode_target(test_df1_oversampled, le_target)\n", + "\n", + "decode_target(train_df1_undersampled, le_target)\n", + "decode_target(val_df1_undersampled, le_target)\n", + "decode_target(test_df1_undersampled, le_target)\n", + "\n", + "# Проверка результатов\n", + "def check_balance(df1, name):\n", + " if 'sex' not in df1.columns:\n", + " print(f\"Столбец 'sex' отсутствует в {name}.\")\n", + " return\n", + " \n", + " counts = df1['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(df1) * 100:.2f}%\")\n", + " print(f\"Процент женщин: {counts['female'] / len(df1) * 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_df1_oversampled, \"обучающей выборке\")\n", + "check_balance(val_df1_oversampled, \"контрольной выборке\")\n", + "check_balance(test_df1_oversampled, \"тестовой выборке\")\n", + "\n", + "# Проверка сбалансированности после undersampling\n", + "print(\"Андерсэмплинг:\")\n", + "check_balance(train_df1_undersampled, \"обучающей выборке\")\n", + "check_balance(val_df1_undersampled, \"контрольной выборке\")\n", + "check_balance(test_df1_undersampled, \"тестовой выборке\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Объекты вокруг Земли\thttps://www.kaggle.com/datasets/sameepvani/nasa-nearest-earth-objects" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['id', 'name', 'est_diameter_min', 'est_diameter_max',\n", + " 'relative_velocity', 'miss_distance', 'orbiting_body', 'sentry_object',\n", + " 'absolute_magnitude', 'hazardous'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "df2 = pd.read_csv(\"..//static//csv//neo.csv\")\n", + "\n", + "print(df2.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "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": [ + "#Получение сведений о пропущенных данных\n", + "\n", + "# Количество пустых значений признаков\n", + "print(df2.isnull().sum())\n", + "\n", + "print()\n", + "\n", + "# Есть ли пустые значения признаков\n", + "print(df2.isnull().any())\n", + "\n", + "print()\n", + "\n", + "# Процент пустых значений признаков\n", + "for i in df2.columns:\n", + " null_rate = df2[i].isnull().sum() / len(df2) * 100\n", + " if null_rate > 0:\n", + " print(f\"{i} процент пустых значений: %{null_rate:.2f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Итог: пропущеных значений нет\n", + "\n", + "Инфографика на сайте и в datawrangelere показывает, что в столбцах orbiting_body и sentry_object у всех записей одно и тоже значение. Значит эти столбцы можно выкинуть из набора данных." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "количество колонок: 8\n", + "колонки: id, name, est_diameter_min, est_diameter_max, relative_velocity, miss_distance, absolute_magnitude, hazardous\n" + ] + } + ], + "source": [ + "df2 = df2.drop(columns=['orbiting_body'])\n", + "df2 = df2.drop(columns=['sentry_object'])\n", + "print('количество колонок: ' + str(df2.columns.size)) \n", + "print('колонки: ' + ', '.join(df2.columns))" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "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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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Список числовых колонок, для которых строим графики, чтобы найти зависимости опасности от других колонок\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(df2['hazardous'], df2[column], alpha=0.5) # Создаем диаграмму рассеяния\n", + " plt.title(f'Зависимость {column} от hazardous')\n", + " plt.xlabel('hazardous (0 = нет, 1 = да)')\n", + " plt.ylabel(column)\n", + " plt.xticks([0, 1]) # Установка меток по оси X\n", + " plt.grid() # Добавление сетки для удобства восприятия\n", + " plt.show() # Отображение графика" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "#Разбиение набора данных на обучающую, контрольную и тестовую выборки\n", + "def split_stratified_into_train_val_test(\n", + " df_input,\n", + " stratify_colname=\"y\",\n", + " frac_train=0.6,\n", + " frac_val=0.15,\n", + " frac_test=0.25,\n", + " random_state=None,\n", + "):\n", + "\n", + " if frac_train + frac_val + frac_test != 1.0:\n", + " raise ValueError(\n", + " \"fractions %f, %f, %f do not add up to 1.0\"\n", + " % (frac_train, frac_val, frac_test)\n", + " )\n", + "\n", + " if stratify_colname not in df_input.columns:\n", + " raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n", + "\n", + " X = df_input # Contains all columns.\n", + " y = df_input[\n", + " [stratify_colname]\n", + " ] # Dataframe of just the column on which to stratify.\n", + "\n", + " # Split original dataframe into train and temp dataframes.\n", + " df_train, df_temp, y_train, y_temp = train_test_split(\n", + " X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n", + " )\n", + "\n", + " # Split the temp dataframe into val and test dataframes.\n", + " relative_frac_test = frac_test / (frac_val + frac_test)\n", + " df_val, df_test, y_val, y_test = train_test_split(\n", + " df_temp,\n", + " y_temp,\n", + " stratify=y_temp,\n", + " test_size=relative_frac_test,\n", + " random_state=random_state,\n", + " )\n", + "\n", + " assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n", + "\n", + " return df_train, df_val, df_test" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hazardous\n", + "False 81996\n", + "True 8840\n", + "Name: count, dtype: int64\n", + "\n", + "Обучающая выборка: (54501, 6)\n", + "hazardous\n", + "False 49197\n", + "True 5304\n", + "Name: count, dtype: int64\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Контрольная выборка: (18167, 6)\n", + "hazardous\n", + "False 16399\n", + "True 1768\n", + "Name: count, dtype: int64\n" + ] + }, + { + "data": { + "image/png": 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Li9N6WaB032VkZCAhIQGrVq2CQCCAj49PtZaVSqUYOnQo0tPTERUVpaqlVfrnn3/g5eWFnj17qqYZGRlh6tSpSEpKUjUdKeXm5mqct7UVHBys+j+Px0NwcDCKi4tx7NgxVYwCgQAzZ85UW2727NlgjOHQoUNq06t7fgJAdnY2MjIykJubW+V85be5/Pzann9lzZs3D506dcKoUaNeGa9Sdc9f5XU2MzOz2veZCAwMVPucuLq6YujQoYiKioJcLodcLseRI0cQEBCAli1bquazs7PDuHHjEB0djZycHLUyp06dqlZT/dFHH0FPT091vpUVGRmJ6dOn4/PPP1f7bACl18KsrCyMHTtW7XgIBAJ07dq12td5oIbNH+vXr4ebmxv09PTQrFkztGrVSu3CrVAosGbNGmzYsAGJiYlqbcJld2p8fDxatWqlqjLUFVdXV7X3PB4PLi4uqjY15UVo0qRJlZaRnZ0Nc3Nz1fuMjAyNcsuLi4sDY6zS+co3U2RlZQFAlfcniIuLQ3Z2tqrKurznz5+rvT9y5Ijahb86Fi5cCHt7e0ybNk2jbTcuLg53796ttMzy669ISkoK/P39kZ+fj8zMzEoTxursDwA4cOAAlixZgpiYGLX2+LLlxsfHg8/no02bNpWWo2y28/DwqHSe5ORkAECrVq00/ubu7o7o6Gi1aY8fP1bbV3Z2dti3b1+17kEhFos19rO5ublGG2l1tr+sKVOmID4+HufPn9f48gsLC8MPP/yAe/fuoaSkRDXdyclJo5zy09LT01FYWFjh571Vq1ZqF7bk5GTw+Xy4uLiozWdrawszMzPVftbG4sWL8e6778LNzQ0eHh4YNGgQ3n//fbRv375ay3fq1En1f5FIhHXr1lXY7FmRwMBA/PvvvxCLxRV+qSQnJ2tUVQNQfTklJyerfe4++OCDaq23uvh8vtoXEwBVFbvyOpicnAx7e3u1H2DlYyyruucnALUmJDMzM4wdOxbLly9X6zSZn5//ymuVtuefUnR0NP7++28cP34cjx49emW8StU9f8teZwUCAdq3b4/vv/9e1axalvK8dHd31/hb69atsW/fPmRkZIAxhoKCggq3tXXr1lAoFHj8+DHatm2rml7+3DMyMoKdnZ1G/5GYmBjs3bsXcrkcL1680Chf+Z2o/LFYnomJSYXTK1Kjb3MvLy/V6I+KfPfdd5g/fz4++OADfPPNN7CwsACfz8esWbM0agC4oIxh+fLlGu3GSmU/RMXFxUhNTX1lO61CoQCPx8OhQ4dUvwYrKxMAnj17BqD0wlpVmTY2NpXewKX8Sdm1a1csWbJEbdq6desQGRlZ4fJ3797F9u3bsWvXrgr7ZigUCrRr1w4rV66scPnmzZtXGrvSw4cP0alTJ6xatQrvv/8+wsLCKkzoqrM/zp49i6FDh6JXr17YsGED7OzsIBQKsW3bNo3OlVxo1qwZdu3aBaA0Md26dSsGDRqE6OhotGvXrsplK/rMlKft9q9ZswZ79uzBrl27ND7ru3btwuTJkxEQEIDPP/8cNjY2EAgECA0NVSVcZZWtIamp2tRAlu+w2qtXL8THxyMyMhJHjhzBL7/8glWrVmHTpk0ICgp6ZXm7du1Cs2bNIJVKceLECXz88ccQi8Uandoqcu3aNURGRiI4OBhTp07FiRMnarpZAErb/pX9iJTeeeedWpWpa8+ePYORkVG1RlMof3gWFRXh1KlTWLFiBQBgw4YNqnnEYjH+/vtvteXOnj2r6vNRG1988QV8fX3Rt29ftZthvUp1z9+y19mnT59i6dKlGDZsWIWdGnVx3tRWbGws/Pz80K9fP3z++eeYMGGCWn8K5Xfizp07K7z+avPDX7dVBP8VERGBPn36YMuWLWrTs7KyVB18gNIeuhcvXkRJSYlOOhsqla8OZYzh4cOHql8wyg6gJiYm1eqUFRsbi5KSkioTKWW5jDE4OTmpfhVU5c6dO+DxeBVmpmXLPHbsGHr06FGtD6eVlZXGNlXVmXLevHl466238N5771W6/tjYWPTr16/GXwjKpqdmzZohMjISs2fPxuDBgzUSoursj3379kEsFuPw4cNq1bDbtm3TiFuhUODOnTuVJo7Kz8GtW7c0fkErOTo6AijtwV0+i79//77q70pisVht/w8dOhQWFhZYt25dpaMitFHd7QdKL9Bz5szBrFmzMH78eI2/R0REoGXLlvjjjz/Uju3ChQurFYu1tTUMDAwqbH64f/++2ntHR0coFArExcWpVSWnpaUhKytLbT+am5urfhUrKRP78iwsLBAYGIjAwEDk5eWhV69eCAkJqVZS0aNHD1XnsyFDhuD27dsIDQ2tVlLxyy+/YOjQoRAIBBgyZAi2bNmC//znP2rbW34fAMC9e/dUfy+rXbt2GudtdZLMyigUCiQkJKhdhx48eAAAqm12dHTEsWPHkJubq1ZbUVmMd+7cqXYzXtkfnv7+/oiNjUVUVJTaPAKBQGObyx93bc8/oPR6d+HChUqbA6tS3fO3/HXWxcUFPXr0wJkzZ9SaAoH/1fBV9nmQSCSq70VDQ8NK5+Pz+Ro/4uLi4tCnTx/V+7y8PKSmpmoMamjXrh3Cw8NhYGCA8PBwTJ06FTdu3FA1KSmvhTY2Nlp1VK5IndymWyAQaAxJCg8P1xgCNGLECGRkZGDdunUaZZRfXhs7duxQa5uLiIhAamoq/Pz8AACenp5wdnbGihUrkJeXp7F8enq6RuzKi0dVhg8fDoFAgEWLFmnEzxhT9QIGSofX7du3D15eXlVWJ44ePRpyuRzffPONxt9kMpnGSaiNCxcuIDIyEt9//32lCcPo0aORkpKCn3/+WeNvhYWFyM/Pf+V63NzcVKMB1q5dC4VCgU8++URtnuruD4FAAB6Pp/arNSkpSSNxCggIAJ/Px+LFizVqx5THZuDAgTA2NkZoaKhGvxPlPJ07d4aNjQ02bdqk1tRw6NAh3L17V220SUWKi4shk8mqNfy2Oqq7/ampqRg9ejR69uxZaR8W5ZdW2c/qxYsXceHChWrH4uvri/3796tVMd+9exeHDx9Wm1d5kVu9erXadGUNWNn96OzsjDNnzqjN99NPP2nUVJQ9n4DSmkAXF5ca7+vCwsJqL6usVfD398eYMWPw+eefq91VePDgwbh06ZLavszPz8dPP/2EFi1aVNkspytlr6uMMaxbtw5CoRD9+vVTxSiXyzWuv6tWrQKPx1NdL4HSZoFz585VWj3+KgqFokZJkrbnn1wux1dffYVx48ZV+mNCG9U9f5XXmIq20draGp07d0ZYWJhaU2Z8fDz++usv+Pn5QSAQQCAQYODAgYiMjFRrvkhLS8Ovv/6Knj17ajRD/PTTT2rNlhs3boRMJlM7dkBpU59EIgGfz8cvv/yCpKQktRohX19fmJiY4LvvvlMrT6n8d2JV6qSmYsiQIVi8eDECAwPRvXt33Lx5E7t379Zo45s4cSJ27NiBzz77DJcuXYK3tzfy8/Nx7NgxTJ8+He+++26N1m9hYYGePXsiMDAQaWlpWL16NVxcXDBlyhQAUO1YPz8/tG3bFoGBgXBwcEBKSgpOnjwJExMT/P3338jPz8f69evx448/ws3NTW1MrzIZuXHjBi5cuIBu3brB2dkZS5Yswbx585CUlISAgAAYGxsjMTERf/75J6ZOnYo5c+bg2LFjmD9/Pm7cuKFR/Veej48Ppk2bhtDQUMTExGDgwIEQCoWIi4tDeHg41qxZg5EjR9ZoPx05cgQDBgyoMjN9//33sXfvXnz44Yc4efIkevToAblcjnv37mHv3r04fPjwK2twyrK1tcXy5csRFBSECRMmYPDgwVrtD39/f6xcuRKDBg3CuHHj8Pz5c6xfvx4uLi64ceOGaj4XFxd8/fXX+Oabb+Dt7Y3hw4dDJBLh8uXLsLe3R2hoKExMTLBq1SoEBQWhS5cuGDduHMzNzREbG4uCggKEhYVBKBRi6dKlCAwMhI+PD8aOHasa0taiRQt8+umnavHl5+erVZ/u3LkTUqlU6yGEtd3+mTNnIj09HXPnzsVvv/2mVkb79u3Rvn17DBkyBH/88QeGDRsGf39/JCYmYtOmTWjTpk2FyXZFFi1ahKioKHh7e2P69OmQyWRYu3Yt2rZtqxZPhw4dMGnSJPz000/IysqCj48PLl26hLCwMAQEBKj92goKCsKHH36IESNGYMCAAYiNjcXhw4fVajkBoE2bNujduzc8PT1hYWGBK1euICIiQqMTWmX2798PKysrVfPH2bNnMWvWrGotW9aaNWvQunVrzJgxA3v37gUAfPnll9izZw/8/Pwwc+ZMWFhYICwsDImJidi3b59G52FdE4vFiIqKwqRJk9C1a1ccOnQIBw8exFdffaWqIXznnXfQp08ffP3110hKSkKHDh1w5MgRREZGYtasWapfrxs3bkRoaCgMDQ01OnVW5sKFC8jIyFA1fxw/fhxz5szReju0Pf+ePHkCfX39CjsqVkd1z19lB12gNIFfunQpTE1N0adPH1WNUFnLli3DwIED0a1bNwQFBamGlIrFYrVhuUuWLMHRo0fRs2dPTJ8+HXp6eti8eTOKioqwbNkyjXKLi4vRr18/jB49Gvfv38eGDRvQs2dPDB06tNJt9PDwwBdffIHvv/8eY8aMQfv27WFiYoKNGzfi/fffR6dOnTBmzBhYW1vj0aNHOHjwIHr06FHhj/8KVXucCKv+cCGpVMpmz57N7OzsmIGBAevRowe7cOFChcOgCgoK2Ndff82cnJyYUChktra2bOTIkaohNTUZUrpnzx42b948ZmNjwwwMDJi/v7/aMCil69evs+HDhzNLS0smEomYo6MjGz16NDt+/Ljaul/1Kj/sa9++faxnz55MIpEwiUTC3N3d2ccff8zu37/PGGNsxowZrFevXiwqKkojpoqGUDJWOrTJ09OTGRgYMGNjY9auXTs2d+5c9vTpU9U82g4p5fF47OrVq2rTKzpGxcXFbOnSpaxt27ZMJBIxc3Nz5unpyRYtWsSys7M11veq8hhjrG/fvuzNN99kubm5Wu+PLVu2MFdXVyYSiZi7uzvbtm1bpftt69atrGPHjqq4fXx82NGjR9Xm+euvv1j37t2ZgYEBMzExYV5eXmzPnj1q8/z++++qciwsLNj48eNVQ6iVlEP5lC8jIyPWqVMntnPnzir3kXLZiobY1XT7fXx8Kv28KodrKhQK9t133zFHR0cmEolYx44d2YEDBzSG1lV1DjLG2OnTp5mnpyfT19dnLVu2ZJs2baow7pKSErZo0SLVud68eXM2b948JpVK1eaTy+Xsiy++YFZWVszQ0JD5+vqyhw8fagwpXbJkCfPy8mJmZmbMwMCAubu7s2+//VZtiF1FlNcx5UtfX5+5uLiwBQsWaMRS2bLlhyeGhYUxAOyvv/5STYuPj2cjR45kZmZmTCwWMy8vL3bgwAG15epqSKlEImHx8fFs4MCBzNDQkDVr1owtXLhQY3h1bm4u+/TTT5m9vT0TCoXM1dWVLV++XG2ovZeXFxs1ahS7d+/eK+Ooat+WHb5c3SGlStqcf5988ona9MqOWUX7rTrnr6Ojo9p8VlZWbODAgezff/9ljGkOKVU6fvw469Gjh+o64+/vz27evKkRx7Vr15ivry8zMjJihoaGrE+fPuz8+fMVbtPp06fZ1KlTmbm5OTMyMmLjx49nmZmZGvGW/xxJpVLm7u7OunTpohpirozd19eXmZqaMrFYzJydndnkyZPZlStXqtx3ZfEYq0U7QwNz6tQp9OnTB+Hh4TX+9V5WUlISnJyckJiYWOmNP0JCQpCUlKRVZyBCCKkrkydPRkRERLVrm0jjo7wh2OXLl7WqKa4P9OhzQgghhOhEnfSpaCqMjIwwfvz4KjsOtm/fXnXbcUIIIeR1RklFFaysrFSddiozfPjweoqGEEIIadiaVJ8KQgghhHCH+lQQQgghRCcoqSCEEEKITlBSQQghhBCdoKSCEEIIITpBSQUhhBBCdIKSCkIIIYToBCUVhBBCCNEJSioIIYQQohOUVBBCCCFEJyipIIQQQohOUFJBCOEEj8er8hUSEsJ1iIQQLdEDxQghnEhNTVX9//fff8eCBQtw//591bSyTwdmjEEul0NPjy5ZhDRkVFNBCOGEra2t6mVqagoej6d6f+/ePRgbG+PQoUPw9PSESCRCdHQ0Jk+ejICAALVyZs2ahd69e6veKxQKhIaGwsnJCQYGBujQoQMiIiLqd+MIeU1R2k8IabC+/PJLrFixAi1btoS5uXm1lgkNDcWuXbuwadMmuLq64syZM5gwYQKsra3h4+NTxxET8nqjpIIQ0mAtXrwYAwYMqPb8RUVF+O6773Ds2DF069YNANCyZUtER0dj8+bNlFQQUscoqSCENFidO3fWav6HDx+ioKBAIxEpLi5Gx44ddRkaIaQClFQQQhosiUSi9p7P54MxpjatpKRE9f+8vDwAwMGDB+Hg4KA2n0gkqqMoCSFKlFQQQhoNa2tr3Lp1S21aTEwMhEIhAKBNmzYQiUR49OgRNXUQwgFKKgghkJbI8Sxbioy8ImTkFf/33yJkF5ZAJmcw5BdjHn8nIBABevqAQB/QEwNmjoCVa+lLX/LqFdVS3759sXz5cuzYsQPdunXDrl27cOvWLVXThrGxMebMmYNPP/0UCoUCPXv2RHZ2Ns6dOwcTExNMmjSpzmMk5HVGSQUhr5nHLwpwJzUH91Jzce9ZDu49y0VyZj4UrPJl7MVFmIetVZTKA0zsAUsXwMrtf4mGlRtg+obOYvf19cX8+fMxd+5cSKVSfPDBB5g4cSJu3rypmuebb76BtbU1QkNDkZCQADMzM3Tq1AlfffWVzuIghFSMx8o3UBJCmpTswhKcf5iBM3EZiH6YjscvCrUuw15chPMIrFkAJg6AS3/AdSDQsjcgMnrlIoSQxomSCkKaoNTsQvwV8xRRt5/hxpNsyKuqhqiGWiUVZQn0AcceQNsAoPVQwNCi9mUSQhoMSioIaSKkJXL8czMVe688xsXEF9Dlma2zpKIsvhBw7gN4jATavAsIxbotnxBS7yipIKSRe54jxS/Ridhz6RFypbI6WUedJBVlGdkCPT4BOgcCQoO6Ww8hpE5RUkFII/UoswCbzsQj4uoTFMsUdbquOk8qlCQ2QPcZQJcgQN+w7tdHCNEpSioIaWQeZRZg5dH7+PtGaq37SlRXvSUVSoZWQPdgoMsU6thJSCNCSQUhjURhsRzrTsbh57OJdV4zUV69JxVKBhZAt4+BrtMAkXH9r58QohVKKghpBP6KfYrQf+4iNVvKyfo5SyqUjO2AIauBVoO4i4EQ8kqUVBDSgKVkFWLO3lhcSMjkNA7Okwql9mMAv+8Bg+o9Bp0QUr/4XAdACKnY37FP4bf6DOcJRYNy4zdg/du4FxfHdSSEkApQUkFIA5NXJMNne2MwY8915NTRENHG7JFZZwza8gAhf91GkUzOdTiEkDIoqSCkAbn9NBv+P57FH9dSuA6lQZIZO+C9JyMBANvPJ2HExvN4xlE/E0KIJkoqCGkgom6lYuTGC0jOLOA6lAaJ8fhYIvwEqVJ91bRbKTl4d300bjzJ4i4wQogKJRWENAAbT8Xjo93XUFhC1fmViX1jArY/1XziaVpOEUZvvoB/bqZyEBUhpCxKKgjhkELBMH//LSyNuqfTZ3U0NYWWbTE+YWClf5eWKPDxr9ew/uTDeoyKEFKeHtcBEPK6UigYPtsbg/0xT7kOpUFjemLMLPoI+fKqfwMxBiw/fB9FMgU+G+BWT9ERQsqimgpCOMAYw9x9NyihqIbDttNwNKP6j0j/8XgcVh97UIcREUIqQ0kFIfWMMYav/ryFiKtPuA6lwXtp2wMfxXtpvdzqY3H48Tjdy4KQ+kZJBSH1LOSv29hz6RHXYTR4CrE5JmROBmO8Gi2/8ugDbDodr+OoCCFVoT4VhNSjDaceIuxCMtdhvJKiqABZZ3ehIO4CFAXZyLR1wuVBcnRxEFQ4/+T9hQiLLdGY3saaj9vTS58yuvtGCb48LkVeMUPgW/pY6StWzZeUpcDAnQW4MlUCE1FpErHFbCZuJ0lqtR1Lo+6hhaUhBnnY1aocQkj10LM/CKknx++mYcqOK6inp5XXSnrkUpSkJ8PCdzoERhbg3z+GnH/34s50IziYaFZwZksZCmX/2zCZAuiwKR8zvIQI6S1GRoECzVflYfu7Bmhpzof/rwXY+q4YQ9yEAIDBuwsQ1EmI4a1L3ye/MRQ+D8foZFsMhAKEf9gNHg6mOimPEFI5av4gpB48fJ6LWb/FNIqEQlFShIL752DWJxDi5h4Qmtujee/34GLBx8YrxRUuYyrmwdaIr3pdeSrHy8LSGgkASHjJYCri4T0PIbo4CNDHSYC76aWPb99zswRCAVQJhcykOd57PFxn21NYIseUHVfwPJfuvElIXaOkgpA6ll1Ygik7riK3qJE8x0MhB5gCPIFQbbKBHg/Rj6p3c64t10vQv6UAjmallxhXCz4KShiup8rxopDhcooc7ZsJ8LKQYf5JKdb5lTaFMB4fi/Rm4lmRflXFay01W4qpO67Ss0IIqWOUVBBSx+aExyIxI5/rMKqNLzKEyN4d2ed/gyw3E0whR/qN07jwRI7UvFdXtTzNVeBQnAxBnf6XGJgb8BAWYICJ+wvh9XMeJnYQwtdFD3OOSBHspY/ELAU6bs6Dy098bDqRWCfbFfM4C8ui7tdJ2YSQUtRRk5A6FHH1CY7eSeM6DK1ZDpmNzENrkLJhEsDjQ2LXEmM9hLia+upf+mExJTAT8xDgrn55GdZaiGGt/1f7cTpJhhvP5Vg7WAyXH/Ow/YM2+LBoOjK3fQ5xcw8IJGa63ixsPZeIXm7W8HGz1nnZhBBKKgipM6nZhVj0922uw6gRobkdbMd9D0WxFIriArxpJUFJxHtoaf6qu1oybI0pwfvthdAXVD4UtEjGMP0fKXYOM8DDFwrIGPDbG19CkWkOoYUDilLvw9Clq643C4wBcyNiceRTH5gaCF+9ACFEK9T8QUgdmRtxA7nSRtKPohJ8fTH0jCwgK8zD4YcyvNuq6t8hp5PlePhCgf90qvoLe8mZIgxy1kMnOwHkCqCQiXAi0xwAwBQyQKHQ2TaUl5ZT1GiTPUIaOqqpIKQO/HrxEc7GZXAdRo0VJlwFAOhZOED2MhW3T29BOysBAt8qTRbmHZMiJZdhxzADteW2XC9BVwcBPGwqvp8FANxJl+P32zJcn1Z6Dwprj14oYNHQjz0CgZE5SjKfQN/OtY62rNQf11IQ8JYDelEzCCE6RUkFITqWVVCMZYfvcR1GrSiKCpB1Jgyy3AwIxMawatMVh/tmQvjfJo3UPIZH2eq1CdlShn13SrBmkLiiIgGUNo9M/VuKlb4iSPR5UBhYYEp2ICwHd8GLoxvB5CWwGPAh9Iyt6nT7AGDJwTs45NILAn7N7thJCNFEN78iRMcWRt5qFHfN1Ia9uAjnEajzcjc2W4SlyXVbK1GVJQEemPC2I2frJ6SpoT4VhOhQQnoedl+k53pUR2LzAE4TCgBYdfQBcqSatxcnhNQMJRWE6NDyw/chawy3zeRYiYkj3ns0jOswkJlfjHUnHnIdBiFNBiUVhOjIrZRsHLr1jOswGjzGE2ChYCaeFzWMIZ1h55OQkVfEdRiENAmUVBCiI5vPJHAdQqNw5Y1J+DW14Tw1tEimwI7zSVyHQUiTQEkFITrwNKsQh26mch1Gg1dg1R4T4vtyHYaGnf8mo7CYngtCSG1RUkGIDmw/n0R9KV6BCQ3xUeE0FCka3mXnZUEJ9l55zHUYhDR6De/sJqSRyS+SYc8lGvHxKn83+win/3vXzIZoS3Qi5JQYElIrlFQQUkt/XHvS6G/HXdcy7Xww86En12FU6dGLAkQ/bLx3QSWkIaCkgpBa2h/zlOsQGjSFgRXGPp/IdRjVEhmTwnUIhDRqlFQQUgspWYW49ugl12E0aBtMZuJBvsGrZ2wAjtxOg7SEOmwSUlOUVBBSCwdin4JudF+5+OYjsCLZheswqi2vSIbjd59zHQYhjRYlFYTUwt83qOmjMiWmThidPJTrMLRGTSCE1BwlFYTU0KPMAtxKyeE6jAaJ8QT4P/4MZBY3jLtmauPUg3RqAiGkhiipIKSGaKRA5S6+EYjfU225DqNGimUKXEx8wXUYhDRKlFQQUkMXEzO5DqFByrd+C5Pie3MdRq1Ex6VzHQIhjRIlFYTU0MUE+jVbHhNKMC2/Yd41Uxv/0rElpEYa95lPCEeSM/PxLEfKdRgNzp820xH9wpTrMGrtTmoO8orohmaEaIuSCkJqgGopNKXb98Vn8R25DkMn5AqG2MdZXIdBSKNDSQUhNXAjJYvrEBoUhaEVxqWN5zoMnXqQlst1CIQ0OpRUEFIDD57lcR1Cg7LWaBbiGsldM6sr7jkdY0K0RUkFITUQ95x+xSrFNR+FVY9ach2Gzj1Mo6SCEG1RUkGIlrILSvCyoITrMBqEYrOWGJ30Dtdh1AlKHAnRHiUVhGgpKTOf6xAaBMbXwzw2Ay9L9LgOpU68LChBZl4R12EQ0qhQUkGIllKyCrkOoUG44PAB9qU14zqMOpWWQ0kFIdqgpIIQLWVR0wfyrDthUrwP12HUuazCYq5DIKRRoaSCEC3lSF/vpILpSzAlbwpKFDyuQ6lzlEASoh1KKgjRUnbh6/1Fs88qGBdeNv67ZlbHywKqqSBEG5RUEKKlnNc4qUiz7485CR24DqPeUE0FIdqhpIIQLeVIX89nQsglNhj7bBzXYdSrfHr+ByFaoaSCEC0xxrgOod4x8PCjZCYSCsRch1KvBPym32+EEF2ipIIQLekLXr/TJlWqjzVN8K6Zr0JJBSHaef2ujoTUkr4enTavCz1KKgjRCl0dCdGS8DWsqXhdCfh0rAnRBp0xhGiJkorXh1BANRWEaIOujoRoyUjcNJ91QTSZGepzHQIhjQolFYRoyc709RoB8TqzMqKkghBtUFJBiJYoqXh9WBuLuA6BkEaFkgpCtGRvZsB1CKSeONCxJkQrlFQQoiVKKl4PEn0B9akgREuUVBCiJSORHkyos2aT52Qt4ToEQhodSioIqYFWtsZch0DqmIf96/EkVkJ0iZIKQmqgjZ0J1yGQOtbWgZIKQrRFSQUhNeBBXzhNnoc9JY6EaIuSCkJqoOObZlyHQOqQHp+H1lQbRYjWKKkgpAacrY2os2YT5trMGGKhgOswCGl0KKkgpAZ4PB66OVtyHQapI73crLgOgZBGiZIKQmqon3szrkMgdaRPKxuuQyCkUaKkgpAa6uNuAx49xLLJMRbrobOjOddhENIoUVJBSA1ZG4vQ/g0zrsMgOubtagU9erw9ITVCZw4htdDfnarJmxpq+iCk5iipIKQW/NrZch0C0SEDoQCDPOiYElJTlFQQUgsuNsbU/t6EDG5nB2OxkOswCGm0KKkgpJbGer3JdQhER8Z4Nec6BEIaNUoqCKkl//Z2MDWgX7eNXUtrCbq0sOA6DEIaNUoqCKklsVCAYR0duA6D1NLozlRLQUhtUVJBiA68380RfLpnRaNlJNKjZixCdICSCkJ0wNnaCO90sOc6DFJD499+k5qwCNEBSioI0ZGZ/VwhoOqKRsdAKEBQz5Zch0FIk0BJBSE64mxthHeptqLRmdjNEdbGIq7DIKRJoKSCEB2aQbUVjYqxSA/TfJy5DoOQJkOP6wAIaUqcrCQY06U5dl98VCflK4oKkHV2FwriLkBRkA19m5Yw7z8VIjs3AABjDNnRu5EXexiKonyIHFrDYuB0CC0qH52Se/0f5F7/B7LsNACA0OpNmHUfCwPnzqp5Xhz/Gfm3joMnFMPMZxKM2vZR/S3/XjTybx2HzciFdbLNdWnWADdYSPS5DoOQJoNqKgjRsc99W9XZF1Vm1FpIk2JgNWQ27D5YB7FTR6T99n+Q5WYAAHIu7kPO1b9h4fsxbN//ATyhGM/3LgCTFVdapsDYEuY+k2A3aTXsJq2G2LEDnv+xBMXpyQCAgocXkX/3NGxGfwPz3oF4EbUW8oJsAICiKB9ZZ3bAYuBHdbK9dam1nQkmd2/BdRiENCmUVBCiY2aG+vhykLvOy1WUFKHg/jmY9QmEuLkHhOb2MOs5HkJzO+RePwTGGHKvRMK023swdH0b+jZOsBryGWR5L1Dw4EKl5Rq6dIWBcxcILRwgtHCAea+J4OuLUfT0PgCgJPMxxM3bQWTnCkkbH/D0DVW1Gi9PboNxx8HQM2lcD+Hi8YAlAR7UVEWIjlFSQUgdGN2lObq1tNRtoQo5wBTgCdSHPvL0RCh6chuy7DTI81/CoMVbqr/xRRKI7Fuh6Om9aq2CKeTIv3MaihIpRA6liZG+tROKnz2EXJqHomcPwWRF0DO3h/TJbRSnxcPY8x2dbWJ9Ge3ZHJ70zBZCdI76VBBSR0KHt8OgNWcgLVHopDy+yBAie3dkn/8NQsvmEEjMkH/3DIqe3oOeuR3keS9L55OYqS0nMDSDPD+ryrKL05PwbOccMFkxePoGsBn2NfStSm8GZdDSE5K2vfEs7FPw9PRh5f8p+EIRXhzeAEv/T0v7ZFw7AIGBCSx8g6Fv7aiT7a0rNsYifOmn+5okQgjVVBBSZ1pYSRDyTludlmk5ZDYAIGXDJDxaMQy5V/+CpHUvALWrxhdaOMAu8EfYTlwJ445+yDi4CsUZ/+tsatZzPBym/Qz7/6yHoVt3ZF8Ih7jFW+DxBci+8Dtsxy+DUfuByDy4slZx1DU+D1g95i2YU+dMQuoEJRWE1KExXm/q9LkgQnM72I77Hs0/jYDD9O2wm7gKTCGH0MwWAqPS6nxFuVoJeUEWBOVqL8rjCYQQmttDZOsCc5/J0LdxQu6VvyqctyTzMfLvnISZ9wRIH92E+A0PCAxNYejujeK0eCiKCnSxqXUiuK8rujtbcR0GIU0WJRWE1LFvh3nA2Vqi0zL5+mLoGVlALs1DYeI1GLi+DT3TZhBIzCFNjlHNpygqQNHT+xDZa1fdzxgDk5dUOD3z8HqY9w0CX98AYAowhey/K/vvv0w3zT265uVkgU/6uXIdBiFNGiUVhNQxQ309bBjvCQOhoNZlFSZcRWHCVZRkPUNh4nWk7ZkHocUbMGrXHzweD8ad30X2+d9REHcRxelJyDi4EnpGFjB066YqI+23r5Bz9W/V+5ent0P6+BZk2WkoTk/Cy9PbUfToJiRtemusPy/2MAQGJjB06QoAEDm0hjT5BopS7iHnciSElm+CLzaq9XbqmoVEHz+O6UijPQipY9RRk5B60MrWGMtHtceMPdfBWM3LURQVIOtMGGS5GRCIjWHYqjvMek0ET1B6Kpt0HQFWIkXm4bVQSPMhfqMNbEYvBk/vf30ISl4+g6gwR/Venp+NjAMrIc9/Ab5IAn3rFrAZvRgGTh3V1i3Pf4nsC3thO2G5aprIvhVMvIbhecQi8A1NYeX/ac03ro6IhXz8MqkzbE3FXIdCSJPHY6w2lzhCiDa2nUvEor/vcB3Ga0PA52HTBE8MaNOM61AIeS1Q8wch9SiwhxM+pGdN1JuQd9pQQkFIPaKkgpB69qWfO0Z0eoPrMJq8j3o74/1uLbgOg5DXCiUVhHBg6Yh28G9vx3UYTdYUbyd8UQe3SieEVI36VBDCEYWC4ev9t7DnUt080fR19XEfZ3zuSwkFIVygpIIQji2NuoeNp+K5DqPR4/GAeX7umNqL+qwQwhVKKghpADafjkfooeo99Ito0hfw8d3wdhjpSX1VCOESJRWENBD/3EzFnPBYFBTLuQ6lUbE1EWPjhE7o+CY9dZQQrlFSQUgDcv9ZLqbtvIKkzIb7/IyGxMvJAuvHdYK1sYjrUAghoKSCkAYnV1qCL/fdxMGbqVyH0qAF9miBrwe3hp6ABrER0lBQUkFIA7Xn0iN8989d5EplXIfSoNiZivHdsHbo427DdSiEkHIoqSCkAUvLkWL+/ls4cieN61AahLFezfHV4NYwFgu5DoUQUgFKKghpBP65mYoFkbeRkVfEdSiceMPcAEtHtEcPFyuuQyGEVIGSCkIaieyCEqw7GYcdF5JRJFNwHU69MDUQ4qPezpjcvQXEOnh0PCGkblFSQUgj8yxbijXH4xB+5TFkiqZ5+oqFfEzu7oSPfJxhakhNHYQ0FpRUENJIJWXkY/WxBzhwI7XJJBdiIR8jOr2BGX1dYWsq5jocQoiWKKkgpJF7li3FrxeTsefyY6TnNs4+F7YmYozv+iYmvO0Ic4k+1+EQQmqIkgpCmogSuQJRt55h57/JuJT4gutwXkko4KGHixXGdHkT/Vvb0P0mCGkCKKkgpIHYvn07Zs2ahaysrFqXlZJViEM3U3Ho1jNcf/QSDaV1RKTHh7erNfw8bNG/TTOYGlB/CUKaEj2uAyCkqZk8eTLCwsI0psfFxcHFxaVeYnAwM0CQd0sEebdEZl4RTt5Px/mHGbj+OAuJGfn1EgMA8HlAK1sTdHY0R9eWFujTygYSEV12CGmq6OwmpA4MGjQI27ZtU5tmbW3NSSyWRiKM9HxD9QTPl/nFuP74Ja4lZ+FmSjYevyjAk6xCFNdymKqBUABHS0M4WhqqEomOb5rRjaoIeY1QUkFIHRCJRLC1tVWbtnLlSmzbtg0JCQmwsLDAO++8g2XLlsHIyKjCMmJjYzFr1ixcuXIFPB4Prq6u2Lx5Mzp37gwAiI6Oxrx583DlyhVYWVlh2LBhCA0NhUQiqTI2c4k++ro3Q1/3ZqppjDGk5RTh0YsCPHlZgOzCEhSWyFFY/N9XiRxyBYNIjw+xUACJSA8mYj2YGerDwdwAjpaGsDGm0RqEvO4oqSCknvD5fPz4449wcnJCQkICpk+fjrlz52LDhg0Vzj9+/Hh07NgRGzduhEAgQExMDITC0l/98fHxGDRoEJYsWYKtW7ciPT0dwcHBCA4O1qghqQ4ejwdbUzFsTcXwcrKo1XYSQl5f1FGTEB2bPHkydu3aBbH4f7/c/fz8EB4erjZfREQEPvzwQ2RkZADQ7KhpYmKCtWvXYtKkSRrrCAoKgkAgwObNm1XToqOj4ePjg/z8fLV1E0JIfaGaCkLqQJ8+fbBx40bVe4lEgmPHjiE0NBT37t1DTk4OZDIZpFIpCgoKYGhoqFHGZ599hqCgIOzcuRP9+/fHqFGj4OzsDKC0aeTGjRvYvXu3an7GGBQKBRITE9G6deu630hCCCmHBoYTUgckEglcXFxUr6KiIgwZMgTt27fHvn37cPXqVaxfvx4AUFxcXGEZISEhuH37Nvz9/XHixAm0adMGf/75JwAgLy8P06ZNQ0xMjOoVGxuLuLg4VeJBCCH1jWoqCKkHV69ehUKhwA8//AA+vzSX37t37yuXc3Nzg5ubGz799FOMHTsW27Ztw7Bhw9CpUyfcuXOn3oaoEkJIdVBNBSH1wMXFBSUlJVi7di0SEhKwc+dObNq0qdL5CwsLERwcjFOnTiE5ORnnzp3D5cuXVc0aX3zxBc6fP4/g4GDExMQgLi4OkZGRCA4Orq9NIoQQDZRUEFIPOnTogJUrV2Lp0qXw8PDA7t27ERoaWun8AoEAmZmZmDhxItzc3DB69Gj4+flh0aJFAID27dvj9OnTePDgAby9vdGxY0csWLAA9vb29bVJhBCigUZ/EEIIIUQnqKaCEEIIITpBSQUhhBBCdIKSCkIIIYToBCUVhBBCCNEJSioIIYQQohOUVBBCCCFEJyipIIQQQohOUFJBCCGEEJ2gpIIQQgghOkFJBSGEEEJ0gpIKQgghhOgEJRWEEEII0QlKKgghhBCiE5RUEEIIIUQnKKkghBBCiE5QUkEIIYQQnaCkghBCCCE68f94mVHfV7++GAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "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(df2.hazardous.value_counts())\n", + "print()\n", + "\n", + "\n", + "data = df2[['est_diameter_min', 'est_diameter_max', 'relative_velocity', 'miss_distance', 'absolute_magnitude', 'hazardous']].copy()\n", + "\n", + "df_train, df_val, df_test = split_stratified_into_train_val_test(\n", + " data, stratify_colname=\"hazardous\", frac_train=0.60, frac_val=0.20, frac_test=0.20\n", + ")\n", + "\n", + "print(\"Обучающая выборка: \", df_train.shape)\n", + "print(df_train.hazardous.value_counts())\n", + "hazardous_counts = df_train['hazardous'].value_counts()\n", + "plt.figure(figsize=(2, 2))# Установка размера графика\n", + "plt.pie(hazardous_counts, labels=hazardous_counts.index, autopct='%1.1f%%', startangle=90)# Построение круговой диаграммы\n", + "plt.title('Распределение классов hazardous в обучающей выборке')# Добавление заголовка\n", + "plt.show()# Отображение графика\n", + "\n", + "print(\"Контрольная выборка: \", df_val.shape)\n", + "print(df_val.hazardous.value_counts())\n", + "hazardous_counts = df_val['hazardous'].value_counts()\n", + "plt.figure(figsize=(2, 2))\n", + "plt.pie(hazardous_counts, labels=hazardous_counts.index, autopct='%1.1f%%', startangle=90)\n", + "plt.title('Распределение классов hazardous в контрольной выборке')\n", + "plt.show()\n", + "\n", + "print(\"Тестовая выборка: \", df_test.shape)\n", + "print(df_test.hazardous.value_counts())\n", + "hazardous_counts = df_test['hazardous'].value_counts()\n", + "plt.figure(figsize=(2, 2))\n", + "plt.pie(hazardous_counts, labels=hazardous_counts.index, autopct='%1.1f%%', startangle=90)\n", + "plt.title('Распределение классов hazardous в тестовой выборке')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Соотношение классов сильно смещено, это может привести к проблемам в обучении модели, так как модель будет обучаться в основном на одном классе. Стоит рассмотреть методы аугментации данных." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Обучающая выборка после oversampling: (100631, 6)\n", + "hazardous\n", + "True 51434\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", + "\n", + "# Создание экземпляра ADASYN\n", + "ada = ADASYN()\n", + "\n", + "# Применение ADASYN\n", + "X_resampled, y_resampled = ada.fit_resample(df_train.drop(columns=['hazardous']), df_train['hazardous'])\n", + "\n", + "# Создание нового DataFrame\n", + "df_train_adasyn = pd.DataFrame(X_resampled)\n", + "df_train_adasyn['hazardous'] = y_resampled # Добавление целевой переменной\n", + "\n", + "# Вывод информации о новой выборке\n", + "print(\"Обучающая выборка после oversampling: \", df_train_adasyn.shape)\n", + "print(df_train_adasyn['hazardous'].value_counts())\n", + "hazardous_counts = df_train_adasyn['hazardous'].value_counts()\n", + "plt.figure(figsize=(2, 2))\n", + "plt.pie(hazardous_counts, labels=hazardous_counts.index, autopct='%1.1f%%', startangle=90)\n", + "plt.title('Распределение классов hazardous в тренировачной выборке после ADASYN')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Обучающая выборка после undersampling: (10608, 6)\n", + "hazardous\n", + "False 5304\n", + "True 5304\n", + "Name: count, dtype: int64\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#андерсемплинг\n", + "rus = RandomUnderSampler()# Создание экземпляра RandomUnderSampler\n", + "\n", + "# Применение RandomUnderSampler\n", + "X_resampled, y_resampled = rus.fit_resample(df_train.drop(columns=['hazardous']), df_train['hazardous'])\n", + "\n", + "# Создание нового DataFrame\n", + "df_train_undersampled = pd.DataFrame(X_resampled)\n", + "df_train_undersampled['hazardous'] = y_resampled # Добавление целевой переменной\n", + "\n", + "# Вывод информации о новой выборке\n", + "print(\"Обучающая выборка после undersampling: \", df_train_undersampled.shape)\n", + "print(df_train_undersampled['hazardous'].value_counts())\n", + "\n", + "# Визуализация распределения классов\n", + "hazardous_counts = df_train_undersampled['hazardous'].value_counts()\n", + "plt.figure(figsize=(2, 2))\n", + "plt.pie(hazardous_counts, labels=hazardous_counts.index, autopct='%1.1f%%', startangle=90)\n", + "plt.title('Распределение классов hazardous в тренировочной выборке после Undersampling')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Заработная плата рабочих мест в области Data Science\thttps://www.kaggle.com/datasets/henryshan/2023-data-scientists-salary" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Целью этого исследования является изучение факторов, влияющих на заработную плату специалистов по обработке данных. Для этого был использован набор данных, содержащий различные релевантные переменные. В этом отчёте описывается исследовательский анализ, проведённый для понимания взаимосвязи между этими факторами и заработной платой специалистов по обработке данных." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['work_year', 'experience_level', 'employment_type', 'job_title',\n", + " 'salary', 'salary_currency', 'salary_in_usd', 'employee_residence',\n", + " 'remote_ratio', 'company_location', 'company_size'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "df3 = pd.read_csv(\"..//static//csv//ds_salaries.csv\")\n", + "\n", + "print(df3.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Визуализация данных - ящик с усами. Выборка относительно сбалансирована, есть среднее смещение в среднюю сторону, медиана уравновешена\n", + "plt.figure(figsize=(10, 6))\n", + "sns.boxplot(x=df3[\"salary_in_usd\"])\n", + "plt.title(\"Box Plot для salary_in_usd\")\n", + "plt.xlabel(\"salary_in_usd\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Визуализируем отношение размера компании и зарплаты\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(df3[\"salary_in_usd\"], df3[\"experience_level\"])\n", + "plt.xlabel(\"salary_in_usd\")\n", + "plt.ylabel(\"experience_level\")\n", + "plt.title(\"salary in usd vs experience_level\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# Статистический анализ для определения выбросов\n", + "Q1 = df3[\"salary_in_usd\"].quantile(0.25)\n", + "Q3 = df3[\"salary_in_usd\"].quantile(0.75)\n", + "IQR = Q3 - Q1\n", + "\n", + "# Определение порога для выбросов\n", + "threshold = 1.5 * IQR\n", + "outliers = (df3[\"salary_in_usd\"] < (Q1 - threshold)) | (\n", + " df3[\"salary_in_usd\"] > (Q3 + threshold)\n", + ")\n", + "\n", + "# Обработка выбросов\n", + "# В данном случае мы уберем выбросы\n", + "median_salary = df3[\"salary_in_usd\"].median()\n", + "df3.loc[outliers, \"salary_in_usd\"] = 0\n", + "df3 = df3[df3.salary_in_usd != 0]\n", + "\n", + "# Визуализация данных после обработки\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(df3[\"salary_in_usd\"], df3[\"experience_level\"])\n", + "plt.xlabel(\"salary_in_usd\")\n", + "plt.ylabel(\"experience_level\")\n", + "plt.title(\"salary in usd vs experience_level\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Размер обучающей выборки: 2214\n", + "Размер контрольной выборки: 739\n", + "Размер тестовой выборки: 739\n", + "Распределение salary_in_usd в обучающей выборке:\n", + "salary_in_usd\n", + "130000 60\n", + "150000 59\n", + "160000 56\n", + "100000 56\n", + "120000 52\n", + " ..\n", + "83864 1\n", + "61467 1\n", + "152380 1\n", + "73742 1\n", + "110037 1\n", + "Name: count, Length: 741, dtype: int64\n", + "\n", + "Распределение salary_in_usd в контрольной выборке:\n", + "salary_in_usd\n", + "100000 25\n", + "150000 20\n", + "140000 19\n", + "120000 16\n", + "135000 16\n", + " ..\n", + "247500 1\n", + "128875 1\n", + "36000 1\n", + "121500 1\n", + "85847 1\n", + "Name: count, Length: 354, dtype: int64\n", + "\n", + "Распределение salary_in_usd в тестовой выборке:\n", + "salary_in_usd\n", + "120000 23\n", + "150000 19\n", + "100000 18\n", + "160000 16\n", + "200000 13\n", + " ..\n", + "75648 1\n", + "169200 1\n", + "198800 1\n", + "219535 1\n", + "172200 1\n", + "Name: count, Length: 364, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "#Разбиение набора данных на обучающую, контрольную и тестовую выборки\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", + "# Разделение на обучающую и тестовую выборки\n", + "train_df, test_df = train_test_split(df3, 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", + "\n", + "def check_balance(df3, name):\n", + " counts = df3[\"salary_in_usd\"].value_counts()\n", + " print(f\"Распределение salary_in_usd 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\")" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Распределение salary_in_usd в обучающей выборке после oversampling:\n", + "salary_in_usd\n", + "127221 60\n", + "105000 60\n", + "100000 60\n", + "260000 60\n", + "130000 60\n", + " ..\n", + "113900 60\n", + "110000 60\n", + "129000 60\n", + "203000 60\n", + "200000 60\n", + "Name: count, Length: 741, dtype: int64\n", + "\n", + "Распределение salary_in_usd в контрольной выборке после oversampling:\n", + "salary_in_usd\n", + "99050 25\n", + "126277 25\n", + "192500 25\n", + "194000 25\n", + "177600 25\n", + " ..\n", + "140000 25\n", + "75000 25\n", + "165000 25\n", + "72914 25\n", + "252000 25\n", + "Name: count, Length: 354, dtype: int64\n", + "\n", + "Распределение salary_in_usd в тестовой выборке после oversampling:\n", + "salary_in_usd\n", + "219000 23\n", + "143860 23\n", + "72500 23\n", + "140000 23\n", + "66837 23\n", + " ..\n", + "109000 23\n", + "126000 23\n", + "70186 23\n", + "199000 23\n", + "60000 23\n", + "Name: count, Length: 364, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "def oversample(df3):\n", + " X = df3.drop(\"salary_in_usd\", axis=1)\n", + " y = df3[\"salary_in_usd\"]\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": "aimenv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}