lab_4 #5
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lab_4/lab_4.ipynb
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lab_4/lab_4.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Вариант: Список людей. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 100000 entries, 0 to 99999\n",
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"Data columns (total 10 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 Id 100000 non-null object \n",
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" 1 Name 100000 non-null object \n",
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" 2 Short description 99923 non-null object \n",
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" 3 Gender 98015 non-null object \n",
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" 4 Country 94533 non-null object \n",
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" 5 Occupation 97299 non-null object \n",
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" 6 Birth year 100000 non-null int64 \n",
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" 7 Death year 99999 non-null float64\n",
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" 8 Manner of death 14821 non-null object \n",
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" 9 Age of death 99999 non-null float64\n",
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"dtypes: float64(2), int64(1), object(7)\n",
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"memory usage: 7.6+ MB\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.preprocessing import LabelEncoder\n",
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"from imblearn.over_sampling import RandomOverSampler\n",
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"from imblearn.under_sampling import RandomUnderSampler\n",
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"from sklearn.preprocessing import OneHotEncoder\n",
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"import numpy as np\n",
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"import featuretools as ft\n",
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"\n",
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"\n",
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"# Функция для применения oversampling\n",
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"def apply_oversampling(X, y):\n",
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" oversampler = RandomOverSampler(random_state=42)\n",
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" X_resampled, y_resampled = oversampler.fit_resample(X, y)\n",
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" return X_resampled, y_resampled\n",
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"\n",
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"# Функция для применения undersampling\n",
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"def apply_undersampling(X, y):\n",
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" undersampler = RandomUnderSampler(random_state=42)\n",
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" X_resampled, y_resampled = undersampler.fit_resample(X, y)\n",
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" return X_resampled, y_resampled\n",
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"\n",
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"def split_stratified_into_train_val_test(\n",
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" df_input,\n",
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" stratify_colname=\"y\",\n",
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" frac_train=0.6,\n",
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" frac_val=0.15,\n",
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" frac_test=0.25,\n",
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" random_state=None,\n",
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"):\n",
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" \"\"\"\n",
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" Splits a Pandas dataframe into three subsets (train, val, and test)\n",
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" following fractional ratios provided by the user, where each subset is\n",
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" stratified by the values in a specific column (that is, each subset has\n",
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" the same relative frequency of the values in the column). It performs this\n",
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" splitting by running train_test_split() twice.\n",
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"\n",
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" Parameters\n",
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" ----------\n",
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" df_input : Pandas dataframe\n",
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" Input dataframe to be split.\n",
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" stratify_colname : str\n",
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" The name of the column that will be used for stratification. Usually\n",
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" this column would be for the label.\n",
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" frac_train : float\n",
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" frac_val : float\n",
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" frac_test : float\n",
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" The ratios with which the dataframe will be split into train, val, and\n",
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" test data. The values should be expressed as float fractions and should\n",
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" sum to 1.0.\n",
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" random_state : int, None, or RandomStateInstance\n",
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" Value to be passed to train_test_split().\n",
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"\n",
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" Returns\n",
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" -------\n",
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" df_train, df_val, df_test :\n",
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" Dataframes containing the three splits.\n",
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" \"\"\"\n",
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"\n",
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" if frac_train + frac_val + frac_test != 1.0:\n",
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" raise ValueError(\n",
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" \"fractions %f, %f, %f do not add up to 1.0\"\n",
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" % (frac_train, frac_val, frac_test)\n",
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" )\n",
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"\n",
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" if stratify_colname not in df_input.columns:\n",
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" raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
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"\n",
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" X = df_input # Contains all columns.\n",
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" y = df_input[\n",
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" [stratify_colname]\n",
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" ] # Dataframe of just the column on which to stratify.\n",
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"\n",
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" # Split original dataframe into train and temp dataframes.\n",
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" df_train, df_temp, y_train, y_temp = train_test_split(\n",
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" X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
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" )\n",
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"\n",
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" # Split the temp dataframe into val and test dataframes.\n",
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" relative_frac_test = frac_test / (frac_val + frac_test)\n",
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" df_val, df_test, y_val, y_test = train_test_split(\n",
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" df_temp,\n",
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" y_temp,\n",
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" stratify=y_temp,\n",
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" test_size=relative_frac_test,\n",
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" random_state=random_state,\n",
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" )\n",
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"\n",
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" assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
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"\n",
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" return df_train, df_val, df_test\n",
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"\n",
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"\n",
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"df = pd.read_csv(\"../data/age.csv\", nrows=100000)\n",
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"df.info()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Как бизнес-цели выделим следующие 2 варианта:\n",
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" 1) GameDev. Создание игры про конкретного персонажа, живущего в конкретном временном промежутке в конкретной стране. \n",
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" 2) Исследование зависимости длительности жизни от страны проживания.\n",
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" \n",
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"Поскольку именно эти бизнес-цели были выбраны в предыдущей лабораторной работе, будем их использовать.\n",
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"Но возникает проблема с 1 целью: её невозможно использовать для машинного обучения. Заменим ее на следующую:\n",
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" Прогнозирование страны. Необходимо не имея такой параметр как страна примерно ее угадать для дальнейшей рекламы."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Выполним подготовку данных"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"df.fillna({\"Gender\": \"NaN\", \"Country\": \"NaN\", \"Occupation\" : \"NaN\", \"Manner of death\" : \"NaN\"}, inplace=True)\n",
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"df = df.dropna()\n",
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"df['Country'] = df['Country'].str.split('; ')\n",
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"df = df.explode('Country')\n",
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"data = df.copy()\n",
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"\n",
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"value_counts = data[\"Country\"].value_counts()\n",
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"rare = value_counts[value_counts < 50].index\n",
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"data = data[~data[\"Country\"].isin(rare)]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Определить достижимый уровень качества модели для каждой задачи. На основе имеющихся данных уровень качества моделей не будет высоким, поскольку все таки длительность жизни лишь примерная и точно ее угадать невозможно. А угадывание страны является трудной задачей, поскольку данные между людьми, живущими в разных странах, могут совпадать между собой."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"data['Age_Category'] = pd.cut(data['Age'], bins=[0, 29, 59, float('inf')], labels=[\"young\", \"middle-aged\", \"old\"])"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "aimvenv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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