revengel66/AIM-PIbd-32-Katysheva-N-E
1
0
Fork 0
Code Issues Pull Requests 1 Actions Packages Projects Releases Wiki Activity
cdbdc2fd39
AIM-PIbd-32-Katysheva-N-E/lab_2/lab2.ipynb

1052 lines
35 KiB
Plaintext
Raw Normal View History

cloned lec2
2024-09-28 14:49:26 +04:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
com
2024-09-28 18:10:56 +04:00
"Загрузка данных в DataFrame \"Список форбс\"\n",
"\n",
"О рейтинге\n",
"The World's Billionaires (\"Миллиардеры мира\") - ежегодный рейтинг самых богатых миллиардеров мира, составляемый и публикуемый в марте американским деловым журналом Forbes. Общее состояние каждого человека, включенного в список, оценивается в долларах США на основе его документально подтвержденных активов, а также с учетом долгов и других факторов. Этот рейтинг представляет собой список самых богатых людей, зарегистрированных по документам, за исключением тех, чье благосостояние не может быть полностью установлено.\n",
"\n",
"Методология Forbes\n",
"Каждый год Forbes нанимает команду из более чем 50 репортеров из разных стран, чтобы отслеживать деятельность самых богатых людей мира, а иногда и групп или семей, которые делятся богатством. Предварительные опросы рассылаются тем, кто может попасть в список. \n",
"\n",
"По данным Forbes, они получили ответы трех типов: одни люди пытаются преувеличить свое богатство, другие сотрудничают, но не раскрывают деталей, а третьи отказываются отвечать на любые вопросы. \n",
"Затем тщательно изучаются деловые сделки и оценивается стоимость ценных активов – земли, домов, транспортных средств, произведений искусства и т.д. – сделаны.\n",
"\n",
"Для проверки данных и уточнения оценки активов отдельных лиц проводятся собеседования. И, наконец, котировки акций, обращающихся на бирже, оцениваются по рыночным ценам примерно за месяц до публикации. \n",
"Частные компании оцениваются в соответствии с преобладающим соотношением цены к продажам или цены к прибыли. \n",
"Известные долги вычитаются из активов, чтобы получить окончательную оценку предполагаемого состояния человека в долларах США. \n",
"Поскольку цены на акции быстро колеблются, истинное состояние человека и его рейтинг на момент публикации могут отличаться от того, в котором он находился на момент составления списка."
cloned lec2
2024-09-28 14:49:26 +04:00
]
},
{
"cell_type": "code",
com
2024-09-28 18:10:56 +04:00
"execution_count": 3,
cloned lec2
2024-09-28 14:49:26 +04:00
"metadata": {},
"outputs": [
{
com
2024-09-28 18:10:56 +04:00
"name": "stdout",
cloned lec2
2024-09-28 14:49:26 +04:00
"output_type": "stream",
"text": [
com
2024-09-28 18:10:56 +04:00
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 2600 entries, Automotive to Food & Beverage \n",
"Data columns (total 6 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Rank 2600 non-null int64 \n",
" 1 Name 2600 non-null object \n",
" 2 Networth 2600 non-null float64\n",
" 3 Age 2600 non-null int64 \n",
" 4 Country 2600 non-null object \n",
" 5 Source 2600 non-null object \n",
"dtypes: float64(1), int64(2), object(3)\n",
"memory usage: 142.2+ KB\n",
"(2600, 6)\n"
cloned lec2
2024-09-28 14:49:26 +04:00
]
},
{
com
2024-09-28 18:10:56 +04:00
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Rank</th>\n",
" <th>Name</th>\n",
" <th>Networth</th>\n",
" <th>Age</th>\n",
" <th>Country</th>\n",
" <th>Source</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Industry</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Automotive</th>\n",
" <td>1</td>\n",
" <td>Elon Musk</td>\n",
" <td>219.0</td>\n",
" <td>50</td>\n",
" <td>United States</td>\n",
" <td>Tesla, SpaceX</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Technology</th>\n",
" <td>2</td>\n",
" <td>Jeff Bezos</td>\n",
" <td>171.0</td>\n",
" <td>58</td>\n",
" <td>United States</td>\n",
" <td>Amazon</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fashion &amp; Retail</th>\n",
" <td>3</td>\n",
" <td>Bernard Arnault &amp; family</td>\n",
" <td>158.0</td>\n",
" <td>73</td>\n",
" <td>France</td>\n",
" <td>LVMH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Technology</th>\n",
" <td>4</td>\n",
" <td>Bill Gates</td>\n",
" <td>129.0</td>\n",
" <td>66</td>\n",
" <td>United States</td>\n",
" <td>Microsoft</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Finance &amp; Investments</th>\n",
" <td>5</td>\n",
" <td>Warren Buffett</td>\n",
" <td>118.0</td>\n",
" <td>91</td>\n",
" <td>United States</td>\n",
" <td>Berkshire Hathaway</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Rank Name Networth Age \\\n",
"Industry \n",
"Automotive 1 Elon Musk 219.0 50 \n",
"Technology 2 Jeff Bezos 171.0 58 \n",
"Fashion & Retail 3 Bernard Arnault & family 158.0 73 \n",
"Technology 4 Bill Gates 129.0 66 \n",
"Finance & Investments 5 Warren Buffett 118.0 91 \n",
"\n",
" Country Source \n",
"Industry \n",
"Automotive United States Tesla, SpaceX \n",
"Technology United States Amazon \n",
"Fashion & Retail France LVMH \n",
"Technology United States Microsoft \n",
"Finance & Investments United States Berkshire Hathaway "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
cloned lec2
2024-09-28 14:49:26 +04:00
}
],
"source": [
"import pandas as pd\n",
"\n",
com
2024-09-28 18:10:56 +04:00
"df = pd.read_csv(\"..//..//static//csv//Forbes Billionaires.csv\", index_col=\"Industry\")\n",
cloned lec2
2024-09-28 14:49:26 +04:00
"\n",
"df.info()\n",
"\n",
"print(df.shape)\n",
"\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Получение сведений о пропущенных данных"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Типы пропущенных данных:\n",
"- None - представление пустых данных в Python\n",
"- NaN - представление пустых данных в Pandas\n",
"- '' - пустая строка"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Survived 0\n",
"Pclass 0\n",
"Name 0\n",
"Sex 0\n",
"Age 177\n",
"SibSp 0\n",
"Parch 0\n",
"Ticket 0\n",
"Fare 0\n",
"Cabin 687\n",
"Embarked 2\n",
"dtype: int64\n",
"\n",
"Survived False\n",
"Pclass False\n",
"Name False\n",
"Sex False\n",
"Age True\n",
"SibSp False\n",
"Parch False\n",
"Ticket False\n",
"Fare False\n",
"Cabin True\n",
"Embarked True\n",
"dtype: bool\n",
"\n",
"Age процент пустых значений: %19.87\n",
"Cabin процент пустых значений: %77.10\n",
"Embarked процент пустых значений: %0.22\n"
]
}
],
"source": [
"# Количество пустых значений признаков\n",
"print(df.isnull().sum())\n",
"\n",
"print()\n",
"\n",
"# Есть ли пустые значения признаков\n",
"print(df.isnull().any())\n",
"\n",
"print()\n",
"\n",
"# Процент пустых значений признаков\n",
"for i in df.columns:\n",
" null_rate = df[i].isnull().sum() / len(df) * 100\n",
" if null_rate > 0:\n",
" print(f\"{i} процент пустых значений: %{null_rate:.2f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Заполнение пропущенных данных\n",
"\n",
"https://pythonmldaily.com/posts/pandas-dataframes-search-drop-empty-values\n",
"\n",
"https://scales.arabpsychology.com/stats/how-to-fill-nan-values-with-median-in-pandas/"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(891, 11)\n",
"Survived False\n",
"Pclass False\n",
"Name False\n",
"Sex False\n",
"Age False\n",
"SibSp False\n",
"Parch False\n",
"Ticket False\n",
"Fare False\n",
"Cabin False\n",
"Embarked False\n",
"dtype: bool\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" <th>AgeFillNA</th>\n",
" <th>AgeFillMedian</th>\n",
" </tr>\n",
" <tr>\n",
" <th>PassengerId</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>887</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>Montvila, Rev. Juozas</td>\n",
" <td>male</td>\n",
" <td>27.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>211536</td>\n",
" <td>13.00</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>27.0</td>\n",
" <td>27.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>888</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Graham, Miss. Margaret Edith</td>\n",
" <td>female</td>\n",
" <td>19.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>112053</td>\n",
" <td>30.00</td>\n",
" <td>B42</td>\n",
" <td>S</td>\n",
" <td>19.0</td>\n",
" <td>19.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>889</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>W./C. 6607</td>\n",
" <td>23.45</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>0.0</td>\n",
" <td>28.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>890</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Behr, Mr. Karl Howell</td>\n",
" <td>male</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>111369</td>\n",
" <td>30.00</td>\n",
" <td>C148</td>\n",
" <td>C</td>\n",
" <td>26.0</td>\n",
" <td>26.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>891</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Dooley, Mr. Patrick</td>\n",
" <td>male</td>\n",
" <td>32.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>370376</td>\n",
" <td>7.75</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>32.0</td>\n",
" <td>32.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Survived Pclass Name \\\n",
"PassengerId \n",
"887 0 2 Montvila, Rev. Juozas \n",
"888 1 1 Graham, Miss. Margaret Edith \n",
"889 0 3 Johnston, Miss. Catherine Helen \"Carrie\" \n",
"890 1 1 Behr, Mr. Karl Howell \n",
"891 0 3 Dooley, Mr. Patrick \n",
"\n",
" Sex Age SibSp Parch Ticket Fare Cabin Embarked \\\n",
"PassengerId \n",
"887 male 27.0 0 0 211536 13.00 NaN S \n",
"888 female 19.0 0 0 112053 30.00 B42 S \n",
"889 female NaN 1 2 W./C. 6607 23.45 NaN S \n",
"890 male 26.0 0 0 111369 30.00 C148 C \n",
"891 male 32.0 0 0 370376 7.75 NaN Q \n",
"\n",
" AgeFillNA AgeFillMedian \n",
"PassengerId \n",
"887 27.0 27.0 \n",
"888 19.0 19.0 \n",
"889 0.0 28.0 \n",
"890 26.0 26.0 \n",
"891 32.0 32.0 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fillna_df = df.fillna(0)\n",
"\n",
"print(fillna_df.shape)\n",
"\n",
"print(fillna_df.isnull().any())\n",
"\n",
"# Замена пустых данных на 0\n",
"df[\"AgeFillNA\"] = df[\"Age\"].fillna(0)\n",
"\n",
"# Замена пустых данных на медиану\n",
"df[\"AgeFillMedian\"] = df[\"Age\"].fillna(df[\"Age\"].median())\n",
"\n",
"df.tail()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" <th>AgeFillNA</th>\n",
" <th>AgeFillMedian</th>\n",
" <th>AgeCopy</th>\n",
" </tr>\n",
" <tr>\n",
" <th>PassengerId</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>887</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>Montvila, Rev. Juozas</td>\n",
" <td>male</td>\n",
" <td>27.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>211536</td>\n",
" <td>13.00</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>27.0</td>\n",
" <td>27.0</td>\n",
" <td>27.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>888</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Graham, Miss. Margaret Edith</td>\n",
" <td>female</td>\n",
" <td>19.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>112053</td>\n",
" <td>30.00</td>\n",
" <td>B42</td>\n",
" <td>S</td>\n",
" <td>19.0</td>\n",
" <td>19.0</td>\n",
" <td>19.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>889</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>W./C. 6607</td>\n",
" <td>23.45</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>0.0</td>\n",
" <td>28.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>890</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Behr, Mr. Karl Howell</td>\n",
" <td>male</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>111369</td>\n",
" <td>30.00</td>\n",
" <td>C148</td>\n",
" <td>C</td>\n",
" <td>26.0</td>\n",
" <td>26.0</td>\n",
" <td>26.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>891</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Dooley, Mr. Patrick</td>\n",
" <td>male</td>\n",
" <td>32.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>370376</td>\n",
" <td>7.75</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>32.0</td>\n",
" <td>32.0</td>\n",
" <td>32.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Survived Pclass Name \\\n",
"PassengerId \n",
"887 0 2 Montvila, Rev. Juozas \n",
"888 1 1 Graham, Miss. Margaret Edith \n",
"889 0 3 Johnston, Miss. Catherine Helen \"Carrie\" \n",
"890 1 1 Behr, Mr. Karl Howell \n",
"891 0 3 Dooley, Mr. Patrick \n",
"\n",
" Sex Age SibSp Parch Ticket Fare Cabin Embarked \\\n",
"PassengerId \n",
"887 male 27.0 0 0 211536 13.00 NaN S \n",
"888 female 19.0 0 0 112053 30.00 B42 S \n",
"889 female NaN 1 2 W./C. 6607 23.45 NaN S \n",
"890 male 26.0 0 0 111369 30.00 C148 C \n",
"891 male 32.0 0 0 370376 7.75 NaN Q \n",
"\n",
" AgeFillNA AgeFillMedian AgeCopy \n",
"PassengerId \n",
"887 27.0 27.0 27.0 \n",
"888 19.0 19.0 19.0 \n",
"889 0.0 28.0 0.0 \n",
"890 26.0 26.0 26.0 \n",
"891 32.0 32.0 32.0 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[\"AgeCopy\"] = df[\"Age\"]\n",
"\n",
"# Замена данных сразу в DataFrame без копирования\n",
"df.fillna({\"AgeCopy\": 0}, inplace=True)\n",
"\n",
"df.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Удаление наблюдений с пропусками"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(183, 14)\n",
"Survived False\n",
"Pclass False\n",
"Name False\n",
"Sex False\n",
"Age False\n",
"SibSp False\n",
"Parch False\n",
"Ticket False\n",
"Fare False\n",
"Cabin False\n",
"Embarked False\n",
"dtype: bool\n"
]
}
],
"source": [
"dropna_df = df.dropna()\n",
"\n",
"print(dropna_df.shape)\n",
"\n",
"print(fillna_df.isnull().any())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Создание выборок данных\n",
"\n",
"Библиотека scikit-learn\n",
"\n",
"https://scikit-learn.org/stable/index.html"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"assets/lec2-split.png\" width=\"600\" style=\"background-color: white\">"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Функция для создания выборок\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"\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",
" Splits a Pandas dataframe into three subsets (train, val, and test)\n",
" following fractional ratios provided by the user, where each subset is\n",
" stratified by the values in a specific column (that is, each subset has\n",
" the same relative frequency of the values in the column). It performs this\n",
" splitting by running train_test_split() twice.\n",
"\n",
" Parameters\n",
" ----------\n",
" df_input : Pandas dataframe\n",
" Input dataframe to be split.\n",
" stratify_colname : str\n",
" The name of the column that will be used for stratification. Usually\n",
" this column would be for the label.\n",
" frac_train : float\n",
" frac_val : float\n",
" frac_test : float\n",
" The ratios with which the dataframe will be split into train, val, and\n",
" test data. The values should be expressed as float fractions and should\n",
" sum to 1.0.\n",
" random_state : int, None, or RandomStateInstance\n",
" Value to be passed to train_test_split().\n",
"\n",
" Returns\n",
" -------\n",
" df_train, df_val, df_test :\n",
" Dataframes containing the three splits.\n",
" \"\"\"\n",
"\n",
" if frac_train + frac_val + frac_test != 1.0:\n",
" raise ValueError(\n",
" \"fractions %f, %f, %f do not add up to 1.0\"\n",
" % (frac_train, frac_val, frac_test)\n",
" )\n",
"\n",
" if stratify_colname not in df_input.columns:\n",
" raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
"\n",
" X = 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": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pclass\n",
"3 491\n",
"1 216\n",
"2 184\n",
"Name: count, dtype: int64\n",
"Обучающая выборка: (534, 3)\n",
"Pclass\n",
"3 294\n",
"1 130\n",
"2 110\n",
"Name: count, dtype: int64\n",
"Контрольная выборка: (178, 3)\n",
"Pclass\n",
"3 98\n",
"1 43\n",
"2 37\n",
"Name: count, dtype: int64\n",
"Тестовая выборка: (179, 3)\n",
"Pclass\n",
"3 99\n",
"1 43\n",
"2 37\n",
"Name: count, dtype: int64\n"
]
}
],
"source": [
"# Вывод распределения количества наблюдений по меткам (классам)\n",
"print(df.Pclass.value_counts())\n",
"\n",
"data = df[[\"Pclass\", \"Survived\", \"AgeFillMedian\"]].copy()\n",
"\n",
"df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
" data, stratify_colname=\"Pclass\", 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.Pclass.value_counts())\n",
"\n",
"print(\"Контрольная выборка: \", df_val.shape)\n",
"print(df_val.Pclass.value_counts())\n",
"\n",
"print(\"Тестовая выборка: \", df_test.shape)\n",
"print(df_test.Pclass.value_counts())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Выборка с избытком (oversampling)\n",
"\n",
"https://www.blog.trainindata.com/oversampling-techniques-for-imbalanced-data/\n",
"\n",
"https://datacrayon.com/machine-learning/class-imbalance-and-oversampling/\n",
"\n",
"Выборка с недостатком (undersampling)\n",
"\n",
"https://machinelearningmastery.com/random-oversampling-and-undersampling-for-imbalanced-classification/\n",
"\n",
"Библиотека imbalanced-learn\n",
"\n",
"https://imbalanced-learn.org/stable/"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Обучающая выборка: (534, 3)\n",
"Pclass\n",
"3 294\n",
"1 130\n",
"2 110\n",
"Name: count, dtype: int64\n",
"Обучающая выборка после oversampling: (864, 3)\n",
"Pclass\n",
"3 294\n",
"2 290\n",
"1 280\n",
"Name: count, dtype: int64\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Pclass</th>\n",
" <th>Survived</th>\n",
" <th>AgeFillMedian</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>28.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>32.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>28.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>45.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>7.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>859</th>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>26.887761</td>\n",
" </tr>\n",
" <tr>\n",
" <th>860</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0.890459</td>\n",
" </tr>\n",
" <tr>\n",
" <th>861</th>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>17.481437</td>\n",
" </tr>\n",
" <tr>\n",
" <th>862</th>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>17.078473</td>\n",
" </tr>\n",
" <tr>\n",
" <th>863</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>17.220445</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>864 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" Pclass Survived AgeFillMedian\n",
"0 3 0 28.000000\n",
"1 3 0 32.000000\n",
"2 3 1 28.000000\n",
"3 1 0 45.000000\n",
"4 3 0 7.000000\n",
".. ... ... ...\n",
"859 2 0 26.887761\n",
"860 2 1 0.890459\n",
"861 2 0 17.481437\n",
"862 2 0 17.078473\n",
"863 2 1 17.220445\n",
"\n",
"[864 rows x 3 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from imblearn.over_sampling import ADASYN\n",
"\n",
"ada = ADASYN()\n",
"\n",
"print(\"Обучающая выборка: \", df_train.shape)\n",
"print(df_train.Pclass.value_counts())\n",
"\n",
"X_resampled, y_resampled = ada.fit_resample(df_train, df_train[\"Pclass\"])\n",
"df_train_adasyn = pd.DataFrame(X_resampled)\n",
"\n",
"print(\"Обучающая выборка после oversampling: \", df_train_adasyn.shape)\n",
"print(df_train_adasyn.Pclass.value_counts())\n",
"\n",
"df_train_adasyn"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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
}
Reference in New Issue Copy Permalink