4576 lines
529 KiB
Plaintext
4576 lines
529 KiB
Plaintext
{
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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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"## Лабораторная 4\n",
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"Датасет: Набор данных для анализа и прогнозирования сердечного приступа"
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Index(['HeartDisease', 'BMI', 'Smoking', 'AlcoholDrinking', 'Stroke',\n",
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" 'PhysicalHealth', 'MentalHealth', 'DiffWalking', 'Sex', 'AgeCategory',\n",
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" 'Race', 'Diabetic', 'PhysicalActivity', 'GenHealth', 'SleepTime',\n",
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" 'Asthma', 'KidneyDisease', 'SkinCancer'],\n",
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" dtype='object')\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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"from sklearn import set_config\n",
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"\n",
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"set_config(transform_output=\"pandas\")\n",
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"df = pd.read_csv(\".//static//csv//heart_2020_cleaned.csv\")\n",
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"print(df.columns)\n",
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"map_heart_disease_to_int = {'No': 0, 'Yes': 1}\n",
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"\n",
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"df['Stroke'] = df['Stroke'].map(map_heart_disease_to_int).astype('int32')"
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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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"## Классификация"
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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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"### Бизнес цель 1: \n",
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"Предсказание сердечного приступа (Stroke) на основе других факторов."
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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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"Формируем выборки"
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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": 3,
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"metadata": {},
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"outputs": [
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{
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||
"data": {
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"text/plain": [
|
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"'X_train'"
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]
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" vertical-align: middle;\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>HeartDisease</th>\n",
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" <th>BMI</th>\n",
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" <th>Smoking</th>\n",
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" <th>AlcoholDrinking</th>\n",
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" <th>Race</th>\n",
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" <th>Diabetic</th>\n",
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" <th>PhysicalActivity</th>\n",
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" <th>GenHealth</th>\n",
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" <th>SleepTime</th>\n",
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" <th>Asthma</th>\n",
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" <th>KidneyDisease</th>\n",
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" <th>SkinCancer</th>\n",
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" <td>Yes</td>\n",
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" <td>Fair</td>\n",
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" <th>314326</th>\n",
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" <td>No</td>\n",
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" <td>23.78</td>\n",
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" <td>No</td>\n",
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" <td>No</td>\n",
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" <td>0</td>\n",
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" <td>55-59</td>\n",
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" <td>Other</td>\n",
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" <td>No</td>\n",
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" <th>23944</th>\n",
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" <td>Yes</td>\n",
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||
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||
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||
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|
||
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|
||
" </tr>\n",
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|
||
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|
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|
||
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|
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|
||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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|
||
" <td>No</td>\n",
|
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|
||
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|
||
" <td>70-74</td>\n",
|
||
" <td>Black</td>\n",
|
||
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|
||
" <td>Yes</td>\n",
|
||
" <td>Very good</td>\n",
|
||
" <td>15.0</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>255836 rows × 18 columns</p>\n",
|
||
"</div>"
|
||
],
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||
"text/plain": [
|
||
" HeartDisease BMI Smoking AlcoholDrinking Stroke PhysicalHealth \\\n",
|
||
"203716 No 30.99 No No 0 0.0 \n",
|
||
"139550 No 32.61 No No 0 5.0 \n",
|
||
"314326 No 23.78 No No 0 0.0 \n",
|
||
"79716 No 30.38 No No 0 1.0 \n",
|
||
"23944 Yes 24.96 No No 0 0.0 \n",
|
||
"... ... ... ... ... ... ... \n",
|
||
"270558 No 25.84 No No 0 0.0 \n",
|
||
"60811 No 29.84 Yes No 0 0.0 \n",
|
||
"263613 Yes 32.92 Yes No 0 0.0 \n",
|
||
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|
||
"50387 No 32.78 No No 0 0.0 \n",
|
||
"\n",
|
||
" MentalHealth DiffWalking Sex AgeCategory Race Diabetic \\\n",
|
||
"203716 0.0 No Female 70-74 White No \n",
|
||
"139550 10.0 Yes Female 65-69 White Yes \n",
|
||
"314326 0.0 No Female 55-59 Other No \n",
|
||
"79716 30.0 Yes Female 80 or older White No \n",
|
||
"23944 0.0 Yes Female 75-79 White Yes \n",
|
||
"... ... ... ... ... ... ... \n",
|
||
"270558 0.0 No Male 65-69 White No \n",
|
||
"60811 3.0 No Male 30-34 White No \n",
|
||
"263613 0.0 Yes Female 50-54 White No \n",
|
||
"268192 0.0 Yes Female 60-64 Hispanic Yes \n",
|
||
"50387 0.0 No Female 70-74 Black Yes \n",
|
||
"\n",
|
||
" PhysicalActivity GenHealth SleepTime Asthma KidneyDisease SkinCancer \n",
|
||
"203716 Yes Fair 6.0 No No No \n",
|
||
"139550 No Good 5.0 Yes No No \n",
|
||
"314326 Yes Very good 7.0 No No No \n",
|
||
"79716 No Good 7.0 No No No \n",
|
||
"23944 Yes Good 8.0 No No No \n",
|
||
"... ... ... ... ... ... ... \n",
|
||
"270558 No Very good 8.0 No No No \n",
|
||
"60811 Yes Excellent 6.0 No No No \n",
|
||
"263613 Yes Good 8.0 No No No \n",
|
||
"268192 No Poor 6.0 No No No \n",
|
||
"50387 Yes Very good 15.0 No No No \n",
|
||
"\n",
|
||
"[255836 rows x 18 columns]"
|
||
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|
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|
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|
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" HeartDisease BMI Smoking AlcoholDrinking Stroke PhysicalHealth \\\n",
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|
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|
||
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|
||
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|
||
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|
||
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|
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" PhysicalActivity GenHealth SleepTime Asthma KidneyDisease SkinCancer \n",
|
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|
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|
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|
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|
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|
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|
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|
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|
||
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|
||
"169637 Yes Very good 7.0 Yes No No \n",
|
||
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|
||
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"source": [
|
||
"from typing import Tuple\n",
|
||
"import pandas as pd\n",
|
||
"from pandas import DataFrame\n",
|
||
"from sklearn.model_selection import train_test_split\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",
|
||
") -> Tuple[DataFrame, DataFrame, DataFrame, DataFrame, DataFrame, DataFrame]:\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",
|
||
" if stratify_colname not in df_input.columns:\n",
|
||
" raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\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",
|
||
" # 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",
|
||
" if frac_val <= 0:\n",
|
||
" assert len(df_input) == len(df_train) + len(df_temp)\n",
|
||
" return df_train, pd.DataFrame(), df_temp, y_train, pd.DataFrame(), y_temp\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",
|
||
" assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
|
||
" return df_train, df_val, df_test, y_train, y_val, y_test\n",
|
||
"\n",
|
||
"X_train, X_val, X_test, y_train, y_val, y_test = split_stratified_into_train_val_test(\n",
|
||
" df, stratify_colname=\"Stroke\", frac_train=0.80, frac_val=0, frac_test=0.20, random_state=9\n",
|
||
")\n",
|
||
"\n",
|
||
"display(\"X_train\", X_train)\n",
|
||
"display(\"y_train\", y_train)\n",
|
||
"\n",
|
||
"display(\"X_test\", X_test)\n",
|
||
"display(\"y_test\", y_test)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Пропущенные значения по столбцам:\n",
|
||
"HeartDisease 0\n",
|
||
"BMI 0\n",
|
||
"Smoking 0\n",
|
||
"AlcoholDrinking 0\n",
|
||
"Stroke 0\n",
|
||
"PhysicalHealth 0\n",
|
||
"MentalHealth 0\n",
|
||
"DiffWalking 0\n",
|
||
"Sex 0\n",
|
||
"AgeCategory 0\n",
|
||
"Race 0\n",
|
||
"Diabetic 0\n",
|
||
"PhysicalActivity 0\n",
|
||
"GenHealth 0\n",
|
||
"SleepTime 0\n",
|
||
"Asthma 0\n",
|
||
"KidneyDisease 0\n",
|
||
"SkinCancer 0\n",
|
||
"dtype: int64\n",
|
||
"\n",
|
||
"Статистический обзор данных:\n",
|
||
" BMI Stroke PhysicalHealth MentalHealth \\\n",
|
||
"count 319795.000000 319795.000000 319795.00000 319795.000000 \n",
|
||
"mean 28.325399 0.037740 3.37171 3.898366 \n",
|
||
"std 6.356100 0.190567 7.95085 7.955235 \n",
|
||
"min 12.020000 0.000000 0.00000 0.000000 \n",
|
||
"25% 24.030000 0.000000 0.00000 0.000000 \n",
|
||
"50% 27.340000 0.000000 0.00000 0.000000 \n",
|
||
"75% 31.420000 0.000000 2.00000 3.000000 \n",
|
||
"max 94.850000 1.000000 30.00000 30.000000 \n",
|
||
"\n",
|
||
" SleepTime \n",
|
||
"count 319795.000000 \n",
|
||
"mean 7.097075 \n",
|
||
"std 1.436007 \n",
|
||
"min 1.000000 \n",
|
||
"25% 6.000000 \n",
|
||
"50% 7.000000 \n",
|
||
"75% 8.000000 \n",
|
||
"max 24.000000 \n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"null_values = df.isnull().sum()\n",
|
||
"print(\"Пропущенные значения по столбцам:\")\n",
|
||
"print(null_values)\n",
|
||
"\n",
|
||
"stat_summary = df.describe()\n",
|
||
"print(\"\\nСтатистический обзор данных:\")\n",
|
||
"print(stat_summary)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Формируем конвеер для классификации данных и проверка конвеера"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"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>BMI</th>\n",
|
||
" <th>Stroke</th>\n",
|
||
" <th>PhysicalHealth</th>\n",
|
||
" <th>MentalHealth</th>\n",
|
||
" <th>SleepTime</th>\n",
|
||
" <th>HeartDisease_Yes</th>\n",
|
||
" <th>Smoking_Yes</th>\n",
|
||
" <th>AlcoholDrinking_Yes</th>\n",
|
||
" <th>DiffWalking_Yes</th>\n",
|
||
" <th>Diabetic_No, borderline diabetes</th>\n",
|
||
" <th>Diabetic_Yes</th>\n",
|
||
" <th>Diabetic_Yes (during pregnancy)</th>\n",
|
||
" <th>PhysicalActivity_Yes</th>\n",
|
||
" <th>GenHealth_Fair</th>\n",
|
||
" <th>GenHealth_Good</th>\n",
|
||
" <th>GenHealth_Poor</th>\n",
|
||
" <th>GenHealth_Very good</th>\n",
|
||
" <th>Asthma_Yes</th>\n",
|
||
" <th>KidneyDisease_Yes</th>\n",
|
||
" <th>SkinCancer_Yes</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>203716</th>\n",
|
||
" <td>0.417528</td>\n",
|
||
" <td>-0.198038</td>\n",
|
||
" <td>-0.424855</td>\n",
|
||
" <td>-0.490470</td>\n",
|
||
" <td>-0.764158</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>139550</th>\n",
|
||
" <td>0.671963</td>\n",
|
||
" <td>-0.198038</td>\n",
|
||
" <td>0.202871</td>\n",
|
||
" <td>0.765292</td>\n",
|
||
" <td>-1.461699</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>314326</th>\n",
|
||
" <td>-0.714865</td>\n",
|
||
" <td>-0.198038</td>\n",
|
||
" <td>-0.424855</td>\n",
|
||
" <td>-0.490470</td>\n",
|
||
" <td>-0.066617</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>79716</th>\n",
|
||
" <td>0.321722</td>\n",
|
||
" <td>-0.198038</td>\n",
|
||
" <td>-0.299310</td>\n",
|
||
" <td>3.276817</td>\n",
|
||
" <td>-0.066617</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>23944</th>\n",
|
||
" <td>-0.529536</td>\n",
|
||
" <td>-0.198038</td>\n",
|
||
" <td>-0.424855</td>\n",
|
||
" <td>-0.490470</td>\n",
|
||
" <td>0.630924</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>270558</th>\n",
|
||
" <td>-0.391324</td>\n",
|
||
" <td>-0.198038</td>\n",
|
||
" <td>-0.424855</td>\n",
|
||
" <td>-0.490470</td>\n",
|
||
" <td>0.630924</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>60811</th>\n",
|
||
" <td>0.236911</td>\n",
|
||
" <td>-0.198038</td>\n",
|
||
" <td>-0.424855</td>\n",
|
||
" <td>-0.113741</td>\n",
|
||
" <td>-0.764158</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>263613</th>\n",
|
||
" <td>0.720651</td>\n",
|
||
" <td>-0.198038</td>\n",
|
||
" <td>-0.424855</td>\n",
|
||
" <td>-0.490470</td>\n",
|
||
" <td>0.630924</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>268192</th>\n",
|
||
" <td>1.427415</td>\n",
|
||
" <td>-0.198038</td>\n",
|
||
" <td>3.341502</td>\n",
|
||
" <td>-0.490470</td>\n",
|
||
" <td>-0.764158</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>50387</th>\n",
|
||
" <td>0.698663</td>\n",
|
||
" <td>-0.198038</td>\n",
|
||
" <td>-0.424855</td>\n",
|
||
" <td>-0.490470</td>\n",
|
||
" <td>5.513713</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>255836 rows × 20 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" BMI Stroke PhysicalHealth MentalHealth SleepTime \\\n",
|
||
"203716 0.417528 -0.198038 -0.424855 -0.490470 -0.764158 \n",
|
||
"139550 0.671963 -0.198038 0.202871 0.765292 -1.461699 \n",
|
||
"314326 -0.714865 -0.198038 -0.424855 -0.490470 -0.066617 \n",
|
||
"79716 0.321722 -0.198038 -0.299310 3.276817 -0.066617 \n",
|
||
"23944 -0.529536 -0.198038 -0.424855 -0.490470 0.630924 \n",
|
||
"... ... ... ... ... ... \n",
|
||
"270558 -0.391324 -0.198038 -0.424855 -0.490470 0.630924 \n",
|
||
"60811 0.236911 -0.198038 -0.424855 -0.113741 -0.764158 \n",
|
||
"263613 0.720651 -0.198038 -0.424855 -0.490470 0.630924 \n",
|
||
"268192 1.427415 -0.198038 3.341502 -0.490470 -0.764158 \n",
|
||
"50387 0.698663 -0.198038 -0.424855 -0.490470 5.513713 \n",
|
||
"\n",
|
||
" HeartDisease_Yes Smoking_Yes AlcoholDrinking_Yes DiffWalking_Yes \\\n",
|
||
"203716 0.0 0.0 0.0 0.0 \n",
|
||
"139550 0.0 0.0 0.0 1.0 \n",
|
||
"314326 0.0 0.0 0.0 0.0 \n",
|
||
"79716 0.0 0.0 0.0 1.0 \n",
|
||
"23944 1.0 0.0 0.0 1.0 \n",
|
||
"... ... ... ... ... \n",
|
||
"270558 0.0 0.0 0.0 0.0 \n",
|
||
"60811 0.0 1.0 0.0 0.0 \n",
|
||
"263613 1.0 1.0 0.0 1.0 \n",
|
||
"268192 0.0 0.0 0.0 1.0 \n",
|
||
"50387 0.0 0.0 0.0 0.0 \n",
|
||
"\n",
|
||
" Diabetic_No, borderline diabetes Diabetic_Yes \\\n",
|
||
"203716 0.0 0.0 \n",
|
||
"139550 0.0 1.0 \n",
|
||
"314326 0.0 0.0 \n",
|
||
"79716 0.0 0.0 \n",
|
||
"23944 0.0 1.0 \n",
|
||
"... ... ... \n",
|
||
"270558 0.0 0.0 \n",
|
||
"60811 0.0 0.0 \n",
|
||
"263613 0.0 0.0 \n",
|
||
"268192 0.0 1.0 \n",
|
||
"50387 0.0 1.0 \n",
|
||
"\n",
|
||
" Diabetic_Yes (during pregnancy) PhysicalActivity_Yes GenHealth_Fair \\\n",
|
||
"203716 0.0 1.0 1.0 \n",
|
||
"139550 0.0 0.0 0.0 \n",
|
||
"314326 0.0 1.0 0.0 \n",
|
||
"79716 0.0 0.0 0.0 \n",
|
||
"23944 0.0 1.0 0.0 \n",
|
||
"... ... ... ... \n",
|
||
"270558 0.0 0.0 0.0 \n",
|
||
"60811 0.0 1.0 0.0 \n",
|
||
"263613 0.0 1.0 0.0 \n",
|
||
"268192 0.0 0.0 0.0 \n",
|
||
"50387 0.0 1.0 0.0 \n",
|
||
"\n",
|
||
" GenHealth_Good GenHealth_Poor GenHealth_Very good Asthma_Yes \\\n",
|
||
"203716 0.0 0.0 0.0 0.0 \n",
|
||
"139550 1.0 0.0 0.0 1.0 \n",
|
||
"314326 0.0 0.0 1.0 0.0 \n",
|
||
"79716 1.0 0.0 0.0 0.0 \n",
|
||
"23944 1.0 0.0 0.0 0.0 \n",
|
||
"... ... ... ... ... \n",
|
||
"270558 0.0 0.0 1.0 0.0 \n",
|
||
"60811 0.0 0.0 0.0 0.0 \n",
|
||
"263613 1.0 0.0 0.0 0.0 \n",
|
||
"268192 0.0 1.0 0.0 0.0 \n",
|
||
"50387 0.0 0.0 1.0 0.0 \n",
|
||
"\n",
|
||
" KidneyDisease_Yes SkinCancer_Yes \n",
|
||
"203716 0.0 0.0 \n",
|
||
"139550 0.0 0.0 \n",
|
||
"314326 0.0 0.0 \n",
|
||
"79716 0.0 0.0 \n",
|
||
"23944 0.0 0.0 \n",
|
||
"... ... ... \n",
|
||
"270558 0.0 0.0 \n",
|
||
"60811 0.0 0.0 \n",
|
||
"263613 0.0 0.0 \n",
|
||
"268192 0.0 0.0 \n",
|
||
"50387 0.0 0.0 \n",
|
||
"\n",
|
||
"[255836 rows x 20 columns]"
|
||
]
|
||
},
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.compose import ColumnTransformer\n",
|
||
"from sklearn.discriminant_analysis import StandardScaler\n",
|
||
"from sklearn.impute import SimpleImputer\n",
|
||
"from sklearn.pipeline import Pipeline\n",
|
||
"from sklearn.preprocessing import OneHotEncoder\n",
|
||
"\n",
|
||
"columns_to_drop = ['AgeCategory', 'Sex', 'Race']\n",
|
||
"num_columns = [\n",
|
||
" column\n",
|
||
" for column in df.columns\n",
|
||
" if column not in columns_to_drop and df[column].dtype != \"object\"\n",
|
||
"]\n",
|
||
"cat_columns = [\n",
|
||
" column\n",
|
||
" for column in df.columns\n",
|
||
" if column not in columns_to_drop and df[column].dtype == \"object\"\n",
|
||
"]\n",
|
||
"\n",
|
||
"num_imputer = SimpleImputer(strategy=\"median\")\n",
|
||
"num_scaler = StandardScaler()\n",
|
||
"preprocessing_num = Pipeline(\n",
|
||
" [\n",
|
||
" (\"imputer\", num_imputer),\n",
|
||
" (\"scaler\", num_scaler),\n",
|
||
" ]\n",
|
||
")\n",
|
||
"\n",
|
||
"cat_imputer = SimpleImputer(strategy=\"constant\", fill_value=\"unknown\")\n",
|
||
"cat_encoder = OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False, drop=\"first\")\n",
|
||
"preprocessing_cat = Pipeline(\n",
|
||
" [\n",
|
||
" (\"imputer\", cat_imputer),\n",
|
||
" (\"encoder\", cat_encoder),\n",
|
||
" ]\n",
|
||
")\n",
|
||
"\n",
|
||
"features_preprocessing = ColumnTransformer(\n",
|
||
" verbose_feature_names_out=False,\n",
|
||
" transformers=[\n",
|
||
" (\"prepocessing_num\", preprocessing_num, num_columns),\n",
|
||
" (\"prepocessing_cat\", preprocessing_cat, cat_columns),\n",
|
||
" ],\n",
|
||
" remainder=\"passthrough\"\n",
|
||
")\n",
|
||
"\n",
|
||
"drop_columns = ColumnTransformer(\n",
|
||
" verbose_feature_names_out=False,\n",
|
||
" transformers=[\n",
|
||
" (\"drop_columns\", \"drop\", columns_to_drop),\n",
|
||
" ],\n",
|
||
" remainder=\"passthrough\",\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"pipeline_end = Pipeline(\n",
|
||
" [\n",
|
||
" (\"features_preprocessing\", features_preprocessing),\n",
|
||
" (\"drop_columns\", drop_columns),\n",
|
||
" ]\n",
|
||
")\n",
|
||
"\n",
|
||
"preprocessing_result = pipeline_end.fit_transform(X_train)\n",
|
||
"preprocessed_df = pd.DataFrame(\n",
|
||
" preprocessing_result,\n",
|
||
" columns=pipeline_end.get_feature_names_out(),\n",
|
||
")\n",
|
||
"\n",
|
||
"preprocessed_df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Формируем набор моделей"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn import ensemble, linear_model, naive_bayes, neighbors, neural_network, tree\n",
|
||
"\n",
|
||
"\n",
|
||
"class_models = {\n",
|
||
" \"logistic\": {\"model\": linear_model.LogisticRegression()},\n",
|
||
" \"ridge\": {\"model\": linear_model.LogisticRegression(penalty=\"l2\", class_weight=\"balanced\")},\n",
|
||
" \"decision_tree\": {\n",
|
||
" \"model\": tree.DecisionTreeClassifier(max_depth=7, random_state=9)\n",
|
||
" },\n",
|
||
" \"knn\": {\"model\": neighbors.KNeighborsClassifier(n_neighbors=7)},\n",
|
||
" \"naive_bayes\": {\"model\": naive_bayes.GaussianNB()},\n",
|
||
" \"gradient_boosting\": {\n",
|
||
" \"model\": ensemble.GradientBoostingClassifier(n_estimators=210)\n",
|
||
" },\n",
|
||
" \"random_forest\": {\n",
|
||
" \"model\": ensemble.RandomForestClassifier(\n",
|
||
" max_depth=11, class_weight=\"balanced\", random_state=9\n",
|
||
" )\n",
|
||
" },\n",
|
||
" \"mlp\": {\n",
|
||
" \"model\": neural_network.MLPClassifier(\n",
|
||
" hidden_layer_sizes=(7,),\n",
|
||
" max_iter=500,\n",
|
||
" early_stopping=True,\n",
|
||
" random_state=9,\n",
|
||
" )\n",
|
||
" },\n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Обучаем модели и тестируем их"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Model: logistic\n",
|
||
"Model: ridge\n",
|
||
"Model: decision_tree\n",
|
||
"Model: knn\n",
|
||
"Model: naive_bayes\n",
|
||
"Model: gradient_boosting\n",
|
||
"Model: random_forest\n",
|
||
"Model: mlp\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"from sklearn import metrics\n",
|
||
"\n",
|
||
"for model_name in class_models.keys():\n",
|
||
" print(f\"Model: {model_name}\")\n",
|
||
" model = class_models[model_name][\"model\"]\n",
|
||
"\n",
|
||
" model_pipeline = Pipeline([(\"pipeline\", pipeline_end), (\"model\", model)])\n",
|
||
" model_pipeline = model_pipeline.fit(X_train, y_train.values.ravel())\n",
|
||
"\n",
|
||
" y_train_predict = model_pipeline.predict(X_train)\n",
|
||
" y_test_probs = model_pipeline.predict_proba(X_test)[:, 1]\n",
|
||
" y_test_predict = np.where(y_test_probs > 0.5, 1, 0)\n",
|
||
"\n",
|
||
" class_models[model_name][\"pipeline\"] = model_pipeline\n",
|
||
" class_models[model_name][\"probs\"] = y_test_probs\n",
|
||
" class_models[model_name][\"preds\"] = y_test_predict\n",
|
||
"\n",
|
||
" class_models[model_name][\"Precision_train\"] = metrics.precision_score(\n",
|
||
" y_train, y_train_predict\n",
|
||
" )\n",
|
||
" class_models[model_name][\"Precision_test\"] = metrics.precision_score(\n",
|
||
" y_test, y_test_predict\n",
|
||
" )\n",
|
||
" class_models[model_name][\"Recall_train\"] = metrics.recall_score(\n",
|
||
" y_train, y_train_predict\n",
|
||
" )\n",
|
||
" class_models[model_name][\"Recall_test\"] = metrics.recall_score(\n",
|
||
" y_test, y_test_predict\n",
|
||
" )\n",
|
||
" class_models[model_name][\"Accuracy_train\"] = metrics.accuracy_score(\n",
|
||
" y_train, y_train_predict\n",
|
||
" )\n",
|
||
" class_models[model_name][\"Accuracy_test\"] = metrics.accuracy_score(\n",
|
||
" y_test, y_test_predict\n",
|
||
" )\n",
|
||
" class_models[model_name][\"ROC_AUC_test\"] = metrics.roc_auc_score(\n",
|
||
" y_test, y_test_probs\n",
|
||
" )\n",
|
||
" class_models[model_name][\"F1_train\"] = metrics.f1_score(y_train, y_train_predict, average=None)\n",
|
||
" class_models[model_name][\"F1_test\"] = metrics.f1_score(y_test, y_test_predict, average=None)\n",
|
||
" class_models[model_name][\"MCC_test\"] = metrics.matthews_corrcoef(\n",
|
||
" y_test, y_test_predict\n",
|
||
" )\n",
|
||
" class_models[model_name][\"Cohen_kappa_test\"] = metrics.cohen_kappa_score(\n",
|
||
" y_test, y_test_predict\n",
|
||
" )\n",
|
||
" class_models[model_name][\"Confusion_matrix\"] = metrics.confusion_matrix(\n",
|
||
" y_test, y_test_predict\n",
|
||
" )"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Матрица неточностей"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 42,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1200x1000 with 16 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"from matplotlib import pyplot as plt\n",
|
||
"from sklearn.metrics import ConfusionMatrixDisplay\n",
|
||
"\n",
|
||
"_, ax = plt.subplots(int(len(class_models) / 2), 2, figsize=(12, 10), sharex=False, sharey=False)\n",
|
||
"for index, key in enumerate(class_models.keys()):\n",
|
||
" c_matrix = class_models[key][\"Confusion_matrix\"]\n",
|
||
" disp = ConfusionMatrixDisplay(\n",
|
||
" confusion_matrix=c_matrix, display_labels=[\"No stroke\", \"Stroke\"]\n",
|
||
" ).plot(ax=ax.flat[index])\n",
|
||
" disp.ax_.set_title(key)\n",
|
||
"\n",
|
||
"plt.subplots_adjust(top=1, bottom=0, hspace=0.4, wspace=0.1)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Точность, полнота, верность (аккуратность), F-мера"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"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",
|
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" .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>Precision_train</th>\n",
|
||
" <th>Precision_test</th>\n",
|
||
" <th>Recall_train</th>\n",
|
||
" <th>Recall_test</th>\n",
|
||
" <th>Accuracy_train</th>\n",
|
||
" <th>Accuracy_test</th>\n",
|
||
" <th>F1_train</th>\n",
|
||
" <th>F1_test</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>logistic</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>[1.0, 1.0]</td>\n",
|
||
" <td>[1.0, 1.0]</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>ridge</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>[1.0, 1.0]</td>\n",
|
||
" <td>[1.0, 1.0]</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>decision_tree</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>[1.0, 1.0]</td>\n",
|
||
" <td>[1.0, 1.0]</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>knn</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>[1.0, 1.0]</td>\n",
|
||
" <td>[1.0, 1.0]</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>naive_bayes</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>[1.0, 1.0]</td>\n",
|
||
" <td>[1.0, 1.0]</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>gradient_boosting</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>[1.0, 1.0]</td>\n",
|
||
" <td>[1.0, 1.0]</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>random_forest</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>[1.0, 1.0]</td>\n",
|
||
" <td>[1.0, 1.0]</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>mlp</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.999896</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0.999996</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>[0.9999979689781726, 0.999948210678958]</td>\n",
|
||
" <td>[1.0, 1.0]</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Precision_train Precision_test Recall_train Recall_test \\\n",
|
||
"logistic 1.0 1.0 1.000000 1.0 \n",
|
||
"ridge 1.0 1.0 1.000000 1.0 \n",
|
||
"decision_tree 1.0 1.0 1.000000 1.0 \n",
|
||
"knn 1.0 1.0 1.000000 1.0 \n",
|
||
"naive_bayes 1.0 1.0 1.000000 1.0 \n",
|
||
"gradient_boosting 1.0 1.0 1.000000 1.0 \n",
|
||
"random_forest 1.0 1.0 1.000000 1.0 \n",
|
||
"mlp 1.0 1.0 0.999896 1.0 \n",
|
||
"\n",
|
||
" Accuracy_train Accuracy_test \\\n",
|
||
"logistic 1.000000 1.0 \n",
|
||
"ridge 1.000000 1.0 \n",
|
||
"decision_tree 1.000000 1.0 \n",
|
||
"knn 1.000000 1.0 \n",
|
||
"naive_bayes 1.000000 1.0 \n",
|
||
"gradient_boosting 1.000000 1.0 \n",
|
||
"random_forest 1.000000 1.0 \n",
|
||
"mlp 0.999996 1.0 \n",
|
||
"\n",
|
||
" F1_train F1_test \n",
|
||
"logistic [1.0, 1.0] [1.0, 1.0] \n",
|
||
"ridge [1.0, 1.0] [1.0, 1.0] \n",
|
||
"decision_tree [1.0, 1.0] [1.0, 1.0] \n",
|
||
"knn [1.0, 1.0] [1.0, 1.0] \n",
|
||
"naive_bayes [1.0, 1.0] [1.0, 1.0] \n",
|
||
"gradient_boosting [1.0, 1.0] [1.0, 1.0] \n",
|
||
"random_forest [1.0, 1.0] [1.0, 1.0] \n",
|
||
"mlp [0.9999979689781726, 0.999948210678958] [1.0, 1.0] "
|
||
]
|
||
},
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"class_metrics = pd.DataFrame.from_dict(class_models, \"index\")[\n",
|
||
" [\n",
|
||
" \"Precision_train\",\n",
|
||
" \"Precision_test\",\n",
|
||
" \"Recall_train\",\n",
|
||
" \"Recall_test\",\n",
|
||
" \"Accuracy_train\",\n",
|
||
" \"Accuracy_test\",\n",
|
||
" \"F1_train\",\n",
|
||
" \"F1_test\",\n",
|
||
" ]\n",
|
||
"]\n",
|
||
"class_metrics.sort_values(\n",
|
||
" by=\"Accuracy_test\", ascending=False\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"ROC-кривая, каппа Коэна, коэффициент корреляции Мэтьюса"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"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>Accuracy_test</th>\n",
|
||
" <th>F1_test</th>\n",
|
||
" <th>ROC_AUC_test</th>\n",
|
||
" <th>Cohen_kappa_test</th>\n",
|
||
" <th>MCC_test</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>logistic</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>[1.0, 1.0]</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>decision_tree</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>[1.0, 1.0]</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>knn</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>[1.0, 1.0]</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>naive_bayes</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>[1.0, 1.0]</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>random_forest</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>[1.0, 1.0]</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>gradient_boosting</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>[1.0, 1.0]</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>mlp</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>[1.0, 1.0]</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>ridge</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>[1.0, 1.0]</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Accuracy_test F1_test ROC_AUC_test Cohen_kappa_test \\\n",
|
||
"logistic 1.0 [1.0, 1.0] 1.0 1.0 \n",
|
||
"decision_tree 1.0 [1.0, 1.0] 1.0 1.0 \n",
|
||
"knn 1.0 [1.0, 1.0] 1.0 1.0 \n",
|
||
"naive_bayes 1.0 [1.0, 1.0] 1.0 1.0 \n",
|
||
"random_forest 1.0 [1.0, 1.0] 1.0 1.0 \n",
|
||
"gradient_boosting 1.0 [1.0, 1.0] 1.0 1.0 \n",
|
||
"mlp 1.0 [1.0, 1.0] 1.0 1.0 \n",
|
||
"ridge 1.0 [1.0, 1.0] 1.0 1.0 \n",
|
||
"\n",
|
||
" MCC_test \n",
|
||
"logistic 1.0 \n",
|
||
"decision_tree 1.0 \n",
|
||
"knn 1.0 \n",
|
||
"naive_bayes 1.0 \n",
|
||
"random_forest 1.0 \n",
|
||
"gradient_boosting 1.0 \n",
|
||
"mlp 1.0 \n",
|
||
"ridge 1.0 "
|
||
]
|
||
},
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"class_metrics = pd.DataFrame.from_dict(class_models, \"index\")[\n",
|
||
" [\n",
|
||
" \"Accuracy_test\",\n",
|
||
" \"F1_test\",\n",
|
||
" \"ROC_AUC_test\",\n",
|
||
" \"Cohen_kappa_test\",\n",
|
||
" \"MCC_test\",\n",
|
||
" ]\n",
|
||
"]\n",
|
||
"class_metrics.sort_values(by=\"ROC_AUC_test\", ascending=False)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Лучшая модель"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"'logistic'"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"best_model = str(class_metrics.sort_values(by=\"MCC_test\", ascending=False).iloc[0].name)\n",
|
||
"\n",
|
||
"display(best_model)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Находим ошибки"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"'Error items count: 0'"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"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>HeartDisease</th>\n",
|
||
" <th>Predicted</th>\n",
|
||
" <th>BMI</th>\n",
|
||
" <th>Smoking</th>\n",
|
||
" <th>AlcoholDrinking</th>\n",
|
||
" <th>Stroke</th>\n",
|
||
" <th>PhysicalHealth</th>\n",
|
||
" <th>MentalHealth</th>\n",
|
||
" <th>DiffWalking</th>\n",
|
||
" <th>Sex</th>\n",
|
||
" <th>AgeCategory</th>\n",
|
||
" <th>Race</th>\n",
|
||
" <th>Diabetic</th>\n",
|
||
" <th>PhysicalActivity</th>\n",
|
||
" <th>GenHealth</th>\n",
|
||
" <th>SleepTime</th>\n",
|
||
" <th>Asthma</th>\n",
|
||
" <th>KidneyDisease</th>\n",
|
||
" <th>SkinCancer</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
"Empty DataFrame\n",
|
||
"Columns: [HeartDisease, Predicted, BMI, Smoking, AlcoholDrinking, Stroke, PhysicalHealth, MentalHealth, DiffWalking, Sex, AgeCategory, Race, Diabetic, PhysicalActivity, GenHealth, SleepTime, Asthma, KidneyDisease, SkinCancer]\n",
|
||
"Index: []"
|
||
]
|
||
},
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"preprocessing_result = pipeline_end.transform(X_test)\n",
|
||
"preprocessed_df = pd.DataFrame(\n",
|
||
" preprocessing_result,\n",
|
||
" columns=pipeline_end.get_feature_names_out(),\n",
|
||
")\n",
|
||
"\n",
|
||
"y_new_pred = class_models[best_model][\"preds\"]\n",
|
||
"\n",
|
||
"error_index = y_test[y_test[\"Stroke\"] != y_new_pred].index.tolist()\n",
|
||
"display(f\"Error items count: {len(error_index)}\")\n",
|
||
"\n",
|
||
"error_predicted = pd.Series(y_new_pred, index=y_test.index).loc[error_index]\n",
|
||
"error_df = X_test.loc[error_index].copy()\n",
|
||
"error_df.insert(loc=1, column=\"Predicted\", value=error_predicted)\n",
|
||
"error_df.sort_index()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Пример использования модели (конвейера) для предсказания"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"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>HeartDisease</th>\n",
|
||
" <th>BMI</th>\n",
|
||
" <th>Smoking</th>\n",
|
||
" <th>AlcoholDrinking</th>\n",
|
||
" <th>Stroke</th>\n",
|
||
" <th>PhysicalHealth</th>\n",
|
||
" <th>MentalHealth</th>\n",
|
||
" <th>DiffWalking</th>\n",
|
||
" <th>Sex</th>\n",
|
||
" <th>AgeCategory</th>\n",
|
||
" <th>Race</th>\n",
|
||
" <th>Diabetic</th>\n",
|
||
" <th>PhysicalActivity</th>\n",
|
||
" <th>GenHealth</th>\n",
|
||
" <th>SleepTime</th>\n",
|
||
" <th>Asthma</th>\n",
|
||
" <th>KidneyDisease</th>\n",
|
||
" <th>SkinCancer</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>8556</th>\n",
|
||
" <td>No</td>\n",
|
||
" <td>38.41</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>Female</td>\n",
|
||
" <td>65-69</td>\n",
|
||
" <td>White</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>Yes</td>\n",
|
||
" <td>Very good</td>\n",
|
||
" <td>6.0</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" HeartDisease BMI Smoking AlcoholDrinking Stroke PhysicalHealth \\\n",
|
||
"8556 No 38.41 No No 1 0.0 \n",
|
||
"\n",
|
||
" MentalHealth DiffWalking Sex AgeCategory Race Diabetic \\\n",
|
||
"8556 0.0 No Female 65-69 White No \n",
|
||
"\n",
|
||
" PhysicalActivity GenHealth SleepTime Asthma KidneyDisease SkinCancer \n",
|
||
"8556 Yes Very good 6.0 No No No "
|
||
]
|
||
},
|
||
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|
||
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|
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|
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|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>BMI</th>\n",
|
||
" <th>Stroke</th>\n",
|
||
" <th>PhysicalHealth</th>\n",
|
||
" <th>MentalHealth</th>\n",
|
||
" <th>SleepTime</th>\n",
|
||
" <th>HeartDisease_Yes</th>\n",
|
||
" <th>Smoking_Yes</th>\n",
|
||
" <th>AlcoholDrinking_Yes</th>\n",
|
||
" <th>DiffWalking_Yes</th>\n",
|
||
" <th>Diabetic_No, borderline diabetes</th>\n",
|
||
" <th>Diabetic_Yes</th>\n",
|
||
" <th>Diabetic_Yes (during pregnancy)</th>\n",
|
||
" <th>PhysicalActivity_Yes</th>\n",
|
||
" <th>GenHealth_Fair</th>\n",
|
||
" <th>GenHealth_Good</th>\n",
|
||
" <th>GenHealth_Poor</th>\n",
|
||
" <th>GenHealth_Very good</th>\n",
|
||
" <th>Asthma_Yes</th>\n",
|
||
" <th>KidneyDisease_Yes</th>\n",
|
||
" <th>SkinCancer_Yes</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>8556</th>\n",
|
||
" <td>1.582904</td>\n",
|
||
" <td>5.049532</td>\n",
|
||
" <td>-0.424855</td>\n",
|
||
" <td>-0.49047</td>\n",
|
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" <td>-0.764158</td>\n",
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||
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
|
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" <td>1.0</td>\n",
|
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.0</td>\n",
|
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" <td>0.0</td>\n",
|
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" <td>0.0</td>\n",
|
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" <td>0.0</td>\n",
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" </tr>\n",
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" </tbody>\n",
|
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"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" BMI Stroke PhysicalHealth MentalHealth SleepTime \\\n",
|
||
"8556 1.582904 5.049532 -0.424855 -0.49047 -0.764158 \n",
|
||
"\n",
|
||
" HeartDisease_Yes Smoking_Yes AlcoholDrinking_Yes DiffWalking_Yes \\\n",
|
||
"8556 0.0 0.0 0.0 0.0 \n",
|
||
"\n",
|
||
" Diabetic_No, borderline diabetes Diabetic_Yes \\\n",
|
||
"8556 0.0 0.0 \n",
|
||
"\n",
|
||
" Diabetic_Yes (during pregnancy) PhysicalActivity_Yes GenHealth_Fair \\\n",
|
||
"8556 0.0 1.0 0.0 \n",
|
||
"\n",
|
||
" GenHealth_Good GenHealth_Poor GenHealth_Very good Asthma_Yes \\\n",
|
||
"8556 0.0 0.0 1.0 0.0 \n",
|
||
"\n",
|
||
" KidneyDisease_Yes SkinCancer_Yes \n",
|
||
"8556 0.0 0.0 "
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"'predicted: 1 (proba: [1.15647247e-04 9.99884353e-01])'"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"'real: 1'"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"model = class_models[best_model][\"pipeline\"]\n",
|
||
"\n",
|
||
"example_id = 8556\n",
|
||
"test = pd.DataFrame(X_test.loc[example_id, :]).T\n",
|
||
"test_preprocessed = pd.DataFrame(preprocessed_df.loc[example_id, :]).T\n",
|
||
"display(test)\n",
|
||
"display(test_preprocessed)\n",
|
||
"result_proba = model.predict_proba(test)[0]\n",
|
||
"result = model.predict(test)[0]\n",
|
||
"real = int(y_test.loc[example_id].values[0])\n",
|
||
"display(f\"predicted: {result} (proba: {result_proba})\")\n",
|
||
"display(f\"real: {real}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Создаем гиперпараметры методом поиска по сетке"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"c:\\Users\\User\\Desktop\\aim\\aimvenv\\Lib\\site-packages\\numpy\\ma\\core.py:2881: RuntimeWarning: invalid value encountered in cast\n",
|
||
" _data = np.array(data, dtype=dtype, copy=copy,\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'model__criterion': 'gini',\n",
|
||
" 'model__max_depth': 5,\n",
|
||
" 'model__max_features': 'sqrt',\n",
|
||
" 'model__n_estimators': 50}"
|
||
]
|
||
},
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.model_selection import GridSearchCV\n",
|
||
"\n",
|
||
"\n",
|
||
"optimized_model_type = 'random_forest'\n",
|
||
"random_state = 9\n",
|
||
"\n",
|
||
"random_forest_model = class_models[optimized_model_type][\"pipeline\"]\n",
|
||
"\n",
|
||
"param_grid = {\n",
|
||
" \"model__n_estimators\": [10, 50, 100],\n",
|
||
" \"model__max_features\": [\"sqrt\", \"log2\"],\n",
|
||
" \"model__max_depth\": [5, 7, 10],\n",
|
||
" \"model__criterion\": [\"gini\", \"entropy\"],\n",
|
||
"}\n",
|
||
"\n",
|
||
"gs_optomizer = GridSearchCV(\n",
|
||
" estimator=random_forest_model, param_grid=param_grid, n_jobs=-1\n",
|
||
")\n",
|
||
"gs_optomizer.fit(X_train, y_train.values.ravel())\n",
|
||
"gs_optomizer.best_params_\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Обучение модели с новыми гиперпараметрами"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"optimized_model = ensemble.RandomForestClassifier(\n",
|
||
" random_state=42,\n",
|
||
" criterion=\"gini\",\n",
|
||
" max_depth=5,\n",
|
||
" max_features=\"sqrt\",\n",
|
||
" n_estimators=50,\n",
|
||
")\n",
|
||
"\n",
|
||
"result = {}\n",
|
||
"\n",
|
||
"result[\"pipeline\"] = Pipeline([(\"pipeline\", pipeline_end), (\"model\", optimized_model)]).fit(X_train, y_train.values.ravel())\n",
|
||
"result[\"train_preds\"] = result[\"pipeline\"].predict(X_train)\n",
|
||
"result[\"probs\"] = result[\"pipeline\"].predict_proba(X_test)[:, 1]\n",
|
||
"result[\"preds\"] = np.where(result[\"probs\"] > 0.5, 1, 0)\n",
|
||
"\n",
|
||
"result[\"Precision_train\"] = metrics.precision_score(y_train, result[\"train_preds\"])\n",
|
||
"result[\"Precision_test\"] = metrics.precision_score(y_test, result[\"preds\"])\n",
|
||
"result[\"Recall_train\"] = metrics.recall_score(y_train, result[\"train_preds\"])\n",
|
||
"result[\"Recall_test\"] = metrics.recall_score(y_test, result[\"preds\"])\n",
|
||
"result[\"Accuracy_train\"] = metrics.accuracy_score(y_train, result[\"train_preds\"])\n",
|
||
"result[\"Accuracy_test\"] = metrics.accuracy_score(y_test, result[\"preds\"])\n",
|
||
"result[\"ROC_AUC_test\"] = metrics.roc_auc_score(y_test, result[\"probs\"])\n",
|
||
"result[\"F1_train\"] = metrics.f1_score(y_train, result[\"train_preds\"])\n",
|
||
"result[\"F1_test\"] = metrics.f1_score(y_test, result[\"preds\"])\n",
|
||
"result[\"MCC_test\"] = metrics.matthews_corrcoef(y_test, result[\"preds\"])\n",
|
||
"result[\"Cohen_kappa_test\"] = metrics.cohen_kappa_score(y_test, result[\"preds\"])\n",
|
||
"result[\"Confusion_matrix\"] = metrics.confusion_matrix(y_test, result[\"preds\"])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Формирование данных для оценки старой и новой версии модели и сама оценка данных"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
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|
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|
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|
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|
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|
||
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|
||
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|
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|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Precision_train</th>\n",
|
||
" <th>Precision_test</th>\n",
|
||
" <th>Recall_train</th>\n",
|
||
" <th>Recall_test</th>\n",
|
||
" <th>Accuracy_train</th>\n",
|
||
" <th>Accuracy_test</th>\n",
|
||
" <th>F1_train</th>\n",
|
||
" <th>F1_test</th>\n",
|
||
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|
||
" <tr>\n",
|
||
" <th>Name</th>\n",
|
||
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|
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|
||
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|
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||
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|
||
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|
||
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|
||
" <tr>\n",
|
||
" <th>Old</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
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" <td>[1.0, 1.0]</td>\n",
|
||
" <td>[1.0, 1.0]</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>New</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
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" <td>1.0</td>\n",
|
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" <td>1.0</td>\n",
|
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" <td>1.0</td>\n",
|
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" <td>1.0</td>\n",
|
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" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Precision_train Precision_test Recall_train Recall_test Accuracy_train \\\n",
|
||
"Name \n",
|
||
"Old 1.0 1.0 1.0 1.0 1.0 \n",
|
||
"New 1.0 1.0 1.0 1.0 1.0 \n",
|
||
"\n",
|
||
" Accuracy_test F1_train F1_test \n",
|
||
"Name \n",
|
||
"Old 1.0 [1.0, 1.0] [1.0, 1.0] \n",
|
||
"New 1.0 1.0 1.0 "
|
||
]
|
||
},
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"optimized_metrics = pd.DataFrame(columns=list(result.keys()))\n",
|
||
"optimized_metrics.loc[len(optimized_metrics)] = pd.Series(\n",
|
||
" data=class_models[optimized_model_type]\n",
|
||
")\n",
|
||
"optimized_metrics.loc[len(optimized_metrics)] = pd.Series(\n",
|
||
" data=result\n",
|
||
")\n",
|
||
"optimized_metrics.insert(loc=0, column=\"Name\", value=[\"Old\", \"New\"])\n",
|
||
"optimized_metrics = optimized_metrics.set_index(\"Name\")\n",
|
||
"\n",
|
||
"optimized_metrics[\n",
|
||
" [\n",
|
||
" \"Precision_train\",\n",
|
||
" \"Precision_test\",\n",
|
||
" \"Recall_train\",\n",
|
||
" \"Recall_test\",\n",
|
||
" \"Accuracy_train\",\n",
|
||
" \"Accuracy_test\",\n",
|
||
" \"F1_train\",\n",
|
||
" \"F1_test\",\n",
|
||
" ]\n",
|
||
"]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
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"<div>\n",
|
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"<style scoped>\n",
|
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|
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|
||
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|
||
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|
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|
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|
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|
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|
||
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|
||
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|
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|
||
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|
||
" <thead>\n",
|
||
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|
||
" <th></th>\n",
|
||
" <th>Accuracy_test</th>\n",
|
||
" <th>F1_test</th>\n",
|
||
" <th>ROC_AUC_test</th>\n",
|
||
" <th>Cohen_kappa_test</th>\n",
|
||
" <th>MCC_test</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Name</th>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <tbody>\n",
|
||
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|
||
" <th>Old</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>[1.0, 1.0]</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>New</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1.0</td>\n",
|
||
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|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Accuracy_test F1_test ROC_AUC_test Cohen_kappa_test MCC_test\n",
|
||
"Name \n",
|
||
"Old 1.0 [1.0, 1.0] 1.0 1.0 1.0\n",
|
||
"New 1.0 1.0 1.0 1.0 1.0"
|
||
]
|
||
},
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"optimized_metrics[\n",
|
||
" [\n",
|
||
" \"Accuracy_test\",\n",
|
||
" \"F1_test\",\n",
|
||
" \"ROC_AUC_test\",\n",
|
||
" \"Cohen_kappa_test\",\n",
|
||
" \"MCC_test\",\n",
|
||
" ]\n",
|
||
"]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 41,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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16dJF0dHRevjhhzVnzhxlZGTo6aef1vDhw21XiIcOHaqFCxfqySef1KOPPqpNmzZp1apVWrduna0do0eP1oABA9S6dWu1adNG8+fP1+XLlzVw4MBifx4AgLJRFrkmeV620YkEALiV77//Xv369dOFCxdUvXp1tW/fXp9//rmqV68uSZo3b558fHzUq1cv5eTkKCEhQa+88ort9b6+vlq7dq2GDRumuLg4Va5cWQMGDND06dNtZerVq6d169Zp1KhRevHFF1WrVi0tWbJECQkJtjJ9+vTRuXPnNHnyZGVkZCgmJkbr168vtCABAAB/xNOyzWS1Wq3F/ExQgrKzsxUSEqIfD9eXOZjZx7CXEBFT1k2AG7pmvarN+reysrLsbvZ3RcHvnpHb7lNAUIVitynn0lXNb/dhsdoEz0Cu4XrINRSFXHM/jEQCABwqq2k/AACUBHLNGFwCBAAAAAA4jZFIAIBDFvnIYsD1RiPqAACguMg1Y9CJBAA4lGc1Kc+AKTtG1AEAQHGRa8bw7i40AAAAAMAljEQCABxiAQIAgCch14zBSCQAAAAAwGmMRAIAHLJafWSxFv96o9WAOgAAKC5yzRh0IgEADuXJpDwZsACBAXUAAFBc5JoxvLsLDQAAAABwCSORAACHLFZjFg+wWA1oDAAAxUSuGYNOJADAIYtB944YUQcAAMVFrhnDu989AAAAAMAljEQCAByyyCSLAYsHGFEHAADFRa4Zg04kAMChPKtJeQbcO2JEHQAAFBe5ZgymswIAAAAAnMZIJADAIRYgAAB4EnLNGN797gEAAAAALmEkEgDgkEUmY56n5eULEAAA3AO5Zgw6kQAAh6wGrWJn9fKwBQC4B3LNGExnBQAAAAA4jZFIAIBDFqtB0368fCl0AIB7INeMQScSAOAQq9gBADwJuWYM7373AAAAAACXMBIJAHCIaT8AAE9CrhmDTiQAwCGLQavYeftS6AAA90CuGYPprAAAAAAApzESCQBwiGk/AABPQq4Zg04kAMAhwhYA4EnINWMwnRUAAAAA4DRGIgEADnHFFgDgScg1YzASCQAAAABwGiORAACHuGILAPAk5Jox6EQCAByyyphnYVmL3xQAAIqNXDMG01kBAAAAAE5jJBIA4BDTfgAAnoRcMwadSACAQ4QtAMCTkGvGYDorAAAAAMBpjEQCABziii0AwJOQa8agEwkAcIiwBQB4EnLNGExnBQAAAAA4jZFIAIBDVqtJVgOuthpRBwAAxUWuGYORSAAAAACA0xiJBAA4ZJFJFhlw74gBdQAAUFzkmjHoRAIAHGIBAgCAJyHXjEEn0gB169bVyJEjNXLkyLJuils6f7qC3nj2Zu34r1k5v/goom6OxsxLV1TzXyRJn30UonXLb9KRvZX0049+euWTQ/pTk1/s6hjXq4H2pAbZ7bv34fN64rnvC50v+6KvhnVuqPOn/fXegb0KCsmTJH29PUhPPtCgUPl/7t6nqjWuGfV2UQb+8sh5PTDsrKpWv6bj31TUK0/X1KHdlcq6WUC5Ra5dH7mGkkauwd2V6T2RjzzyiEwmk2bPnm23f/Xq1TKZSq53n5ycrNDQ0BKrH7/6KdNXo7vfIl8/q55567he33xQj00+ZQtASbrys49ubXNZg/7v1HXruifxvP65e59tG/x00eVfGFNb9RpfcVjPG1sP2NUTWo2gLc/uuO9HPTbllN5+IVzDE6J0/JtAPbviuEJuulrWTfMIBQsQGLF5A3LN85FrKGnkWski14xR5iORgYGBeu655/T//t//U5UqVcq6OXZyc3Pl7+9f1s0o11a9XEPVInI1dv5J277w2rl2ZeIf+FGSlHHy+p91QEXrH15ZXbPsJl3O9lXiqAzt2GQuskxotWt2YY/yredj57V+RVV9srKqJGnB+Fpqc3e2Evpd1KqFYWXcuvKPaT+uI9c8G7mGkkaulSxyzRhlvjprfHy8wsPDNWvWrOuWe++993TrrbcqICBAdevW1dy5c69b/uuvv1anTp0UHBwss9msVq1aaefOndq8ebMGDhyorKwsmUwmmUwmTZ06VVL+9J0ZM2aof//+MpvNeuyxx27o3EuWLFFoaKhSUlIkSfv27dM999yjoKAghYWF6eGHH9b58+ed/ITKt88/CVFU85/1zGN11bvprXq8c5Q+ervqDdX13/er6MFbm+ixTg315sybdeVn+3+83x0O0Ip54Rr34ncyXecn+/HODdUv5lZN6PMn7f+y8g21Be7Br4JFtzT7WV9tDbbts1pN2rU1WNGtfi7DlsGbkWuejVxDSSLXUF6UeSfS19dXM2fO1EsvvaTvvy98H4AkpaWlqXfv3urbt6/27t2rqVOnatKkSUpOTnZYb2JiomrVqqUdO3YoLS1NEyZMUIUKFXT77bdr/vz5MpvNOn36tE6fPq2xY8faXvf3v/9dzZs3165duzRp0iSXzz1nzhxNmDBBn3zyie6++25lZmbqrrvuUosWLbRz506tX79eZ86cUe/evYt8fU5OjrKzs+228ux0ur/WLq+miHo5mrniuP484IIWTaqljatcuzrf6f4f9eTC7zTnX0fVd8RZpbxXRXNG1LEdz80xadbjdTV40inVqFX0dI+qNa7qb8+d1KQlJ/T06ydUPSJX4x5ooCN7KhbrPaLsmKvmyddPyjxnP6nix/N+qlKd6VxGYNqP68g1e+Ra0cg1FIVcK3nkmjHKfDqrJN1///2KiYnRlClT9MYbbxQ6/sILL+juu+/WpEmTJElRUVH65ptv9Pzzz+uRRx4pss709HSNGzdOjRo1kiTdcssttmMhISEymUwKDw8v9Lq77rpLY8aMsX2dmJjo9LnHjx+vf/zjH9qyZYtuvfVWSdLChQvVokULzZw501buzTffVGRkpA4fPqyoqCi7OmbNmqVp06Y5+qjKHatFuqXZL3p04mlJUoOmv+jbg4Fa949q6tz7R6frufevF2z/Xa/xFVWtcVXjezfQqW/9FVE3V0tn3azaDa7o7l6O64xskKPIBjm2r2+97Wed/i5AH7xeXU++lH4D7w7wfFaDpv14W9iSa78i14pGrgFlg1wzRpmPRBZ47rnntGzZMh04cKDQsQMHDqhdu3Z2+9q1a6cjR44oL6/oewBGjx6twYMHKz4+XrNnz9axY8ecakfr1q1v6Nxz587V66+/rs8++8wWtFL+9KP//ve/CgoKsm0FfwAU1aaJEycqKyvLtp08ebJQmfKkao1rqhNlvxhA5C1XdPaHCsWqt1HL/Ckdp74NkCTt/ixYW9eG6p7I5ronsrkm9P6TJOnBJk20/PnCf1QVaBjzs60OlD/ZF32Vd00K/d3V2SrVrunHc25xjQxejFzLR645h1yDRK6h/HCbTmTHjh2VkJCgiRMnGlLf1KlTtX//fnXr1k2bNm1SdHS0Pvjggz98XeXKN3YvQYcOHZSXl6dVq1bZ7b906ZL+8pe/aPfu3XbbkSNH1LFjx0L1BAQEyGw2223lWfRtl3XymH2Y/XA8QDVqFm+FsWP78qfqVK2RX8+kJSe06D+HtGhj/jby7/l/pMz94IjuG+j4Pp1j+yva6kD5c+2qj47sqaQW7X+y7TOZrIppf0nfpLEUuhGskqxWA7ayfiNlgFzLR645h1yDRK6VBnLNGG51SWP27NmKiYlRw4YN7fY3btxY27Zts9u3bds2RUVFydfX12F9UVFRioqK0qhRo9SvXz8tXbpU999/v/z9/R1e6f09Z8/dpk0bJSUlqWvXrvLz87Pdj9KyZUu99957qlu3rvz83OrjLhU9HzurUfdF6Z8LaqjjXzJ1aFclffTWTRr5/K/3CWX/6KtzP/jrwpn8z6cgnKvUuKqqNa7p1Lf++u8HVdTm7mwFV8nTiW8C9erUmmra9pLqR+dfDY6oa78yXtbF/Lpq35JjW7Hu/derKzwyR3UaXtHVHB99vOImfb0tSDP/6dzVfLin91+rprHzT+rw15V0aFcl3T/knAIrWfTJOze20AXsWWSSSQasYmdAHeURueZ5yDWUNHKtZJFrxnCbkUhJatq0qRITE7VgwQK7/WPGjFFKSopmzJihw4cPa9myZVq4cKHdwgG/9csvvygpKUmbN2/Wd999p23btmnHjh1q3LixpPzV6i5duqSUlBSdP39eP//seLUrV859++2366OPPtK0adM0f/58SdLw4cN18eJF9evXTzt27NCxY8e0YcMGDRw40OnAL88axvyiyW+c0ObVVfT/7mqkFfPDNXT6D7qr56/3eHz+SYge79JQkx7On6oza1hdPd6lodYtryZJ8qtg1a6twZrY708a3LGRXpteU+3vzdS0Zcddasu1XJNem15TQ+9upLG9Guj4N4GatfKYWnS4ZNwbRqnb8mEVvT4jQv3HZeiVjYf1p1uv6KnEeso8X7ypZXAfs2fPlslksnvw/ZUrVzR8+HDddNNNCgoKUq9evXTmzBm716Wnp6tbt26qVKmSatSooXHjxunaNfspYps3b1bLli0VEBCgBg0aFLm4zMsvv6y6desqMDBQsbGx+vLLL51uO7nmecg1lDRyzfOV51wr4HaXEKdPn66VK1fa7WvZsqVWrVqlyZMna8aMGbr55ps1ffp0h4sP+Pr66sKFC+rfv7/OnDmjatWqqWfPnrYb+2+//XYNHTpUffr00YULFzRlyhTbcui/5+q527dvr3Xr1unee++Vr6+vRowYoW3btmn8+PHq0qWLcnJyVKdOHXXt2lU+Pm7Vhy8xbTtnq21nx6vxdelzUV36XHR4vEbNq/r7+0ddOmfz2y9pw6nddvt6Dz+r3sPPulQPyocPl1bTh0urlXUzPJJRK9DdaB07duzQq6++qmbNmtntHzVqlNatW6d3331XISEhSkpKUs+ePW0jbHl5eerWrZvCw8O1fft2nT59Wv3791eFChVsC8KcOHFC3bp109ChQ/X2228rJSVFgwcP1s0336yEhARJ0sqVKzV69GgtXrxYsbGxmj9/vhISEnTo0CHVqFHDqfdArnkecg0ljVwrOeRa8XNNkkxWq9Xbp/S6tezsbIWEhOjHw/VlDvaOcIbzEiJiyroJcEPXrFe1Wf9WVlbWDd9/VvC7p9m7Y+VbqfiLdOT9nKM9D/7dpTZdunRJLVu21CuvvKJnnnlGMTExmj9/vrKyslS9enWtWLFCDzzwgCTp4MGDaty4sVJTU9W2bVt9/PHH+vOf/6xTp04pLCz/4dyLFy/W+PHjde7cOfn7+2v8+PFat26d9u3bZztn3759lZmZqfXr10uSYmNjddttt2nhwoWSJIvFosjISI0YMUITJkwo9ufijcg1XA+5hqKQa+6Xa/z2BgA4ZPnfUuhGbJIKPS8wJyfH4bmHDx+ubt26KT4+3m5/Wlqarl69are/UaNGql27tlJTUyVJqampatq0qS1oJSkhIUHZ2dnav3+/rczv605ISLDVkZubq7S0NLsyPj4+io+Pt5UBAJQv5JoxuUYnEgDgkCEr2P1vk6TIyEiFhITYtlmzZhV53nfeeUdfffVVkcczMjLk7++v0NBQu/1hYWHKyMiwlflt0BYcLzh2vTLZ2dn65ZdfdP78eeXl5RVZpqAOAED5Qq4Zk2tud08kAMBznTx50m7aT0BA4SlFJ0+e1BNPPKGNGzcqMDCwNJsHAIBLvDXXGIkEADhUsACBEZukQs8LLCps09LSdPbsWbVs2VJ+fn7y8/PTli1btGDBAvn5+SksLEy5ubnKzMy0e92ZM2cUHp7/EPbw8PBCq9oVfP1HZcxmsypWrKhq1arJ19e3yDIFdQAAyhdyzZhcoxMJAHDI6LB1xt133629e/faPci+devWSkxMtP13hQoVlJKSYnvNoUOHlJ6erri4OElSXFyc9u7dq7Nnf125cuPGjTKbzYqOjraV+W0dBWUK6vD391erVq3sylgsFqWkpNjKAADKF3LNmFxjOisAwK0EBwerSZMmdvsqV66sm266ybZ/0KBBGj16tKpWrSqz2awRI0YoLi5Obdu2lSR16dJF0dHRevjhhzVnzhxlZGTo6aef1vDhw21XiYcOHaqFCxfqySef1KOPPqpNmzZp1apVWrdune28o0eP1oABA9S6dWu1adNG8+fP1+XLlzVw4MBS+jQAAOWdJ+YanUgAgEMWq0kmA56nZTGgjt+aN2+efHx81KtXL+Xk5CghIUGvvPKK7bivr6/Wrl2rYcOGKS4uTpUrV9aAAQM0ffp0W5l69epp3bp1GjVqlF588UXVqlVLS5YssT1LS5L69Omjc+fOafLkycrIyFBMTIzWr19faFECAED5QK4Zk2s8J9LN8TwtXA/P00JRjHyeVtTbEwx7ntbhxNnFahM8A7mG6yHXUBRyzf3w2xsAAAAA4DSmswIAHMp/Flbxp+ww5wUA4A7INWMwEgkAAAAAcBojkQAAh1xdxvx69QAAUNbINWPQiQQAOGT932ZEPQAAlDVyzRhMZwUAAAAAOI2RSACAQ0z7AQB4EnLNGHQiAQCOMe8HAOBJyDVDMJ0VAAAAAOA0RiIBAI4ZNO1HXj7tBwDgJsg1Q9CJBAA4lP9QZmPqAQCgrJFrxmA6KwAAAADAaYxEAgAcYhU7AIAnIdeMQScSAOCY1WTMfR9eHrYAADdBrhmC6awAAAAAAKcxEgkAcIgFCAAAnoRcMwYjkQAAAAAApzESCQBwzPq/zYh6AAAoa+SaIehEAgAcYhU7AIAnIdeMwXRWAAAAAIDTGIkEAFyfl0/ZAQB4GHKt2JzqRH744YdOV3jffffdcGMAAO7FU6f9kGsA4J08NddKm1OdyB49ejhVmclkUl5eXnHaAwBAiSPXAAC4cU51Ii0WS0m3AwDgjjx0FTtyDQC8lIfmWmkr1sI6V65cMaodAAC3ZDJwc3/kGgB4Ou/KtZLicicyLy9PM2bMUM2aNRUUFKTjx49LkiZNmqQ33njD8AYCAFCSyDUAAFzjcify2WefVXJysubMmSN/f3/b/iZNmmjJkiWGNg4AUMasBm5uilwDAC/iBblWGlzuRC5fvlyvvfaaEhMT5evra9vfvHlzHTx40NDGAQBQ0sg1AABc4/JzIn/44Qc1aNCg0H6LxaKrV68a0igAgJvwggUIyDUA8CJekGulweWRyOjoaG3durXQ/n/9619q0aKFIY0CALgJq8m4zU2RawDgRbwg10qDyyORkydP1oABA/TDDz/IYrHo/fff16FDh7R8+XKtXbu2JNoIAECJIdcAAHCNyyOR3bt315o1a/Sf//xHlStX1uTJk3XgwAGtWbNGnTt3Lok2AgDKiNVq3OauyDUA8B7ekGulweWRSEnq0KGDNm7caHRbAADuxkvuHSHXAMBLeEmulbQb6kRK0s6dO3XgwAFJ+feTtGrVyrBGAQBQ2sg1AACc43In8vvvv1e/fv20bds2hYaGSpIyMzN1++2365133lGtWrWMbiMAoKwYtXiAGy9AQK4BgBfxglwrDS7fEzl48GBdvXpVBw4c0MWLF3Xx4kUdOHBAFotFgwcPLok2AgDKiMlq3OauyDUA8B7ekGulweWRyC1btmj79u1q2LChbV/Dhg310ksvqUOHDoY2DgCAkkauAQDgGpc7kZGRkUU+fDkvL08RERGGNAoA4Ca8YAECcg0AvIgX5FppcHk66/PPP68RI0Zo586dtn07d+7UE088ob///e+GNg4AUMa84KHM5BoAeBEvyLXS4NRIZJUqVWQy/fpBXb58WbGxsfLzy3/5tWvX5Ofnp0cffVQ9evQokYYCAGAUcg0AgBvnVCdy/vz5JdwMAIBb8tBpP+QaAHgpD8210uZUJ3LAgAEl3Q4AAEoNuQYAwI1zeWGd37py5Ypyc3Pt9pnN5mI1CADgRrzsii25BgAezstyraS4vLDO5cuXlZSUpBo1aqhy5cqqUqWK3QYA8CBWAzc3Ra4BgBfxglwrDS53Ip988klt2rRJixYtUkBAgJYsWaJp06YpIiJCy5cvL4k2AgBQYsg1AABc4/J01jVr1mj58uW68847NXDgQHXo0EENGjRQnTp19PbbbysxMbEk2gkAKAtGLWPuxkuhk2sA4EW8INdKg8sjkRcvXlT9+vUl5d8ncvHiRUlS+/bt9emnnxrbOgBAmTJZjdvcFbkGAN7DG3KtNLjciaxfv75OnDghSWrUqJFWrVolKf9KbmhoqKGNAwCgpJFrAAC4xuVO5MCBA/X1119LkiZMmKCXX35ZgYGBGjVqlMaNG2d4AwEAZcgLFiAg1wDAi3hBrpUGl++JHDVqlO2/4+PjdfDgQaWlpalBgwZq1qyZoY0DAKCkkWsAALimWM+JlKQ6deqoTp06RrQFAIAyR64BAHB9TnUiFyxY4HSFf/vb3264MQAA92KSMYsHuNsaduQaAHgnT8210uZUJ3LevHlOVWYymQjbEnJ/VFP5mSqUdTPgZnzN5rJuAtyQ1ZorZZd1K9wbuVb2yDUUhVxDUcg19+NUJ7Jg1ToAgJfx0OdpkWsA4KU8NNdKm8urswIAvEgZrGK3aNEiNWvWTGazWWazWXFxcfr4449tx69cuaLhw4frpptuUlBQkHr16qUzZ87Y1ZGenq5u3bqpUqVKqlGjhsaNG6dr167Zldm8ebNatmypgIAANWjQQMnJyYXa8vLLL6tu3boKDAxUbGysvvzyS+ffCADA/ZTR6qyelm10IgEAbqVWrVqaPXu20tLStHPnTt11113q3r279u/fLyl/NdU1a9bo3Xff1ZYtW3Tq1Cn17NnT9vq8vDx169ZNubm52r59u5YtW6bk5GRNnjzZVubEiRPq1q2bOnXqpN27d2vkyJEaPHiwNmzYYCuzcuVKjR49WlOmTNFXX32l5s2bKyEhQWfPni29DwMA4BE8LdtMVqvVy59y4t6ys7MVEhKiO9Wde0dQCPeOoCjXrLlKyX5LWVlZMt/gz0jB7546M5+VT2BgsdtkuXJF3/3fUzfcpqpVq+r555/XAw88oOrVq2vFihV64IEHJEkHDx5U48aNlZqaqrZt2+rjjz/Wn//8Z506dUphYWGSpMWLF2v8+PE6d+6c/P39NX78eK1bt0779u2znaNv377KzMzU+vXrJUmxsbG67bbbtHDhwvz3YLEoMjJSI0aM0IQJE4r7kXgtcg3XQ66hKJ6Ya1L5zjZGIgEADpmsxm1Sfoj/dsvJybnu+fPy8vTOO+/o8uXLiouLU1pamq5evar4+HhbmUaNGql27dpKTU2VJKWmpqpp06a2kJWkhIQEZWdn2674pqam2tVRUKagjtzcXKWlpdmV8fHxUXx8vK0MAKD8Ketckzwj2+hEAgBKTWRkpEJCQmzbrFmziiy3d+9eBQUFKSAgQEOHDtUHH3yg6OhoZWRkyN/fX6GhoXblw8LClJGRIUnKyMiwC9mC4wXHrlcmOztbv/zyi86fP6+8vLwiyxTUAQCAs7kmeVa2ObU66+9t3bpVr776qo4dO6Z//etfqlmzpv7xj3+oXr16at++/Y1UCQBwRzeweIDDeiSdPHnSbtpPQEBAkcUbNmyo3bt3KysrS//61780YMAAbdmyxYCGFG3r1q16/vnnJUk//PCDKlWqJEn6+uuvFRcXV2LnBQCUsjLKNan0s60kuTwS+d577ykhIUEVK1bUrl27bEO2WVlZmjlzpuENBACUIYNXsStYla5gcxS2/v7+atCggVq1aqVZs2apefPmevHFFxUeHq7c3FxlZmbalT9z5ozCw8MlSeHh4YVWtCv4uqgyBblmsVgk5U/tqVatmnx8fPT66687PA8AoBwqo1yTSjfbflvGbDarYsWKqlatmnx9fYss42q2udyJfOaZZ7R48WK9/vrrqlDh1xvi27Vrp6+++srV6gAA+EMWi0U5OTlq1aqVKlSooJSUFNuxQ4cOKT093TZiGBcXp71799qtNLdx40aZzWZFR0fbyhTUUZBrNWvWlK+vr6T8oI+OjtaBAwfs2pCSksLIJADAECWZbb8tU1CHv7+/WrVqZVfmRrPN5emshw4dUseOHQvtDwkJKdR7BgCUb79dPKC49Thr4sSJuueee1S7dm399NNPWrFihTZv3qwNGzYoJCREgwYN0ujRo1W1alWZzWaNGDFCcXFxatu2rSSpS5cuio6O1sMPP6w5c+YoIyNDTz/9tIYPH267Qjx06FAtXLhQTz75pA4cOKDvvvtOq1atsrs4OmTIED3xxBNatmyZ2rRpo/nz5+vy5csaOHBg8T8QAECZKItck0o/2x599FFt2rRJq1at0rp162ztGD16tAYMGKDWrVsXK9tc7kSGh4fr6NGjqlu3rt3+zz77TPXr13e1OgCAO7Oa8jcj6nHS2bNn1b9/f50+fVohISFq1qyZNmzYoM6dO0uS5s2bJx8fH/Xq1Us5OTlKSEjQK6+8Ynu9r6+v1q5dq2HDhikuLk6VK1fWgAEDNH36dFuZevXqad26dRo1apRycnK0aNEiLVmyRCNGjLCVCQ0NVVhYmCZPnqyMjAzFxMRo/fr1hRYkAACUI2WQa1LpZ9uLL76oWrVqacmSJUpISLCV6dOnj86dO1fsbHP5OZGzZs3SW2+9pTfffFOdO3fWRx99pO+++06jRo3SpEmT7AIYxcfztHA9PE8LRTHyeVr1ps007HlaJ6b8X7HaVFLItdJFruF6yDUUhVxzPy6PRE6YMEEWi0V33323fv75Z3Xs2FEBAQEaO3YsQQsAnsbgVezcEbkGAF7EC3KtNLjciTSZTHrqqac0btw4HT16VJcuXVJ0dLSCgoJKon0AAJQocg0AANfc0HMipV9XrgMAeK6yWoCgLJBrAOD5vCnXSpLLnchOnTrJZHJ8I+mmTZuK1SAAgBvxgmk/5BoAeBEvyLXS4HInMiYmxu7rq1evavfu3dq3b58GDBhgVLsAACgV5BoAAK5xuRM5b968IvdPnTpVly5dKnaDAABuxKBpP+58xZZcAwAv4gW5Vhp8jKror3/9q958802jqgMAuAOrgVs5Q64BgAfy4lwzkmGdyNTUVAUa8MwVAADcAbkGAEDRXJ7O2rNnT7uvrVarTp8+rZ07d2rSpEmGNQwA4Aa8YAECcg0AvIgX5FppcLkTGRISYve1j4+PGjZsqOnTp6tLly6GNQwAUPa8YSl0cg0AvIc35FppcKkTmZeXp4EDB6pp06aqUqVKSbUJAIBSQa4BAOA6l+6J9PX1VZcuXZSZmVlCzQEAoPSQawAAuM7lhXWaNGmi48ePl0RbAAAodeQaAACucbkT+cwzz2js2LFau3atTp8+rezsbLsNAOBBvGApdHINALyIF+RaaXD6nsjp06drzJgxuvfeeyVJ9913n0wmk+241WqVyWRSXl6e8a0EAJQJT16AgFwDAO/jyblWmpzuRE6bNk1Dhw7Vf//735JsDwAApYJcAwDgxjjdibRa87vbd9xxR4k1BgDghjz0aiu5BgBeykNzrTS59IiP307zAQB4AQ9/KDO5BgBexsNzrbS41ImMior6w8C9ePFisRoEAEBpIdcAAHCdS53IadOmKSQkpKTaAgBwM56+AAG5BgDexdNzrbS41Ins27evatSoUVJtAQC4Gw+f9kOuAYCX8fBcKy1OPyeS+0YAAJ6EXAMA4Ma4vDorAMB7ePK0H3INALyPJ+daaXK6E2mxWEqyHQAAd+TB037INQDwQh6ca6XJ6emsAAAAAAC4tLAOAMDLcMUWAOBJyDVDMBIJAAAAAHAaI5EAAIdYgAAA4EnINWPQiQQAOMa0HwCAJyHXDMF0VgAAAACA0xiJBAA4xhVbAIAnIdcMQScSAOAQ944AADwJuWYMprMCAAAAAJzGSCQAwDGm/QAAPAm5Zgg6kQAAh5j2AwDwJOSaMZjOCgAAAABwGiORAADHmPYDAPAk5JohGIkEAAAAADiNkUgAgGNcsQUAeBJyzRB0IgEADpn+txlRDwAAZY1cMwbTWQEAAAAATmMkEgDgGNN+AACehFwzBJ1IAIBDPE8LAOBJyDVjMJ0VAAAAAOA0RiIBAI4x7QcA4EnINUPQiQQAXJ+XByUAwMOQa8XGdFYAAAAAgNMYiQQAOMQCBAAAT0KuGYNOJADAMe4dAQB4EnLNEHQiUW785ZHzemDYWVWtfk3Hv6moV56uqUO7K5V1s2CQ3o+d1O2dz6tW/V+Ue8VHB3aZ9ebcuvrhRFHfY6umv7ZfrTv+qBnDGys1pZrtyP976piiW2ar7i2XlX6skkbc39LhOW+u/Yte+mCXLHlS7za3l8C7AgDHyDXPRq7Bk3FPZDFNnTpVMTExZd0Mj3fHfT/qsSmn9PYL4RqeEKXj3wTq2RXHFXLT1bJuGgzS5LYsrV0RodF9muupR5vI18+iZ5fsU0DFvEJleww4Jet1rgBufC9Mn35U/brn8/WzaPzcg9q/01zcpnu0gmk/RmwoH8i10kGueT5yzT2Ra8bwyE7kuXPnNGzYMNWuXVsBAQEKDw9XQkKCtm3bJkkymUxavXp12TYSLun52HmtX1FVn6ysqvQjgVowvpZyfjEpod/Fsm4aDDJ5SBP954MwpR+trBOHgvTCxCjVqJmjW269ZFeufqNL6jnwe81/KqrIel599k9auyJCGd8HXvd8/Z/4Tt8fr6St668fyoA7INc8D7nm+cg1eDKPnM7aq1cv5ebmatmyZapfv77OnDmjlJQUXbhwwek6cnNz5e/vX4KthLP8Klh0S7Of9c7CGrZ9VqtJu7YGK7rVz2XYMpSkysH5V2p/yvr111RAYJ6e/PtBvTK9gX48f+P/PpvHZqp91/NK6tFC7bo4/3vBK3HviFsg1zwLueadyDU3Qa4ZwuNGIjMzM7V161Y999xz6tSpk+rUqaM2bdpo4sSJuu+++1S3bl1J0v333y+TyWT7umD6zpIlS1SvXj0FBuZf7UlPT1f37t0VFBQks9ms3r1768yZMw7Pf+zYMdWvX19JSUmyWq3KycnR2LFjVbNmTVWuXFmxsbHavHlzCX8KnsVcNU++flLmOftrHj+e91OV6tfKqFUoSSaTVf/v/45rf5pZ3x2pbNs/ZOJxHdhl1uebbrrhuoNDr2rUrMOaNzFKv1z2yOtohmLaT9kj1zwPueZ9yDX3Qa4Zw+M6kUFBQQoKCtLq1auVk5NT6PiOHTskSUuXLtXp06dtX0vS0aNH9d577+n999/X7t27ZbFY1L17d128eFFbtmzRxo0bdfz4cfXp06fIc+/Zs0ft27fXQw89pIULF8pkMikpKUmpqal65513tGfPHj344IPq2rWrjhw5UmQdOTk5ys7OttsAb/P45KOqc8tlzR7dyLYvttMFNY/N1Kuz/lSsuv82/Yg2r62ufTtDittMlJBZs2bptttuU3BwsGrUqKEePXro0KFDdmWuXLmi4cOH66abblJQUJB69epVqCOUnp6ubt26qVKlSqpRo4bGjRuna9fs/0DfvHmzWrZsqYCAADVo0EDJycmF2vPyyy+rbt26CgwMVGxsrL788kvD3/P1kGtA+UeueTdPzDWP60T6+fkpOTlZy5YtU2hoqNq1a6f/+7//0549eyRJ1avnzxMPDQ1VeHi47Wspf6rP8uXL1aJFCzVr1kwpKSnau3evVqxYoVatWik2NlbLly/Xli1b7EJakrZv364777xTY8eO1TPPPCMp/xu9dOlSvfvuu+rQoYP+9Kc/aezYsWrfvr2WLl1aZPtnzZqlkJAQ2xYZGVkSH1O5kn3RV3nXpNDfXZ2tUu2afjzHFTdPM2zSUbW586Im9G+mC2cCbPubt83UzbWv6N0vt2vNvq1as2+rJOn/FhzQ7OV7nK6/edtM9Xr0e1sdTzxzWEHmPK3Zt1Wde2YY/n7KPauBm5O2bNmi4cOH6/PPP9fGjRt19epVdenSRZcvX7aVGTVqlNasWaN3331XW7Zs0alTp9SzZ0/b8by8PHXr1k25ubnavn27li1bpuTkZE2ePNlW5sSJE+rWrZs6deqk3bt3a+TIkRo8eLA2bNhgK7Ny5UqNHj1aU6ZM0VdffaXmzZsrISFBZ8+edeVTLBZyzfOQa96FXHMz5JohueaRv6l69eqlbt26aevWrfr888/18ccfa86cOVqyZIkeeeQRh6+rU6eOXfgeOHBAkZGRdoEXHR2t0NBQHThwQLfddpuk/FDt3Lmznn32WY0cOdJWdu/evcrLy1NUlP2N0jk5ObrppqKnLUycOFGjR4+2fZ2dne31gXvtqo+O7KmkFu1/Uur6/KtsJpNVMe0v6cPkG5/+AXdj1bBJxxQXf0ET+jfTmR/sFxB49/VIbfhXuN2+RWu+0uuz6+sLF6YBjekbIx/fX3/zt73rgh4c8r3G9GuuC2e4X6yQMrh3ZP369XZfJycnq0aNGkpLS1PHjh2VlZWlN954QytWrNBdd90lKX8UrnHjxvr888/Vtm1bffLJJ/rmm2/0n//8R2FhYYqJidGMGTM0fvx4TZ06Vf7+/lq8eLHq1aunuXPnSpIaN26szz77TPPmzVNCQoIk6YUXXtCQIUM0cOBASdLixYu1bt06vfnmm5owYYIBH4xzyDXPQq55C3LNLZFrhuSaR3YiJSkwMFCdO3dW586dNWnSJA0ePFhTpky5bthWrlzZ4bHrqV69uiIiIvTPf/5Tjz76qMzm/KWVL126JF9fX6WlpcnX19fuNUFBQUXWFRAQoICAgCKPebP3X6umsfNP6vDXlXRoVyXdP+ScAitZ9Mk7Vcu6aTDI45OP6c4/n9X04dH65bKvqlTLlSRd/slXuTm++vG8f5GLDpw7FWAXzDfX/kUVK+WpSrVcBQRaVL9R/ip46ccq6dpVH508bv98rluaXJLFIrt7VFByfj+V0ZnfeVlZWZKkqlXz/72npaXp6tWrio+Pt5Vp1KiRateurdTUVLVt21apqalq2rSpwsLCbGUSEhI0bNgw7d+/Xy1atFBqaqpdHQVlCjpNubm5SktL08SJE23HfXx8FB8fr9TUVNfffDGRa56FXPN85Jp38NZc89hO5O9FR0fblj+vUKGC8vIKP6Pn9xo3bqyTJ0/q5MmTtqum33zzjTIzMxUdHW0rV7FiRa1du1b33nuvEhIS9Mknnyg4OFgtWrRQXl6ezp49qw4dOpTI+/IWWz6sopCb8tR/XIaqVL+m4/sr6qnEeso8X6GsmwaD/Pmh05KkOf/Ya7f/hYlR+s8HYUW9pEhPPHNEzdpk2b5euHqXJOmRu2/T2R+uvzw6CjNq8YCCOn4/AjVlyhRNnTrV4essFotGjhypdu3aqUmTJpKkjIwM+fv7KzQ01K5sWFiYMjIybGV+G7QFxwuOXa9Mdna2fvnlF/3444/Ky8srsszBgwf/+E2XMHKtfCPXPB+55p7INWNyzeM6kRcuXNCDDz6oRx99VM2aNVNwcLB27typOXPmqHv37pKkunXrKiUlRe3atVNAQICqVKlSZF3x8fFq2rSpEhMTNX/+fF27dk2PP/647rjjDrVu3dqubOXKlbVu3Trdc889uueee7R+/XpFRUUpMTFR/fv319y5c9WiRQudO3dOKSkpatasmbp161bin4cn+XBpNX24tFpZNwMl5N5Grv9BWtRrJvRv5lId//kgzKUw9zoGT/s5efKkbVRL0h9erR0+fLj27dunzz77zIBGlE/kmuci1zwbueamyDVDeNzCOkFBQYqNjdW8efPUsWNHNWnSRJMmTdKQIUO0cOFCSdLcuXO1ceNGRUZGqkWLFg7rMplM+ve//60qVaqoY8eOio+PV/369bVy5UqH5/74449ltVrVrVs3Xb58WUuXLlX//v01ZswYNWzYUD169NCOHTtUu3btEnn/AODOzGaz3Xa9sE1KStLatWv13//+V7Vq1bLtDw8PV25urjIzM+3KnzlzRuHh4bYyv1/VruDrPypjNptVsWJFVatWTb6+vkWWKaijNJBrAOC+vDXXTFar1cufcuLesrOzFRISojvVXX4mprjAnu9vrnwBBa5Zc5WS/ZaysrLsro66ouB3T8zDz8rXv/jTpfJyr2j3P55yqk1Wq1UjRozQBx98oM2bN+uWW26xO56VlaXq1avrn//8p3r16iVJOnTokBo1amS7d+Tjjz/Wn//8Z50+fVo1auQ/0P21117TuHHjdPbsWQUEBGj8+PH66KOPtHfvr1PNHnroIV28eNG2CEJsbKzatGmjl156SVL+NKTatWsrKSmpVBfW8STkGq6HXENRyDX3yzWPm84KACjfhg8frhUrVujf//63goODbfd6hISEqGLFigoJCdGgQYM0evRoVa1aVWazWSNGjFBcXJzatm0rSerSpYuio6P18MMPa86cOcrIyNDTTz+t4cOH264SDx06VAsXLtSTTz6pRx99VJs2bdKqVau0bt06W1tGjx6tAQMGqHXr1mrTpo3mz5+vy5cv21a1AwDgj3hirtGJBAA4VgZLoS9atEiSdOedd9rtX7p0qW0l0nnz5snHx0e9evVSTk6OEhIS9Morr9jK+vr6au3atRo2bJji4uJUuXJlDRgwQNOnT7eVqVevntatW6dRo0bpxRdfVK1atbRkyRLbMuiS1KdPH507d06TJ09WRkaGYmJitH79+kKLEgAAyglyzZBcYzqrm2PaD66HaT8oipHTflokGjftZ9fbzk37gWcj13A95BqKQq65H49bWAcAAAAAUHKYzgoAcKwMpv0AAFBiyDVD0IkEADhk9EOZAQAoS+SaMZjOCgAAAABwGiORAADHmPYDAPAk5Joh6EQCABxi2g8AwJOQa8ZgOisAAAAAwGmMRAIAHGPaDwDAk5BrhqATCQC4Lm+fsgMA8CzkWvExnRUAAAAA4DRGIgEAjlmt+ZsR9QAAUNbINUMwEgkAAAAAcBojkQAAh1gKHQDgScg1Y9CJBAA4xip2AABPQq4ZgumsAAAAAACnMRIJAHDIZMnfjKgHAICyRq4Zg04kAMAxpv0AADwJuWYIprMCAAAAAJzGSCQAwCFWsQMAeBJyzRh0IgEAjvFQZgCAJyHXDMF0VgAAAACA0xiJBAA4xLQfAIAnIdeMwUgkAAAAAMBpjEQCABxjKXQAgCch1wxBJxIA4BDTfgAAnoRcMwbTWQEAAAAATmMkEgDgGEuhAwA8CblmCDqRAACHmPYDAPAk5JoxmM4KAAAAAHAaI5EAAMdYxQ4A4EnINUPQiQQAOMS0HwCAJyHXjMF0VgAAAACA0xiJBAA4ZrHmb0bUAwBAWSPXDEEnEgDgGPeOAAA8CblmCKazAgAAAACcxkgkAMAhkwxagKD4VQAAUGzkmjEYiQQAAAAAOI2RSACAY1Zr/mZEPQAAlDVyzRB0IgEADvE8LQCAJyHXjMF0VgAAAACA0xiJBAA4xlLoAABPQq4Zgk4kAMAhk9UqkwH3fRhRBwAAxUWuGYPprAAAAAAApzESCQBwzPK/zYh6AAAoa+SaIehEAgAcYtoPAMCTkGvGYDorAAAAAMBpjEQCABxjFTsAgCch1wzBSCQAAAAAwGmMRAIAHLNa8zcj6gEAoKyRa4agEwkAcMhkzd+MqAcAgLJGrhmD6awAAAAAAKcxEgkAcIxpPwAAT0KuGYJOJADAIZMlfzOiHgAAyhq5ZgymswIAAAAAnEYnEgDgWMG0HyM2F3z66af6y1/+ooiICJlMJq1evfp3zbJq8uTJuvnmm1WxYkXFx8fryJEjdmUuXryoxMREmc1mhYaGatCgQbp06ZJdmT179qhDhw4KDAxUZGSk5syZU6gt7777rho1aqTAwEA1bdpUH330kUvvBQDgRsg1Q3KNTiQAwDGrgZsLLl++rObNm+vll18u8vicOXO0YMECLV68WF988YUqV66shIQEXblyxVYmMTFR+/fv18aNG7V27Vp9+umneuyxx2zHs7Oz1aVLF9WpU0dpaWl6/vnnNXXqVL322mu2Mtu3b1e/fv00aNAg7dq1Sz169FCPHj20b98+194QAMA9kGuG5JrJavXyu0LdXFZWlkJDQ9Ve98pPFcq6OXAzvubgsm4C3NA1a662/LRKmZmZCgkJuaE6srOzFRISojtve0p+foHFb9O1K9q841llZWXJbDa79FqTyaQPPvhAPXr0kJR/tTYiIkJjxozR2LFjJeX/rgwLC1NycrL69u2rAwcOKDo6Wjt27FDr1q0lSevXr9e9996r77//XhEREVq0aJGeeuopZWRkyN/fX5I0YcIErV69WgcPHpQk9enTR5cvX9batWtt7Wnbtq1iYmK0ePHi4n4sXolcw/WQaygKueZ+ucbCOm7up59+kiR9JqZPoQjZZd0AuLOffvrphsO2gMlqlcmAa40FdWRn2//QBgQEKCAgwKW6Tpw4oYyMDMXHx9v2hYSEKDY2Vqmpqerbt69SU1MVGhpqC1pJio+Pl4+Pj7744gvdf//9Sk1NVceOHW1BK0kJCQl67rnn9OOPP6pKlSpKTU3V6NGj7c6fkJBQaBoSnEeu4brINVwHueY+uUYn0s1FRETo5MmTCg4OlslkKuvmlLns7GxFRkbq5MmTLl/1gefi58Ke1WrVTz/9pIiICCMqM3Qp9MjISLvdU6ZM0dSpU12qKiMjQ5IUFhZmtz8sLMx2LCMjQzVq1LA77ufnp6pVq9qVqVevXqE6Co5VqVJFGRkZ1z0PXEeu2eP3F4rCz4U9cs39co1OpJvz8fFRrVq1yroZbsdsNvNLFYXwc/Gr4l6pLSm//4PI1au1KP/ItaLx+wtF4efiV+Sae2FhHQCAY1ZJFgO2/130LfiDqGC7kbANDw+XJJ05c8Zu/5kzZ2zHwsPDdfbsWbvj165d08WLF+3KFFXHb8/hqEzBcQBAOUOuGZJrdCIBAOVKvXr1FB4erpSUFNu+7OxsffHFF4qLi5MkxcXFKTMzU2lpabYymzZtksViUWxsrK3Mp59+qqtXr9rKbNy4UQ0bNlSVKlVsZX57noIyBecBAKC4ymOu0YlEuRIQEKApU6Z4zVQBOIefi5JTsACBEZsrLl26pN27d2v37t2S8hcd2L17t9LT02UymTRy5Eg988wz+vDDD7V37171799fERERtpXuGjdurK5du2rIkCH68ssvtW3bNiUlJalv3762e2oeeugh+fv7a9CgQdq/f79WrlypF1980W7BgSeeeELr16/X3LlzdfDgQU2dOlU7d+5UUlKSIZ8vwO8vFIWfi5JDrhmTazziAwBQSMFS6HfFTJCfb/H/iLmWl6NNu2c7vRT65s2b1alTp0L7BwwYoOTkZFmtVk2ZMkWvvfaaMjMz1b59e73yyiuKioqylb148aKSkpK0Zs0a+fj4qFevXlqwYIGCgoJsZfbs2aPhw4drx44dqlatmkaMGKHx48fbnfPdd9/V008/rW+//Va33HKL5syZo3vvvbcYnwYAoLSRa78yItfoRAIACinrsAUAwEjkmrFYnRUA4JjBS6EDAFCmyDVD0IkEADhmkWTEo/wsBtQBAEBxkWuGYGEdAAAAAIDT6ETCa9WtW1fz588v62agFEydOlUxMTFl3YxyqaxWsQPgOnLNe5BrN45cMwadSLjskUcekclk0uzZs+32r169WiaTEfMDipacnKzQ0NASqx+l69y5cxo2bJhq166tgIAAhYeHKyEhQdu2bZMkmUwmrV69umwbiV/vHTFiA9wUuQYjkGvlBLlmCDqRuCGBgYF67rnn9OOPP5Z1UwrJzc0t6ybACb169dKuXbu0bNkyHT58WB9++KHuvPNOXbhwwek6+F4DMAq5huIi1+BN6ETihsTHxys8PFyzZs26brn33ntPt956qwICAlS3bl3NnTv3uuW//vprderUScHBwTKbzWrVqpV27typzZs3a+DAgcrKypLJZJLJZNLUqVMl5U/fmTFjhvr37y+z2azHHnvshs69ZMkShYaGKiUlRZK0b98+3XPPPQoKClJYWJgefvhhnT9/3slPCNeTmZmprVu36rnnnlOnTp1Up04dtWnTRhMnTtR9992nunXrSpLuv/9+mUwm29cF03eWLFmievXqKTAwUJKUnp6u7t27KygoSGazWb1799aZM2ccnv/YsWOqX7++kpKSZLValZOTo7Fjx6pmzZqqXLmyYmNjtXnz5hL+FMoJrtjCS5BrKA5yrRwh1wxBJxI3xNfXVzNnztRLL72k77//vsgyaWlp6t27t/r27au9e/dq6tSpmjRpkpKTkx3Wm5iYqFq1amnHjh1KS0vThAkTVKFCBd1+++2aP3++zGazTp8+rdOnT2vs2LG21/39739X8+bNtWvXLk2aNMnlc8+ZM0cTJkzQJ598orvvvluZmZm666671KJFC+3cuVPr16/XmTNn1Lt37+J8bPifoKAgBQUFafXq1crJySl0fMeOHZKkpUuX6vTp07avJeno0aN677339P7772v37t2yWCzq3r27Ll68qC1btmjjxo06fvy4+vTpU+S59+zZo/bt2+uhhx7SwoULZTKZlJSUpNTUVL3zzjvas2ePHnzwQXXt2lVHjhwpmQ8AgNsh11Ac5Bq8DY/4wA27//77FRMToylTpuiNN94odPyFF17Q3XffrUmTJkmSoqKi9M033+j555/XI488UmSd6enpGjdunBo1aiRJuuWWW2zHQkJCZDKZFB4eXuh1d911l8aMGWP7OjEx0elzjx8/Xv/4xz+0ZcsW3XrrrZKkhQsXqkWLFpo5c6at3JtvvqnIyEgdPnxYUVFRTnxCcMTPz0/JyckaMmSIFi9erJYtW+qOO+5Q37591axZM1WvXl2SFBoaWuj7nZubq+XLl9vKbNy4UXv37tWJEycUGRkpSVq+fLluvfVW7dixQ7fddpvttdu3b9ef//xnPfXUU7afl/T0dC1dulTp6emKiIiQJI0dO1br16/X0qVL7X4GvBLP04IXIddwo8i1coRcMwQjkSiW5557TsuWLdOBAwcKHTtw4IDatWtnt69du3Y6cuSI8vLyiqxv9OjRGjx4sOLj4zV79mwdO3bMqXa0bt36hs49d+5cvf766/rss89sQSvlTz/673//a7uyGBQUZPsDwNk24fp69eqlU6dO6cMPP1TXrl21efNmtWzZ8rpX9CWpTp06tqCV8r/XkZGRtqCVpOjoaIWGhtr9XKanp6tz586aPHmy3R9me/fuVV5enqKiouy+31u2bOF7LeU/B8uoDSgHyDXcKHKtnCDXDEEnEsXSsWNHJSQkaOLEiYbUN3XqVO3fv1/dunXTpk2bFB0drQ8++OAPX1e5cuUbOl+HDh2Ul5enVatW2e2/dOmS/vKXv2j37t1225EjR9SxY8cbOhcKCwwMVOfOnTVp0iRt375djzzyiKZMmXLd19zo97p69epq06aN/vnPfyo7O9u2/9KlS/L19VVaWprd9/rAgQN68cUXb+hcAMovcg3FQa7BWzCdFcU2e/ZsxcTEqGHDhnb7GzdubFvWusC2bdsUFRUlX19fh/VFRUUpKipKo0aNUr9+/bR06VLdf//98vf3d3il9/ecPXebNm2UlJSkrl27ys/Pz3Y/SsuWLfXee++pbt268vPjn0lpiY6Oti1/XqFCBae+340bN9bJkyd18uRJ21Xbb775RpmZmYqOjraVq1ixotauXat7771XCQkJ+uSTTxQcHKwWLVooLy9PZ8+eVYcOHUrkfZVnRj0Ly9ufp4XyhVyDUcg190OuGYORSBRb06ZNlZiYqAULFtjtHzNmjFJSUjRjxgwdPnxYy5Yt08KFC+0WDvitX375RUlJSdq8ebO+++47bdu2TTt27FDjxo0l5a9Wd+nSJaWkpOj8+fP6+eefHbbJlXPffvvt+uijjzRt2jTbQ5qHDx+uixcvql+/ftqxY4eOHTumDRs2aODAgU4HPhy7cOGC7rrrLr311lvas2ePTpw4oXfffVdz5sxR9+7dJeV/v1NSUpSRkXHdJffj4+NtP4NfffWVvvzyS/Xv31933HFHoelglStX1rp16+Tn56d77rlHly5dUlRUlBITE9W/f3+9//77OnHihL788kvNmjVL69atK9HPoVxgFTt4IXINriLXyhFyzRB0ImGI6dOny2KxnxzesmVLrVq1Su+8846aNGmiyZMna/r06Q4XH/D19dWFCxfUv39/RUVFqXfv3rrnnns0bdo0SfmhOHToUPXp00fVq1fXnDlzHLbH1XO3b99e69at09NPP62XXnpJERER2rZtm/Ly8tSlSxc1bdpUI0eOVGhoqHx8+GdTXEFBQYqNjdW8efPUsWNHNWnSRJMmTdKQIUO0cOFCSfn39WzcuFGRkZFq0aKFw7pMJpP+/e9/q0qVKurYsaPi4+NVv359rVy50uG5P/74Y1mtVnXr1k2XL1/W0qVL1b9/f40ZM0YNGzZUjx49tGPHDtWuXbtE3j8A90euwRXkGryNyWr18m40AKCQ7OxshYSEKP5PI+XnG1Ds+q7l5eg/x+YrKytLZrPZgBYCAOA8cs1YTIoHADjGUugAAE9CrhmC+QsAAAAAAKcxEgkAuA6jFg/w7iu2AAB3Qa4ZgU4kAMAxpv0AADwJuWYIprMCAAAAAJzGSCQAwDGLVYZM2bF49xVbAICbINcMwUgkAAAAAMBpdCKBEvTII4+oR48etq/vvPNOjRw5stTbsXnzZplMJmVmZjosYzKZtHr1aqfrnDp1qmJiYorVrm+//VYmk0m7d+8uVj0oQVaLcRuAco9cuz5yrRwg1wxBJxJe55FHHpHJZJLJZJK/v78aNGig6dOn69q1ayV+7vfff18zZsxwqqwzAQmUuIIFCIzYAJQIcg1wAblmCO6JhFfq2rWrli5dqpycHH300UcaPny4KlSooIkTJxYqm5ubK39/f0POW7VqVUPqAQDgt8g1AKWJkUh4pYCAAIWHh6tOnToaNmyY4uPj9eGHH0r6darOs88+q4iICDVs2FCSdPLkSfXu3VuhoaGqWrWqunfvrm+//dZWZ15enkaPHq3Q0FDddNNNevLJJ2X93VWq30/7ycnJ0fjx4xUZGamAgAA1aNBAb7zxhr799lt16tRJklSlShWZTCY98sgjkiSLxaJZs2apXr16qlixopo3b65//etfduf56KOPFBUVpYoVK6pTp0527XTW+PHjFRUVpUqVKql+/fqaNGmSrl69Wqjcq6++qsjISFWqVEm9e/dWVlaW3fElS5aocePGCgwMVKNGjfTKK6+43BaUIYvVuA1AiSHX/hi5BknkmkEYiQQkVaxYURcuXLB9nZKSIrPZrI0bN0qSrl69qoSEBMXFxWnr1q3y8/PTM888o65du2rPnj3y9/fX3LlzlZycrDfffFONGzfW3Llz9cEHH+iuu+5yeN7+/fsrNTVVCxYsUPPmzXXixAmdP39ekZGReu+999SrVy8dOnRIZrNZFStWlCTNmjVLb731lhYvXqxbbrlFn376qf7617+qevXquuOOO3Ty5En17NlTw4cP12OPPaadO3dqzJgxLn8mwcHBSk5OVkREhPbu3ashQ4YoODhYTz75pK3M0aNHtWrVKq1Zs0bZ2dkaNGiQHn/8cb399tuSpLfffluTJ0/WwoUL1aJFC+3atUtDhgxR5cqVNWDAAJfbhDLA87SAcolcK4xcgyRyzSB0IuHVrFarUlJStGHDBo0YMcK2v3LlylqyZIltus9bb70li8WiJUuWyGQySZKWLl2q0NBQbd68WV26dNH8+fM1ceJE9ezZU5K0ePFibdiwweG5Dx8+rFWrVmnjxo2Kj4+XJNWvX992vGCKUI0aNRQaGiop/wrvzJkz9Z///EdxcXG213z22Wd69dVXdccdd2jRokX605/+pLlz50qSGjZsqL179+q5555z6bN5+umnbf9dt25djR07Vu+8845d2F65ckXLly9XzZo1JUkvvfSSunXrprlz5yo8PFxTpkzR3LlzbZ9JvXr19M033+jVV18lbAGgBJBrjpFrgHHoRMIrrV27VkFBQbp69aosFoseeughTZ061Xa8adOmdveLfP311zp69KiCg4Pt6rly5YqOHTumrKwsnT59WrGxsbZjfn5+at26daGpPwV2794tX19f3XHHHU63++jRo/r555/VuXNnu/25ublq0aKFJOnAgQN27ZBkC2ZXrFy5UgsWLNCxY8d06dIlXbt2TWaz2a5M7dq1bUFbcB6LxaJDhw4pODhYx44d06BBgzRkyBBbmWvXrikkJMTl9qCMWGXQFdviVwHAMXLtj5FrkESuGYROJLxSp06dtGjRIvn7+ysiIkJ+fvb/FCpXrmz39aVLl9SqVSvbdJbfql69+g21oWAajysuXbokSVq3bp1dyEn598MYJTU1VYmJiZo2bZoSEhIUEhKid955x3YV2JW2vv7664XC39fX17C2ooQx7QcoF8i16yPXYEOuGYJOJLxS5cqV1aBBA6fLt2zZUitXrlSNGjUKXbUscPPNN+uLL75Qx44dJeVfmUxLS1PLli2LLN+0aVNZLBZt2bLFNu3ntwquGOfl5dn2RUdHKyAgQOnp6Q6v9DZu3Ni2mEKBzz///I/f5G9s375dderU0VNPPWXb99133xUql56erlOnTikiIsJ2Hh8fHzVs2FBhYWGKiIjQ8ePHlZiY6NL5AQCuIdeuj1wDjMXqrIATEhMTVa1aNXXv3l1bt27ViRMntHnzZv3tb3/T999/L0l64oknNHv2bK1evVoHDx7U448/ft1nYdWtW1cDBgzQo48+qtWrV9vqXLVqlSSpTp06MplMWrt2rc6dO6dLly4pODhYY8eO1ahRo7Rs2TIdO3ZMX331lV566SUtW7ZMkjR06FAdOXJE48aN06FDh7RixQolJye79H5vueUWpaen65133tGxY8e0YMECffDBB4XKBQYGasCAAfr666+1detW/e1vf1Pv3r0VHh4uSZo2bZpmzZqlBQsW6PDhw9q7d6+WLl2qF154waX2oAxZLMZtANwGuUaueS1yzRB0IgEnVKpUSZ9++qlq166tnj17qnHjxho0aJCuXLliu4I7ZswYPfzwwxowYIDi4uIUHBys+++//7r1Llq0SA888IAef/xxNWrUSEOGDNHly5clSTVr1tS0adM0YcIEhYWFKSkpSZI0Y8YMTZo0SbNmzVLjxo3VtWtXrVu3TvXq1ZOUfz/He++9p9WrV6t58+ZavHixZs6c6dL7ve+++zRq1CglJSUpJiZG27dv16RJkwqVa9CggXr27Kl7771XXbp0UbNmzeyWOh88eLCWLFmipUuXqmnTprrjjjuUnJxsaysAoGyQa+QaUBwmq6O7owEAXis7O1shISGKrz5Ifj7Ffyj5NUuu/nPuDWVlZTmcOgcAQEkh14zFPZEAAMdYgAAA4EnINUMwnRUAAAAA4DRGIgEAjlmsMuRhWBbvvmILAHAT5Joh6EQCAByyWi2yWou/Ap0RdQAAUFzkmjGYzgoAAAAAcBojkQAAx6xWY6bsePkCBAAAN0GuGYJOJADAMatB9454edgCANwEuWYIprMCAAAAAJzGSCQAwDGLRTIZsHiAly9AAABwE+SaIehEAgAcY9oPAMCTkGuGYDorAAAAAMBpjEQCAByyWiyyGjDtx9ufpwUAcA/kmjEYiQQAAAAAOI2RSACAY9w7AgDwJOSaIehEAgAcs1glE2ELAPAQ5JohmM4KAAAAAHAaI5EAAMesVklGPE/Lu6/YAgDcBLlmCDqRAACHrBarrAZM+7F6edgCANwDuWYMprMCAAAAAJxGJxIA4JjVYtzmopdffll169ZVYGCgYmNj9eWXX5bAGwQAeBVyzRB0IgEADlktVsM2V6xcuVKjR4/WlClT9NVXX6l58+ZKSEjQ2bNnS+idAgC8AblmDDqRAAC388ILL2jIkCEaOHCgoqOjtXjxYlWqVElvvvlmWTcNAACXeVqu0YkEADhWBtN+cnNzlZaWpvj4eNs+Hx8fxcfHKzU1tSTeJQDAW5BrhmB1VgCAQ9d0VTJgAbpruipJys7OttsfEBCggIAAu33nz59XXl6ewsLC7PaHhYXp4MGDxW8MAMBrkWvGoBMJACjE399f4eHh+izjI8PqDAoKUmRkpN2+KVOmaOrUqYadAwCAopBrxqITCQAoJDAwUCdOnFBubq5hdVqtVplMJrt9v79aK0nVqlWTr6+vzpw5Y7f/zJkzCg8PN6w9AADvQa4Zi04kAKBIgYGBCgwMLPXz+vv7q1WrVkpJSVGPHj0kSRaLRSkpKUpKSir19gAAPAO5Zhw6kQAAtzN69GgNGDBArVu3Vps2bTR//nxdvnxZAwcOLOumAQDgMk/LNTqRAAC306dPH507d06TJ09WRkaGYmJitH79+kKLEgAAUB54Wq6ZrFarAesTAQAAAAC8Ac+JBAAAAAA4jU4kAAAAAMBpdCIBAAAAAE6jEwkAAAAAcBqdSAAAAACA0+hEAgAAAACcRicSAAAAAOA0OpEAAAAAAKfRiQQAAAAAOI1OJAAAAADAaXQiAQAAAABOoxMJAAAAAHDa/wfqnmK3ZC8xDgAAAABJRU5ErkJggg==",
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"text/plain": [
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"<Figure size 1000x400 with 4 Axes>"
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]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"_, ax = plt.subplots(1, 2, figsize=(10, 4), sharex=False, sharey=False\n",
|
||
")\n",
|
||
"\n",
|
||
"for index in range(0, len(optimized_metrics)):\n",
|
||
" c_matrix = optimized_metrics.iloc[index][\"Confusion_matrix\"]\n",
|
||
" disp = ConfusionMatrixDisplay(\n",
|
||
" confusion_matrix=c_matrix, display_labels=[\"No stroke\", \"Stroke\"]\n",
|
||
" ).plot(ax=ax.flat[index])\n",
|
||
"\n",
|
||
"plt.subplots_adjust(top=1, bottom=0, hspace=0.4, wspace=0.3)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Модель идеально классифицировала объекты, которые относятся к \"No stroke\" и \"Stroke\"."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Регрессия"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Бизнес цель 2: \n",
|
||
"Предсказание среднего количества часов сна в день (SleepTime) на основе других факторов."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Формируем выборки"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 43,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"'X_train'"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"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>HeartDisease</th>\n",
|
||
" <th>BMI</th>\n",
|
||
" <th>Smoking</th>\n",
|
||
" <th>AlcoholDrinking</th>\n",
|
||
" <th>Stroke</th>\n",
|
||
" <th>PhysicalHealth</th>\n",
|
||
" <th>MentalHealth</th>\n",
|
||
" <th>DiffWalking</th>\n",
|
||
" <th>Sex</th>\n",
|
||
" <th>AgeCategory</th>\n",
|
||
" <th>Race</th>\n",
|
||
" <th>Diabetic</th>\n",
|
||
" <th>PhysicalActivity</th>\n",
|
||
" <th>GenHealth</th>\n",
|
||
" <th>Asthma</th>\n",
|
||
" <th>KidneyDisease</th>\n",
|
||
" <th>SkinCancer</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>95877</th>\n",
|
||
" <td>No</td>\n",
|
||
" <td>23.33</td>\n",
|
||
" <td>Yes</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>Male</td>\n",
|
||
" <td>75-79</td>\n",
|
||
" <td>White</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>Yes</td>\n",
|
||
" <td>Very good</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>228939</th>\n",
|
||
" <td>Yes</td>\n",
|
||
" <td>27.46</td>\n",
|
||
" <td>Yes</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>Yes</td>\n",
|
||
" <td>30.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>Male</td>\n",
|
||
" <td>55-59</td>\n",
|
||
" <td>White</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>Yes</td>\n",
|
||
" <td>Good</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>260256</th>\n",
|
||
" <td>No</td>\n",
|
||
" <td>32.69</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>Male</td>\n",
|
||
" <td>50-54</td>\n",
|
||
" <td>Hispanic</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>Very good</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>84785</th>\n",
|
||
" <td>No</td>\n",
|
||
" <td>31.32</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>Female</td>\n",
|
||
" <td>25-29</td>\n",
|
||
" <td>White</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>Yes</td>\n",
|
||
" <td>Excellent</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>83845</th>\n",
|
||
" <td>Yes</td>\n",
|
||
" <td>24.63</td>\n",
|
||
" <td>Yes</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>10.0</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>Male</td>\n",
|
||
" <td>80 or older</td>\n",
|
||
" <td>White</td>\n",
|
||
" <td>Yes</td>\n",
|
||
" <td>Yes</td>\n",
|
||
" <td>Good</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>119879</th>\n",
|
||
" <td>No</td>\n",
|
||
" <td>29.65</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>Male</td>\n",
|
||
" <td>60-64</td>\n",
|
||
" <td>White</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>Good</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>259178</th>\n",
|
||
" <td>No</td>\n",
|
||
" <td>42.60</td>\n",
|
||
" <td>Yes</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>5.0</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>Male</td>\n",
|
||
" <td>35-39</td>\n",
|
||
" <td>White</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>Yes</td>\n",
|
||
" <td>Good</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>131932</th>\n",
|
||
" <td>No</td>\n",
|
||
" <td>31.19</td>\n",
|
||
" <td>Yes</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>12.0</td>\n",
|
||
" <td>6.0</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>Male</td>\n",
|
||
" <td>65-69</td>\n",
|
||
" <td>White</td>\n",
|
||
" <td>No, borderline diabetes</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>Very good</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>146867</th>\n",
|
||
" <td>No</td>\n",
|
||
" <td>22.24</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>7.0</td>\n",
|
||
" <td>5.0</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>Female</td>\n",
|
||
" <td>18-24</td>\n",
|
||
" <td>White</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>Yes</td>\n",
|
||
" <td>Excellent</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>121958</th>\n",
|
||
" <td>No</td>\n",
|
||
" <td>36.39</td>\n",
|
||
" <td>Yes</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>Female</td>\n",
|
||
" <td>30-34</td>\n",
|
||
" <td>Black</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>Yes</td>\n",
|
||
" <td>Good</td>\n",
|
||
" <td>Yes</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>255836 rows × 17 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" HeartDisease BMI Smoking AlcoholDrinking Stroke PhysicalHealth \\\n",
|
||
"95877 No 23.33 Yes No No 0.0 \n",
|
||
"228939 Yes 27.46 Yes No Yes 30.0 \n",
|
||
"260256 No 32.69 No No No 2.0 \n",
|
||
"84785 No 31.32 No No No 0.0 \n",
|
||
"83845 Yes 24.63 Yes No No 2.0 \n",
|
||
"... ... ... ... ... ... ... \n",
|
||
"119879 No 29.65 No No No 0.0 \n",
|
||
"259178 No 42.60 Yes No No 0.0 \n",
|
||
"131932 No 31.19 Yes No No 12.0 \n",
|
||
"146867 No 22.24 No No No 7.0 \n",
|
||
"121958 No 36.39 Yes No No 0.0 \n",
|
||
"\n",
|
||
" MentalHealth DiffWalking Sex AgeCategory Race \\\n",
|
||
"95877 0.0 No Male 75-79 White \n",
|
||
"228939 0.0 No Male 55-59 White \n",
|
||
"260256 2.0 No Male 50-54 Hispanic \n",
|
||
"84785 0.0 No Female 25-29 White \n",
|
||
"83845 10.0 No Male 80 or older White \n",
|
||
"... ... ... ... ... ... \n",
|
||
"119879 0.0 No Male 60-64 White \n",
|
||
"259178 5.0 No Male 35-39 White \n",
|
||
"131932 6.0 No Male 65-69 White \n",
|
||
"146867 5.0 No Female 18-24 White \n",
|
||
"121958 0.0 No Female 30-34 Black \n",
|
||
"\n",
|
||
" Diabetic PhysicalActivity GenHealth Asthma \\\n",
|
||
"95877 No Yes Very good No \n",
|
||
"228939 No Yes Good No \n",
|
||
"260256 No No Very good No \n",
|
||
"84785 No Yes Excellent No \n",
|
||
"83845 Yes Yes Good No \n",
|
||
"... ... ... ... ... \n",
|
||
"119879 No No Good No \n",
|
||
"259178 No Yes Good No \n",
|
||
"131932 No, borderline diabetes No Very good No \n",
|
||
"146867 No Yes Excellent No \n",
|
||
"121958 No Yes Good Yes \n",
|
||
"\n",
|
||
" KidneyDisease SkinCancer \n",
|
||
"95877 No No \n",
|
||
"228939 No No \n",
|
||
"260256 No No \n",
|
||
"84785 No No \n",
|
||
"83845 No No \n",
|
||
"... ... ... \n",
|
||
"119879 No No \n",
|
||
"259178 No No \n",
|
||
"131932 No No \n",
|
||
"146867 No No \n",
|
||
"121958 No No \n",
|
||
"\n",
|
||
"[255836 rows x 17 columns]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"'y_train'"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"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>SleepTime</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>95877</th>\n",
|
||
" <td>7.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>228939</th>\n",
|
||
" <td>6.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>260256</th>\n",
|
||
" <td>8.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>84785</th>\n",
|
||
" <td>8.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>83845</th>\n",
|
||
" <td>7.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>119879</th>\n",
|
||
" <td>8.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>259178</th>\n",
|
||
" <td>6.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>131932</th>\n",
|
||
" <td>8.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>146867</th>\n",
|
||
" <td>8.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>121958</th>\n",
|
||
" <td>8.0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>255836 rows × 1 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" SleepTime\n",
|
||
"95877 7.0\n",
|
||
"228939 6.0\n",
|
||
"260256 8.0\n",
|
||
"84785 8.0\n",
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||
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||
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|
||
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||
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|
||
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|
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|
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
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|
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||
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|
||
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|
||
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||
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||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>No</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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||
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||
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|
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|
||
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>No</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
" <td>No</td>\n",
|
||
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|
||
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|
||
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|
||
" <td>No</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>Other</td>\n",
|
||
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|
||
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|
||
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|
||
" <td>Yes</td>\n",
|
||
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|
||
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|
||
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|
||
" <tr>\n",
|
||
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|
||
" <td>No</td>\n",
|
||
" <td>21.11</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>4.0</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>Good</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>Yes</td>\n",
|
||
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|
||
" <tr>\n",
|
||
" <th>94526</th>\n",
|
||
" <td>No</td>\n",
|
||
" <td>23.99</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>Yes</td>\n",
|
||
" <td>Excellent</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
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|
||
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|
||
" <tr>\n",
|
||
" <th>107129</th>\n",
|
||
" <td>No</td>\n",
|
||
" <td>31.87</td>\n",
|
||
" <td>Yes</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>30.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>No</td>\n",
|
||
" <td>Poor</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" <td>No</td>\n",
|
||
" </tr>\n",
|
||
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|
||
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|
||
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|
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|
||
],
|
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|
||
" HeartDisease BMI Smoking AlcoholDrinking Stroke PhysicalHealth \\\n",
|
||
"271884 No 27.63 Yes No No 0.0 \n",
|
||
"270361 No 21.95 No No No 0.0 \n",
|
||
"219060 No 31.32 Yes No No 0.0 \n",
|
||
"24010 No 40.35 No No No 30.0 \n",
|
||
"181930 No 35.61 Yes No No 30.0 \n",
|
||
"... ... ... ... ... ... ... \n",
|
||
"181387 No 28.06 Yes No No 0.0 \n",
|
||
"13791 No 29.68 Yes No No 7.0 \n",
|
||
"180164 No 21.11 No No No 4.0 \n",
|
||
"94526 No 23.99 No No No 0.0 \n",
|
||
"107129 No 31.87 Yes No No 30.0 \n",
|
||
"\n",
|
||
" MentalHealth DiffWalking Sex AgeCategory Race Diabetic \\\n",
|
||
"271884 25.0 No Female 25-29 Hispanic No \n",
|
||
"270361 20.0 No Female 30-34 White No \n",
|
||
"219060 0.0 No Female 40-44 White No \n",
|
||
"24010 0.0 No Female 65-69 White No \n",
|
||
"181930 30.0 Yes Female 60-64 White No \n",
|
||
"... ... ... ... ... ... ... \n",
|
||
"181387 15.0 No Male 80 or older White No \n",
|
||
"13791 25.0 No Male 35-39 Other No \n",
|
||
"180164 0.0 No Female 35-39 White No \n",
|
||
"94526 0.0 No Male 70-74 White No \n",
|
||
"107129 0.0 Yes Male 60-64 White Yes \n",
|
||
"\n",
|
||
" PhysicalActivity GenHealth Asthma KidneyDisease SkinCancer \n",
|
||
"271884 Yes Very good No No No \n",
|
||
"270361 Yes Excellent No No Yes \n",
|
||
"219060 Yes Very good Yes No No \n",
|
||
"24010 No Good No No No \n",
|
||
"181930 No Fair Yes No Yes \n",
|
||
"... ... ... ... ... ... \n",
|
||
"181387 Yes Very good No No Yes \n",
|
||
"13791 No Excellent Yes No No \n",
|
||
"180164 Yes Good No No Yes \n",
|
||
"94526 Yes Excellent No No No \n",
|
||
"107129 No Poor No No No \n",
|
||
"\n",
|
||
"[63959 rows x 17 columns]"
|
||
]
|
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|
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|
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|
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|
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|
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|
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|
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|
||
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|
||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||
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|
||
" <th>219060</th>\n",
|
||
" <td>6.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>24010</th>\n",
|
||
" <td>8.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>181930</th>\n",
|
||
" <td>4.0</td>\n",
|
||
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|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
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|
||
" <tr>\n",
|
||
" <th>181387</th>\n",
|
||
" <td>7.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13791</th>\n",
|
||
" <td>3.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>180164</th>\n",
|
||
" <td>7.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>94526</th>\n",
|
||
" <td>8.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>107129</th>\n",
|
||
" <td>7.0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
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|
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"<p>63959 rows × 1 columns</p>\n",
|
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|
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],
|
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"text/plain": [
|
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" SleepTime\n",
|
||
"271884 7.0\n",
|
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"270361 6.0\n",
|
||
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|
||
"24010 8.0\n",
|
||
"181930 4.0\n",
|
||
"... ...\n",
|
||
"181387 7.0\n",
|
||
"13791 3.0\n",
|
||
"180164 7.0\n",
|
||
"94526 8.0\n",
|
||
"107129 7.0\n",
|
||
"\n",
|
||
"[63959 rows x 1 columns]"
|
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]
|
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},
|
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"metadata": {},
|
||
"output_type": "display_data"
|
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}
|
||
],
|
||
"source": [
|
||
"df = pd.read_csv(\".//static//csv//heart_2020_cleaned.csv\")\n",
|
||
"\n",
|
||
"def split_into_train_test(\n",
|
||
" df_input: DataFrame,\n",
|
||
" target_colname: str,\n",
|
||
" frac_train: float = 0.8,\n",
|
||
" random_state: int = None,\n",
|
||
") -> Tuple[DataFrame, DataFrame, DataFrame, DataFrame]:\n",
|
||
" \n",
|
||
" if not (0 < frac_train < 1):\n",
|
||
" raise ValueError(\"Fraction must be between 0 and 1.\")\n",
|
||
" \n",
|
||
" if target_colname not in df_input.columns:\n",
|
||
" raise ValueError(f\"{target_colname} is not a column in the DataFrame.\")\n",
|
||
" \n",
|
||
" X = df_input.drop(columns=[target_colname])\n",
|
||
" y = df_input[[target_colname]]\n",
|
||
"\n",
|
||
" X_train, X_test, y_train, y_test = train_test_split(\n",
|
||
" X, y,\n",
|
||
" test_size=(1.0 - frac_train),\n",
|
||
" random_state=random_state\n",
|
||
" )\n",
|
||
" return X_train, X_test, y_train, y_test\n",
|
||
"\n",
|
||
"X_train, X_test, y_train, y_test = split_into_train_test(\n",
|
||
" df, \n",
|
||
" target_colname=\"SleepTime\", \n",
|
||
" frac_train=0.8, \n",
|
||
" random_state=42\n",
|
||
")\n",
|
||
"\n",
|
||
"display(\"X_train\", X_train)\n",
|
||
"display(\"y_train\", y_train)\n",
|
||
"\n",
|
||
"display(\"X_test\", X_test)\n",
|
||
"display(\"y_test\", y_test)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Выполним one-hot encoding, чтобы избавиться от категориальных признаков"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 44,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>BMI</th>\n",
|
||
" <th>PhysicalHealth</th>\n",
|
||
" <th>MentalHealth</th>\n",
|
||
" <th>HeartDisease_Yes</th>\n",
|
||
" <th>Smoking_Yes</th>\n",
|
||
" <th>AlcoholDrinking_Yes</th>\n",
|
||
" <th>Stroke_Yes</th>\n",
|
||
" <th>DiffWalking_Yes</th>\n",
|
||
" <th>Sex_Male</th>\n",
|
||
" <th>AgeCategory_25-29</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>Diabetic_Yes</th>\n",
|
||
" <th>Diabetic_Yes (during pregnancy)</th>\n",
|
||
" <th>PhysicalActivity_Yes</th>\n",
|
||
" <th>GenHealth_Fair</th>\n",
|
||
" <th>GenHealth_Good</th>\n",
|
||
" <th>GenHealth_Poor</th>\n",
|
||
" <th>GenHealth_Very good</th>\n",
|
||
" <th>Asthma_Yes</th>\n",
|
||
" <th>KidneyDisease_Yes</th>\n",
|
||
" <th>SkinCancer_Yes</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>95877</th>\n",
|
||
" <td>23.33</td>\n",
|
||
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||
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||
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||
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||
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||
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||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>228939</th>\n",
|
||
" <td>27.46</td>\n",
|
||
" <td>30.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>True</td>\n",
|
||
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||
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||
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||
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||
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|
||
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|
||
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|
||
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|
||
" <td>False</td>\n",
|
||
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|
||
" <td>False</td>\n",
|
||
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|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>260256</th>\n",
|
||
" <td>32.69</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>True</td>\n",
|
||
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|
||
" <td>...</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>84785</th>\n",
|
||
" <td>31.32</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>False</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>True</td>\n",
|
||
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|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>83845</th>\n",
|
||
" <td>24.63</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>10.0</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>True</td>\n",
|
||
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|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
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|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
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|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>119879</th>\n",
|
||
" <td>29.65</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>259178</th>\n",
|
||
" <td>42.60</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>5.0</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>131932</th>\n",
|
||
" <td>31.19</td>\n",
|
||
" <td>12.0</td>\n",
|
||
" <td>6.0</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>146867</th>\n",
|
||
" <td>22.24</td>\n",
|
||
" <td>7.0</td>\n",
|
||
" <td>5.0</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>121958</th>\n",
|
||
" <td>36.39</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>255836 rows × 37 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" BMI PhysicalHealth MentalHealth HeartDisease_Yes Smoking_Yes \\\n",
|
||
"95877 23.33 0.0 0.0 False True \n",
|
||
"228939 27.46 30.0 0.0 True True \n",
|
||
"260256 32.69 2.0 2.0 False False \n",
|
||
"84785 31.32 0.0 0.0 False False \n",
|
||
"83845 24.63 2.0 10.0 True True \n",
|
||
"... ... ... ... ... ... \n",
|
||
"119879 29.65 0.0 0.0 False False \n",
|
||
"259178 42.60 0.0 5.0 False True \n",
|
||
"131932 31.19 12.0 6.0 False True \n",
|
||
"146867 22.24 7.0 5.0 False False \n",
|
||
"121958 36.39 0.0 0.0 False True \n",
|
||
"\n",
|
||
" AlcoholDrinking_Yes Stroke_Yes DiffWalking_Yes Sex_Male \\\n",
|
||
"95877 False False False True \n",
|
||
"228939 False True False True \n",
|
||
"260256 False False False True \n",
|
||
"84785 False False False False \n",
|
||
"83845 False False False True \n",
|
||
"... ... ... ... ... \n",
|
||
"119879 False False False True \n",
|
||
"259178 False False False True \n",
|
||
"131932 False False False True \n",
|
||
"146867 False False False False \n",
|
||
"121958 False False False False \n",
|
||
"\n",
|
||
" AgeCategory_25-29 ... Diabetic_Yes Diabetic_Yes (during pregnancy) \\\n",
|
||
"95877 False ... False False \n",
|
||
"228939 False ... False False \n",
|
||
"260256 False ... False False \n",
|
||
"84785 True ... False False \n",
|
||
"83845 False ... True False \n",
|
||
"... ... ... ... ... \n",
|
||
"119879 False ... False False \n",
|
||
"259178 False ... False False \n",
|
||
"131932 False ... False False \n",
|
||
"146867 False ... False False \n",
|
||
"121958 False ... False False \n",
|
||
"\n",
|
||
" PhysicalActivity_Yes GenHealth_Fair GenHealth_Good GenHealth_Poor \\\n",
|
||
"95877 True False False False \n",
|
||
"228939 True False True False \n",
|
||
"260256 False False False False \n",
|
||
"84785 True False False False \n",
|
||
"83845 True False True False \n",
|
||
"... ... ... ... ... \n",
|
||
"119879 False False True False \n",
|
||
"259178 True False True False \n",
|
||
"131932 False False False False \n",
|
||
"146867 True False False False \n",
|
||
"121958 True False True False \n",
|
||
"\n",
|
||
" GenHealth_Very good Asthma_Yes KidneyDisease_Yes SkinCancer_Yes \n",
|
||
"95877 True False False False \n",
|
||
"228939 False False False False \n",
|
||
"260256 True False False False \n",
|
||
"84785 False False False False \n",
|
||
"83845 False False False False \n",
|
||
"... ... ... ... ... \n",
|
||
"119879 False False False False \n",
|
||
"259178 False False False False \n",
|
||
"131932 True False False False \n",
|
||
"146867 False False False False \n",
|
||
"121958 False True False False \n",
|
||
"\n",
|
||
"[255836 rows x 37 columns]"
|
||
]
|
||
},
|
||
"execution_count": 44,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"cat_features = ['HeartDisease', 'Smoking', 'AlcoholDrinking', 'Stroke',\n",
|
||
" 'DiffWalking', 'Sex', 'AgeCategory',\n",
|
||
" 'Race', 'Diabetic', 'PhysicalActivity', 'GenHealth',\n",
|
||
" 'Asthma', 'KidneyDisease', 'SkinCancer']\n",
|
||
"\n",
|
||
"X_test = pd.get_dummies(X_test, columns=cat_features, drop_first=True)\n",
|
||
"X_train = pd.get_dummies(X_train, columns=cat_features, drop_first=True)\n",
|
||
"\n",
|
||
"X_test\n",
|
||
"X_train"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Определение перечня алгоритмов решения задачи регрессии"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 45,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Model: linear\n",
|
||
"Model: linear_poly\n",
|
||
"Model: linear_interact\n",
|
||
"Model: ridge\n",
|
||
"Model: decision_tree\n",
|
||
"Model: knn\n",
|
||
"Model: random_forest\n",
|
||
"Model: mlp\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import math\n",
|
||
"from sklearn.pipeline import make_pipeline\n",
|
||
"from sklearn.preprocessing import PolynomialFeatures\n",
|
||
"\n",
|
||
"\n",
|
||
"models = {\n",
|
||
" \"linear\": {\"model\": linear_model.LinearRegression(n_jobs=-1)},\n",
|
||
" \"linear_poly\": {\n",
|
||
" \"model\": make_pipeline(\n",
|
||
" PolynomialFeatures(degree=2),\n",
|
||
" linear_model.LinearRegression(fit_intercept=False, n_jobs=-1),\n",
|
||
" )\n",
|
||
" },\n",
|
||
" \"linear_interact\": {\n",
|
||
" \"model\": make_pipeline(\n",
|
||
" PolynomialFeatures(interaction_only=True),\n",
|
||
" linear_model.LinearRegression(fit_intercept=False, n_jobs=-1),\n",
|
||
" )\n",
|
||
" },\n",
|
||
" \"ridge\": {\"model\": linear_model.RidgeCV()},\n",
|
||
" \"decision_tree\": {\n",
|
||
" \"model\": tree.DecisionTreeRegressor(max_depth=7, random_state=random_state)\n",
|
||
" },\n",
|
||
" \"knn\": {\"model\": neighbors.KNeighborsRegressor(n_neighbors=7, n_jobs=-1)},\n",
|
||
" \"random_forest\": {\n",
|
||
" \"model\": ensemble.RandomForestRegressor(\n",
|
||
" max_depth=7, random_state=random_state, n_jobs=-1\n",
|
||
" )\n",
|
||
" },\n",
|
||
" \"mlp\": {\n",
|
||
" \"model\": neural_network.MLPRegressor(\n",
|
||
" activation=\"tanh\",\n",
|
||
" hidden_layer_sizes=(3),\n",
|
||
" max_iter=500,\n",
|
||
" early_stopping=True,\n",
|
||
" random_state=random_state,\n",
|
||
" )\n",
|
||
" },\n",
|
||
"}\n",
|
||
"\n",
|
||
"for model_name in models.keys():\n",
|
||
" print(f\"Model: {model_name}\")\n",
|
||
"\n",
|
||
" fitted_model = models[model_name][\"model\"].fit(\n",
|
||
" X_train.values, y_train.values.ravel()\n",
|
||
" )\n",
|
||
" y_train_pred = fitted_model.predict(X_train.values)\n",
|
||
" y_test_pred = fitted_model.predict(X_test.values)\n",
|
||
" models[model_name][\"fitted\"] = fitted_model\n",
|
||
" models[model_name][\"train_preds\"] = y_train_pred\n",
|
||
" models[model_name][\"preds\"] = y_test_pred\n",
|
||
" models[model_name][\"RMSE_train\"] = math.sqrt(\n",
|
||
" metrics.mean_squared_error(y_train, y_train_pred)\n",
|
||
" )\n",
|
||
" models[model_name][\"RMSE_test\"] = math.sqrt(\n",
|
||
" metrics.mean_squared_error(y_test, y_test_pred)\n",
|
||
" )\n",
|
||
" models[model_name][\"RMAE_test\"] = math.sqrt(\n",
|
||
" metrics.mean_absolute_error(y_test, y_test_pred)\n",
|
||
" )\n",
|
||
" models[model_name][\"R2_test\"] = metrics.r2_score(y_test, y_test_pred)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Выводим результаты оценки"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 46,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<style type=\"text/css\">\n",
|
||
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|
||
" background-color: #90d743;\n",
|
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" color: #000000;\n",
|
||
"}\n",
|
||
"#T_d0fa9_row0_col1, #T_d0fa9_row1_col1, #T_d0fa9_row7_col0 {\n",
|
||
" background-color: #26818e;\n",
|
||
" color: #f1f1f1;\n",
|
||
"}\n",
|
||
"#T_d0fa9_row0_col2, #T_d0fa9_row1_col2 {\n",
|
||
" background-color: #5302a3;\n",
|
||
" color: #f1f1f1;\n",
|
||
"}\n",
|
||
"#T_d0fa9_row0_col3, #T_d0fa9_row1_col3, #T_d0fa9_row7_col2 {\n",
|
||
" background-color: #da5a6a;\n",
|
||
" color: #f1f1f1;\n",
|
||
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|
||
"#T_d0fa9_row2_col0 {\n",
|
||
" background-color: #a2da37;\n",
|
||
" color: #000000;\n",
|
||
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|
||
"#T_d0fa9_row2_col1, #T_d0fa9_row3_col1, #T_d0fa9_row4_col1 {\n",
|
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|
||
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|
||
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|
||
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|
||
" background-color: #5801a4;\n",
|
||
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|
||
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|
||
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|
||
" background-color: #d6556d;\n",
|
||
" color: #f1f1f1;\n",
|
||
"}\n",
|
||
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|
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|
||
" color: #000000;\n",
|
||
"}\n",
|
||
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|
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" background-color: #5601a4;\n",
|
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|
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|
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|
||
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|
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|
||
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|
||
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|
||
" background-color: #24878e;\n",
|
||
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|
||
"}\n",
|
||
"#T_d0fa9_row5_col2, #T_d0fa9_row7_col3 {\n",
|
||
" background-color: #4e02a2;\n",
|
||
" color: #f1f1f1;\n",
|
||
"}\n",
|
||
"#T_d0fa9_row5_col3 {\n",
|
||
" background-color: #d5536f;\n",
|
||
" color: #f1f1f1;\n",
|
||
"}\n",
|
||
"#T_d0fa9_row6_col0, #T_d0fa9_row7_col1 {\n",
|
||
" background-color: #a8db34;\n",
|
||
" color: #000000;\n",
|
||
"}\n",
|
||
"#T_d0fa9_row6_col1 {\n",
|
||
" background-color: #228b8d;\n",
|
||
" color: #f1f1f1;\n",
|
||
"}\n",
|
||
"#T_d0fa9_row6_col3 {\n",
|
||
" background-color: #d14e72;\n",
|
||
" color: #f1f1f1;\n",
|
||
"}\n",
|
||
"</style>\n",
|
||
"<table id=\"T_d0fa9\">\n",
|
||
" <thead>\n",
|
||
" <tr>\n",
|
||
" <th class=\"blank level0\" > </th>\n",
|
||
" <th id=\"T_d0fa9_level0_col0\" class=\"col_heading level0 col0\" >RMSE_train</th>\n",
|
||
" <th id=\"T_d0fa9_level0_col1\" class=\"col_heading level0 col1\" >RMSE_test</th>\n",
|
||
" <th id=\"T_d0fa9_level0_col2\" class=\"col_heading level0 col2\" >RMAE_test</th>\n",
|
||
" <th id=\"T_d0fa9_level0_col3\" class=\"col_heading level0 col3\" >R2_test</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th id=\"T_d0fa9_level0_row0\" class=\"row_heading level0 row0\" >linear_poly</th>\n",
|
||
" <td id=\"T_d0fa9_row0_col0\" class=\"data row0 col0\" >1.397226</td>\n",
|
||
" <td id=\"T_d0fa9_row0_col1\" class=\"data row0 col1\" >1.413139</td>\n",
|
||
" <td id=\"T_d0fa9_row0_col2\" class=\"data row0 col2\" >0.999215</td>\n",
|
||
" <td id=\"T_d0fa9_row0_col3\" class=\"data row0 col3\" >0.042532</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th id=\"T_d0fa9_level0_row1\" class=\"row_heading level0 row1\" >linear_interact</th>\n",
|
||
" <td id=\"T_d0fa9_row1_col0\" class=\"data row1 col0\" >1.397316</td>\n",
|
||
" <td id=\"T_d0fa9_row1_col1\" class=\"data row1 col1\" >1.413193</td>\n",
|
||
" <td id=\"T_d0fa9_row1_col2\" class=\"data row1 col2\" >0.999216</td>\n",
|
||
" <td id=\"T_d0fa9_row1_col3\" class=\"data row1 col3\" >0.042460</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th id=\"T_d0fa9_level0_row2\" class=\"row_heading level0 row2\" >mlp</th>\n",
|
||
" <td id=\"T_d0fa9_row2_col0\" class=\"data row2 col0\" >1.404383</td>\n",
|
||
" <td id=\"T_d0fa9_row2_col1\" class=\"data row2 col1\" >1.416410</td>\n",
|
||
" <td id=\"T_d0fa9_row2_col2\" class=\"data row2 col2\" >1.000126</td>\n",
|
||
" <td id=\"T_d0fa9_row2_col3\" class=\"data row2 col3\" >0.038095</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th id=\"T_d0fa9_level0_row3\" class=\"row_heading level0 row3\" >linear</th>\n",
|
||
" <td id=\"T_d0fa9_row3_col0\" class=\"data row3 col0\" >1.405231</td>\n",
|
||
" <td id=\"T_d0fa9_row3_col1\" class=\"data row3 col1\" >1.416610</td>\n",
|
||
" <td id=\"T_d0fa9_row3_col2\" class=\"data row3 col2\" >0.999855</td>\n",
|
||
" <td id=\"T_d0fa9_row3_col3\" class=\"data row3 col3\" >0.037823</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th id=\"T_d0fa9_level0_row4\" class=\"row_heading level0 row4\" >ridge</th>\n",
|
||
" <td id=\"T_d0fa9_row4_col0\" class=\"data row4 col0\" >1.405231</td>\n",
|
||
" <td id=\"T_d0fa9_row4_col1\" class=\"data row4 col1\" >1.416611</td>\n",
|
||
" <td id=\"T_d0fa9_row4_col2\" class=\"data row4 col2\" >0.999852</td>\n",
|
||
" <td id=\"T_d0fa9_row4_col3\" class=\"data row4 col3\" >0.037821</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th id=\"T_d0fa9_level0_row5\" class=\"row_heading level0 row5\" >random_forest</th>\n",
|
||
" <td id=\"T_d0fa9_row5_col0\" class=\"data row5 col0\" >1.401999</td>\n",
|
||
" <td id=\"T_d0fa9_row5_col1\" class=\"data row5 col1\" >1.418929</td>\n",
|
||
" <td id=\"T_d0fa9_row5_col2\" class=\"data row5 col2\" >0.998045</td>\n",
|
||
" <td id=\"T_d0fa9_row5_col3\" class=\"data row5 col3\" >0.034671</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th id=\"T_d0fa9_level0_row6\" class=\"row_heading level0 row6\" >decision_tree</th>\n",
|
||
" <td id=\"T_d0fa9_row6_col0\" class=\"data row6 col0\" >1.406670</td>\n",
|
||
" <td id=\"T_d0fa9_row6_col1\" class=\"data row6 col1\" >1.422338</td>\n",
|
||
" <td id=\"T_d0fa9_row6_col2\" class=\"data row6 col2\" >0.999876</td>\n",
|
||
" <td id=\"T_d0fa9_row6_col3\" class=\"data row6 col3\" >0.030026</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th id=\"T_d0fa9_level0_row7\" class=\"row_heading level0 row7\" >knn</th>\n",
|
||
" <td id=\"T_d0fa9_row7_col0\" class=\"data row7 col0\" >1.296527</td>\n",
|
||
" <td id=\"T_d0fa9_row7_col1\" class=\"data row7 col1\" >1.507555</td>\n",
|
||
" <td id=\"T_d0fa9_row7_col2\" class=\"data row7 col2\" >1.039156</td>\n",
|
||
" <td id=\"T_d0fa9_row7_col3\" class=\"data row7 col3\" >-0.089685</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n"
|
||
],
|
||
"text/plain": [
|
||
"<pandas.io.formats.style.Styler at 0x1d24abd35f0>"
|
||
]
|
||
},
|
||
"execution_count": 46,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"reg_metrics = pd.DataFrame.from_dict(models, \"index\")[\n",
|
||
" [\"RMSE_train\", \"RMSE_test\", \"RMAE_test\", \"R2_test\"]\n",
|
||
"]\n",
|
||
"reg_metrics.sort_values(by=\"RMSE_test\").style.background_gradient(\n",
|
||
" cmap=\"viridis\", low=1, high=0.3, subset=[\"RMSE_train\", \"RMSE_test\"]\n",
|
||
").background_gradient(cmap=\"plasma\", low=0.3, high=1, subset=[\"RMAE_test\", \"R2_test\"])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Выводим лучшую модель"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 47,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"'linear_poly'"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"best_model = str(reg_metrics.sort_values(by=\"RMSE_test\").iloc[0].name)\n",
|
||
"\n",
|
||
"display(best_model)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Подбираем гиперпараметры методом поиска по сетке"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 48,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Fitting 3 folds for each of 8 candidates, totalling 24 fits\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"c:\\Users\\User\\Desktop\\aim\\aimvenv\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
|
||
" return fit_method(estimator, *args, **kwargs)\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Лучшие параметры: {'max_depth': 10, 'min_samples_split': 10, 'n_estimators': 50}\n",
|
||
"Лучший результат (MSE): 1.9790374490880065\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.ensemble import RandomForestRegressor\n",
|
||
"\n",
|
||
"\n",
|
||
"X = df[['HeartDisease', 'BMI', 'Smoking', 'AlcoholDrinking', 'Stroke',\n",
|
||
" 'PhysicalHealth', 'MentalHealth', 'DiffWalking',\n",
|
||
" 'Diabetic', 'PhysicalActivity', 'GenHealth',\n",
|
||
" 'Asthma', 'KidneyDisease', 'SkinCancer']]\n",
|
||
"y = df['SleepTime'] \n",
|
||
"\n",
|
||
"model = RandomForestRegressor() \n",
|
||
"\n",
|
||
"param_grid = {\n",
|
||
" 'n_estimators': [50, 100], \n",
|
||
" 'max_depth': [10, 20], \n",
|
||
" 'min_samples_split': [5, 10] \n",
|
||
"}\n",
|
||
"\n",
|
||
"grid_search = GridSearchCV(estimator=model, param_grid=param_grid,\n",
|
||
" scoring='neg_mean_squared_error', cv=3, n_jobs=-1, verbose=2)\n",
|
||
"\n",
|
||
"grid_search.fit(X_train, y_train)\n",
|
||
"\n",
|
||
"print(\"Лучшие параметры:\", grid_search.best_params_)\n",
|
||
"print(\"Лучший результат (MSE):\", -grid_search.best_score_)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Обучаем модель с новыми гиперпараметрами и сравниваем новых данных со старыми"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 49,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Fitting 3 folds for each of 8 candidates, totalling 24 fits\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"c:\\Users\\User\\Desktop\\aim\\aimvenv\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
|
||
" return fit_method(estimator, *args, **kwargs)\n",
|
||
"c:\\Users\\User\\Desktop\\aim\\aimvenv\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
|
||
" return fit_method(estimator, *args, **kwargs)\n",
|
||
"c:\\Users\\User\\Desktop\\aim\\aimvenv\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
|
||
" return fit_method(estimator, *args, **kwargs)\n",
|
||
"c:\\Users\\User\\Desktop\\aim\\aimvenv\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
|
||
" return fit_method(estimator, *args, **kwargs)\n",
|
||
"c:\\Users\\User\\Desktop\\aim\\aimvenv\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
|
||
" return fit_method(estimator, *args, **kwargs)\n",
|
||
"c:\\Users\\User\\Desktop\\aim\\aimvenv\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
|
||
" return fit_method(estimator, *args, **kwargs)\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Старые параметры: {'max_depth': 10, 'min_samples_split': 10, 'n_estimators': 100}\n",
|
||
"Лучший результат (MSE) на старых параметрах: 1.9789879323889759\n",
|
||
"\n",
|
||
"Новые параметры: {'max_depth': 10, 'min_samples_split': 5, 'n_estimators': 100}\n",
|
||
"Лучший результат (MSE) на новых параметрах: 1.9835849471109568\n",
|
||
"Среднеквадратическая ошибка (MSE) на тестовых данных: 2.005535804883726\n",
|
||
"Корень среднеквадратичной ошибки (RMSE) на тестовых данных: 1.416169412494044\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Old data\n",
|
||
"\n",
|
||
"old_param_grid = param_grid\n",
|
||
"old_grid_search = grid_search\n",
|
||
"old_grid_search.fit(X_train, y_train)\n",
|
||
"\n",
|
||
"old_best_params = old_grid_search.best_params_\n",
|
||
"old_best_mse = -old_grid_search.best_score_ \n",
|
||
"\n",
|
||
"# New data\n",
|
||
"\n",
|
||
"new_param_grid = {\n",
|
||
" 'n_estimators': [100],\n",
|
||
" 'max_depth': [10],\n",
|
||
" 'min_samples_split': [5]\n",
|
||
" }\n",
|
||
"new_grid_search = GridSearchCV(estimator=RandomForestRegressor(), \n",
|
||
" param_grid=new_param_grid,\n",
|
||
" scoring='neg_mean_squared_error', cv=2)\n",
|
||
"\n",
|
||
"new_grid_search.fit(X_train, y_train)\n",
|
||
"\n",
|
||
"new_best_params = new_grid_search.best_params_\n",
|
||
"new_best_mse = -new_grid_search.best_score_\n",
|
||
"\n",
|
||
"new_best_model = RandomForestRegressor(**new_best_params)\n",
|
||
"new_best_model.fit(X_train, y_train)\n",
|
||
"\n",
|
||
"old_best_model = RandomForestRegressor(**old_best_params)\n",
|
||
"old_best_model.fit(X_train, y_train)\n",
|
||
"\n",
|
||
"y_new_pred = new_best_model.predict(X_test)\n",
|
||
"y_old_pred = old_best_model.predict(X_test)\n",
|
||
"\n",
|
||
"mse = metrics.mean_squared_error(y_test, y_new_pred)\n",
|
||
"rmse = np.sqrt(mse)\n",
|
||
"\n",
|
||
"print(\"Старые параметры:\", old_best_params)\n",
|
||
"print(\"Лучший результат (MSE) на старых параметрах:\", old_best_mse)\n",
|
||
"print(\"\\nНовые параметры:\", new_best_params)\n",
|
||
"print(\"Лучший результат (MSE) на новых параметрах:\", new_best_mse)\n",
|
||
"print(\"Среднеквадратическая ошибка (MSE) на тестовых данных:\", mse)\n",
|
||
"print(\"Корень среднеквадратичной ошибки (RMSE) на тестовых данных:\", rmse)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Визуализация данных"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 54,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1600x800 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure(figsize=(16, 8))\n",
|
||
"plt.scatter(range(len(y_test)), y_test, label=\"Истинные значения\", color=\"black\", alpha=0.5)\n",
|
||
"plt.scatter(range(len(y_test)), y_new_pred, label=\"Предсказанные (новые параметры)\", color=\"blue\", alpha=0.5)\n",
|
||
"plt.scatter(range(len(y_test)), y_old_pred, label=\"Предсказанные (старые параметры)\", color=\"red\", alpha=0.5)\n",
|
||
"plt.xlabel(\"Выборка\")\n",
|
||
"plt.ylabel(\"Значения\")\n",
|
||
"plt.legend()\n",
|
||
"plt.title(\"Сравнение предсказанных и истинных значений\")\n",
|
||
"plt.show()"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "aimvenv",
|
||
"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
|
||
}
|