From 4ca2448febbd0bb6f815e0fa540bf103f430ee87 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=95=D0=BB=D0=B5=D0=BD=D0=B0=20=D0=91=D0=B0=D0=BA=D0=B0?= =?UTF-8?q?=D0=BB=D1=8C=D1=81=D0=BA=D0=B0=D1=8F?= Date: Wed, 27 Nov 2024 22:27:38 +0400 Subject: [PATCH] =?UTF-8?q?=D1=80=D0=B0=D0=B7=D0=B1=D0=B8=D0=BB=D0=B0=20?= =?UTF-8?q?=D0=BD=D0=B0=20=D1=84=D1=83=D0=BD=D0=BA=D1=86=D0=B8=D0=B8=20?= =?UTF-8?q?=D0=BA=D0=BE=D0=B4,=20=D1=81=D1=82=D0=B0=D0=BB=D0=BE=20=D1=83?= =?UTF-8?q?=D0=B4=D0=BE=D0=B1=D0=BD=D0=B5=D0=B5.=20=D0=BD=D0=B0=D1=87?= =?UTF-8?q?=D0=B0=D0=BB=D0=B0=20=D0=BA=D0=BE=D0=BD=D1=81=D1=82=D1=80=D1=83?= =?UTF-8?q?=D0=B8=D1=80=D0=BE=D0=B2=D0=B0=D0=BD=D0=B8=D0=B5=20=D0=BF=D1=80?= =?UTF-8?q?=D0=B8=D0=B7=D0=BD=D0=B0=D0=BA=D0=BE=D0=B2?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- lab_3/lab3.ipynb | 189 +++++++++++++++++++++++++++++++++-------------- 1 file changed, 134 insertions(+), 55 deletions(-) diff --git a/lab_3/lab3.ipynb b/lab_3/lab3.ipynb index e008f7d..c3eeb23 100644 --- a/lab_3/lab3.ipynb +++ b/lab_3/lab3.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 65, + "execution_count": 96, "metadata": {}, "outputs": [ { @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 100, "metadata": {}, "outputs": [ { @@ -54,24 +54,10 @@ "bmi 201\n", "smoking_status 0\n", "stroke 0\n", - "dtype: int64\n" - ] - } - ], - "source": [ - "print(\"\\nНаличие пропущенных значений:\")\n", - "print(data.isnull().sum())" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ + "dtype: int64\n", + "\n", + "\n", + "\n", "Напишем функцию и сделаем аугментацию данных

" + ] }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 114, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "После OverSampling:\n", + "Данные ДО аугментации в ОБУЧАЮЩЕЙ ВЫБОРКЕ (60-80% данных)\n", + "\n", "stroke\n", - "0 3403\n", - "1 1701\n", + "0 3889\n", + "1 199\n", "Name: count, dtype: int64\n", - "После комбинированного подхода (Over + Under Sampling):\n", + "\n", + "После оверсемплинга\n", + "\n", "stroke\n", - "0 1701\n", - "1 1701\n", + "0 3889\n", + "1 1944\n", + "Name: count, dtype: int64\n", + "\n", + "После балансировки данных (андерсемплинга)\n", + "\n", + "stroke\n", + "0 1944\n", + "1 1944\n", "Name: count, dtype: int64\n" ] }, { "data": { - "image/png": "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", + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Данные ДО аугментации в ТЕСТОВОЙ ВЫБОРКЕ (10-20% данных)\n", + "\n", + "stroke\n", + "0 486\n", + "1 25\n", + "Name: count, dtype: int64\n", + "\n", + "После оверсемплинга\n", + "\n", + "stroke\n", + "0 486\n", + "1 243\n", + "Name: count, dtype: int64\n", + "\n", + "После балансировки данных (андерсемплинга)\n", + "\n", + "stroke\n", + "0 243\n", + "1 243\n", + "Name: count, dtype: int64\n" + ] + }, + { + "data": { + "image/png": "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", "text/plain": [ "
" ] @@ -374,30 +413,70 @@ "from imblearn.over_sampling import RandomOverSampler\n", "from imblearn.under_sampling import RandomUnderSampler\n", "\n", - "# 1. Увеличение меньшинственного класса\n", - "oversampler = RandomOverSampler(sampling_strategy=0.5, random_state=42) # Увеличиваем класс 1 до 50% от класса 0\n", - "X_train_over, y_train_over = oversampler.fit_resample(X_train, y_train)\n", + "def over_under_sampling(x_selection, y_selection):\n", "\n", - "print(\"После OverSampling:\")\n", - "print(y_train_over.value_counts())\n", + " # сначала увеличение меньшинства\n", "\n", - "# 2. Уменьшение большинства\n", - "undersampler = RandomUnderSampler(sampling_strategy=1.0, random_state=42) # Балансируем классы 1:1\n", - "X_train_balanced, y_train_balanced = undersampler.fit_resample(X_train_over, y_train_over)\n", + " oversampler = RandomOverSampler(sampling_strategy=0.5, random_state=42) \n", + " x_over, y_over = oversampler.fit_resample(x_selection, y_selection) \n", "\n", - "print(\"После комбинированного подхода (Over + Under Sampling):\")\n", - "print(y_train_balanced.value_counts())\n", + " print(\"\\nПосле оверсемплинга\\n\")\n", + " print(y_over.value_counts())\n", "\n", - "plt.pie(\n", - " y_train_balanced.value_counts(), \n", + " # потом уменьшение большинства\n", + "\n", + " undersampler = RandomUnderSampler(sampling_strategy=1.0, random_state=42)\n", + " x_balanced, y_balanced = undersampler.fit_resample(x_over, y_over)\n", + "\n", + " print(\"\\nПосле балансировки данных (андерсемплинга)\\n\")\n", + " print(y_balanced.value_counts())\n", + "\n", + " plt.pie(\n", + " y_balanced.value_counts(), \n", " labels=class_counts.index, # Метки классов (0 и 1)\n", " autopct='%1.1f%%', # Отображение процентов\n", - " colors=['skyblue', 'lightcoral'], # Цвета для классов\n", + " colors=['lightgreen', 'lightcoral'], # Цвета для классов\n", " startangle=45, # Поворот диаграммы\n", - " explode=(0, 0.1) # Небольшое смещение для класса 1\n", - ")\n", - "plt.title(\"Распределение классов (stroke)\")\n", - "plt.show()\n" + " explode=(0, 0.05) # Небольшое смещение для класса 1\n", + " )\n", + " plt.title(\"Распределение классов (stroke)\")\n", + " plt.show()\n", + "\n", + "print(\"Данные ДО аугментации в ОБУЧАЮЩЕЙ ВЫБОРКЕ (60-80% данных)\\n\")\n", + "print(y_train.value_counts())\n", + "over_under_sampling(X_train, y_train)\n", + "\n", + "print(\"Данные ДО аугментации в ТЕСТОВОЙ ВЫБОРКЕ (10-20% данных)\\n\")\n", + "print(y_test.value_counts())\n", + "over_under_sampling(X_test, y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Теперь можно и к конструированию признаков приступить) данные ведь сбалансированы (в выборках)

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Унитарное кодирование категориальных признаков

Применяем к категориальным (НЕ числовым) признакам: 'gender', 'ever_married', 'work_type', 'Residence_type', 'smoking_status'

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# One-Hot Encoding\n", + "categorical_columns = ['gender', 'ever_married', 'work_type', 'Residence_type', 'smoking_status']\n", + "X_encoded = pd.get_dummies(X_train, columns=categorical_columns, drop_first=True)\n", + "\n", + "print(\"Данные после унитарного кодирования:\")\n", + "print(X_encoded.head())\n" ] } ],