From 3b528edbf8224a012c91a0d7b936f5da66ead07e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=90=D0=BB=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9A=D1=80?= =?UTF-8?q?=D1=8E=D0=BA=D0=BE=D0=B2?= Date: Sun, 20 Oct 2024 21:33:54 +0400 Subject: [PATCH] lab_2_done --- .gitignore | 4 + Lab_2/lab2.ipynb | 649 +++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 653 insertions(+) create mode 100644 Lab_2/lab2.ipynb diff --git a/.gitignore b/.gitignore index 8c2b884..9953c5f 100644 --- a/.gitignore +++ b/.gitignore @@ -12,3 +12,7 @@ # Built Visual Studio Code Extensions *.vsix +static/csv/diabetes.csv +static/csv/healthcare-dataset-stroke-data.csv +static/csv/heart_2020_cleaned.csv +static/csv/neo_v2.csv diff --git a/Lab_2/lab2.ipynb b/Lab_2/lab2.ipynb new file mode 100644 index 0000000..9096c9f --- /dev/null +++ b/Lab_2/lab2.ipynb @@ -0,0 +1,649 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Лабораторная работа №2\n", + "\n", + "## Общие данные\n", + "\n", + "Типы пропущенных данных:\n", + "\n", + "None - представление пустых данных в Python \n", + "NaN - представление пустых данных в Pandas \n", + "' ' - пустая строка\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 Датасет: NASA - Nearest Earth Objects \n", + "https://www.kaggle.com/datasets/sameepvani/nasa-nearest-earth-objects\n", + "\n", + "Перевод контекста со страницы на Kaggle: \n", + " В космическом пространстве существует бесконечное количество объектов. Некоторые из них находятся ближе, чем мы думаем. Несмотря на то, что мы можем думать, что расстояние в 70 000 км потенциально не может причинить нам вреда, по астрономическим меркам это очень небольшое расстояние, которое может нарушить многие природные явления. Таким образом, эти объекты/астероиды могут оказаться опасными. Следовательно, разумно знать, что нас окружает и что из этого может причинить нам вред. Таким образом, этот набор данных составляет список сертифицированных НАСА астероидов, которые классифицируются как ближайшие к Земле объекты.\n", + "\n", + "- По описаню можно понять, что объектами исследования являютя объекты, которые находятся в близи Земли\n", + "- Атрибуты обьекта: id, name, est_diameter_min, est_diameter_max, relative_velocity, miss_distance, orbiting_body, sentry_object, absolute_magnitude, hazardous\n", + "- В описании говорится о возможной опасности объектов, поэтому можно сделать вывод, что цель данного датасета научится определять опасность околоземных объектов\n", + "\n", + "## 2 Датасет: Indicators of Heart Disease \n", + "https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease\n", + "\n", + "Перевод контекста со страницы на Kaggle: \n", + " По данным Всемирной организации здравоохранения (ВОЗ), инсульт является второй по значимости причиной смертности во всем мире, на его долю приходится примерно 11% от общего числа смертей. Этот набор данных используется для прогнозирования вероятности инсульта у пациента на основе таких входных параметров, как пол, возраст, различные заболевания и статус курильщика. Каждая строка данных содержит соответствующую информацию о пациенте.\n", + "- Из этого описания очевидно что объектами иследования являются реальные пациенты.\n", + "- Атрибуты объектов: id, gender, age, hypertension, heart_disease, ever_married, work_type, Residence_type, avg_glucose_level, bmi, smoking_status, stroke\n", + "- Очевидная цель этого датасета - это научиться определять будет у человека сердечный приступ или нет.\n", + "\n", + "## 3 Датасет: Pima Indians Diabetes Database\n", + "https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database\n", + "\n", + "Перевод контекста со страницы на Kaggle: \n", + " Этот набор данных изначально был получен из Национального института диабета, заболеваний пищеварительной системы и почек. Целью набора данных является диагностическое прогнозирование наличия или отсутствия у пациента диабета на основе определенных диагностических измерений, включенных в набор данных. На выбор этих случаев из более крупной базы данных налагалось несколько ограничений. В частности, все пациенты здесь — женщины в возрасте не менее 21 года индейского происхождения пима.\n", + "- объект иследования - женьщины индейци пима\n", + "- очевидно цель датасета это предсказание диабета.\n", + "- атрибуты: Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age, Outcome" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "колонки: id, name, est_diameter_min, est_diameter_max, relative_velocity, miss_distance, orbiting_body, sentry_object, absolute_magnitude, hazardous\n", + "колонки: id, gender, age, hypertension, heart_disease, ever_married, work_type, Residence_type, avg_glucose_level, bmi, smoking_status, stroke\n", + "колонки: Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age, Outcome\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "neo = pd.read_csv(\"..//static//csv//neo_v2.csv\", sep=\",\")\n", + "healthcare = pd.read_csv(\"..//static//csv//healthcare-dataset-stroke-data.csv\", sep=\",\")\n", + "diabetes = pd.read_csv(\"..//static//csv//diabetes.csv\", sep=\",\")\n", + "\n", + "print('колонки околоземных обьектов: ' + ', '.join(neo.columns))\n", + "print('колонки пациентов: ' + ', '.join(healthcare.columns))\n", + "print('колонки индейцев: ' + ', '.join(diabetes.columns))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Проверим пустые занчения" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Околоземные обьекты\n", + "id 0\n", + "name 0\n", + "est_diameter_min 0\n", + "est_diameter_max 0\n", + "relative_velocity 0\n", + "miss_distance 0\n", + "orbiting_body 0\n", + "sentry_object 0\n", + "absolute_magnitude 0\n", + "hazardous 0\n", + "dtype: int64\n", + "\n", + "id False\n", + "name False\n", + "est_diameter_min False\n", + "est_diameter_max False\n", + "relative_velocity False\n", + "miss_distance False\n", + "orbiting_body False\n", + "sentry_object False\n", + "absolute_magnitude False\n", + "hazardous False\n", + "dtype: bool\n", + "\n", + "Пациенты\n", + "id 0\n", + "gender 0\n", + "age 0\n", + "hypertension 0\n", + "heart_disease 0\n", + "ever_married 0\n", + "work_type 0\n", + "Residence_type 0\n", + "avg_glucose_level 0\n", + "bmi 201\n", + "smoking_status 0\n", + "stroke 0\n", + "dtype: int64\n", + "\n", + "id False\n", + "gender False\n", + "age False\n", + "hypertension False\n", + "heart_disease False\n", + "ever_married False\n", + "work_type False\n", + "Residence_type False\n", + "avg_glucose_level False\n", + "bmi True\n", + "smoking_status False\n", + "stroke False\n", + "dtype: bool\n", + "\n", + "bmi процент пустых значений: %3.93\n", + "\n", + "Индейцы\n", + "Pregnancies 0\n", + "Glucose 0\n", + "BloodPressure 0\n", + "SkinThickness 0\n", + "Insulin 0\n", + "BMI 0\n", + "DiabetesPedigreeFunction 0\n", + "Age 0\n", + "Outcome 0\n", + "dtype: int64\n", + "\n", + "Pregnancies False\n", + "Glucose False\n", + "BloodPressure False\n", + "SkinThickness False\n", + "Insulin False\n", + "BMI False\n", + "DiabetesPedigreeFunction False\n", + "Age False\n", + "Outcome False\n", + "dtype: bool\n", + "\n" + ] + } + ], + "source": [ + "# Околоземные обьекты\n", + "print(\"Околоземные обьекты\")\n", + "# Количество пустых значений признаков\n", + "print(neo.isnull().sum())\n", + "\n", + "print()\n", + "\n", + "# Есть ли пустые значения признаков\n", + "print(neo.isnull().any())\n", + "\n", + "print()\n", + "\n", + "# Процент пустых значений признаков\n", + "for i in neo.columns:\n", + " null_rate = neo[i].isnull().sum() / len(neo) * 100\n", + " if null_rate > 0:\n", + " print(f\"{i} процент пустых значений: %{null_rate:.2f}\\n\")\n", + "\n", + "# Пациенты\n", + "print(\"Пациенты\")\n", + "# Количество пустых значений признаков\n", + "print(healthcare.isnull().sum())\n", + "\n", + "print()\n", + "\n", + "# Есть ли пустые значения признаков\n", + "print(healthcare.isnull().any())\n", + "\n", + "print()\n", + "\n", + "# Процент пустых значений признаков\n", + "for i in healthcare.columns:\n", + " null_rate = healthcare[i].isnull().sum() / len(healthcare) * 100\n", + " if null_rate > 0:\n", + " print(f\"{i} процент пустых значений: %{null_rate:.2f}\\n\")\n", + "\n", + "\n", + "# Индейцы\n", + "print(\"Индейцы\")\n", + "# Количество пустых значений признаков\n", + "print(diabetes.isnull().sum())\n", + "\n", + "print()\n", + "\n", + "# Есть ли пустые значения признаков\n", + "print(diabetes.isnull().any())\n", + "\n", + "print()\n", + "\n", + "# Процент пустых значений признаков\n", + "for i in diabetes.columns:\n", + " null_rate = diabetes[i].isnull().sum() / len(diabetes) * 100\n", + " if null_rate > 0:\n", + " print(f\"{i} процент пустых значений: %{null_rate:.2f}\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "После этого видно, что в атрибуде bmi датасета Indicators of Heart Disease есть пустые значения, заполним значением Unknown\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "healthcare['bmi'] = healthcare['bmi'].fillna('Unknown')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Проверим датасет по числовым данным, для выявления аномальных распределений" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " id est_diameter_min est_diameter_max relative_velocity \\\n", + "count 9.083600e+04 90836.000000 90836.000000 90836.000000 \n", + "mean 1.438288e+07 0.127432 0.284947 48066.918918 \n", + "std 2.087202e+07 0.298511 0.667491 25293.296961 \n", + "min 2.000433e+06 0.000609 0.001362 203.346433 \n", + "25% 3.448110e+06 0.019256 0.043057 28619.020645 \n", + "50% 3.748362e+06 0.048368 0.108153 44190.117890 \n", + "75% 3.884023e+06 0.143402 0.320656 62923.604633 \n", + "max 5.427591e+07 37.892650 84.730541 236990.128088 \n", + "\n", + " miss_distance absolute_magnitude \n", + "count 9.083600e+04 90836.000000 \n", + "mean 3.706655e+07 23.527103 \n", + "std 2.235204e+07 2.894086 \n", + "min 6.745533e+03 9.230000 \n", + "25% 1.721082e+07 21.340000 \n", + "50% 3.784658e+07 23.700000 \n", + "75% 5.654900e+07 25.700000 \n", + "max 7.479865e+07 33.200000 \n", + " id age hypertension heart_disease \\\n", + "count 5110.000000 5110.000000 5110.000000 5110.000000 \n", + "mean 36517.829354 43.226614 0.097456 0.054012 \n", + "std 21161.721625 22.612647 0.296607 0.226063 \n", + "min 67.000000 0.080000 0.000000 0.000000 \n", + "25% 17741.250000 25.000000 0.000000 0.000000 \n", + "50% 36932.000000 45.000000 0.000000 0.000000 \n", + "75% 54682.000000 61.000000 0.000000 0.000000 \n", + "max 72940.000000 82.000000 1.000000 1.000000 \n", + "\n", + " avg_glucose_level bmi stroke \n", + "count 5110.000000 5110.000000 5110.000000 \n", + "mean 106.147677 28.893237 0.048728 \n", + "std 45.283560 7.698018 0.215320 \n", + "min 55.120000 10.300000 0.000000 \n", + "25% 77.245000 23.800000 0.000000 \n", + "50% 91.885000 28.400000 0.000000 \n", + "75% 114.090000 32.800000 0.000000 \n", + "max 271.740000 97.600000 1.000000 \n", + " Pregnancies Glucose BloodPressure SkinThickness Insulin \\\n", + "count 768.000000 768.000000 768.000000 768.000000 768.000000 \n", + "mean 3.845052 120.894531 69.105469 20.536458 79.799479 \n", + "std 3.369578 31.972618 19.355807 15.952218 115.244002 \n", + "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "25% 1.000000 99.000000 62.000000 0.000000 0.000000 \n", + "50% 3.000000 117.000000 72.000000 23.000000 30.500000 \n", + "75% 6.000000 140.250000 80.000000 32.000000 127.250000 \n", + "max 17.000000 199.000000 122.000000 99.000000 846.000000 \n", + "\n", + " BMI DiabetesPedigreeFunction Age Outcome \n", + "count 768.000000 768.000000 768.000000 768.000000 \n", + "mean 31.992578 0.471876 33.240885 0.348958 \n", + "std 7.884160 0.331329 11.760232 0.476951 \n", + "min 0.000000 0.078000 21.000000 0.000000 \n", + "25% 27.300000 0.243750 24.000000 0.000000 \n", + "50% 32.000000 0.372500 29.000000 0.000000 \n", + "75% 36.600000 0.626250 41.000000 1.000000 \n", + "max 67.100000 2.420000 81.000000 1.000000 \n" + ] + } + ], + "source": [ + "print(neo.describe())\n", + "print(healthcare.describe())\n", + "print(diabetes.describe())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Аномальное рапределение будем искать по z-индексую. Z-индекс показывает, насколько далеко значение находится от среднего в стандартных отклонениях. Значения Z-индекса больше 3 или меньше -3 обычно считаются аномальными." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Аномалии в наборе данных Neo:\n", + "В атрибуте 'est_diameter_min' обнаружены аномалии: [1.1982708007, 1.0293308202, 1.5085335612, 1.0246014747, 1.4078454339, 1.5507970872, 1.5224918504, 2.8350124902, 1.4208720673, 1.1818299089, 2.2727673228, 1.8053232555, 1.6693791177, 1.7240703244, 1.1709948272, 1.4809997207, 1.332155667, 1.0878148336, 4.5767266723, 1.2780709882, 1.2093582639, 1.5651464359, 1.1982708007, 2.2727673228, 1.4208720673, 1.332155667, 1.0340819954, 1.1080388213, 4.5767266723, 1.4208720673, 2.2832579402, 2.3689449936, 1.1390819672, 2.8090209395, 1.214940408, 2.0443487103, 1.0581688593, 2.9549829311, 1.5507970872, 1.5224918504, 1.0828167784, 2.6825941712, 1.2839702958, 1.133848361, 3.4084346887, 1.1818299089, 2.091967709, 1.0878148336, 1.6089960451, 1.0679599752, 2.3689449936, 1.9344387205, 1.3018321019, 1.6389095149, 1.6464743776, 1.4208720673, 2.1016237932, 1.7805532918, 3.1956188672, 1.0340819954, 1.5651464359, 1.1872849879, 1.2261821132, 1.1080388213, 4.1740243339, 1.0878148336, 1.7642290811, 1.2721987854, 1.2780709882, 1.85590173, 3.0658787593, 1.9344387205, 1.6617090174, 1.7161489408, 2.9549829311, 1.2663535629, 3.4399725466, 1.4013769717, 1.1818299089, 1.2318419127, 1.3694777373, 1.2958507267, 4.1740243339, 1.8904055193, 1.6848256925, 1.5224918504, 1.4208720673, 1.038855101, 1.128638801, 1.1982708007, 1.5016024791, 1.2547435637, 2.091967709, 1.0878148336, 1.1709948272, 1.3885290704, 1.1496217629, 1.4208720673, 1.3694777373, 1.1818299089, 2.0349557812, 1.128638801, 1.1496217629, 1.8991312347, 1.1709948272, 2.9549829311, 1.4208720673, 1.0878148336, 1.4208720673, 1.2721987854, 1.2780709882, 1.3260349677, 1.5651464359, 1.1602590821, 1.2093582639, 1.7723723926, 2.9549829311, 1.1496217629, 1.5085335612, 1.0581688593, 1.2605351968, 1.3383046182, 1.0828167784, 1.0828167784, 3.4084346887, 2.1905591097, 2.7450798165, 1.5507970872, 1.5224918504, 1.332155667, 1.4208720673, 1.1602590821, 1.4340192346, 2.091967709, 1.2605351968, 1.1182913782, 1.2721987854, 1.2038017674, 1.1872849879, 1.7004151927, 1.6693791177, 1.1982708007, 1.5085335612, 1.3018321019, 1.0246014747, 1.8991312347, 1.0293308202, 1.6313794097, 1.7805532918, 2.4578477463, 1.5224918504, 1.4013769717, 1.0878148336, 1.0293308202, 1.2721987854, 2.6336313095, 1.0293308202, 1.2432400055, 1.2780709882, 1.0246014747, 2.3043846658, 1.1763998937, 1.4340192346, 1.6770846216, 1.6464743776, 3.602093458, 1.3018321019, 1.2261821132, 1.6089960451, 1.2958507267, 2.6825941712, 2.3689449936, 2.3043846658, 2.1406958967, 1.0630531449, 1.0581688593, 1.4606796427, 1.0878148336, 1.4208720673, 1.2898968334, 1.2721987854, 1.0728894545, 2.091967709, 2.9549829311, 1.7723723926, 1.4809997207, 1.4208720673, 1.4809997207, 1.2038017674, 1.4340192346, 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147117.3950382462, 126313.541578967, 131738.4262679008, 131537.8513943586, 127227.216994286, 149843.7442418908, 127874.948421361, 139317.8394963845, 157742.4051444262, 132417.1136575519, 141452.9943771182, 132308.7361709476, 124647.2806434275, 129142.4219427542, 130740.2847594194, 154581.8076981536, 187221.0733344144, 127015.9871583006, 149160.6452428978, 129256.6666398854, 138084.7165128119, 154243.0836316415, 126058.6942420756, 128214.7144037726, 139606.3015661095, 125252.2987777217]\n", + "В атрибуте 'absolute_magnitude' обнаружены аномалии: [13.82, 13.82, 14.77, 14.46, 14.6, 14.02, 14.69, 14.77, 14.44, 14.02, 14.77, 14.77, 14.46, 14.34, 14.77, 14.6, 10.31, 14.7, 14.46, 14.6, 14.77, 12.44, 13.8, 14.13, 13.53, 12.58, 14.4, 14.34, 14.6, 14.81, 32.3, 14.6, 14.35, 33.2, 14.77, 14.44, 14.46, 32.3, 32.56, 14.77, 14.46, 14.04, 32.56, 14.46, 14.77, 14.6, 32.3, 14.27, 9.23, 14.13, 10.31, 14.13, 14.34, 14.77, 14.46, 14.02, 14.82, 14.4, 14.13, 32.95, 14.77, 14.46, 14.6, 32.56, 14.04, 32.56, 14.56, 14.46, 14.4, 32.95, 13.76, 12.76, 32.95, 32.3, 10.31, 14.6, 12.76, 13.53, 14.69, 14.77, 14.6, 13.76, 14.13, 14.77, 14.69, 14.31, 14.74, 14.84, 14.46, 14.77, 14.34, 13.84, 14.6, 12.44, 14.46, 32.95, 32.56, 14.84, 14.77, 14.34, 14.74, 32.95, 14.13, 32.95, 14.6]\n", + "\n", + "Аномалии в наборе данных Healthcare:\n", + "В атрибуте 'hypertension' обнаружены аномалии: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n", + "В атрибуте 'heart_disease' обнаружены аномалии: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n", + "В атрибуте 'avg_glucose_level' обнаружены аномалии: [252.72, 243.58, 259.63, 249.31, 263.32, 271.74, 242.52, 250.89, 247.51, 243.53, 242.3, 243.5, 251.6, 247.69, 250.2, 254.6, 254.63, 246.34, 251.46, 267.76, 246.53, 244.28, 251.99, 253.16, 242.84, 249.29, 242.94, 247.48, 266.59, 243.73, 243.59, 250.8, 255.17, 267.61, 260.85, 248.37, 263.56, 247.97, 248.24, 253.93, 254.95, 247.87, 261.67, 256.74, 244.3, 242.62, 243.52, 267.6, 253.86]\n", + "В атрибуте 'bmi' обнаружены аномалии: [56.6, 54.6, 60.9, 54.7, 64.8, 54.7, 60.2, 71.9, 54.6, 55.7, 55.7, 57.5, 54.2, 52.3, 78.0, 53.4, 55.2, 55.0, 54.8, 52.8, 66.8, 55.1, 55.9, 57.3, 56.0, 57.7, 54.0, 56.1, 97.6, 53.9, 53.8, 52.7, 52.8, 55.7, 53.5, 63.3, 52.8, 61.2, 58.1, 52.7, 53.4, 59.7, 52.5, 52.9, 54.7, 61.6, 53.8, 54.3, 55.0, 57.2, 64.4, 92.0, 55.9, 57.9, 55.7, 57.2, 60.9, 54.1, 56.6]\n", + "В атрибуте 'stroke' обнаружены аномалии: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n", + "\n", + "Аномалии в наборе данных Diabetes:\n", + "В атрибуте 'Pregnancies' обнаружены аномалии: [15, 17, 14, 14]\n", + "В атрибуте 'Glucose' обнаружены аномалии: [0, 0, 0, 0, 0]\n", + "В атрибуте 'BloodPressure' обнаружены аномалии: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n", + "В атрибуте 'SkinThickness' обнаружены аномалии: [99]\n", + "В атрибуте 'Insulin' обнаружены аномалии: [543, 846, 495, 485, 495, 478, 744, 680, 545, 465, 579, 474, 480, 600, 440, 540, 480, 510]\n", + "В атрибуте 'BMI' обнаружены аномалии: [0.0, 0.0, 0.0, 0.0, 0.0, 67.1, 0.0, 0.0, 59.4, 0.0, 0.0, 57.3, 0.0, 0.0]\n", + "В атрибуте 'DiabetesPedigreeFunction' обнаружены аномалии: [2.288, 1.893, 1.781, 2.329, 1.476, 2.137, 1.731, 1.6, 2.42, 1.699, 1.698]\n", + "В атрибуте 'Age' обнаружены аномалии: [69, 72, 81, 70, 69]\n" + ] + } + ], + "source": [ + "from scipy import stats\n", + "# Вычисляем Z-индексы только для числовых столбцов\n", + "neo_zscores = neo.select_dtypes(include=['float64', 'int64']).apply(stats.zscore, nan_policy='omit')\n", + "healthcare_zscores = healthcare.select_dtypes(include=['float64', 'int64']).apply(stats.zscore, nan_policy='omit')\n", + "diabetes_zscores = diabetes.select_dtypes(include=['float64', 'int64']).apply(stats.zscore, nan_policy='omit')\n", + "\n", + "# Устанавливаем порог для поиска аномалий\n", + "threshold = 3\n", + "\n", + "# Функция для нахождения аномалий и вывода сообщения\n", + "def find_anomalies(zscores, df):\n", + " for column in zscores.columns:\n", + " # Проверяем, есть ли аномалии в Z-индексах\n", + " anomalies = df[column][(zscores[column].abs() > threshold)]\n", + " if not anomalies.empty:\n", + " print(f\"В атрибуте '{column}' обнаружены аномалии: {anomalies.tolist()}\")\n", + "\n", + "# Находим аномалии\n", + "try:\n", + " print(\"Аномалии в наборе данных Neo:\")\n", + " find_anomalies(neo_zscores, neo)\n", + "\n", + " print(\"\\nАномалии в наборе данных Healthcare:\")\n", + " find_anomalies(healthcare_zscores, healthcare)\n", + "\n", + " print(\"\\nАномалии в наборе данных Diabetes:\")\n", + " find_anomalies(diabetes_zscores, diabetes)\n", + "\n", + "except Exception as e:\n", + " print(f\"Произошла ошибка: {e}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Теперь выполним 10 пункт, разобьем данные на выборки" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Набор данных Neo:\n", + "Обучающая выборка:\n", + "hazardous\n", + "False 0.902681\n", + "True 0.097319\n", + "Name: proportion, dtype: float64\n", + "\n", + "Контрольная выборка:\n", + "hazardous\n", + "False 0.902686\n", + "True 0.097314\n", + "Name: proportion, dtype: float64\n", + "\n", + "Тестовая выборка:\n", + "hazardous\n", + "False 0.902686\n", + "True 0.097314\n", + "Name: proportion, dtype: float64\n", + "\n", + "Набор данных Healthcare:\n", + "Обучающая выборка:\n", + "stroke\n", + "0 0.951321\n", + "1 0.048679\n", + "Name: proportion, dtype: float64\n", + "\n", + "Контрольная выборка:\n", + "stroke\n", + "0 0.951076\n", + "1 0.048924\n", + "Name: proportion, dtype: float64\n", + "\n", + "Тестовая выборка:\n", + "stroke\n", + "0 0.951076\n", + "1 0.048924\n", + "Name: proportion, dtype: float64\n", + "\n", + "Набор данных Diabetes:\n", + "Обучающая выборка:\n", + "Outcome\n", + "0 0.651466\n", + "1 0.348534\n", + "Name: proportion, dtype: float64\n", + "\n", + "Контрольная выборка:\n", + "Outcome\n", + "0 0.649351\n", + "1 0.350649\n", + "Name: proportion, dtype: float64\n", + "\n", + "Тестовая выборка:\n", + "Outcome\n", + "0 0.649351\n", + "1 0.350649\n", + "Name: proportion, dtype: float64\n", + "Набор данных Neo:\n", + "Аугментация данных не требуется.\n", + "\n", + "Набор данных Healthcare:\n", + "Необходима аугментация данных.\n", + "\n", + "Набор данных Diabetes:\n", + "Аугментация данных не требуется.\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "def split_data(df, target_column, test_size=0.2, random_state=42):\n", + " # Разделяем данные на обучающую и временную выборки\n", + " X_train, X_temp, y_train, y_temp = train_test_split(df.drop(columns=[target_column]), \n", + " df[target_column], \n", + " test_size=test_size, \n", + " random_state=random_state, \n", + " stratify=df[target_column])\n", + " # Делим временную выборку на контрольную и тестовую\n", + " X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, \n", + " test_size=0.5, \n", + " random_state=random_state, \n", + " stratify=y_temp)\n", + " \n", + " return X_train, X_val, X_test, y_train, y_val, y_test\n", + "\n", + "# Для набора данных neo\n", + "neo_train, neo_val, neo_test, neo_train_labels, neo_val_labels, neo_test_labels = split_data(neo, 'hazardous')\n", + "\n", + "# Для набора данных healthcare\n", + "healthcare_train, healthcare_val, healthcare_test, healthcare_train_labels, healthcare_val_labels, healthcare_test_labels = split_data(healthcare, 'stroke')\n", + "\n", + "# Для набора данных diabetes\n", + "diabetes_train, diabetes_val, diabetes_test, diabetes_train_labels, diabetes_val_labels, diabetes_test_labels = split_data(diabetes, 'Outcome')\n", + "def check_balance(y_train, y_val, y_test):\n", + " print(\"Обучающая выборка:\")\n", + " print(y_train.value_counts(normalize=True))\n", + " print(\"\\nКонтрольная выборка:\")\n", + " print(y_val.value_counts(normalize=True))\n", + " print(\"\\nТестовая выборка:\")\n", + " print(y_test.value_counts(normalize=True))\n", + "\n", + "print(\"Набор данных Neo:\")\n", + "check_balance(neo_train_labels, neo_val_labels, neo_test_labels)\n", + "\n", + "print(\"\\nНабор данных Healthcare:\")\n", + "check_balance(healthcare_train_labels, healthcare_val_labels, healthcare_test_labels)\n", + "\n", + "print(\"\\nНабор данных Diabetes:\")\n", + "check_balance(diabetes_train_labels, diabetes_val_labels, diabetes_test_labels)\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Для набота Neo \n", + "- Пропорция классов сильно несбалансирована: только 9.73% объектов относятся к классу True (опасные), а 90.27% — к классу False (неопасные).\n", + "- В данном случае, если модель будет обучаться только на этих данных, она может иметь высокую точность, просто предсказывая, что все объекты неопасные. Это приведет к тому, что модель будет плохо определять опасные объекты.\n", + "\n", + "# Набор данных Healthcare\n", + "- Пропорция классов также сильно несбалансирована: только 4.87% объектов относятся к классу 1 (с инсультом).\n", + "- Как и в предыдущем случае, если модель будет обучаться на этих данных, она может показывать высокую точность, просто предсказывая, что все объекты без инсульта.\n", + "\n", + "# Набор данных Diabetes\n", + "- Здесь классы более сбалансированы, чем в предыдущих примерах, хотя класс 0 все еще составляет 65.15% и класс 1 34.85%.\n", + "- Модель может научиться определять оба класса, но если точность по классу 1 будет низкой, можно рассмотреть методы аугментации.\n", + "\n", + "1. Oversampling (приращение данных): Увеличение числа примеров для меньшинства классов.\n", + "2. Undersampling (уменьшение данных): Уменьшение числа примеров для большинства классов." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Oversampling для Neo:\n", + "hazardous\n", + "False 81996\n", + "True 81996\n", + "Name: count, dtype: int64\n", + "\n", + "Undersampling для Healthcare:\n", + "stroke\n", + "0 249\n", + "1 249\n", + "Name: count, dtype: int64\n", + "\n", + "Oversampling для Diabetes:\n", + "Outcome\n", + "1 500\n", + "0 500\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "from imblearn.over_sampling import RandomOverSampler\n", + "from imblearn.under_sampling import RandomUnderSampler\n", + "\n", + "# Пример Oversampling для Neo\n", + "X_neo = neo.drop('hazardous', axis=1) \n", + "y_neo = neo['hazardous'] \n", + "\n", + "# Oversampling\n", + "ros_neo = RandomOverSampler(random_state=42)\n", + "X_neo_resampled, y_neo_resampled = ros_neo.fit_resample(X_neo, y_neo)\n", + "neo_resampled = pd.DataFrame(X_neo_resampled, columns=X_neo.columns)\n", + "neo_resampled['hazardous'] = y_neo_resampled\n", + "\n", + "print(\"Oversampling для Neo:\")\n", + "print(neo_resampled['hazardous'].value_counts())\n", + "\n", + "\n", + "X_healthcare = healthcare.drop('stroke', axis=1)\n", + "y_healthcare = healthcare['stroke']\n", + "\n", + "# Пример Undersampling для Healthcare\n", + "rus_healthcare = RandomUnderSampler(random_state=42)\n", + "X_healthcare_resampled_under, y_healthcare_resampled_under = rus_healthcare.fit_resample(X_healthcare, y_healthcare)\n", + "healthcare_resampled_under = pd.DataFrame(X_healthcare_resampled_under, columns=X_healthcare.columns)\n", + "healthcare_resampled_under['stroke'] = y_healthcare_resampled_under\n", + "\n", + "print(\"\\nUndersampling для Healthcare:\")\n", + "print(healthcare_resampled_under['stroke'].value_counts())\n", + "\n", + "# Пример Oversampling для Diabetes\n", + "X_diabetes = diabetes.drop('Outcome', axis=1)\n", + "y_diabetes = diabetes['Outcome']\n", + "\n", + "# Oversampling\n", + "ros_diabetes = RandomOverSampler(random_state=42)\n", + "X_diabetes_resampled, y_diabetes_resampled = ros_diabetes.fit_resample(X_diabetes, y_diabetes)\n", + "diabetes_resampled = pd.DataFrame(X_diabetes_resampled, columns=X_diabetes.columns)\n", + "diabetes_resampled['Outcome'] = y_diabetes_resampled\n", + "\n", + "print(\"\\nOversampling для Diabetes:\")\n", + "print(diabetes_resampled['Outcome'].value_counts())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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 +}