From 5e0058b82ebdc55a4178904a0697821322200612 Mon Sep 17 00:00:00 2001 From: acidmikk Date: Sun, 29 Oct 2023 17:07:56 +0400 Subject: [PATCH] basharin_sevastyan_lab_2 is ready --- .gitignore | 1 + basharin_sevastyan_lab_2/README.md | 42 ++++++++++++++++++++++++++ basharin_sevastyan_lab_2/main.py | 46 +++++++++++++++++++++++------ basharin_sevastyan_lab_2/res.png | Bin 0 -> 6134 bytes basharin_sevastyan_lab_2/rfe.png | Bin 0 -> 11475 bytes basharin_sevastyan_lab_2/rfr.png | Bin 0 -> 11407 bytes basharin_sevastyan_lab_2/ridge.png | Bin 0 -> 13923 bytes 7 files changed, 80 insertions(+), 9 deletions(-) create mode 100644 basharin_sevastyan_lab_2/README.md create mode 100644 basharin_sevastyan_lab_2/res.png create mode 100644 basharin_sevastyan_lab_2/rfe.png create mode 100644 basharin_sevastyan_lab_2/rfr.png create mode 100644 basharin_sevastyan_lab_2/ridge.png diff --git a/.gitignore b/.gitignore index 7e86cf0..d13d270 100644 --- a/.gitignore +++ b/.gitignore @@ -138,3 +138,4 @@ dmypy.json # Cython debug symbols cython_debug/ +.idea \ No newline at end of file diff --git a/basharin_sevastyan_lab_2/README.md b/basharin_sevastyan_lab_2/README.md new file mode 100644 index 0000000..9aa5309 --- /dev/null +++ b/basharin_sevastyan_lab_2/README.md @@ -0,0 +1,42 @@ +## Лабораторная работа 2. Вариант 5. +### Задание +Выполнить ранжирование признаков. Отобразить получившиеся значения\оценки каждого признака каждым методом\моделью и среднюю оценку. Провести анализ получившихся результатов. Какие четыре признака оказались самыми важными по среднему значению? + +Модели: + +- Гребневая регрессия `Ridge`, +- Рекурсивное сокращение признаков `Recursive Feature Elimination – RFE`, +- Сокращение признаков Случайными деревьями `Random Forest Regressor` + +### Как запустить +Для запуска программы необходимо с помощью командной строки в корневой директории файлов прокета прописать: +``` +python main.py +``` + +### Используемые технологии +- `numpy` (псевдоним `np`): NumPy - это библиотека для научных вычислений в Python. +- `sklearn` (scikit-learn): Scikit-learn - это библиотека для машинного обучения и анализа данных в Python. Из данной библиотеки были использованы следующие модули: + - `LinearRegression` - линейная регрессия - это алгоритм машинного обучения, используемый для задач бинарной классификации. + - `Ridge` - инструмент работы с моделью "Гребневая регрессия" + - `RFE` - инструмент оценки важности признаков "Рекурсивное сокращение признаков" + - `RandomForestRegressor` - инструмент работы с моделью "Регрессор случайного леса" + +### Описание работы +1. Программа генерирует данные для обучения моделей, содержащие матрицу признаков X и вектор целевой переменной y. +1. Создает DataFrame data, в котором столбцы представляют признаки, а последний столбец - целевую переменную. +1. Разделяет данные на матрицу признаков X и вектор целевой переменной y +1. Создает список обученных моделей для ранжирования признаков: гребневой регрессии, рекурсивного сокращения признаков и сокращения признаков случайными деревьями. +1. Создает словарь model_scores для хранения оценок каждой модели. +1. Выводит оценки признаков каждой модели и их средние оценки. +1. Находит четыре наиболее важных признака по средней оценке и выводит их индексы и значения. + +### Результат работы +![](ridge.png "Гребневая регрессия") +![](rfe.png "Рекурсивное сокращение признаков") +![](rfr.png "Сокращение признаков Случайными деревьями") +![](res.png "Четыре самых важных") + +### Вывод +Четыре наиболее важных признака, определенных на основе средних оценок, включают +Признак 1, Признак 3, Признак 12 и Признак 6. \ No newline at end of file diff --git a/basharin_sevastyan_lab_2/main.py b/basharin_sevastyan_lab_2/main.py index e0f801e..8603b2a 100644 --- a/basharin_sevastyan_lab_2/main.py +++ b/basharin_sevastyan_lab_2/main.py @@ -1,7 +1,10 @@ import numpy as np +import pandas as pd +from sklearn.datasets import make_regression from sklearn.linear_model import Ridge, LinearRegression from sklearn.ensemble import RandomForestRegressor from sklearn.feature_selection import RFE +from sklearn.preprocessing import MinMaxScaler ''' Задание Используя код из [1](пункт «Решение задачи ранжирования признаков», стр. 205), выполните ранжирование признаков с @@ -15,25 +18,50 @@ from sklearn.feature_selection import RFE ''' # создание данных -rs = np.random.RandomState(2) +random_state = np.random.RandomState(2) X, y = make_regression(n_samples=750, n_features=15, noise=0.1, random_state=random_state) data = pd.DataFrame(X, columns=[f'Признак {i}' for i in range(X.shape[1])]) data['Целевая переменная'] = y X = data.drop('Целевая переменная', axis=1) y = data['Целевая переменная'] -ridge = Ridge(alpha=1) # Создаём модель гребневой регрессии и обучаем её -ridge.fit(X, Y) +ridge = Ridge(alpha=1) # Гребневая регрессия +ridge.fit(X, y) -recFE = RFE(LinearRegression(), n_features_to_select=1) # -recFE.fit(X, Y) +recFE = RFE(LinearRegression(), n_features_to_select=1) # Рекурсивное сокращение признаков +recFE.fit(X, y) -rfr = RandomForestRegressor() # Создаём и обучаем регрессор случайного леса -rfr.fit(X, Y) +rfr = RandomForestRegressor() # Сокращение признаков Случайными деревьями +rfr.fit(X, y) -models = [('Гребневая регрессия', ridge), +models = [('Ridge', ridge), ('RFE', recFE), ('RFR', rfr)] +model_scores = [] for name, model in models: - pass + if name == 'Ridge': + coef = model.coef_ + normalized_coef = MinMaxScaler().fit_transform(coef.reshape(-1, 1)) + model_scores.append((name, normalized_coef.flatten())) + elif name == 'RFE': + rankings = model.ranking_ + normalized_rankings = 1 - (rankings - 1) / (np.max(rankings) - 1) + model_scores.append((name, normalized_rankings)) + elif name == 'RFR': + feature_importances = model.feature_importances_ + normalized_importances = MinMaxScaler().fit_transform(feature_importances.reshape(-1, 1)) + model_scores.append((name, normalized_importances.flatten())) + +for name, scores in model_scores: + print(f"{name} оценки признаков:") + for feature, score in enumerate(scores, start=1): + print(f"Признак {feature}: {score:.2f}") + print(f"Средняя оценка: {np.mean(scores):.2f}") + +all_feature_scores = np.mean(list(map(lambda x: x[1], model_scores)), axis=0) +sorted_features = sorted(enumerate(all_feature_scores, start=1), key=lambda x: x[1], reverse=True) +top_features = sorted_features[:4] +print("Четыре наиболее важных признака:") +for feature, score in top_features: + print(f"Признак {feature}: {score:.2f}") diff --git a/basharin_sevastyan_lab_2/res.png b/basharin_sevastyan_lab_2/res.png new file mode 100644 index 0000000000000000000000000000000000000000..1dc7f4ba2cbe25c6863e4202864e71d673c12f99 GIT binary patch literal 6134 zcma)gbyO5wv@as*&>#&%gVLdcq|zcS$j~Vu-3=q%ARCg?Ga}FFjQSoZ2zw?K=U_{)OfX9Gv_LzeH$CW4}yzwDO+q)$11&pEz*e6!XP{!vi13!YotHJLql3f;1<&c%Uk0 z-<34SriZu9tgWd!=wJ1h*XP2?_>}8#9>nqu>gf;l>TTxzW+^^65Lu-}VsC@5TBOIv z_C^}UF_&4Xw>@V&GYRFR8Drn$&A z@yPZG>84G&cw}CP($~3q)m=y6(@O_R;Ns$%qpxoKlqb`j)ar@)39AU%{(6(FCkEa) z6GEy{wiNk!$#7~g`|9mE{bUmkj0a2H04I;KM^JGM$Uc|jmoXnc-R=EGOMgB_fY;a- z8~c-L=3Bn_+}gNnio1}IiBG{3U-;lNfT$mwE0J{`XtuDyzU?YSgFYtSB^Q>C7*rJi z@9KhET^VraOUh^MjbzUlK;*SAZ|Y8MW%3{L>43}Mf2;Of0do?Pt|WLx{s(&QnnT(89ZY$gExC#j(|D;umsxg)e(N zwadfz0XAnwbp*pV{1lbto;FbTr<+}>FUhS3`cu{qc7og}UNyNtdp_+TkUlNR%=GGlkAV5G`Q&F4G{8nsg!U%23OB$U!! 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