From 90954dfa898f688e30e7099e4901fe3703bf49e0 Mon Sep 17 00:00:00 2001 From: KirillTepechin Date: Wed, 29 Nov 2023 15:43:19 +0400 Subject: [PATCH] tepechin_kirill_lab1 --- tepechin_kirill_lab_1/READMY.md | 35 +++++++++++++++ tepechin_kirill_lab_1/lab1.py | 74 ++++++++++++++++++++++++++++++++ tepechin_kirill_lab_1/plots.png | Bin 0 -> 70995 bytes 3 files changed, 109 insertions(+) create mode 100644 tepechin_kirill_lab_1/READMY.md create mode 100644 tepechin_kirill_lab_1/lab1.py create mode 100644 tepechin_kirill_lab_1/plots.png diff --git a/tepechin_kirill_lab_1/READMY.md b/tepechin_kirill_lab_1/READMY.md new file mode 100644 index 0000000..e7acc62 --- /dev/null +++ b/tepechin_kirill_lab_1/READMY.md @@ -0,0 +1,35 @@ +## Лабораторная работа №1, ПИбд-42 Тепечин Кирилл, Вариант 26(5) + +### Задание +#### Данные: +make_circles (noise=0.2, factor=0.5, random_state=rs) +#### Модели: +* Линейная регрессия +* Полиномиальная регрессия (со степенью 4) +* Гребневая полиномиальная регрессия (со степенью 4, alpha = 1.0) +### Как запустить лабораторную работу: + +Для запуска лабораторной работы необходимо запустить файл lab1.py + +### Используемые технологии: + +* Python 3.12 +* numpy +* matplotlib +* scikit-learn + +### Что делает лабораторная работа: + +Эта лабораторная работа создает и сравнивает различные модели регрессии для сгенерированного набора данных, оценивая их производительность на основе среднеквадратичной ошибки и визуализируя их предсказания на графиках. + +### Результат: +* *Линейная регрессия*, среднеквадратичная ошибка: 0.29507570583195913 +* *Полиноминальная регрессия*, среднеквадратичная ошибка: 0.1803255642844966 +* *Гребневая полиномиальная регрессия*, среднеквадратичная ошибка: 0.17274375314846807 + +### Вывод +Самое маленькое значение mse у *Гребневая полиномиальная регрессия* + +### График + +![График](plots.png) diff --git a/tepechin_kirill_lab_1/lab1.py b/tepechin_kirill_lab_1/lab1.py new file mode 100644 index 0000000..89a2251 --- /dev/null +++ b/tepechin_kirill_lab_1/lab1.py @@ -0,0 +1,74 @@ +import matplotlib.pyplot as plt +import numpy as np +from matplotlib.colors import ListedColormap +from sklearn.datasets import make_circles +from sklearn.linear_model import LinearRegression +from sklearn.linear_model import Ridge +from sklearn.metrics import mean_squared_error +from sklearn.model_selection import train_test_split +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import PolynomialFeatures, StandardScaler + +rs = 42 + +X, y = make_circles(noise=0.2, factor=0.5, random_state=rs) +X = StandardScaler().fit_transform(X) +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4, random_state=rs) + +# Инициализируем модели +linear_model = LinearRegression() +poly_model = make_pipeline(PolynomialFeatures(4), LinearRegression()) +ridge_model = make_pipeline(PolynomialFeatures(4), Ridge(alpha=1.0)) + +# Обучаем модели +linear_model.fit(X_train, y_train) +poly_model.fit(X_train, y_train) +ridge_model.fit(X_train, y_train) + +# Предсказываем значения для тестового набора +y_pred_linear = linear_model.predict(X_test) +y_pred_poly = poly_model.predict(X_test) +y_pred_ridge = ridge_model.predict(X_test) + +# Качество моделей +mse_linear = mean_squared_error(y_test, y_pred_linear) +mse_poly = mean_squared_error(y_test, y_pred_poly) +mse_ridge = mean_squared_error(y_test, y_pred_ridge) +models_data = [(linear_model, "Линейная", mse_linear), (poly_model, "Полиномиальная", mse_poly), + (ridge_model, "Греб. полиномиальная", mse_ridge)] +# Печатаем MSE +print(f"mse линейная: {mse_linear}") +print(f"mse полиномиальная: {mse_poly}") +print(f"mse гребневая полиномиальная: {mse_ridge}") + +cm = plt.cm.RdBu +cm_bright = ListedColormap(['#FF0000', '#0000FF']) + +fig, axs = plt.subplots(1, 3, figsize=(15, 7)) +fig.suptitle('Сравнение регрессионных моделей') + + +# Функция отрисовки моделей +def draw_plot(model_data, i): + h = .02 # шаг регулярной сетки + x0_min, x0_max = X[:, 0].min() - .5, X[:, 0].max() + .5 + x1_min, x1_max = X[:, 1].min() - .5, X[:, 1].max() + .5 + xx0, xx1 = np.meshgrid(np.arange(x0_min, x0_max, h), np.arange(x1_min, x1_max, h)) + Z = model_data[0].predict(np.c_[xx0.ravel(), xx1.ravel()]) # 3 + + Z = Z.reshape(xx0.shape) + axs[i].contourf(xx0, xx1, Z, cmap=cm, alpha=.8) + axs[i].set_xlim(xx0.min(), xx0.max()) + axs[i].set_ylim(xx0.min(), xx1.max()) + axs[i].set_xticks(()) + axs[i].set_yticks(()) + axs[i].set_title(model_data[1]) + axs[i].text(xx0.max() - .3, xx1.min() + .3, ('%.2f' % model_data[2]).lstrip('0'), + size=15, horizontalalignment='right') + axs[i].scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6) + + +for i, model_data in enumerate(models_data): + draw_plot(model_data, i) +plt.savefig("plots.png") +plt.show() diff --git a/tepechin_kirill_lab_1/plots.png b/tepechin_kirill_lab_1/plots.png new file mode 100644 index 0000000000000000000000000000000000000000..37f2099e61fc30e659fa6863d6a6c7f45454ec2e GIT binary patch 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