75 lines
2.9 KiB
Python
75 lines
2.9 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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from matplotlib.colors import ListedColormap
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from sklearn.datasets import make_circles
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from sklearn.linear_model import LinearRegression
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from sklearn.linear_model import Ridge
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from sklearn.metrics import mean_squared_error
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from sklearn.model_selection import train_test_split
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from sklearn.pipeline import make_pipeline
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from sklearn.preprocessing import PolynomialFeatures, StandardScaler
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rs = 42
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X, y = make_circles(noise=0.2, factor=0.5, random_state=rs)
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X = StandardScaler().fit_transform(X)
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4, random_state=rs)
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# Инициализируем модели
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linear_model = LinearRegression()
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poly_model = make_pipeline(PolynomialFeatures(4), LinearRegression())
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ridge_model = make_pipeline(PolynomialFeatures(4), Ridge(alpha=1.0))
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# Обучаем модели
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linear_model.fit(X_train, y_train)
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poly_model.fit(X_train, y_train)
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ridge_model.fit(X_train, y_train)
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# Предсказываем значения для тестового набора
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y_pred_linear = linear_model.predict(X_test)
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y_pred_poly = poly_model.predict(X_test)
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y_pred_ridge = ridge_model.predict(X_test)
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# Качество моделей
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mse_linear = mean_squared_error(y_test, y_pred_linear)
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mse_poly = mean_squared_error(y_test, y_pred_poly)
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mse_ridge = mean_squared_error(y_test, y_pred_ridge)
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models_data = [(linear_model, "Линейная", mse_linear), (poly_model, "Полиномиальная", mse_poly),
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(ridge_model, "Греб. полиномиальная", mse_ridge)]
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# Печатаем MSE
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print(f"mse линейная: {mse_linear}")
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print(f"mse полиномиальная: {mse_poly}")
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print(f"mse гребневая полиномиальная: {mse_ridge}")
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cm = plt.cm.RdBu
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cm_bright = ListedColormap(['#FF0000', '#0000FF'])
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fig, axs = plt.subplots(1, 3, figsize=(15, 7))
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fig.suptitle('Сравнение регрессионных моделей')
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# Функция отрисовки моделей
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def draw_plot(model_data, i):
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h = .02 # шаг регулярной сетки
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x0_min, x0_max = X[:, 0].min() - .5, X[:, 0].max() + .5
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x1_min, x1_max = X[:, 1].min() - .5, X[:, 1].max() + .5
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xx0, xx1 = np.meshgrid(np.arange(x0_min, x0_max, h), np.arange(x1_min, x1_max, h))
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Z = model_data[0].predict(np.c_[xx0.ravel(), xx1.ravel()]) # 3
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Z = Z.reshape(xx0.shape)
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axs[i].contourf(xx0, xx1, Z, cmap=cm, alpha=.8)
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axs[i].set_xlim(xx0.min(), xx0.max())
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axs[i].set_ylim(xx0.min(), xx1.max())
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axs[i].set_xticks(())
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axs[i].set_yticks(())
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axs[i].set_title(model_data[1])
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axs[i].text(xx0.max() - .3, xx1.min() + .3, ('%.2f' % model_data[2]).lstrip('0'),
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size=15, horizontalalignment='right')
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axs[i].scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)
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for i, model_data in enumerate(models_data):
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draw_plot(model_data, i)
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plt.savefig("plots.png")
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plt.show()
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