98 lines
3.5 KiB
Python
98 lines
3.5 KiB
Python
import numpy as np
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from matplotlib import pyplot as plt
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from skimage.metrics import mean_squared_error
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from sklearn.datasets import make_moons, make_circles, make_classification
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from sklearn.linear_model import LinearRegression, Ridge
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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 StandardScaler, PolynomialFeatures
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X, y = make_classification(
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n_features=2,
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n_redundant=0,
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n_informative=2,
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random_state=0,
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n_clusters_per_class=1
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)
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rng = np.random.RandomState(2)
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X += 2 * rng.uniform(size=X.shape)
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linearly_dataset = (X, y)
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moon_dataset = make_moons(noise=0.3, random_state=0)
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circles_dataset = make_circles(noise=0.2, factor=0.5, random_state=1)
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datasets = [moon_dataset, circles_dataset, linearly_dataset]
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"""
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Данные:
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· moon_dataset
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· circles_dataset
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· linearly_dataset
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"""
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for ds_cnt, ds in enumerate(datasets):
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X, y = ds
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X = StandardScaler().fit_transform(X)
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=.4, random_state=42
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)
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"""
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Модели:
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· Линейную регрессию
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· Полиномиальную регрессию (со степенью 3)
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· Гребневую полиномиальную регрессию (со степенью 3, alpha = 1.0)
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"""
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# Линейная
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linear_regression = LinearRegression()
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linear_regression.fit(X_train, y_train)
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linear_predictions = linear_regression.predict(X_test)
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linear_mse = mean_squared_error(y_test, linear_predictions)
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# Полиномиальная (degree=3)
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poly_regression = make_pipeline(PolynomialFeatures(degree=3), LinearRegression())
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poly_regression.fit(X_train, y_train)
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poly_predictions = poly_regression.predict(X_test)
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poly_mse = mean_squared_error(y_test, poly_predictions)
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# Гребневая (degree=3, alpha=1.0)
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poly_regression_alpha = make_pipeline(PolynomialFeatures(degree=3), Ridge(alpha=1.0))
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poly_regression_alpha.fit(X_train, y_train)
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poly_alpha_predictions = poly_regression_alpha.predict(X_test)
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poly_alpha_mse = mean_squared_error(y_test, poly_alpha_predictions)
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# График данных
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plt.figure(figsize=(10, 6))
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plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap='coolwarm')
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plt.title('Датасет №' + str(ds_cnt))
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plt.xlabel('X')
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plt.ylabel('Y')
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# График линейной модели
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plt.figure(figsize=(10, 6))
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plt.scatter(X_test[:, 0], X_test[:, 1], c=linear_predictions, cmap='coolwarm')
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plt.title('Линейная ds'+ str(ds_cnt))
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plt.xlabel('X')
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plt.ylabel('Y')
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plt.show()
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# График полиномиальной модели (degree=3)
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plt.figure(figsize=(10, 6))
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plt.scatter(X_test[:, 0], X_test[:, 1], c=poly_predictions, cmap='coolwarm')
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plt.title('Полиномиальная (degree=3) ds' + str(ds_cnt))
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plt.xlabel('X')
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plt.ylabel('Y')
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plt.show()
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# График гребневой модели (degree=3, alpha=1.0)
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plt.figure(figsize=(10, 6))
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plt.scatter(X_test[:, 0], X_test[:, 1], c=poly_alpha_predictions, cmap='coolwarm')
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plt.title('Гребневая (degree=3, alpha=1.0) ds' + str(ds_cnt))
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plt.xlabel('X')
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plt.ylabel('Y')
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plt.show()
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# Сравнение качества
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print('Линейная MSE:', linear_mse)
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print('Полиномиальная (degree=3) MSE:', poly_mse)
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print('Гребневая (degree=3, alpha=1.0) MSE:', poly_alpha_mse)
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