57 lines
2.3 KiB
Python
57 lines
2.3 KiB
Python
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import numpy as np
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from sklearn.datasets import make_classification
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from sklearn.preprocessing import StandardScaler
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from sklearn.model_selection import train_test_split
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from matplotlib.colors import ListedColormap
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from matplotlib import pyplot as plt
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from sklearn.linear_model import LinearRegression, Perceptron, Ridge
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from sklearn.preprocessing import PolynomialFeatures
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from sklearn.pipeline import Pipeline
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#Создаем набор данных
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X, Y = make_classification(n_samples=500, n_features=2, n_redundant=0, n_informative=2, random_state=0, n_clusters_per_class=1)
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rng = np.random.RandomState(2)
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X += 2 * rng.uniform(size=X.shape)
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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=40)
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#Создаем модели
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linear = LinearRegression()
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perseptron = Perceptron()
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ridge = Ridge(alpha=1.0)
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polynomial_features = PolynomialFeatures(degree=3)
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rid_poly = Pipeline([("polynomial_features", polynomial_features),("ridge_regression", ridge)])
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#Тренируем модель
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def train(model, description):
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model.fit(X_train, Y_train)
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Y_pred = model.predict(X_test)
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print(description + ", качество модели = ", model.score(X_test, Y_test))
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#Выводим результат на график
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def plot(model, name):
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cmap = ListedColormap(['#8b00ff', '#ff294d'])
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plt.figure(figsize=(10, 7))
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subplot = plt.subplot(111)
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h = .5 # 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.predict(np.c_[xx0.ravel(), xx1.ravel()])
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Z = Z.reshape(xx0.shape)
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subplot.contourf(xx0, xx1, Z, cmap=cmap, alpha=.3)
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subplot.scatter(X_train[:, 0], X_train[:, 1], c=Y_train, cmap=cmap)
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subplot.scatter(X_test[:, 0], X_test[:, 1], c=Y_test, cmap=cmap, alpha=0.4)
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plt.savefig(name + ".png")
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#Вызов функций
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train(linear, "Линейная регрессия")
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train(perseptron, "Персептрон")
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train(rid_poly, "Гребневая полиномиальная регрессия")
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plot(linear, "linear_plot")
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plot(perseptron, "perseptron_plot")
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plot(rid_poly, "rid_poly_plot")
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