62 lines
2.1 KiB
Python
62 lines
2.1 KiB
Python
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from flask import Flask, request, render_template
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from sklearn.datasets import make_moons
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from sklearn.linear_model import LinearRegression
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from sklearn.preprocessing import PolynomialFeatures
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from sklearn.neural_network import MLPRegressor
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from sklearn.model_selection import train_test_split
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import matplotlib.pyplot as plt
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app = Flask(__name__)
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@app.route('/')
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def home():
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return render_template('index.html')
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@app.route('/compare_models', methods=['POST'])
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def compare_models():
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# Генерация данных
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rs = 0
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X, y = make_moons(noise=0.3, random_state=rs)
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=rs)
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# Линейная регрессия
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lr = LinearRegression()
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lr.fit(X_train, y_train)
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lr_score = lr.score(X_test, y_test)
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# Полиномиальная регрессия (степень 3)
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poly = PolynomialFeatures(degree=3)
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X_poly = poly.fit_transform(X_train)
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poly_reg = LinearRegression()
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poly_reg.fit(X_poly, y_train)
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poly_score = poly_reg.score(poly.transform(X_test), y_test)
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# Многослойный персептрон (100 нейронов в скрытом слое, alpha=0.01)
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mlp = MLPRegressor(hidden_layer_sizes=(100,), alpha=0.01, random_state=rs)
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mlp.fit(X_train, y_train)
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mlp_score = mlp.score(X_test, y_test)
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# Создание графиков
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plt.figure(figsize=(15, 4))
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plt.subplot(131)
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plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=plt.cm.RdBu)
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plt.title('Линейная регрессия\n(Score: {:.2f})'.format(lr_score))
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plt.subplot(132)
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plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=plt.cm.RdBu)
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plt.title('Полиномиальная регрессия\n(Score: {:.2f})'.format(poly_score))
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plt.subplot(133)
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plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=plt.cm.RdBu)
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plt.title('Многослойный персептрон\n(Score: {:.2f})'.format(mlp_score))
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plt.tight_layout()
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plt.savefig('static/models_comparison.png')
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return render_template('index.html', result=True)
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if __name__ == '__main__':
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app.run(debug=True)
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