70 lines
2.0 KiB
Python
70 lines
2.0 KiB
Python
"""
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========================================
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Lasso and Elastic Net for Sparse Signals
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========================================
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Estimates Lasso and Elastic-Net regression models on a manually generated
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sparse signal corrupted with an additive noise. Estimated coefficients are
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compared with the ground-truth.
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"""
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print(__doc__)
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.metrics import r2_score
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# #############################################################################
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# Generate some sparse data to play with
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np.random.seed(42)
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n_samples, n_features = 50, 200
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X = np.random.randn(n_samples, n_features)
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coef = 3 * np.random.randn(n_features)
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inds = np.arange(n_features)
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np.random.shuffle(inds)
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coef[inds[10:]] = 0 # sparsify coef
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y = np.dot(X, coef)
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# add noise
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y += 0.01 * np.random.normal(size=n_samples)
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# Split data in train set and test set
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n_samples = X.shape[0]
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X_train, y_train = X[:n_samples // 2], y[:n_samples // 2]
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X_test, y_test = X[n_samples // 2:], y[n_samples // 2:]
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# #############################################################################
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# Lasso
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from sklearn.linear_model import Lasso
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alpha = 0.1
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lasso = Lasso(alpha=alpha)
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y_pred_lasso = lasso.fit(X_train, y_train).predict(X_test)
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r2_score_lasso = r2_score(y_test, y_pred_lasso)
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print(lasso)
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print("r^2 on test data : %f" % r2_score_lasso)
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# #############################################################################
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# ElasticNet
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from sklearn.linear_model import ElasticNet
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enet = ElasticNet(alpha=alpha, l1_ratio=0.7)
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y_pred_enet = enet.fit(X_train, y_train).predict(X_test)
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r2_score_enet = r2_score(y_test, y_pred_enet)
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print(enet)
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print("r^2 on test data : %f" % r2_score_enet)
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plt.plot(enet.coef_, color='lightgreen', linewidth=2,
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label='Elastic net coefficients')
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plt.plot(lasso.coef_, color='gold', linewidth=2,
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label='Lasso coefficients')
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plt.plot(coef, '--', color='navy', label='original coefficients')
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plt.legend(loc='best')
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plt.title("Lasso R^2: %f, Elastic Net R^2: %f"
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% (r2_score_lasso, r2_score_enet))
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plt.show()
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