70 lines
2.0 KiB
Python
70 lines
2.0 KiB
Python
"""
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=====================
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Lasso and Elastic Net
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=====================
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Lasso and elastic net (L1 and L2 penalisation) implemented using a
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coordinate descent.
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"""
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# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
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# License: BSD Style.
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from datetime import datetime
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from itertools import cycle
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import numpy as np
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import pylab as pl
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from scikits.learn.glm import lasso_path, enet_path
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n_samples, n_features, maxit = 442, 10, 50
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from scikits.learn import datasets
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np.random.seed(0)
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y = np.random.randn(n_samples)
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X = np.random.randn(n_samples, n_features)
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data = datasets.load_diabetes()
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X = data.data
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y = data.target
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################################################################################
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# Fit models
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################################################################################
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################################################################################
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# Demo path functions
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################################################################################
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eps = 1e-2 # the smaller it is the longer is the path
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print "Computing regularization path using the lasso..."
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start = datetime.now()
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models = lasso_path(X, y, eps=eps)
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print "This took ", datetime.now() - start
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alphas_lasso = np.array([model.alpha for model in models])
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coefs_lasso = np.array([model.coef_ for model in models])
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print "Computing regularization path using the elastic net..."
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start = datetime.now()
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models = enet_path(X, y, eps=eps, rho=0.6)
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print "This took ", datetime.now() - start
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alphas_enet = np.array([model.alpha for model in models])
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coefs_enet = np.array([model.coef_ for model in models])
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# Display results
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color_iter = cycle(['b', 'g', 'r', 'c', 'm', 'y', 'k'])
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for color, coef_lasso, coef_enet in zip(color_iter,
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coefs_lasso.T, coefs_enet.T):
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pl.plot(-np.log10(alphas_lasso), coef_lasso, color)
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pl.plot(-np.log10(alphas_enet), coef_enet, color + 'x')
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pl.xlabel('-Log(lambda)')
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pl.ylabel('weights')
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pl.title('Lasso and Elastic-Net Paths')
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pl.legend(['Lasso','Elastic-Net'])
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pl.axis('tight')
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pl.show()
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