scikit-learn/examples/plot_lasso_coordinate_desce...

64 lines
1.8 KiB
Python

"""
=====================
Lasso and Elastic Net
=====================
Lasso and elastic net (L1 and L2 penalisation) implemented using a
coordinate descent.
"""
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD Style.
# $Id$
from datetime import datetime
from itertools import cycle
import numpy as np
import pylab as pl
from scikits.learn.glm.coordinate_descent import Lasso, ElasticNet, lasso_path, \
enet_path
n_samples, n_features, maxit = 5, 10, 30
np.random.seed(0)
y = np.random.randn(n_samples)
X = np.random.randn(n_samples, n_features)
################################################################################
# Fit models
################################################################################
################################################################################
# Demo path functions
################################################################################
eps = 1e-2 # the smaller it is the longer is the path
print "Computing regularization path using the lasso..."
start = datetime.now()
alphas_lasso, weights_lasso = lasso_path(X, y, eps=eps)
print "This took ", datetime.now() - start
print "Computing regularization path using the elastic net..."
start = datetime.now()
alphas_enet, weights_enet = enet_path(X, y, rho=0.6, eps=eps)
print "This took ", datetime.now() - start
# Display results
color_iter = cycle(['b', 'g', 'r', 'c', 'm', 'y', 'k'])
for color, weight_lasso, weight_enet in zip(color_iter,
weights_lasso.T, weights_enet.T):
pl.plot(-np.log10(alphas_lasso), weight_lasso, color)
pl.plot(-np.log10(alphas_enet), weight_enet, color+'x')
pl.xlabel('-Log(lambda)')
pl.ylabel('weights')
pl.title('Lasso and Elastic-Net Paths')
pl.legend(['Lasso','Elastic-Net'])
pl.axis('tight')
pl.show()