# Author: Fabian Pedregosa <*****@*****.**>
# Alexandre Gramfort <*****@*****.**>
# License: BSD Style.
import numpy as np
import pylab as pl
from scikits.learn import glm
from scikits.learn import datasets
diabetes = datasets.load_diabetes()
X = diabetes.data
y = diabetes.target
X[:,6] *= -1 # To reproduce wikipedia LASSO page
print "Computing regularization path using the LARS ..."
_, _, coefs_ = glm.lars_path(X, y, method='lasso', verbose=True)
xx = np.sum(np.abs(coefs_.T), axis=1)
xx /= xx[-1]
pl.plot(xx, coefs_.T)
ymin, ymax = pl.ylim()
pl.vlines(xx, ymin, ymax, linestyle='dashed')
pl.xlabel('|coef| / max|coef|')
pl.ylabel('Coefficients')
pl.title('LASSO Path')
pl.axis('tight')
pl.show()
_xmean = X.mean(0)
_ymean = y.mean(0)
X = X - _xmean
y = y - _ymean
_norms = np.apply_along_axis (np.linalg.norm, 0, X)
nonzeros = np.flatnonzero(_norms)
X[:, nonzeros] /= _norms[nonzeros]
################################################################################
# Demo path functions
################################################################################
G = np.dot(X.T, X)
print "Computing regularization path using the LARS ..."
start = datetime.now()
alphas, active, path = glm.lars_path(X, y, Gram=G, method='lasso')
print "This took ", datetime.now() - start
alphas = np.sum(np.abs(path.T), axis=1)
alphas /= alphas[-1]
# # Display results
color_iter = itertools.cycle(['r', 'g', 'b', 'c'])
for coef_, color in zip(path, color_iter):
pl.plot(alphas, coef_.T, color)
ymin, ymax = pl.ylim()
pl.vlines(alphas, ymin, ymax, linestyle='dashed')
pl.xlabel('-Log(lambda)') # XXX : wrong label
pl.ylabel('Coefficients')
from scikits.learn import glm
from scikits.learn import datasets
diabetes = datasets.load_diabetes()
X = diabetes.data
y = diabetes.target
X[:,6] *= -1 # To reproduce wikipedia LAR page
################################################################################
# Compute path functions
print "Computing regularization path using the LARS ..."
start = datetime.now()
_, _, coefs_ = glm.lars_path(X, y, max_features=10, method="lar")
print "This took ", datetime.now() - start
###############################################################################
# Display path
xx = np.sum(np.abs(coefs_), axis=0)
xx /= xx[-1]
pl.plot(xx, coefs_.T)
ymin, ymax = pl.ylim()
pl.vlines(xx, ymin, ymax, linestyle='dashed')
pl.xlabel('|coef| / max|coef|')
pl.ylabel('Coefficients')
pl.title('Least Angle Regression (LAR) Path')
pl.axis('tight')
pl.show()
import numpy as np
import pylab as pl
from scikits.learn import glm
from scikits.learn import datasets
diabetes = datasets.load_diabetes()
X = diabetes.data
y = diabetes.target
X[:,6] *= -1 # To reproduce wikipedia LASSO page
################################################################################
# Demo path functions
print "Computing regularization path using the LARS ..."
start = datetime.now()
alphas_, _, coefs_ = glm.lars_path(X, y, method='lasso')
print "This took ", datetime.now() - start
xx = np.sum(np.abs(coefs_.T), axis=1)
xx /= xx[-1]
pl.plot(xx, coefs_.T)
ymin, ymax = pl.ylim()
pl.vlines(xx, ymin, ymax, linestyle='dashed')
pl.xlabel('|coef| / max|coef|')
pl.ylabel('Coefficients')
pl.title('LASSO Path')
pl.axis('tight')
pl.show()
