y += 0.01*np.random.normal((n_samples,))
# Split data in train set and test set
n_samples = X.shape[0]
X_train, y_train = X[:n_samples/2], y[:n_samples/2]
X_test, y_test = X[n_samples/2:], y[n_samples/2:]
################################################################################
# Lasso
from scikits.learn.glm import Lasso
alpha = 0.1
lasso = Lasso(alpha=alpha)
y_pred_lasso = lasso.fit(X_train, y_train).predict(X_test)
print lasso
print "r^2 on test data : %f" % (1 - np.linalg.norm(y_test - y_pred_lasso)**2
/ np.linalg.norm(y_test)**2)
################################################################################
# ElasticNet
from scikits.learn.glm import ElasticNet
enet = ElasticNet(alpha=alpha, rho=0.7)
y_pred_enet = enet.fit(X_train, y_train).predict(X_test)
print enet
print "r^2 on test data : %f" % (1 - np.linalg.norm(y_test - y_pred_enet)**2
/ np.linalg.norm(y_test)**2)
def test_enet_toy():
"""
Test ElasticNet for various parameters of alpha and rho.
Actualy, the parameters alpha = 0 should not be alowed. However,
we test it as a border case.
"""
X = [[-1], [0], [1]]
Y = [-1, 0, 1] # just a straight line
T = [[2], [3], [4]] # test sample
# this should be the same as lasso
clf = ElasticNet(alpha=0, rho=1.0)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [1])
assert_array_almost_equal(pred, [2, 3, 4])
assert_almost_equal(clf.dual_gap_, 0)
clf = ElasticNet(alpha=0.5, rho=0.3)
clf.fit(X, Y, maxit=1000)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [0.50819], decimal=3)
assert_array_almost_equal(pred, [1.0163, 1.5245, 2.0327], decimal=3)
assert_almost_equal(clf.dual_gap_, 0)
clf = ElasticNet(alpha=0.5, rho=0.5)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [0.45454], 3)
assert_array_almost_equal(pred, [0.9090, 1.3636, 1.8181], 3)
assert_almost_equal(clf.dual_gap_, 0)
