from scikits.learn.glm.sparse import Lasso as SparseLasso
from scikits.learn.glm import Lasso as DenseLasso
###############################################################################
# The two Lasso implementations on Dense data
print "--- Dense matrices"
n_samples, n_features = 200, 10000
np.random.seed(0)
y = np.random.randn(n_samples)
X = np.random.randn(n_samples, n_features)
alpha = 1
sparse_lasso = SparseLasso(alpha=alpha, fit_intercept=False)
dense_lasso = DenseLasso(alpha=alpha, fit_intercept=False)
t0 = time()
sparse_lasso.fit(X, y, maxit=1000)
print "Sparse Lasso done in %fs" % (time() - t0)
t0 = time()
dense_lasso.fit(X, y, maxit=1000)
print "Dense Lasso done in %fs" % (time() - t0)
print "Distance between coefficients : %s" % linalg.norm(sparse_lasso.coef_
- dense_lasso.coef_)
###############################################################################
# The two Lasso implementations on Sparse data
print "--- Sparse matrices"
y = np.dot(X, coef)
# add noise
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
def test_lasso_toy():
"""
Test Lasso on a toy example for various values of alpha.
When validating this against glmnet notice that glmnet divides it
against nobs.
"""
X = [[-1], [0], [1]]
Y = [-1, 0, 1] # just a straight line
T = [[2], [3], [4]] # test sample
clf = Lasso(alpha=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 = Lasso(alpha=0.1)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [.85])
assert_array_almost_equal(pred, [ 1.7 , 2.55, 3.4 ])
assert_almost_equal(clf.dual_gap_, 0)
clf = Lasso(alpha=0.5)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [.25])
assert_array_almost_equal(pred, [0.5, 0.75, 1.])
assert_almost_equal(clf.dual_gap_, 0)
clf = Lasso(alpha=1)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [.0])
assert_array_almost_equal(pred, [0, 0, 0])
assert_almost_equal(clf.dual_gap_, 0)