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"""
==============================
Lasso on dense and sparse data
==============================
We show that linear_model.Lasso and linear_model.sparse.Lasso
provide the same results and that in the case of
sparse data linear_model.sparse.Lasso improves the speed.
"""
print __doc__
from time import time
from scipy import sparse
from scipy import linalg
from sklearn.datasets.samples_generator import make_regression
from sklearn.linear_model.sparse import Lasso as SparseLasso
from sklearn.linear_model import Lasso as DenseLasso
###############################################################################
# The two Lasso implementations on Dense data
print "--- Dense matrices"
X, y = make_regression(n_samples=200, n_features=5000, random_state=0)
alpha = 1
sparse_lasso = SparseLasso(alpha=alpha, fit_intercept=False, max_iter=1000)
dense_lasso = DenseLasso(alpha=alpha, fit_intercept=False, max_iter=1000)
t0 = time()
sparse_lasso.fit(X, y)
print "Sparse Lasso done in %fs" % (time() - t0)
t0 = time()
dense_lasso.fit(X, y)
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"
Xs = X.copy()
Xs[Xs < 2.5] = 0.0
Xs = sparse.coo_matrix(Xs)
Xs = Xs.tocsc()
print "Matrix density : %s %%" % (Xs.nnz / float(X.size) * 100)
alpha = 0.1
sparse_lasso = SparseLasso(alpha=alpha, fit_intercept=False, max_iter=10000)
dense_lasso = DenseLasso(alpha=alpha, fit_intercept=False, max_iter=10000)
t0 = time()
sparse_lasso.fit(Xs, y)
print "Sparse Lasso done in %fs" % (time() - t0)
t0 = time()
dense_lasso.fit(Xs.todense(), y)
print "Dense Lasso done in %fs" % (time() - t0)
print "Distance between coefficients : %s" % linalg.norm(sparse_lasso.coef_
- dense_lasso.coef_)
|
