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""" Test the graph_lasso module.
"""
import sys
from StringIO import StringIO
import numpy as np
from scipy import linalg
from sklearn.covariance import graph_lasso, GraphLasso, GraphLassoCV, \
empirical_covariance
from sklearn.datasets.samples_generator import make_sparse_spd_matrix
from sklearn.utils import check_random_state
def test_graph_lasso(random_state=0):
# Sample data from a sparse multivariate normal
dim = 20
n_samples = 100
random_state = check_random_state(random_state)
prec = make_sparse_spd_matrix(dim, alpha=.95,
random_state=random_state)
cov = linalg.inv(prec)
X = random_state.multivariate_normal(np.zeros(dim), cov, size=n_samples)
emp_cov = empirical_covariance(X)
for alpha in (.1, .01):
covs = dict()
for method in ('cd', 'lars'):
cov_, _, costs = graph_lasso(emp_cov, alpha=.1, return_costs=True)
covs[method] = cov_
costs, dual_gap = np.array(costs).T
# Check that the costs always decrease
np.testing.assert_array_less(np.diff(costs), 0)
# Check that the 2 approaches give similar results
np.testing.assert_array_almost_equal(covs['cd'], covs['lars'])
# Smoke test the estimator
model = GraphLasso(alpha=.1).fit(X)
np.testing.assert_array_almost_equal(model.covariance_, covs['cd'])
def test_graph_lasso_cv(random_state=1):
# Sample data from a sparse multivariate normal
dim = 5
n_samples = 6
random_state = check_random_state(random_state)
prec = make_sparse_spd_matrix(dim, alpha=.96,
random_state=random_state)
cov = linalg.inv(prec)
X = random_state.multivariate_normal(np.zeros(dim), cov, size=n_samples)
# Capture stdout, to smoke test the verbose mode
orig_stdout = sys.stdout
try:
sys.stdout = StringIO()
# We need verbose very high so that Parallel prints on stdout
GraphLassoCV(verbose=100, alphas=3).fit(X)
finally:
sys.stdout = orig_stdout
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