def test_MultiTaskLasso(fit_intercept):
"""Test that our MultiTaskLasso behaves as sklearn's."""
X, Y = build_dataset(n_samples=20, n_features=30, n_targets=10)
alpha_max = np.max(norm(X.T.dot(Y), axis=1)) / X.shape[0]
alpha = alpha_max / 2.
params = dict(alpha=alpha, fit_intercept=fit_intercept, tol=1e-10)
clf = MultiTaskLasso(**params)
clf.verbose = 2
clf.fit(X, Y)
clf2 = sklearn_MultiTaskLasso(**params)
clf2.fit(X, Y)
np.testing.assert_allclose(clf.coef_, clf2.coef_, rtol=1e-5)
if fit_intercept:
np.testing.assert_allclose(clf.intercept_, clf2.intercept_)
def test_dropin_MultiTaskLasso():
"""Test that our MultiTaskLasso class behaves as sklearn's."""
X, Y, _, _ = build_dataset(n_samples=20, n_features=30, n_targets=10)
alpha_max = np.max(norm(X.T.dot(Y), axis=1)) / X.shape[0]
alpha = alpha_max / 2.
params = dict(alpha=alpha, fit_intercept=False, tol=1e-10, normalize=True)
clf = MultiTaskLasso(**params)
clf.fit(X, Y)
clf2 = sklearn_MultiTaskLasso(**params)
clf2.fit(X, Y)
np.testing.assert_allclose(clf.coef_, clf2.coef_, rtol=1e-5)
# if fit_intercept:
# np.testing.assert_allclose(clf.intercept_, clf2.intercept_)
check_estimator(MultiTaskLasso)