def test_dropin_LassoCV(sparse_X):
"""Test that our LassoCV behaves like sklearn's LassoCV."""
X, y, _, _ = build_dataset(n_samples=30, n_features=50, sparse_X=sparse_X)
params = dict(eps=1e-1, n_alphas=100, tol=1e-10, cv=2)
clf = LassoCV(**params)
clf.fit(X, y)
clf2 = sklearn_LassoCV(**params)
clf2.fit(X, y)
np.testing.assert_allclose(clf.mse_path_, clf2.mse_path_, rtol=1e-04)
np.testing.assert_allclose(clf.alpha_, clf2.alpha_, rtol=1e-05)
np.testing.assert_allclose(clf.coef_, clf2.coef_, rtol=1e-05)
check_estimator(LassoCV)
def test_LassoCV(sparse_X, fit_intercept, positive):
"""Test that our LassoCV behaves like sklearn's LassoCV."""
X, y = build_dataset(n_samples=20, n_features=30, sparse_X=sparse_X)
params = dict(eps=0.05, n_alphas=10, tol=1e-10, cv=2,
fit_intercept=fit_intercept, positive=positive, verbose=2,
n_jobs=-1)
clf = LassoCV(**params)
clf.fit(X, y)
clf2 = sklearn_LassoCV(**params, max_iter=10000)
clf2.fit(X, y)
np.testing.assert_allclose(clf.mse_path_, clf2.mse_path_, atol=1e-4)
np.testing.assert_allclose(clf.alpha_, clf2.alpha_)
np.testing.assert_allclose(clf.coef_, clf2.coef_, atol=1e-5)