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)