def test_feature_importance(X_y_regression): X, y = X_y_regression sk_clf = SKElasticNetRegressor(alpha=0.5, l1_ratio=0.5, random_state=0, normalize=False, max_iter=1000) sk_clf.fit(X, y) clf = ElasticNetRegressor() clf.fit(X, y) np.testing.assert_almost_equal(sk_clf.coef_, clf.feature_importance, decimal=5)
def test_fit_predict(X_y_regression): X, y = X_y_regression sk_clf = SKElasticNetRegressor(alpha=0.5, l1_ratio=0.5, random_state=0, normalize=False, max_iter=1000) sk_clf.fit(X, y) y_pred_sk = sk_clf.predict(X) clf = ElasticNetRegressor() fitted = clf.fit(X, y) assert isinstance(fitted, ElasticNetRegressor) y_pred = clf.predict(X) np.testing.assert_almost_equal(y_pred, y_pred_sk, decimal=5)