def test_correct_feature_importances_for_svc_w_linear_kernel(trained_models): feature_importances = get_feature_importances( trained_models['SVC_w_linear_kernel']) assert feature_importances.shape == (30, )
def test_correct_feature_importances_for_lr(trained_models): feature_importances = get_feature_importances(trained_models['LR']) ## It returns the intercept, too assert feature_importances.shape == (30, )
def test_correct_feature_importances_for_rf(trained_models): feature_importances = get_feature_importances(trained_models['RF']) assert feature_importances.shape == (30, )
def test_throwing_warning_if_SVC_wo_linear_kernel(trained_models): with pytest.warns(UserWarning): get_feature_importances(trained_models['SVC_wo_linear_kernel'])
def test_throwing_warning_if_dummyclassifier(trained_models): with pytest.warns(UserWarning): get_feature_importances(trained_models['Dummy'])
def test_throwing_warning_if_lr(trained_models): with pytest.warns(UserWarning): get_feature_importances(trained_models['LR'])
def test_correct_feature_importances_for_dummy(trained_models): feature_importances = get_feature_importances(trained_models["Dummy"]) assert feature_importances is None
def test_correct_feature_importances_for_svc_wo_linear_kernel(trained_models): feature_importances = get_feature_importances( trained_models["SVC_wo_linear_kernel"]) assert feature_importances is None