def test_features_features_list_exclude_transformed(
data_classification_balanced, feature_descriptor, transformed_features
):
"""Testing if returning feature list with transformed columns excluded works properly."""
col_list = ["AgeGroup", "bool", "Height", "Price", "Product", "Sex", "Target"]
X, y = data_classification_balanced
f = Features(X, y, feature_descriptor, transformed_features)
actual_result = f.features(exclude_transformed=True)
if transformed_features:
expected_result = [feature for feature in col_list if feature not in transformed_features]
else:
expected_result = col_list
assert actual_result == expected_result
def test_features_features_list_no_target(
data_classification_balanced, feature_descriptor_type, feature_descriptor, feature_descriptor_forced_categories
):
"""Testing if .features() returns correct values when drop_target = True (without Target feature name)."""
expected = ["AgeGroup", "bool", "Height", "Price", "Product", "Sex"]
X, y = data_classification_balanced
# couldn't find a way to incorporate fixtures into @pytest.mark.parametrize
if feature_descriptor_type == "normal":
fd = feature_descriptor
elif feature_descriptor_type == "forced":
fd = feature_descriptor_forced_categories
else:
raise
f = Features(X, y, fd)
actual = f.features(drop_target=True)
assert actual == expected