def test_features_analyze_features_transformed_features(
data_classification_balanced, feature_descriptor, transformed_features
):
"""Testing if creating features properly assigns Transformed flag based on provided transformed_features
sequence."""
X, y = data_classification_balanced
f = Features(X, y, feature_descriptor, transformed_features)
f.original_dataframe = pd.concat([X, y], axis=1) # original_dataframe needs to be set up
actual = f._analyze_features(feature_descriptor)
for feature in actual.keys():
if feature in transformed_features:
assert actual[feature].transformed
else:
assert not actual[feature].transformed
def test_features_analyze_features(data_classification_balanced, feature_descriptor):
"""Testing if .analyze_features() method of Features class returns a dictionary with a correct content"""
n = NumericalFeature
c = CategoricalFeature
expected = {
"Sex": c,
"AgeGroup": c,
"Height": n,
"Product": c,
"Price": n,
"bool": c,
"Target": c
}
X, y = data_classification_balanced
f = Features(X, y, feature_descriptor)
f.original_dataframe = pd.concat([X, y], axis=1) # original_dataframe needs to be set up
actual = f._analyze_features(feature_descriptor)
assert isinstance(actual, dict)
for key, item in expected.items():
assert isinstance(actual[key], item)