def test_get_feature_names_out(_varnames, _drop, df_vartypes):
# set up transformer
transformer = MathFeatures(
variables=["Age", "Marks"],
func=["sum", "mean"],
new_variables_names=_varnames,
drop_original=_drop,
)
# fit transformer
X = transformer.fit_transform(df_vartypes)
# assert functionality
assert list(
X.columns) == transformer.get_feature_names_out(input_features=None)
assert list(
X.columns) == transformer.get_feature_names_out(input_features=False)
if _varnames is not None:
assert _varnames == transformer.get_feature_names_out(
input_features=True)
else:
assert ["sum_Age_Marks", "mean_Age_Marks"
] == transformer.get_feature_names_out(input_features=True)
def test_get_feature_names_out_raises_error_when_wrong_param(
_input_features, df_vartypes):
transformer = MathFeatures(
variables=["Age", "Marks"],
func=["sum", "mean"],
)
transformer.fit(df_vartypes)
with pytest.raises(ValueError):
transformer.get_feature_names_out(input_features=_input_features)