def test_get_feature_names_out_input_features_is_list(df_na):
input_features = ["Age", "Marks"]
# when add_indicators is false, we've got the generic check from estimator_checks.
# We need to test only when true.
tr = Winsorizer(tail="left", add_indicators=True, missing_values="ignore")
tr.fit(df_na)
out = [f + "_left" for f in input_features]
assert tr.get_feature_names_out(input_features) == input_features + out
tr = Winsorizer(tail="right", add_indicators=True, missing_values="ignore")
tr.fit(df_na)
out = [f + "_right" for f in input_features]
assert tr.get_feature_names_out(input_features) == input_features + out
tr = Winsorizer(tail="both", add_indicators=True, missing_values="ignore")
tr.fit(df_na)
out = ["Age_left", "Age_right", "Marks_left", "Marks_right"]
assert tr.get_feature_names_out(input_features) == input_features + out
def test_get_feature_names_out_input_features_is_none(df_na):
original_features = df_na.columns.to_list()
input_features = ["Age", "Marks"]
# when indicators is false, we've got the generic check.
# We need to test only when true
tr = Winsorizer(tail="left", add_indicators=True, missing_values="ignore")
tr.fit(df_na)
out = [f + "_left" for f in input_features]
assert tr.get_feature_names_out() == original_features + out
tr = Winsorizer(tail="right", add_indicators=True, missing_values="ignore")
tr.fit(df_na)
out = [f + "_right" for f in input_features]
assert tr.get_feature_names_out() == original_features + out
tr = Winsorizer(tail="both", add_indicators=True, missing_values="ignore")
tr.fit(df_na)
out = ["Age_left", "Age_right", "Marks_left", "Marks_right"]
assert tr.get_feature_names_out() == original_features + out