def test_renamer_works_correctly_if_only_given_string(numerical):
single_column = numerical.iloc[:, 1].to_frame()
renamer = Renamer('test')
result = renamer.fit_transform(single_column)
assert isinstance(result, pd.DataFrame)
assert ['test'] == result.columns
assert len(numerical) == len(result)
def test_renamer_returns_correctly(numerical):
new_col_names = ['test_a', 'test_b']
renamer = Renamer(new_col_names)
result = renamer.fit_transform(numerical)
assert isinstance(result, pd.DataFrame)
assert len(numerical) == len(result)
assert set(new_col_names) == set(result.columns)
def test_mismatched_no_of_names_raises(numerical):
new_col_names = ['test_a']
renamer = Renamer(new_col_names)
with pytest.raises(TransformerError):
renamer.fit_transform(numerical)
def test_renamer_works_gridsearch(self, train_iris_dataset):
grid = create_gridsearch(Renamer(["1", "2", "3", "4"]))
model = Model(grid)
result = model.score_estimator(train_iris_dataset)
assert isinstance(result, Result)
def test_renamer_works_in_cv(self, train_iris_dataset):
model = create_model(Renamer(["1", "2", "3", "4"]))
result = model.score_estimator(train_iris_dataset, cv=2)
assert isinstance(result, Result)
def test_mismatched_no_of_names_raises(self, numerical: pd.DataFrame):
new_col_names = ["test_a"]
renamer = Renamer(new_col_names)
with pytest.raises(TransformerError):
renamer.fit_transform(numerical)
def test_works_without_args(self):
assert Renamer()