def test_function_transformer_feature_names_out_is_None():
transformer = FunctionTransformer()
X = np.random.rand(100, 2)
transformer.fit_transform(X)
msg = "This 'FunctionTransformer' has no attribute 'get_feature_names_out'"
with pytest.raises(AttributeError, match=msg):
transformer.get_feature_names_out()
def test_function_transformer_feature_names_out_string(feature_names_out):
transformer = FunctionTransformer(feature_names_out=feature_names_out)
X = np.random.rand(100, 2)
transformer.fit_transform(X)
msg = """must either be "one-to-one" or a callable"""
with pytest.raises(ValueError, match=msg):
transformer.get_feature_names_out()
def test_function_transformer_get_feature_names_out_without_validation():
transformer = FunctionTransformer(feature_names_out="one-to-one", validate=False)
X = np.random.rand(100, 2)
transformer.fit_transform(X)
msg = "When 'feature_names_out' is 'one-to-one', either"
with pytest.raises(ValueError, match=msg):
transformer.get_feature_names_out()
names = transformer.get_feature_names_out(("a", "b"))
assert isinstance(names, np.ndarray)
assert names.dtype == object
assert_array_equal(names, ("a", "b"))
def test_function_transformer_get_feature_names_out(
X, feature_names_out, input_features, expected
):
if isinstance(X, dict):
pd = pytest.importorskip("pandas")
X = pd.DataFrame(X)
transformer = FunctionTransformer(
feature_names_out=feature_names_out, validate=True
)
transformer.fit_transform(X)
names = transformer.get_feature_names_out(input_features)
assert isinstance(names, np.ndarray)
assert names.dtype == object
assert_array_equal(names, expected)
def test_function_transformer_feature_names_out_uses_estimator():
def add_n_random_features(X, n):
return np.concatenate([X, np.random.rand(len(X), n)], axis=1)
def feature_names_out(transformer, input_features):
n = transformer.kw_args["n"]
return list(input_features) + [f"rnd{i}" for i in range(n)]
transformer = FunctionTransformer(
func=add_n_random_features,
feature_names_out=feature_names_out,
kw_args=dict(n=3),
validate=True,
)
pd = pytest.importorskip("pandas")
df = pd.DataFrame({"a": np.random.rand(100), "b": np.random.rand(100)})
transformer.fit_transform(df)
names = transformer.get_feature_names_out()
assert isinstance(names, np.ndarray)
assert names.dtype == object
assert_array_equal(names, ("a", "b", "rnd0", "rnd1", "rnd2"))
