def test_sklearn_compliance(self):
for encoder_name in encoders.__all__:
with self.subTest(encoder_name=encoder_name):
# in sklearn < 0.19.0, these methods require classes,
# in sklearn >= 0.19.0, these methods require instances
if sklearn.__version__ < '0.19.0':
encoder = getattr(encoders, encoder_name)
else:
encoder = getattr(encoders, encoder_name)()
check_transformer_general(encoder_name, encoder)
check_transformers_unfitted(encoder_name, encoder)
def test_sparse_coder_common_transformer():
rng = np.random.RandomState(777)
n_components, n_features = 40, 3
init_dict = rng.rand(n_components, n_features)
sc = SparseCoder(init_dict)
check_transformer_data_not_an_array(sc.__class__.__name__, sc)
check_transformer_general(sc.__class__.__name__, sc)
check_transformer_general_memmap = partial(check_transformer_general,
readonly_memmap=True)
check_transformer_general_memmap(sc.__class__.__name__, sc)
check_transformers_unfitted(sc.__class__.__name__, sc)
def test_sklearn_compliance(self):
for encoder_name in encoders.__all__:
with self.subTest(encoder_name=encoder_name):
# in sklearn < 0.19.0, these methods require classes,
# in sklearn >= 0.19.0, these methods require instances
if sklearn.__version__ < '0.19.0':
encoder = getattr(encoders, encoder_name)
else:
encoder = getattr(encoders, encoder_name)()
check_transformer_general(encoder_name, encoder)
check_transformers_unfitted(encoder_name, encoder)
