def test_types_encoded_by_expected_functions(self):
things = np.array([[0, 0], [1, 0], [2, 0.5673]], dtype=np.float32)
mock_entity_relation_encoder = Mock(
return_value=np.array([[0, 0, 0], [0, 0, 0]], dtype=np.float32))
mock_attribute_encoder = Mock(return_value=np.array(
[[0.9527, 0.2367, 0.7582]], dtype=np.float32))
encoders_for_types = {
lambda: mock_entity_relation_encoder: [0, 1],
lambda: mock_attribute_encoder: [2]
}
tm = TypewiseEncoder(encoders_for_types, 3)
encoding = tm(things) # The function under test
np.testing.assert_array_equal(
[[np.array([[0], [0]], dtype=np.float32)]],
get_call_args(mock_entity_relation_encoder))
np.testing.assert_array_equal(
[[np.array([[0.5673]], dtype=np.float32)]],
get_call_args(mock_attribute_encoder))
expected_encoding = np.array(
[[0, 0, 0], [0, 0, 0], [0.9527, 0.2367, 0.7582]], dtype=np.float32)
np.testing.assert_array_equal(expected_encoding, encoding.numpy())
def test_encoders_do_not_fulfil_classes(self):
mock_entity_relation_encoder = Mock()
encoders_for_types = {lambda: mock_entity_relation_encoder: [0, 2]}
with self.assertRaises(ValueError) as context:
TypewiseEncoder(encoders_for_types, 3)
self.assertEqual(
'Encoder categories are inconsistent. Expected [0, 1, 2], but got [0, 2]',
str(context.exception))
def test_basic_encoding(self):
things = np.array([[0], [1], [2]], dtype=np.float32)
mock_entity_relation_encoder = Mock(return_value=np.array(
[[0.1, 0, 0], [0.1, 0, 0], [0.1, 0, 0]], dtype=np.float32))
encoders_for_types = {lambda: mock_entity_relation_encoder: [0, 1, 2]}
tm = TypewiseEncoder(encoders_for_types, 3)
encoding = tm(things) # The function under test
expected_encoding = np.array([[0.1, 0, 0], [0.1, 0, 0], [0.1, 0, 0]],
dtype=np.float32)
np.testing.assert_array_equal(expected_encoding, encoding.numpy())
def test_with_tensors(self):
tf.reset_default_graph()
tf.set_random_seed(1)
things = tf.convert_to_tensor(
np.array([[0, 0], [1, 0], [2, 0.5673]], dtype=np.float32))
entity_relation = lambda x: x
continuous_attribute = lambda x: x
encoders_for_types = {
lambda: entity_relation: [0, 1],
lambda: continuous_attribute: [2]
}
tm = TypewiseEncoder(encoders_for_types, 1)
encoded_things = tm(things) # The function under test
# Check that tensorflow was actually used
self.assertEqual(EagerTensor, type(encoded_things))
def embed_attribute(features, attr_encoders, attr_embedding_dim):
typewise_attribute_encoder = TypewiseEncoder(attr_encoders,
attr_embedding_dim)
attr_embedding = typewise_attribute_encoder(features[:, 1:])
tf.summary.histogram('attribute_embedding_histogram', attr_embedding)
return attr_embedding
def attribute_embedding(features, attr_encoders, attr_embedding_dim):
typewise_attribute_encoder = TypewiseEncoder(attr_encoders,
attr_embedding_dim)
return typewise_attribute_encoder(features[:, 1:])