def get_config(self):
return {
"inputs": [[
preprocessors_module.serialize(preprocessor)
for preprocessor in preprocessors
] for preprocessors in self.inputs],
"outputs": [[
preprocessors_module.serialize(preprocessor)
for preprocessor in preprocessors
] for preprocessors in self.outputs],
}
def test_multi_label_deserialize_without_error():
encoder = encoders.MultiLabelEncoder()
dataset = tf.data.Dataset.from_tensor_slices([1, 2]).batch(32)
encoder = preprocessors.deserialize(preprocessors.serialize(encoder))
assert encoder.transform(dataset) is dataset
def test_softmax_deserialize_without_error():
postprocessor = postprocessors.SoftmaxPostprocessor()
dataset = tf.data.Dataset.from_tensor_slices([1, 2]).batch(32)
postprocessor = preprocessors.deserialize(
preprocessors.serialize(postprocessor))
assert postprocessor.transform(dataset) is dataset
def test_one_hot_encoder_deserialize_transforms_to_np():
encoder = encoders.OneHotEncoder(["a", "b", "c"])
encoder.fit(np.array(["a", "b", "a"]))
encoder = preprocessors.deserialize(preprocessors.serialize(encoder))
one_hot = encoder.transform(
tf.data.Dataset.from_tensor_slices([["a"], ["c"], ["b"]]).batch(2))
for data in one_hot:
assert data.shape[1:] == [3]
def get_config(self):
vocab = []
for encoding_layer in self.layer.encoding_layers:
if encoding_layer is None:
vocab.append([])
else:
vocab.append(encoding_layer.get_vocabulary())
return {
"column_types": self.column_types,
"column_names": self.column_names,
"encoding_layer": preprocessors.serialize(self.layer),
"encoding_vocab": vocab,
}
def get_config(self):
config = super().get_config()
config.update(
{"preprocessor": preprocessors.serialize(self.preprocessor)})
return config
