def get_hyper_preprocessors(self):
hyper_preprocessors = []
if self._add_one_dimension:
hyper_preprocessors.append(
hpps_module.DefaultHyperPreprocessor(preprocessors.AddOneDimension())
)
return hyper_preprocessors
def test_serialize_and_deserialize_default_hpps():
preprocessor = preprocessors.AddOneDimension()
hyper_preprocessor = hyper_preprocessors.DefaultHyperPreprocessor(preprocessor)
hyper_preprocessor = hyper_preprocessors.deserialize(
hyper_preprocessors.serialize(hyper_preprocessor)
)
assert isinstance(hyper_preprocessor.preprocessor, preprocessors.AddOneDimension)
def get_hyper_preprocessors(self):
hyper_preprocessors = []
if self._add_one_dimension:
hyper_preprocessors.append(
hpps_module.DefaultHyperPreprocessor(
preprocessors.AddOneDimension()))
if self.dtype in [tf.uint8, tf.uint16, tf.uint32, tf.uint64]:
hyper_preprocessors.append(
hpps_module.DefaultHyperPreprocessor(
preprocessors.CastToInt32()))
if not self._encoded and self.dtype != tf.string:
hyper_preprocessors.append(
hpps_module.DefaultHyperPreprocessor(
preprocessors.CastToString()))
if self.multi_label:
hyper_preprocessors.append(
hpps_module.DefaultHyperPreprocessor(
preprocessors.MultiLabelEncoder()))
if not self._encoded:
if self.num_classes == 2 and not self.multi_label:
hyper_preprocessors.append(
hpps_module.DefaultHyperPreprocessor(
preprocessors.LabelEncoder(self._labels)))
else:
hyper_preprocessors.append(
hpps_module.DefaultHyperPreprocessor(
preprocessors.OneHotEncoder(self._labels)))
return hyper_preprocessors
def get_hyper_preprocessors(self):
hyper_preprocessors = []
if not self.has_channel_dim:
hyper_preprocessors.append(
hpps_module.DefaultHyperPreprocessor(
preprocessors.AddOneDimension()))
return hyper_preprocessors