def get_hyper_preprocessors(self): hyper_preprocessors = [] if self.column_names: hyper_preprocessors.append( hpps_module.DefaultHyperPreprocessor( preprocessors.CategoricalToNumericalPreprocessor( column_names=self.column_names, column_types=self.column_types, ))) hyper_preprocessors.append( hpps_module.DefaultHyperPreprocessor( preprocessors.SlidingWindow(lookback=self.lookback, batch_size=self.batch_size))) return hyper_preprocessors
def test_serialize_and_deserialize_default_hpps_categorical(): x_train = np.array([["a", "ab", 2.1], ["b", "bc", 1.0], ["a", "bc", "nan"]]) preprocessor = preprocessors.CategoricalToNumericalPreprocessor( column_names=["column_a", "column_b", "column_c"], column_types={ "column_a": "categorical", "column_b": "categorical", "column_c": "numerical", }, ) hyper_preprocessor = hyper_preprocessors.DefaultHyperPreprocessor( preprocessor) dataset = tf.data.Dataset.from_tensor_slices(x_train).batch(32) hyper_preprocessor.preprocessor.fit( tf.data.Dataset.from_tensor_slices(x_train).batch(32)) hyper_preprocessor = hyper_preprocessors.deserialize( hyper_preprocessors.serialize(hyper_preprocessor)) assert isinstance( hyper_preprocessor.preprocessor, preprocessors.CategoricalToNumericalPreprocessor, ) result = hyper_preprocessor.preprocessor.transform(dataset) assert result[0][0] == result[2][0] assert result[0][0] != result[1][0] assert result[0][1] != result[1][1] assert result[0][1] != result[2][1] assert result[2][2] == 0 assert result.dtype == tf.float32
def get_hyper_preprocessors(self): hyper_preprocessors = [] if self._add_one_dimension: hyper_preprocessors.append( hpps_module.DefaultHyperPreprocessor( preprocessors.AddOneDimension())) return hyper_preprocessors
def get_hyper_preprocessors(self): hyper_preprocessors = [] hyper_preprocessors.append( hpps_module.DefaultHyperPreprocessor( preprocessors.SlidingWindow(lookback=self.lookback, batch_size=self.batch_size))) 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 not self.has_channel_dim: hyper_preprocessors.append( hpps_module.DefaultHyperPreprocessor( preprocessors.AddOneDimension())) return hyper_preprocessors
def get_hyper_preprocessors(self): hyper_preprocessors = [] if self.dtype != tf.string: hyper_preprocessors.append( hpps_module.DefaultHyperPreprocessor( preprocessors.CastToString())) return hyper_preprocessors
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._encoded_for_sigmoid: hyper_preprocessors.append( hpps_module.DefaultHyperPreprocessor( preprocessors.SigmoidPostprocessor())) elif self._encoded_for_softmax: hyper_preprocessors.append( hpps_module.DefaultHyperPreprocessor( preprocessors.SoftmaxPostprocessor())) elif self.num_classes == 2: 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 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