def test_categorical_to_numerical():
block = preprocessing.CategoricalToNumerical()
block.column_names = ['a']
block.column_types = {'a': 'num'}
utils.block_basic_exam(
block,
tf.keras.Input(shape=(1, ), dtype=tf.string),
[],
)
def build(self, hp, inputs=None):
input_node = nest.flatten(inputs)[0]
output_node = input_node
if self.categorical_encoding:
block = preprocessing.CategoricalToNumerical()
block.column_types = self.column_types
block.column_names = self.column_names
output_node = block.build(hp, output_node)
output_node = basic.DenseBlock().build(hp, output_node)
return output_node
def build_categorical_encoding(self, hp, input_node):
output_node = input_node
categorical_encoding = self.categorical_encoding
if categorical_encoding is None:
categorical_encoding = hp.Choice('categorical_encoding',
[True, False],
default=True)
if categorical_encoding:
block = preprocessing.CategoricalToNumerical()
block.column_types = self.column_types
block.column_names = self.column_names
output_node = block.build(hp, output_node)
return output_node
def build(self, hp, inputs=None):
input_node = nest.flatten(inputs)[0]
output_node = input_node
if self.categorical_encoding:
block = preprocessing.CategoricalToNumerical()
block.column_types = self.column_types
block.column_names = self.column_names
output_node = block.build(hp, output_node)
if self.normalize is None and hp.Boolean(NORMALIZE):
with hp.conditional_scope(NORMALIZE, [True]):
output_node = preprocessing.Normalization().build(hp, output_node)
elif self.normalize:
output_node = preprocessing.Normalization().build(hp, output_node)
output_node = basic.DenseBlock().build(hp, output_node)
return output_node
