def test_timeseries_get_config_has_all_attributes():
block = blocks.TimeseriesBlock()
config = block.get_config()
assert test_utils.get_func_args(blocks.TimeseriesBlock.__init__).issubset(
config.keys())
def _assemble(self):
"""Assemble the Blocks based on the input output nodes."""
inputs = nest.flatten(self.inputs)
outputs = nest.flatten(self.outputs)
middle_nodes = []
for input_node in inputs:
if isinstance(input_node, input_module.TextInput):
middle_nodes.append(blocks.TextBlock()(input_node))
if isinstance(input_node, input_module.ImageInput):
middle_nodes.append(blocks.ImageBlock()(input_node))
if isinstance(input_node, input_module.StructuredDataInput):
middle_nodes.append(blocks.StructuredDataBlock()(input_node))
if isinstance(input_node, input_module.TimeseriesInput):
middle_nodes.append(blocks.TimeseriesBlock()(input_node))
# Merge the middle nodes.
if len(middle_nodes) > 1:
output_node = blocks.Merge()(middle_nodes)
else:
output_node = middle_nodes[0]
outputs = nest.flatten(
[output_blocks(output_node) for output_blocks in outputs])
return graph_module.Graph(inputs=inputs, outputs=outputs)
def test_timeseries_build_return_tensor():
block = blocks.TimeseriesBlock()
block.column_names = ["0", "1"]
block.column_types = {"0": analysers.NUMERICAL, "1": analysers.NUMERICAL}
outputs = block.build(kerastuner.HyperParameters(),
tf.keras.Input(shape=(32, 2), dtype=tf.float32))
assert len(nest.flatten(outputs)) == 1
def test_timeseries_build_return_tensor():
block = blocks.TimeseriesBlock()
block.column_names = ['0', '1']
block.column_types = {'0': adapters.NUMERICAL, '1': adapters.NUMERICAL}
outputs = block.build(kerastuner.HyperParameters(),
tf.keras.Input(shape=(32, 2), dtype=tf.float32))
assert len(nest.flatten(outputs)) == 1
assert isinstance(nest.flatten(outputs)[0], tf.Tensor)
def test_timeseries_deserialize_to_timeseries():
serialized_block = blocks.serialize(blocks.TimeseriesBlock())
block = blocks.deserialize(serialized_block)
assert isinstance(block, blocks.TimeseriesBlock)
def get_block(self):
return blocks.TimeseriesBlock()
