def _meta_build(self, dataset): # Using functional API. if all([isinstance(output, base.Node) for output in self.outputs]): self.hyper_graph = graph.HyperGraph(inputs=self.inputs, outputs=self.outputs) # Using input/output API. elif all([isinstance(output, base.Head) for output in self.outputs]): self.hyper_graph = meta_model.assemble(inputs=self.inputs, outputs=self.outputs, dataset=dataset, seed=self.seed) self.outputs = self.hyper_graph.outputs
def test_partial_column_types(): input_node = node.StructuredDataInput( column_names=common.COLUMN_NAMES_FROM_CSV, column_types=common.PARTIAL_COLUMN_TYPES_FROM_CSV) (x, y), (val_x, val_y) = common.dataframe_numpy() dataset = tf.data.Dataset.zip( ((tf.data.Dataset.from_tensor_slices(x.values.astype(np.unicode)), ), (tf.data.Dataset.from_tensor_slices(y), ))) hm = meta_model.assemble(input_node, ak.ClassificationHead(), dataset) for block in hm._blocks: if isinstance(block, ak.FeatureEngineering): assert block.input_node.column_types['fare'] == 'categorical'
def _meta_build(self, dataset): self.outputs = meta_model.assemble(inputs=self.inputs, outputs=self.outputs, dataset=dataset) self._build_network()
def _meta_build(self, dataset): self.hypermodel = meta_model.assemble(inputs=self.inputs, outputs=self.outputs, dataset=dataset, seed=self.seed) self.outputs = self.hypermodel.outputs