def setUp(self): super(ModelFactoryTest, self).setUp() self.addCleanup(mock.patch.stopall) mock.patch.object(utils, 'fetch_explanation_metadata', return_value=explain_metadata.ExplainMetadata( inputs=[], framework='xgboost')).start()
def get_metadata(self): """Returns the current metadata as a dictionary.""" current_md = explain_metadata.ExplainMetadata( inputs=list(self._inputs.values()), outputs=list(self._outputs.values()), framework=explain_metadata.Framework.TENSORFLOW2, tags=[constants.METADATA_TAG]) return current_md.to_dict()
def get_metadata(self): """Returns the current metadata.""" current_md = explain_metadata.ExplainMetadata( inputs=list(self._inputs.values()), outputs=list(self._outputs.values()), framework='Tensorflow', tags=[constants.METADATA_TAG]) return current_md.to_dict()
def _create_metadata_dict(self, features_dict, crossed_columns, desired_columns, output_dict): """Creates metadata from given tensor information. Args: features_dict: Dictionary from feature name to FeatureTensors class. crossed_columns: A set of crossed column names. desired_columns: A list of feature column names. Only the columns in this list will be added to input metadata. output_dict: Dictionary from tf.feature_columns to list of dense tensors. Returns: A dictionary that abides to explanation metadata. """ return explain_metadata.ExplainMetadata( inputs=self._create_input_metadata(features_dict, crossed_columns, desired_columns), outputs=self._create_output_metadata(output_dict), framework='Tensorflow', tags=[constants.METADATA_TAG]).to_dict()
def _create_metadata_dict( self, features_dict: Dict[Text, List[monkey_patch_utils.FeatureTensors]], crossed_columns: Set[Text], desired_columns: List[Text], output_dict: Dict[Text, tf.Tensor], drop_duplicate_features: bool = False, group_duplicate_features: bool = False) -> Dict[Text, Any]: """Creates metadata from given tensor information. Args: features_dict: Dictionary from feature name to FeatureTensors class. crossed_columns: A set of crossed column names. desired_columns: A list of feature column names. Only the columns in this list will be added to input metadata. output_dict: Dictionary from tf.feature_columns to list of dense tensors. drop_duplicate_features: If there are multiple inputs for the same feature column, then we will drop all but one if drop_duplicate_features is True. If False, we will include them all with unique suffix added to the input names to disambiguate. group_duplicate_features: If there are multiple inputs for the same feature column, then we will group them all as one feature group if this parameter is set to True. Returns: A dictionary that abides to explanation metadata. """ return explain_metadata.ExplainMetadata( inputs=self._create_input_metadata( features_dict, crossed_columns, desired_columns, drop_duplicate_features=drop_duplicate_features, group_duplicate_features=group_duplicate_features), outputs=self._create_output_metadata(output_dict), framework='Tensorflow', tags=[constants.METADATA_TAG]).to_dict()