def _update_predictions_metadata(
cls, inputs_metadata: metadata_base.DataMetadata,
outputs: Optional[Outputs], target_columns_metadata: List[OrderedDict]
) -> metadata_base.DataMetadata:
outputs_metadata = inputs_metadata.clear(for_value=outputs,
generate_metadata=True)
for column_index, column_metadata in enumerate(
target_columns_metadata):
outputs_metadata = outputs_metadata.update_column(
column_index, column_metadata)
return outputs_metadata
def _update_metadata(
cls,
metadata: metadata_base.DataMetadata,
resource_id: str,
target: str,
features: list,
update_target: bool = True,
for_value: DataFrame = None,
source: typing.Any = None) -> metadata_base.DataMetadata:
if source is None:
source = cls
resource_metadata = dict(metadata.query((resource_id, )))
resource_metadata.update(
{
'schema': metadata_base.CONTAINER_SCHEMA_VERSION,
'structural_type': DataFrame,
'target_column': target,
}, )
new_metadata = metadata.clear(resource_metadata,
for_value=for_value,
source=source)
new_metadata = cls._copy_elements_metadata(metadata, (resource_id, ),
(),
new_metadata,
source=source)
old_resource_metadata = metadata.query((resource_id, ALL_ELEMENTS))
resource_metadata = dict(old_resource_metadata)
# TODO: what if we don't include target? len(features) + 1
resource_metadata['dimension'] = {'length': len(features) + 1}
if update_target:
resource_metadata['dimension'] = {'length': len(features) + 2}
resource_metadata['ft_features'] = cloudpickle.dumps(features)
new_metadata = new_metadata.update((ALL_ELEMENTS, ), resource_metadata)
for i, f in enumerate(features):
resource_metadata = {
'semantic_types': [
D3MMetadataTypes.to_d3m(f.variable_type),
D3MMetadataTypes.Attribute
],
'structural_type':
D3MMetadataTypes.to_default_structural_type(f.variable_type),
'name':
f.get_name()
}
if for_value is not None:
structural_type = type(for_value[f.get_name()].iloc[0])
resource_metadata['structural_type'] = structural_type
new_metadata = new_metadata.update((ALL_ELEMENTS, i),
resource_metadata)
# update index
resource_metadata = {
'semantic_types': [D3MMetadataTypes.PrimaryKey],
'name': 'd3mIndex'
}
if for_value is not None:
structural_type = type(for_value['d3mIndex'].iloc[0])
resource_metadata['structural_type'] = structural_type
new_metadata = new_metadata.update((ALL_ELEMENTS, len(features)),
resource_metadata)
if update_target:
resource_metadata = {
'semantic_types': [
D3MMetadataTypes.TrueTarget,
D3MMetadataTypes.SuggestedTarget
],
'name':
target
}
if for_value is not None:
structural_type = type(for_value[target].iloc[0])
resource_metadata['structural_type'] = structural_type
new_metadata = new_metadata.update(
(ALL_ELEMENTS, len(features) + 1), resource_metadata)
return new_metadata
