ibm_aigov_facts_client.export.export_facts module

class ExportFacts(facts_service_client: FactsClientAdapter, **kwargs)

Export updated payloads for any run

export_payload(run_id: str, root_directory: str = None) → DetailedResponse

Export single run to factsheet service.

Parameters:

run_id (str) – Id of run to be exported
root_directory (str) – (Optional) Absolute path for directory containing experiments and runs.

Returns:

A DetailedResponse containing the factsheet response result

Return type:

DetailedResponse

A way you might use me is:

>>> client.export_facts.export_payload(<RUN_ID>)

prepare_model_meta(wml_client: object, meta_props: Dict[str, Any], experiment_name: str = None) → Dict

Add current experiment attributes to model meta properties

Parameters:

wml_client (object) – Watson Machine learning client object.
meta_props (dict) – Current model meta properties.
experiment_name (str) – (Optional) Explicit name any experiment to be used.

Returns:

A Dict containing the updated meta properties.

Return type:

Dict

A way you might use me is:

>>> client.export_facts.prepare_model_meta(wml_client=<wml_client>,meta_props=<wml_model_meta_props>)

class ExportFactsAutolog(run_id, guid)

Bases: FactsheetServiceClientAutolog

Generate and export payload to factsheet as part of autolog