def take_action(self, parsed_args: Namespace) -> None:
warnings.warn(
"'best-trials' is an experimental CLI command. The interface can change in the "
"future.",
ExperimentalWarning,
)
storage_url = _check_storage_url(self.app_args.storage)
study = optuna.load_study(storage=storage_url,
study_name=parsed_args.study_name)
best_trials = [trial.number for trial in study.best_trials]
attrs = (
"number",
"value" if not study._is_multi_objective() else "values",
"datetime_start",
"datetime_complete",
"duration",
"params",
"user_attrs",
"state",
)
records, columns = _dataframe._create_records_and_aggregate_column(
study, attrs)
best_records = list(
filter(lambda record: record[("number", "")] in best_trials,
records))
print(
_format_output(best_records, columns, parsed_args.format,
parsed_args.flatten))
def take_action(
self, parsed_args: Namespace
) -> Tuple[List[str], List[List[Union[int, float, str]]]]:
warnings.warn(
"'best-trials' is an experimental CLI command. The interface can change in the "
"future.",
ExperimentalWarning,
)
storage_url = _check_storage_url(self.app_args.storage)
study = optuna.load_study(storage=storage_url,
study_name=parsed_args.study_name)
best_trials = [trial.number for trial in study.best_trials]
attrs = (
"number",
"value" if not study._is_multi_objective() else "values",
"datetime_start",
"datetime_complete",
"duration",
"params",
"user_attrs",
"state",
)
records, columns = _dataframe._create_records_and_aggregate_column(
study, attrs)
best_records = filter(
lambda record: record[("number", "")] in best_trials, records)
list_of_trial_values = [
_format_trial_values(record, columns) for record in best_records
]
return _dataframe._flatten_columns(columns), list_of_trial_values