def test_log_completed_trial_skip_storage_access() -> None:
study = optuna.multi_objective.create_study(["minimize", "maximize"])
new_trial_id = study._study._storage.create_new_trial(
study._study._study_id)
trial = optuna.Trial(study._study, new_trial_id)
storage = study._storage
with patch.object(storage, "get_trial",
wraps=storage.get_trial) as mock_object:
optuna.multi_objective.study._log_completed_trial(study, trial, 1.0)
# Trial.params and MultiObjectiveTrial._get_values access storage.
assert mock_object.call_count == 2
optuna.logging.set_verbosity(optuna.logging.WARNING)
with patch.object(storage, "get_trial",
wraps=storage.get_trial) as mock_object:
optuna.multi_objective.study._log_completed_trial(study, trial, 1.0)
assert mock_object.call_count == 0
optuna.logging.set_verbosity(optuna.logging.DEBUG)
with patch.object(storage, "get_trial",
wraps=storage.get_trial) as mock_object:
optuna.multi_objective.study._log_completed_trial(study, trial, 1.0)
assert mock_object.call_count == 2
def test_log_completed_trial_skip_storage_access() -> None:
study = optuna.create_study()
# Create a trial to retrieve it as the `study.best_trial`.
study.optimize(lambda _: 0.0, n_trials=1)
trial = optuna.Trial(study,
study._storage.create_new_trial(study._study_id))
storage = study._storage
with patch.object(storage, "get_best_trial",
wraps=storage.get_best_trial) as mock_object:
study._log_completed_trial(trial, 1.0)
# Trial.best_trial and Trial.best_params access storage.
assert mock_object.call_count == 2
optuna.logging.set_verbosity(optuna.logging.WARNING)
with patch.object(storage, "get_best_trial",
wraps=storage.get_best_trial) as mock_object:
study._log_completed_trial(trial, 1.0)
assert mock_object.call_count == 0
optuna.logging.set_verbosity(optuna.logging.DEBUG)
with patch.object(storage, "get_best_trial",
wraps=storage.get_best_trial) as mock_object:
study._log_completed_trial(trial, 1.0)
assert mock_object.call_count == 2
def test_prepare_params(self):
study = optuna.study.create_study()
study.enqueue_trial({})
trial = optuna.Trial(study, 0)
params_file = r"../autolearn\params\xgboost.yml"
params = utils.read_yaml(params_file)
params = autolearn._Objective._eval_params(trial, params)
assert isinstance(params, dict)
