def test_run_trial_pruned(storage_mode: str,
caplog: LogCaptureFixture) -> None:
def gen_func(
intermediate: Optional[float] = None) -> Callable[[Trial], float]:
def func(trial: Trial) -> float:
if intermediate is not None:
trial.report(step=1, value=intermediate)
raise TrialPruned
return func
with StorageSupplier(storage_mode) as storage:
study = create_study(storage=storage)
caplog.clear()
frozen_trial = _optimize._run_trial(study, gen_func(), catch=())
assert frozen_trial.state == TrialState.PRUNED
assert frozen_trial.value is None
assert "Trial 0 pruned." in caplog.text
caplog.clear()
frozen_trial = _optimize._run_trial(study,
gen_func(intermediate=1),
catch=())
assert frozen_trial.state == TrialState.PRUNED
assert frozen_trial.value == 1
assert "Trial 1 pruned." in caplog.text
caplog.clear()
frozen_trial = _optimize._run_trial(
study, gen_func(intermediate=float("nan")), catch=())
assert frozen_trial.state == TrialState.PRUNED
assert frozen_trial.value is None
assert "Trial 2 pruned." in caplog.text
def test_run_trial(storage_mode: str, caplog: LogCaptureFixture) -> None:
with StorageSupplier(storage_mode) as storage:
study = create_study(storage=storage)
caplog.clear()
frozen_trial = _optimize._run_trial(study, lambda _: 1.0, catch=())
assert frozen_trial.state == TrialState.COMPLETE
assert frozen_trial.value == 1.0
assert "Trial 0 finished with value: 1.0 and parameters" in caplog.text
caplog.clear()
frozen_trial = _optimize._run_trial(study,
lambda _: float("inf"),
catch=())
assert frozen_trial.state == TrialState.COMPLETE
assert frozen_trial.value == float("inf")
assert "Trial 1 finished with value: inf and parameters" in caplog.text
caplog.clear()
frozen_trial = _optimize._run_trial(study,
lambda _: -float("inf"),
catch=())
assert frozen_trial.state == TrialState.COMPLETE
assert frozen_trial.value == -float("inf")
assert "Trial 2 finished with value: -inf and parameters" in caplog.text
def test_run_trial_exception(storage_mode: str) -> None:
with StorageSupplier(storage_mode) as storage:
study = create_study(storage=storage)
with pytest.raises(ValueError):
_optimize._run_trial(study, fail_objective, ())
# Test trial with unacceptable exception.
with StorageSupplier(storage_mode) as storage:
study = create_study(storage=storage)
with pytest.raises(ValueError):
_optimize._run_trial(study, fail_objective, (ArithmeticError, ))
def test_run_trial_catch_exception(storage_mode: str) -> None:
with StorageSupplier(storage_mode) as storage:
study = create_study(storage=storage)
frozen_trial = _optimize._run_trial(study,
fail_objective,
catch=(ValueError, ))
assert frozen_trial.state == TrialState.FAIL
assert STUDY_TELL_WARNING_KEY not in frozen_trial.system_attrs
def test_run_trial_invoke_tell_with_suppressing_warning(
storage_mode: str) -> None:
def func_numerical(trial: Trial) -> float:
return trial.suggest_float("v", 0, 10)
with StorageSupplier(storage_mode) as storage:
study = create_study(storage=storage)
with mock.patch("optuna.study._optimize._tell_with_warning",
side_effect=_tell_with_warning) as mock_obj:
_optimize._run_trial(study, func_numerical, ())
mock_obj.assert_called_once_with(
study=mock.ANY,
trial=mock.ANY,
values=mock.ANY,
state=mock.ANY,
suppress_warning=True,
)
def test_run_trial(storage_mode: str) -> None:
with StorageSupplier(storage_mode) as storage:
study = create_study(storage=storage)
# Test trial with acceptable exception.
def func_value_error(_: Trial) -> float:
raise ValueError
trial = _optimize._run_trial(study,
func_value_error,
catch=(ValueError, ))
frozen_trial = study._storage.get_trial(trial._trial_id)
assert frozen_trial.state == TrialState.FAIL
# Test trial with unacceptable exception.
with pytest.raises(ValueError):
_optimize._run_trial(study,
func_value_error,
catch=(ArithmeticError, ))
# Test trial with invalid objective value: None
def func_none(_: Trial) -> float:
return None # type: ignore
trial = _optimize._run_trial(study, func_none, catch=(Exception, ))
frozen_trial = study._storage.get_trial(trial._trial_id)
assert frozen_trial.state == TrialState.FAIL
# Test trial with invalid objective value: nan
def func_nan(_: Trial) -> float:
return float("nan")
trial = _optimize._run_trial(study, func_nan, catch=(Exception, ))
frozen_trial = study._storage.get_trial(trial._trial_id)
assert frozen_trial.state == TrialState.FAIL
def test_run_trial_automatically_fail(storage_mode: str,
caplog: LogCaptureFixture) -> None:
with StorageSupplier(storage_mode) as storage:
study = create_study(storage=storage)
caplog.clear()
frozen_trial = _optimize._run_trial(study,
lambda _: float("nan"),
catch=())
assert frozen_trial.state == TrialState.FAIL
assert frozen_trial.value is None
assert "Trial 0 failed because of the following error:" in caplog.text
assert "The value nan is not acceptable." in caplog.text
caplog.clear()
frozen_trial = _optimize._run_trial(
study, lambda _: None,
catch=()) # type: ignore[arg-type,return-value] # noqa: E501
assert frozen_trial.state == TrialState.FAIL
assert frozen_trial.value is None
assert "Trial 1 failed because of the following error:" in caplog.text
assert "The value None could not be cast to float." in caplog.text
caplog.clear()
frozen_trial = _optimize._run_trial(
study, lambda _: object(),
catch=()) # type: ignore[arg-type,return-value] # noqa: E501
assert frozen_trial.state == TrialState.FAIL
assert frozen_trial.value is None
assert "Trial 2 failed because of the following error:" in caplog.text
assert "The value <object object at" in caplog.text
assert "> could not be cast to float." in caplog.text
caplog.clear()
frozen_trial = _optimize._run_trial(study, lambda _: [0, 1], catch=())
assert frozen_trial.state == TrialState.FAIL
assert frozen_trial.value is None
assert "Trial 3 failed because of the following error: The number" in caplog.text
assert "of the values 2 did not match the number of the objectives 1." in caplog.text
def test_run_trial_with_trial_pruned(trial_pruned_class: Callable[[],
TrialPruned],
report_value: Optional[float]) -> None:
study = create_study()
def func_with_trial_pruned(trial: Trial) -> float:
if report_value is not None:
trial.report(report_value, 1)
raise trial_pruned_class()
trial = _optimize._run_trial(study, func_with_trial_pruned, catch=())
frozen_trial = study._storage.get_trial(trial._trial_id)
assert frozen_trial.value == report_value
assert frozen_trial.state == TrialState.PRUNED
