def test_optuna_callback(self) -> None:
params = {"verbose": -1} # type: Dict[str, Any]
dataset = lgb.Dataset(np.zeros((10, 10)))
callback_mock = mock.MagicMock()
study = optuna.create_study()
tuner = LightGBMTunerCV(params, dataset, study=study, optuna_callbacks=[callback_mock],)
with mock.patch.object(OptunaObjectiveCV, "_get_cv_scores", return_value=[1.0]):
tuner.tune_params(["num_leaves"], 10, optuna.samplers.TPESampler(), "num_leaves")
assert callback_mock.call_count == 10
def test_resume_run(self) -> None:
params = {"verbose": -1} # type: Dict
dataset = lgb.Dataset(np.zeros((10, 10)))
study = optuna.create_study()
tuner = LightGBMTunerCV(params, dataset, study=study)
with mock.patch.object(OptunaObjectiveCV, "_get_cv_scores", return_value=[1.0]):
tuner.tune_regularization_factors()
n_trials = len(study.trials)
assert n_trials == len(study.trials)
tuner2 = LightGBMTuner(params, dataset, valid_sets=dataset, study=study)
with mock.patch.object(OptunaObjectiveCV, "_get_cv_scores", return_value=[1.0]):
tuner2.tune_regularization_factors()
assert n_trials == len(study.trials)
def test_inconsistent_study_direction(self, metric: str, study_direction: str) -> None:
params = {} # type: Dict[str, Any]
if metric is not None:
params["metric"] = metric
train_set = lgb.Dataset(None)
study = optuna.create_study(direction=study_direction)
with pytest.raises(ValueError) as excinfo:
LightGBMTunerCV(
params, train_set, num_boost_round=5, early_stopping_rounds=2, study=study,
)
assert excinfo.type == ValueError
assert str(excinfo.value).startswith("Study direction is inconsistent with the metric")
def _get_tunercv_object(
self,
params: Dict[str, Any] = {},
train_set: lgb.Dataset = None,
kwargs_options: Dict[str, Any] = {},
study: Optional[optuna.study.Study] = None,
) -> LightGBMTunerCV:
# Required keyword arguments.
kwargs = dict(num_boost_round=5, early_stopping_rounds=2,
study=study) # type: Dict[str, Any]
kwargs.update(kwargs_options)
runner = LightGBMTunerCV(params, train_set, **kwargs)
return runner