def test_get_best_booster(self) -> None:
unexpected_value = 20 # out of scope.
params = {"verbose": -1, "lambda_l1": unexpected_value} # type: Dict
dataset = lgb.Dataset(np.zeros((10, 10)))
study = optuna.create_study()
tuner = LightGBMTuner(params, dataset, valid_sets=dataset, study=study)
with pytest.raises(ValueError):
tuner.get_best_booster()
with mock.patch.object(BaseTuner, "_get_booster_best_score", return_value=0.0):
tuner.tune_regularization_factors()
best_booster = tuner.get_best_booster()
assert best_booster.params["lambda_l1"] != unexpected_value
# TODO(toshihikoyanase): Remove this check when LightGBMTuner.best_booster is removed.
with pytest.warns(DeprecationWarning):
tuner.best_booster
tuner2 = LightGBMTuner(params, dataset, valid_sets=dataset, study=study)
# Resumed study does not have the best booster.
with pytest.raises(ValueError):
tuner2.get_best_booster()
def test_best_booster_with_model_dir(self) -> None:
params = {"verbose": -1} # type: Dict
dataset = lgb.Dataset(np.zeros((10, 10)))
study = optuna.create_study()
with TemporaryDirectory() as tmpdir:
tuner = LightGBMTuner(params,
dataset,
valid_sets=dataset,
study=study,
model_dir=tmpdir)
with mock.patch.object(BaseTuner,
"_get_booster_best_score",
return_value=0.0):
tuner.tune_regularization_factors()
best_booster = tuner.get_best_booster()
tuner2 = LightGBMTuner(params,
dataset,
valid_sets=dataset,
study=study,
model_dir=tmpdir)
best_booster2 = tuner2.get_best_booster()
assert best_booster.params == best_booster2.params
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)
