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()
with TemporaryDirectory() as tmpdir:
tuner = LightGBMTunerCV(params,
dataset,
study=study,
model_dir=tmpdir,
return_cvbooster=True)
with pytest.raises(ValueError):
tuner.get_best_booster()
with mock.patch.object(_OptunaObjectiveCV,
"_get_cv_scores",
return_value=[1.0]):
tuner.tune_regularization_factors()
best_boosters = tuner.get_best_booster().boosters
for booster in best_boosters:
assert booster.params["lambda_l1"] != unexpected_value
tuner2 = LightGBMTunerCV(params,
dataset,
study=study,
model_dir=tmpdir,
return_cvbooster=True)
best_boosters2 = tuner2.get_best_booster().boosters
for booster, booster2 in zip(best_boosters, best_boosters2):
assert booster.params == booster2.params
def test_get_best_booster_with_error(self) -> None:
params: Dict = {"verbose": -1}
dataset = lgb.Dataset(np.zeros((10, 10)))
study = optuna.create_study()
tuner = LightGBMTunerCV(
params, dataset, study=study, model_dir=None, return_cvbooster=True
)
# No trial is completed yet.
with pytest.raises(ValueError):
tuner.get_best_booster()
with mock.patch.object(_OptunaObjectiveCV, "_get_cv_scores", return_value=[1.0]):
tuner.tune_regularization_factors()
tuner2 = LightGBMTunerCV(
params, dataset, study=study, model_dir=None, return_cvbooster=True
)
# Resumed the study does not have the best booster.
with pytest.raises(ValueError):
tuner2.get_best_booster()
with TemporaryDirectory() as tmpdir:
tuner3 = LightGBMTunerCV(
params, dataset, study=study, model_dir=tmpdir, return_cvbooster=True
)
# The booster was not saved hence not found in the `model_dir`.
with pytest.raises(ValueError):
tuner3.get_best_booster()
def test_resume_run(self) -> None:
params: Dict = {"verbose": -1}
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)
