def test_run_verbosity(self, verbosity: int, level: int) -> None:
# We need to reconstruct our default handler to properly capture stderr.
optuna.logging._reset_library_root_logger()
optuna.logging.set_verbosity(optuna.logging.INFO)
params = {"verbose": -1} # type: Dict
dataset = lgb.Dataset(np.zeros((10, 10)))
study = optuna.create_study()
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=FutureWarning)
tuner = LightGBMTuner(
params,
dataset,
valid_sets=dataset,
study=study,
verbosity=verbosity,
time_budget=1,
)
with mock.patch.object(_BaseTuner,
"_get_booster_best_score",
return_value=1.0):
tuner.run()
assert optuna.logging.get_verbosity() == level
assert tuner.lgbm_params["verbose"] == -1
def test_run_show_progress_bar(self, show_progress_bar: bool, expected: int) -> None:
params: Dict = {"verbose": -1}
dataset = lgb.Dataset(np.zeros((10, 10)))
study = optuna.create_study()
tuner = LightGBMTuner(
params,
dataset,
valid_sets=dataset,
study=study,
time_budget=1,
show_progress_bar=show_progress_bar,
)
with mock.patch.object(
_BaseTuner, "_get_booster_best_score", return_value=1.0
), mock.patch("tqdm.tqdm") as mock_tqdm:
tuner.run()
assert mock_tqdm.call_count == expected
def train(*args: Any, **kwargs: Any) -> Any:
"""Wrapper of LightGBM Training API to tune hyperparameters.
It tunes important hyperparameters (e.g., ``min_child_samples`` and ``feature_fraction``) in a
stepwise manner. It is a drop-in replacement for `lightgbm.train()`_. See
`a simple example of LightGBM Tuner <https://github.com/optuna/optuna/blob/master/examples/lig
htgbm_tuner_simple.py>`_ which optimizes the validation log loss of cancer detection.
:func:`~optuna.integration.lightgbm.train` is a wrapper function of
:class:`~optuna.integration.lightgbm.LightGBMTuner`. To use feature in Optuna such as
suspended/resumed optimization and/or parallelization, refer to
:class:`~optuna.integration.lightgbm.LightGBMTuner` instead of this function.
Arguments and keyword arguments for `lightgbm.train()`_ can be passed.
.. _lightgbm.train(): https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.train.html
"""
_imports.check()
auto_booster = LightGBMTuner(*args, **kwargs)
auto_booster.run()
return auto_booster.get_best_booster()
