from typing import Any
from optuna.integration._lightgbm_tuner.optimize import _imports
from optuna.integration._lightgbm_tuner.optimize import LightGBMTuner
from optuna.integration._lightgbm_tuner.optimize import LightGBMTunerCV # NOQA
if _imports.is_successful():
from optuna.integration._lightgbm_tuner.sklearn import LGBMClassifier # NOQA
from optuna.integration._lightgbm_tuner.sklearn import LGBMModel # NOQA
from optuna.integration._lightgbm_tuner.sklearn import LGBMRegressor # NOQA
[docs]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-examples/tree/main/
lightgbm/lightgbm_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()