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. Arguments and keyword arguments for `lightgbm.train()
<https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.train.html>`_ can be passed.
"""
_check_lightgbm_availability()
auto_booster = LightGBMTuner(*args, **kwargs)
auto_booster.run()
return auto_booster.get_best_booster()
def train(*args, **kwargs):
# type: (List[Any], Optional[Dict[Any, Any]]) -> Any
"""Wrapper function of LightGBM API: train()
Arguments and keyword arguments for `lightgbm.train()` can be passed.
"""
auto_booster = LightGBMTuner(*args, **kwargs)
booster = auto_booster.run()
return booster
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_tuner.LightGBMTuner`. To use feature in Optuna such as
suspended/resumed optimization and/or parallelization, refer to
:class:`~optuna.integration.lightgbm_tuner.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()
def train(*args, **kwargs):
# type: (Any, Any) -> Any
"""Wrapper of LightGBM Training API to tune hyperparameters.
.. warning::
This feature is experimental. The interface may be changed in the future.
It tunes important hyperparameters (e.g., `min_child_samples` and `feature_fraction`) in a
stepwise manner. Arguments and keyword arguments for `lightgbm.train()
<https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.train.html>`_ can be passed.
"""
auto_booster = LightGBMTuner(*args, **kwargs)
booster = auto_booster.run()
return booster
