optuna.integration.lightgbm.LightGBMTunerCV

class optuna.integration.lightgbm.LightGBMTunerCV(params: Dict[str, Any], train_set: lgb.Dataset, num_boost_round: int = 1000, folds: Optional[Union[Generator[Tuple[int, int], None, None], Iterator[Tuple[int, int]], BaseCrossValidator]] = None, nfold: int = 5, stratified: bool = True, shuffle: bool = True, fobj: Optional[Callable[[…], Any]] = None, feval: Optional[Callable[[…], Any]] = None, feature_name: str = 'auto', categorical_feature: str = 'auto', early_stopping_rounds: Optional[int] = None, fpreproc: Optional[Callable[[…], Any]] = None, verbose_eval: Optional[Union[bool, int]] = True, show_stdv: bool = True, seed: int = 0, callbacks: Optional[List[Callable[[…], Any]]] = None, time_budget: Optional[int] = None, sample_size: Optional[int] = None, study: Optional[optuna.study.Study] = None, optuna_callbacks: Optional[List[Callable[[optuna.study.Study, optuna.trial._frozen.FrozenTrial], None]]] = None, verbosity: int = 1)

Hyperparameter tuner for LightGBM with cross-validation.

It employs the same stepwise approach as LightGBMTuner. LightGBMTunerCV invokes lightgbm.cv() to train and validate boosters while LightGBMTuner invokes lightgbm.train(). See a simple example which optimizes the validation log loss of cancer detection.

Arguments and keyword arguments for lightgbm.cv() can be passed except metrics, init_model and eval_train_metric. The arguments that only LightGBMTunerCV has are listed below:

Parameters

• time_budget – A time budget for parameter tuning in seconds.

• study – A Study instance to store optimization results. The Trial instances in it has the following user attributes: elapsed_secs is the elapsed time since the optimization starts. average_iteration_time is the average time of iteration to train the booster model in the trial. lgbm_params is a JSON-serialized dictionary of LightGBM parameters used in the trial.

• optuna_callbacks – List of Optuna callback functions that are invoked at the end of each trial. Each function must accept two parameters with the following types in this order: Study and FrozenTrial. Please note that this is not a callbacks argument of lightgbm.train() .

__init__(params: Dict[str, Any], train_set: lgb.Dataset, num_boost_round: int = 1000, folds: Optional[Union[Generator[Tuple[int, int], None, None], Iterator[Tuple[int, int]], BaseCrossValidator]] = None, nfold: int = 5, stratified: bool = True, shuffle: bool = True, fobj: Optional[Callable[[…], Any]] = None, feval: Optional[Callable[[…], Any]] = None, feature_name: str = 'auto', categorical_feature: str = 'auto', early_stopping_rounds: Optional[int] = None, fpreproc: Optional[Callable[[…], Any]] = None, verbose_eval: Optional[Union[bool, int]] = True, show_stdv: bool = True, seed: int = 0, callbacks: Optional[List[Callable[[…], Any]]] = None, time_budget: Optional[int] = None, sample_size: Optional[int] = None, study: Optional[optuna.study.Study] = None, optuna_callbacks: Optional[List[Callable[[optuna.study.Study, optuna.trial._frozen.FrozenTrial], None]]] = None, verbosity: int = 1) → None

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(params, train_set[, …])	Initialize self.
compare_validation_metrics(val_score, best_score)
higher_is_better()
run()	Perform the hyperparameter-tuning with given parameters.
sample_train_set()	Make subset of self.train_set Dataset object.
tune_bagging([n_trials])
tune_feature_fraction([n_trials])
tune_feature_fraction_stage2([n_trials])
tune_min_data_in_leaf()
tune_num_leaves([n_trials])
tune_regularization_factors([n_trials])

Attributes

best_params	Return parameters of the best booster.
best_score	Return the score of the best booster.

property best_params

Return parameters of the best booster.

property best_score

Return the score of the best booster.

run() → None

Perform the hyperparameter-tuning with given parameters.

sample_train_set() → None

Make subset of self.train_set Dataset object.