optuna_integration.lightgbm.train

optuna_integration.lightgbm.train(params, train_set, num_boost_round=1000, valid_sets=None, valid_names=None, feval=None, feature_name='auto', categorical_feature='auto', keep_training_booster=False, callbacks=None, time_budget=None, sample_size=None, study=None, optuna_callbacks=None, model_dir=None, *, show_progress_bar=True, optuna_seed=None)[source]

Wrapper of LightGBM Training API to tune hyperparameters.

It optimizes the following hyperparameters in a stepwise manner: lambda_l1, lambda_l2, num_leaves, feature_fraction, bagging_fraction, bagging_freq and min_child_samples. It is a drop-in replacement for lightgbm.train(). See a simple example of LightGBM Tuner which optimizes the validation log loss of cancer detection.

train() is a wrapper function of LightGBMTuner. To use feature in Optuna such as suspended/resumed optimization and/or parallelization, refer to LightGBMTuner instead of this function.

Note

Arguments and keyword arguments for lightgbm.train() can be passed. For params, please check the official documentation for LightGBM.

Parameters:

  • time_budget (int | None) – A time budget for parameter tuning in seconds.

  • study (Study | None) – 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[Callable[[Study, FrozenTrial], None]] | None) – 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() .

  • model_dir (str | None) – A directory to save boosters. By default, it is set to None and no boosters are saved. Please set shared directory (e.g., directories on NFS) if you want to access get_best_booster() in distributed environments. Otherwise, it may raise ValueError. If the directory does not exist, it will be created. The filenames of the boosters will be {model_dir}/{trial_number}.pkl (e.g., ./boosters/0.pkl).

  • show_progress_bar (bool) –

    Flag to show progress bars or not. To disable progress bar, set this False.

    Note

    Progress bars will be fragmented by logging messages of LightGBM and Optuna. Please suppress such messages to show the progress bars properly.

  • optuna_seed (int | None) –

    seed of TPESampler for random number generator that affects sampling for num_leaves, bagging_fraction, bagging_freq, lambda_l1, and lambda_l2.

    Note

    The deterministic parameter of LightGBM makes training reproducible. Please enable it when you use this argument.

  • params (dict[str, Any])

  • train_set (lgb.Dataset)

  • num_boost_round (int)

  • valid_sets (list['lgb.Dataset'] | tuple['lgb.Dataset', ...] | 'lgb.Dataset' | None)

  • valid_names (Any | None)

  • feval (Callable[..., Any] | None)

  • feature_name (str)

  • categorical_feature (str)

  • keep_training_booster (bool)

  • callbacks (list[Callable[..., Any]] | None)

  • sample_size (int | None)

Return type:

lgb.Booster