optuna.integration.LightGBMPruningCallback¶
- class optuna.integration.LightGBMPruningCallback(trial: optuna.trial._trial.Trial, metric: str, valid_name: str = 'valid_0')[source]¶
Callback for LightGBM to prune unpromising trials.
See the example if you want to add a pruning callback which observes AUC of a LightGBM model.
- Parameters
trial – A
Trial
corresponding to the current evaluation of the objective function.metric – An evaluation metric for pruning, e.g.,
binary_error
andmulti_error
. Please refer to LightGBM reference for further details.valid_name – The name of the target validation. Validation names are specified by
valid_names
option of train method. If omitted,valid_0
is used which is the default name of the first validation. Note that this argument will be ignored if you are calling cv method instead of train method.
Methods
__init__
(trial, metric[, valid_name])