optuna_integration.LightGBMPruningCallback

class optuna_integration.LightGBMPruningCallback(trial, metric, valid_name='valid_0', report_interval=1)[source]

Callback for LightGBM to prune unpromising trials.

See the example if you want to add a pruning callback which observes accuracy of a LightGBM model.

Parameters:

trial (optuna.trial.Trial) – A Trial corresponding to the current evaluation of the objective function.
metric (str) – An evaluation metric for pruning, e.g., binary_error and multi_error. Please refer to LightGBM reference for further details.
valid_name (str) – 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.
report_interval (int) – Check if the trial should report intermediate values for pruning every n-th boosting iteration. By default report_interval=1 and reporting is performed after every iteration. Note that the pruning itself is performed according to the interval definition of the pruner.