- 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.
trial (Trial) – A
Trialcorresponding to the current evaluation of the objective function.
metric (str) – An evaluation metric for pruning, e.g.,
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_namesoption of train method. If omitted,
valid_0is 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=1and reporting is performed after every iteration. Note that the pruning itself is performed according to the interval definition of the pruner.