optuna_integration.MXNetPruningCallback

class optuna_integration.MXNetPruningCallback(trial, eval_metric)[source]

MXNet callback to prune unpromising trials.

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

Parameters:

trial (optuna.trial.Trial) – A Trial corresponding to the current evaluation of the objective function.
eval_metric (str) – An evaluation metric name for pruning, e.g., cross-entropy and accuracy. If using default metrics like mxnet.metrics.Accuracy, use it’s default metric name. For custom metrics, use the metric_name provided to constructor. Please refer to mxnet.metrics reference for further details.

Warning

Deprecated in v4.1.0. This feature will be removed in the future. The removal of this feature is currently scheduled for v6.0.0, but this schedule is subject to change. See https://github.com/optuna/optuna/releases/tag/v4.1.0.

MXNet development ended. For more details, please check the MXNet website.