optuna.integration.AllenNLPPruningCallback¶

class optuna.integration.AllenNLPPruningCallback(trial: optuna.trial._trial.Trial, monitor: str)[source]¶

AllenNLP callback to prune unpromising trials.

See the example if you want to add a proning callback which observes a metric.

Parameters

trial – A Trial corresponding to the current evaluation of the objective function.
monitor – An evaluation metric for pruning, e.g. validation_loss or validation_accuracy.

Note

Added in v2.0.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v2.0.0.

__init__(trial: optuna.trial._trial.Trial, monitor: str)[source]¶: Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(trial, monitor)

Initialize self.