optuna.pruners
The pruners
module defines a BasePruner
class characterized by an abstract prune()
method, which, for a given trial and its associated study, returns a boolean value representing whether the trial should be pruned. This determination is made based on stored intermediate values of the objective function, as previously reported for the trial using optuna.trial.Trial.report()
. The remaining classes in this module represent child classes, inheriting from BasePruner
, which implement different pruning strategies.
See also
Efficient Optimization Algorithms tutorial explains the concept of the pruner classes and a minimal example.
See also
User-Defined Pruner tutorial could be helpful if you want to implement your own pruner classes.
Base class for pruners. |
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Pruner using the median stopping rule. |
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Pruner which never prunes trials. |
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Pruner which wraps another pruner with tolerance. |
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Pruner to keep the specified percentile of the trials. |
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Pruner using Asynchronous Successive Halving Algorithm. |
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Pruner using Hyperband. |
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Pruner to detect outlying metrics of the trials. |