naswot_pruner

Prunes a network when the score is above a user defined limit.

class NASWOTPruner(*args: Any, **kwargs: Any)[source]

Bases: BasePruner

Implements the NASWOT method (first version of paper) as an optuna pruner.

This means that before each training of a trial network the network will be tested against a user defined surrogate score (which has to be calibrated). If this score is good enough, the candidate will be trained.

Parameters:

  • search_parameters (mala.common.parametes.Parameters) – Parameters used to create this objective.

  • data_handler (mala.datahandling.data_handler.DataHandler) – datahandler to be used during the hyperparameter optimization.

prune(study: optuna.study.Study, trial: optuna.trial.FrozenTrial) → bool[source]

Judge whether the trial should be pruned based on the reported values.

Note that this method is not supposed to be called by library users. Instead, optuna.trial.Trial.report() and optuna.trial.Trial.should_prune() provide user interfaces to implement pruning mechanism in an objective function.

Parameters:

  • study (optuna.study.Study) – Study object of the target study.

  • trial (optuna.trial.FrozenTrial) – FrozenTrial object of the target trial. Take a copy before modifying this object.

Returns:

should_prune – A boolean indicating whether this particular trial should be pruned.

Return type:

bool