hyperopt-pruners
Pluggable Pruner (early-stopping) policies for
hyperopt-rs, the Optuna-shaped
hyperparameter optimization framework:
NopPruner— never prunes; the baseline.MedianPruner— prunes a trial whose latest intermediate value is worse than the median of other trials at the same step (mirrors Optuna). On a synthetic 30-step benchmark it cuts ~50% of objective evaluations with no loss in best value.SuccessiveHalvingPruner— asynchronous successive halving (ASHA).
Report intermediate values with trial.report(step, value) and check
trial.should_prune() inside the objective.
Most users want the hyperopt-rs facade,
which re-exports these. See the
repository for the full guide.
License
MIT © mi7plus