Source code for optuna.pruners.median

from optuna.pruners.percentile import PercentilePruner


[docs]class MedianPruner(PercentilePruner): """Pruner using the median stopping rule. Prune if the trial's best intermediate result is worse than median of intermediate results of previous trials at the same step. Example: We minimize an objective function with the median stopping rule. .. code:: >>> from optuna import create_study >>> from optuna.pruners import MedianPruner >>> >>> def objective(trial): >>> ... >>> >>> study = create_study(pruner=MedianPruner()) >>> study.optimize(objective) Args: n_startup_trials: Pruning is disabled until the given number of trials finish in the same study. n_warmup_steps: Pruning is disabled until the trial reaches the given number of step. interval_steps: Interval in number of steps between the pruning checks, offset by the warmup steps. If no value has been reported at the time of a pruning check, that particular check will be postponed until a value is reported. """ def __init__(self, n_startup_trials=5, n_warmup_steps=0, interval_steps=1): # type: (int, int, int) -> None super(MedianPruner, self).__init__(50.0, n_startup_trials, n_warmup_steps, interval_steps)