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. .. testcode:: import numpy as np from sklearn.datasets import load_iris from sklearn.linear_model import SGDClassifier from sklearn.model_selection import train_test_split import optuna X, y = load_iris(return_X_y=True) X_train, X_valid, y_train, y_valid = train_test_split(X, y) classes = np.unique(y) def objective(trial): alpha = trial.suggest_float("alpha", 0.0, 1.0) clf = SGDClassifier(alpha=alpha) n_train_iter = 100 for step in range(n_train_iter): clf.partial_fit(X_train, y_train, classes=classes) intermediate_value = clf.score(X_valid, y_valid), step) if trial.should_prune(): raise optuna.TrialPruned() return clf.score(X_valid, y_valid) study = optuna.create_study( direction="maximize", pruner=optuna.pruners.MedianPruner( n_startup_trials=5, n_warmup_steps=30, interval_steps=10 ), ) study.optimize(objective, n_trials=20) 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 exceeds the given number of step. Note that this feature assumes that ``step`` starts at zero. 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. n_min_trials: Minimum number of reported trial results at a step to judge whether to prune. If the number of reported intermediate values from all trials at the current step is less than ``n_min_trials``, the trial will not be pruned. This can be used to ensure that a minimum number of trials are run to completion without being pruned. """ def __init__( self, n_startup_trials: int = 5, n_warmup_steps: int = 0, interval_steps: int = 1, *, n_min_trials: int = 1, ) -> None: super().__init__( 50.0, n_startup_trials, n_warmup_steps, interval_steps, n_min_trials=n_min_trials )