class optuna.terminator.TerminatorCallback(terminator=None)[source]

A callback that terminates the optimization using Terminator.

This class implements a callback which wraps Terminator so that it can be used with the optimize() method.


terminator (Optional[BaseTerminator]) – A terminator object which determines whether to terminate the optimization by assessing the room for optimization and statistical error. Defaults to a Terminator object with default improvement_evaluator and error_evaluator.


from sklearn.datasets import load_wine
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold

import optuna
from optuna.terminator import TerminatorCallback
from optuna.terminator import report_cross_validation_scores

def objective(trial):
    X, y = load_wine(return_X_y=True)

    clf = RandomForestClassifier(
        max_depth=trial.suggest_int("max_depth", 2, 32),
        min_samples_split=trial.suggest_float("min_samples_split", 0, 1),
        criterion=trial.suggest_categorical("criterion", ("gini", "entropy")),

    scores = cross_val_score(clf, X, y, cv=KFold(n_splits=5, shuffle=True))
    report_cross_validation_scores(trial, scores)
    return scores.mean()

study = optuna.create_study(direction="maximize")
terminator = TerminatorCallback()
study.optimize(objective, n_trials=50, callbacks=[terminator])

See also

Please refer to Terminator for the details of the terminator mechanism.


Added in v3.2.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v3.2.0.