Source code for optuna.terminator.callback

from __future__ import annotations

from typing import Optional

from optuna._experimental import experimental_class
from optuna.logging import get_logger
from import Study
from optuna.terminator.terminator import BaseTerminator
from optuna.terminator.terminator import Terminator
from optuna.trial import FrozenTrial

_logger = get_logger(__name__)

[docs] @experimental_class("3.2.0") class TerminatorCallback: """A callback that terminates the optimization using Terminator. This class implements a callback which wraps :class:`~optuna.terminator.Terminator` so that it can be used with the :func:`` method. Args: terminator: A terminator object which determines whether to terminate the optimization by assessing the room for optimization and statistical error. Defaults to a :class:`~optuna.terminator.Terminator` object with default ``improvement_evaluator`` and ``error_evaluator``. Example: .. testcode:: 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]) .. seealso:: Please refer to :class:`~optuna.terminator.Terminator` for the details of the terminator mechanism. """ def __init__( self, terminator: Optional[BaseTerminator] = None, ) -> None: self._terminator = terminator or Terminator() def __call__(self, study: Study, trial: FrozenTrial) -> None: should_terminate = self._terminator.should_terminate(study=study) if should_terminate:"The study has been stopped by the terminator.") study.stop()