Method optimize

optuna/study/study.py:414–526

Optimize an objective function. Optimization is done by choosing a suitable set of hyperparameter values from a given range. Uses a sampler which implements the task of value suggestion based on a specified distribution. The sampler is specified in :func:`~optuna.study.creat

(
        self,
        func: ObjectiveFuncType,
        n_trials: int | None = None,
        timeout: float | None = None,
        n_jobs: int = 1,
        catch: Iterable[type[Exception]] | type[Exception] = (),
        callbacks: Iterable[Callable[[Study, FrozenTrial], None]] | None = None,
        gc_after_trial: bool = False,
        show_progress_bar: bool = False,
    )

        return self._storage.get_study_system_attrs(self._study_id).get(_SYSTEM_ATTR_METRIC_NAMES)

    def optimize(
        self,
        func: ObjectiveFuncType,
        n_trials: int | None = None,
        timeout: float | None = None,
        n_jobs: int = 1,
        catch: Iterable[type[Exception]] | type[Exception] = (),
        callbacks: Iterable[Callable[[Study, FrozenTrial], None]] | None = None,
        gc_after_trial: bool = False,
        show_progress_bar: bool = False,
    ) -> None:
        """Optimize an objective function.

        Optimization is done by choosing a suitable set of hyperparameter values from a given
        range. Uses a sampler which implements the task of value suggestion based on a specified
        distribution. The sampler is specified in :func:`~optuna.study.create_study` and the
        default choice for the sampler is TPE.
        See also :class:`~optuna.samplers.TPESampler` for more details on 'TPE'.

        Optimization will be stopped when receiving a termination signal such as SIGINT and
        SIGTERM. Unlike other signals, a trial is automatically and cleanly failed when receiving
        SIGINT (Ctrl+C). If ``n_jobs`` is greater than one or if another signal than SIGINT
        is used, the interrupted trial state won't be properly updated.

        Example:

            .. testcode::

                import optuna


                def objective(trial):
                    x = trial.suggest_float("x", -1, 1)
                    return x**2


                study = optuna.create_study()
                study.optimize(objective, n_trials=3)

        Args:
            func:
                A callable that implements objective function.
            n_trials:
                The number of trials for each process. :obj:`None` represents no limit in terms of
                the number of trials. The study continues to create trials until the number of
                trials reaches ``n_trials``, ``timeout`` period elapses,
                :func:`~optuna.study.Study.stop` is called, or a termination signal such as
                SIGTERM or Ctrl+C is received.

                .. seealso::
                    :class:`optuna.study.MaxTrialsCallback` can ensure how many times trials
                    will be performed across all processes.
            timeout:
                Stop study after the given number of second(s). :obj:`None` represents no limit in
                terms of elapsed time. The study continues to create trials until the number of
                trials reaches ``n_trials``, ``timeout`` period elapses,
                :func:`~optuna.study.Study.stop` is called or, a termination signal such as
                SIGTERM or Ctrl+C is received.