Exemplo n.º 1
0
 def _make_pruner(self):
     if isinstance(self.pruner_method, str):
         if self.pruner_method == 'halving':
             pruner = SuccessiveHalvingPruner(
                 min_resource=self.n_timesteps // 6,
                 reduction_factor=4,
                 min_early_stopping_rate=0)
         elif self.pruner_method == 'median':
             pruner = MedianPruner(n_startup_trials=5,
                                   n_warmup_steps=self.n_timesteps // 6)
         elif self.pruner_method == 'none':
             # Do not prune
             pruner = NopPruner()
         else:
             raise ValueError('Unknown pruner: {}'.format(
                 self.pruner_method))
     elif isinstance(self.pruner_method, dict):
         method_copy = deepcopy(self.pruner_method)
         method = method_copy.pop('method')
         if method == 'halving':
             pruner = SuccessiveHalvingPruner(**method_copy)
         elif method == 'median':
             pruner = MedianPruner(**method_copy)
         elif method == 'none':
             # Do not prune
             pruner = NopPruner()
         else:
             raise ValueError('Unknown pruner: {}'.format(
                 self.pruner_method))
     else:
         raise ValueError("Wrong type for pruner settings!")
     return pruner
Exemplo n.º 2
0
 def _create_pruner(self, pruner_method: str) -> BasePruner:
     if pruner_method == "halving":
         pruner = SuccessiveHalvingPruner(min_resource=1,
                                          reduction_factor=4,
                                          min_early_stopping_rate=0)
     elif pruner_method == "median":
         pruner = MedianPruner(n_startup_trials=self.n_startup_trials,
                               n_warmup_steps=self.n_evaluations // 3)
     elif pruner_method == "none":
         # Do not prune
         pruner = NopPruner()
     else:
         raise ValueError(f"Unknown pruner: {pruner_method}")
     return pruner
Exemplo n.º 3
0
def _create_study(mo_study: "multi_objective.study.MultiObjectiveStudy") -> "optuna.Study":
    study = create_study(
        storage=mo_study._storage,
        sampler=_MultiObjectiveSamplerAdapter(mo_study.sampler),
        pruner=NopPruner(),
        study_name="_motpe-" + mo_study._storage.get_study_name_from_id(mo_study._study_id),
        directions=mo_study.directions,
        load_if_exists=True,
    )
    for mo_trial in mo_study.trials:
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", ExperimentalWarning)
            study.add_trial(_create_trial(mo_trial))
    return study
Exemplo n.º 4
0
def create_study(
    directions: List[str],
    study_name: Optional[str] = None,
    storage: Optional[Union[str, BaseStorage]] = None,
    sampler: Optional["multi_objective.samplers.BaseMultiObjectiveSampler"] = None,
    load_if_exists: bool = False,
) -> "multi_objective.study.MultiObjectiveStudy":
    """Create a new :class:`~optuna.multi_objective.study.MultiObjectiveStudy`.

    Example:

        .. testcode::

            import optuna


            def objective(trial):
                # Binh and Korn function.
                x = trial.suggest_float("x", 0, 5)
                y = trial.suggest_float("y", 0, 3)

                v0 = 4 * x ** 2 + 4 * y ** 2
                v1 = (x - 5) ** 2 + (y - 5) ** 2
                return v0, v1


            study = optuna.multi_objective.create_study(["minimize", "minimize"])
            study.optimize(objective, n_trials=3)

    Args:
        directions:
            Optimization direction for each objective value.
            Set ``minimize`` for minimization and ``maximize`` for maximization.
        study_name:
            Study's name. If this argument is set to None, a unique name is generated
            automatically.
        storage:
            Database URL. If this argument is set to None, in-memory storage is used, and the
            :class:`~optuna.study.Study` will not be persistent.

            .. note::
                When a database URL is passed, Optuna internally uses `SQLAlchemy`_ to handle
                the database. Please refer to `SQLAlchemy's document`_ for further details.
                If you want to specify non-default options to `SQLAlchemy Engine`_, you can
                instantiate :class:`~optuna.storages.RDBStorage` with your desired options and
                pass it to the ``storage`` argument instead of a URL.

             .. _SQLAlchemy: https://www.sqlalchemy.org/
             .. _SQLAlchemy's document:
                 https://docs.sqlalchemy.org/en/latest/core/engines.html#database-urls
             .. _SQLAlchemy Engine: https://docs.sqlalchemy.org/en/latest/core/engines.html

        sampler:
            A sampler object that implements background algorithm for value suggestion.
            If :obj:`None` is specified,
            :class:`~optuna.multi_objective.samplers.NSGAIIMultiObjectiveSampler` is used
            as the default. See also :class:`~optuna.multi_objective.samplers`.
        load_if_exists:
            Flag to control the behavior to handle a conflict of study names.
            In the case where a study named ``study_name`` already exists in the ``storage``,
            a :class:`~optuna.exceptions.DuplicatedStudyError` is raised if ``load_if_exists`` is
            set to :obj:`False`.
            Otherwise, the creation of the study is skipped, and the existing one is returned.

    Returns:
        A :class:`~optuna.multi_objective.study.MultiObjectiveStudy` object.
    """

    # TODO(ohta): Support pruner.
    mo_sampler = sampler or multi_objective.samplers.NSGAIIMultiObjectiveSampler()
    sampler_adapter = multi_objective.samplers._MultiObjectiveSamplerAdapter(mo_sampler)

    if not isinstance(directions, Iterable):
        raise TypeError("`directions` must be a list or other iterable types.")

    if not all(d in ["minimize", "maximize"] for d in directions):
        raise ValueError("`directions` includes unknown direction names.")

    study = _create_study(
        study_name=study_name,
        storage=storage,
        sampler=sampler_adapter,
        pruner=NopPruner(),
        load_if_exists=load_if_exists,
    )

    study.set_system_attr("multi_objective:study:directions", list(directions))

    return MultiObjectiveStudy(study)