optuna.multi_objective.study.create_study

optuna.multi_objective.study.create_study(directions, study_name=None, storage=None, sampler=None, load_if_exists=False)[source]

Create a new MultiObjectiveStudy.

Example

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)
Parameters

  • directions (List[str]) – Optimization direction for each objective value. Set minimize for minimization and maximize for maximization.

  • study_name (Optional[str]) – Study’s name. If this argument is set to None, a unique name is generated automatically.

  • storage (Optional[Union[str, BaseStorage]]) –

    Database URL. If this argument is set to None, in-memory storage is used, and the 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 RDBStorage with your desired options and pass it to the storage argument instead of a URL.

  • sampler (Optional[BaseMultiObjectiveSampler]) – A sampler object that implements background algorithm for value suggestion. If None is specified, NSGAIIMultiObjectiveSampler is used as the default. See also samplers.

  • load_if_exists (bool) – 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 DuplicatedStudyError is raised if load_if_exists is set to False. Otherwise, the creation of the study is skipped, and the existing one is returned.

Returns

A MultiObjectiveStudy object.

Return type

MultiObjectiveStudy

Warning

Deprecated in v2.4.0. This feature will be removed in the future. The removal of this feature is currently scheduled for v4.0.0, but this schedule is subject to change. See https://github.com/optuna/optuna/releases/tag/v2.4.0.