optuna.multi_objective.study.load_study(study_name, storage, sampler=None)[source]

Load the existing MultiObjectiveStudy that has the specified name.

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(
directions=["minimize", "minimize"],
study_name="my_study",
storage="sqlite:///example.db",
)
study.optimize(objective, n_trials=3)

study_name="my_study", storage="sqlite:///example.db"
)

Parameters
• study_name (str) – Study’s name. Each study has a unique name as an identifier.

• storage (Union[str, optuna.storages._base.BaseStorage]) – Database URL such as sqlite:///example.db. Please see also the documentation of create_study() for further details.

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

Returns

A MultiObjectiveStudy object.

Return type

optuna.multi_objective.study.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.