optuna.multi_objective.study.load_study¶
-
optuna.multi_objective.study.
load_study
(study_name: str, storage: Union[str, optuna.storages._base.BaseStorage], sampler: Optional[multi_objective.samplers.BaseMultiObjectiveSampler] = None) → optuna.multi_objective.study.MultiObjectiveStudy[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) loaded_study = optuna.multi_objective.study.load_study( study_name="my_study", storage="sqlite:///example.db" ) assert len(loaded_study.trials) == len(study.trials)
- Parameters
study_name – Study’s name. Each study has a unique name as an identifier.
storage – Database URL such as
sqlite:///example.db
. Please see also the documentation ofcreate_study()
for further details.sampler – A sampler object that implements background algorithm for value suggestion. If
None
is specified,RandomMultiObjectiveSampler
is used as the default. See alsosamplers
.
- Returns
A
MultiObjectiveStudy
object.
Note
Added in v1.4.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v1.4.0.