optuna_dashboard.preferential.load_study
- optuna_dashboard.preferential.load_study(*, study_name, storage, sampler=None)
Like
optuna.load_study()
, but for preferential optimization.Example
import optuna from optuna_dashboard.preferential import create_study from optuna_dashboard.preferential import load_study study = create_study(storage="sqlite:///example.db", study_name="my_study") study.ask() loaded_study = load_study(study_name="my_study", storage="sqlite:///example.db") assert len(loaded_study.trials) == len(study.trials)
- Parameters:
study_name (str | None) – Study’s name. Each study has a unique name as an identifier. If
None
, checks whether the storage contains a single study, and if so loads that study.study_name
is required if there are multiple studies in the storage.storage (str | BaseStorage) – Database URL such as
sqlite:///example.db
. Please see also the documentation ofcreate_study()
for further details.sampler (BaseSampler | None) – A sampler object that implements background algorithm for value suggestion. If
None
is specified,PreferentialGPSampler
is used. Please note that most Optuna samplers does not work efficiently for preferential optimization.
- Returns:
A
PreferentialStudy
object.- Return type:
Note
Preferential optimization is an experimental feature (introduced in v0.13.0). The interface may change in newer versions without prior notice.