def test_copy_study(from_storage_mode: str, to_storage_mode: str) -> None:
with StorageSupplier(from_storage_mode) as from_storage, StorageSupplier(
to_storage_mode) as to_storage:
from_study = create_study(storage=from_storage,
directions=["maximize", "minimize"])
from_study.set_system_attr("foo", "bar")
from_study.set_user_attr("baz", "qux")
from_study.optimize(
lambda t:
(t.suggest_float("x0", 0, 1), t.suggest_float("x1", 0, 1)),
n_trials=3)
copy_study(
from_study_name=from_study.study_name,
from_storage=from_storage,
to_storage=to_storage,
)
to_study = load_study(study_name=from_study.study_name,
storage=to_storage)
assert to_study.study_name == from_study.study_name
assert to_study.directions == from_study.directions
assert to_study.system_attrs == from_study.system_attrs
assert to_study.user_attrs == from_study.user_attrs
assert len(to_study.trials) == len(from_study.trials)
def test_copy_study_to_study_name(from_storage_mode: str, to_storage_mode: str) -> None:
with StorageSupplier(from_storage_mode) as from_storage, StorageSupplier(
to_storage_mode
) as to_storage:
from_study = create_study(study_name="foo", storage=from_storage)
_ = create_study(study_name="foo", storage=to_storage)
with pytest.raises(DuplicatedStudyError):
copy_study(
from_study_name=from_study.study_name,
from_storage=from_storage,
to_storage=to_storage,
)
copy_study(
from_study_name=from_study.study_name,
from_storage=from_storage,
to_storage=to_storage,
to_study_name="bar",
)
_ = load_study(study_name="bar", storage=to_storage)
import optuna # pip install optuna optuna.create_study() optuna.load_study() optuna.delete_study() optuna.copy_study() optuna.get_all_study_summaries() optuna.TrialPruned trial = Trial(study, trial_id) trial.datetime_start trial.distributions trial.number trial.params trial.system_attrs trial.user_attrs trial.report(value, step) trial.set_system_attr(key, value) trial.set_user_attr(key, value) trial.should_prune() trial.suggest_categorical(name, choices) trial.suggest_discrete_uniform(name, low, high, q) trial.suggest_float(name, low, high, *, ?step, ?log) trial.suggest_int(name, low, high, ?step, ?log) trial.suggest_loguniform(name, low, high) trial.suggest_uniform(name, low, high) #@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ import tensorflow as tf from tf import keras
