def test_infer_relative_search_space() -> None:
sampler = TPESampler()
search_space = {
"a": distributions.UniformDistribution(1.0, 100.0),
"b": distributions.LogUniformDistribution(1.0, 100.0),
"c": distributions.DiscreteUniformDistribution(1.0, 100.0, 3.0),
"d": distributions.IntUniformDistribution(1, 100),
"e": distributions.IntUniformDistribution(0, 100, step=2),
"f": distributions.IntLogUniformDistribution(1, 100),
"g": distributions.CategoricalDistribution(["x", "y", "z"]),
}
def obj(t: Trial) -> float:
t.suggest_uniform("a", 1.0, 100.0)
t.suggest_loguniform("b", 1.0, 100.0)
t.suggest_discrete_uniform("c", 1.0, 100.0, 3.0)
t.suggest_int("d", 1, 100)
t.suggest_int("e", 0, 100, step=2)
t.suggest_int("f", 1, 100, log=True)
t.suggest_categorical("g", ["x", "y", "z"])
return 0.0
# Study and frozen-trial are not supposed to be accessed.
study1 = Mock(spec=[])
frozen_trial = Mock(spec=[])
assert sampler.infer_relative_search_space(study1, frozen_trial) == {}
study2 = optuna.create_study(sampler=sampler)
study2.optimize(obj, n_trials=1)
assert sampler.infer_relative_search_space(study2, study2.best_trial) == {}
with warnings.catch_warnings():
warnings.simplefilter("ignore", optuna.exceptions.ExperimentalWarning)
sampler = TPESampler(multivariate=True)
study3 = optuna.create_study(sampler=sampler)
study3.optimize(obj, n_trials=1)
assert sampler.infer_relative_search_space(
study3, study3.best_trial) == search_space
def test_infer_relative_search_space() -> None:
sampler = TPESampler()
# Study and frozen-trial are not supposed to be accessed.
study = Mock(spec=[])
frozen_trial = Mock(spec=[])
assert sampler.infer_relative_search_space(study, frozen_trial) == {}