def objective(trial: Trial) -> float:
x1 = trial.suggest_float("x1", 0.1, 3)
if trial.number % 2 == 0:
_ = trial.suggest_float("x2", 0.1, 3, log=True)
raise optuna.TrialPruned
return x1**2
def test_check_distribution_suggest_uniform(
storage_init_func: Callable[[], storages.BaseStorage]) -> None:
sampler = samplers.RandomSampler()
study = create_study(storage_init_func(), sampler=sampler)
trial = Trial(study, study._storage.create_new_trial(study._study_id))
with pytest.warns(None) as record:
trial.suggest_uniform("x", 10, 20)
trial.suggest_uniform("x", 10, 20)
trial.suggest_uniform("x", 10, 30)
# we expect exactly one warning
assert len(record) == 1
with pytest.raises(ValueError):
trial.suggest_int("x", 10, 20)
trial = Trial(study, study._storage.create_new_trial(study._study_id))
with pytest.raises(ValueError):
trial.suggest_int("x", 10, 20)
def objective(trial: Trial) -> float:
return trial.suggest_int("a", 0, 100)
def test_report() -> None:
study = create_study()
trial = Trial(study, study._storage.create_new_trial(study._study_id))
# Report values that can be cast to `float` (OK).
trial.report(1.23, 1)
trial.report(float("nan"), 2)
trial.report("1.23", 3) # type: ignore
trial.report("inf", 4) # type: ignore
trial.report(1, 5)
trial.report(np.array([1], dtype=np.float32)[0], 6)
# Report values that cannot be cast to `float` or steps that are negative (Error).
with pytest.raises(TypeError):
trial.report(None, 7) # type: ignore
with pytest.raises(TypeError):
trial.report("foo", 7) # type: ignore
with pytest.raises(TypeError):
trial.report([1, 2, 3], 7) # type: ignore
with pytest.raises(TypeError):
trial.report("foo", -1) # type: ignore
with pytest.raises(ValueError):
trial.report(1.23, -1)
def objective(trial: Trial) -> Tuple[float, float]:
with pytest.raises(NotImplementedError):
trial.report(1.0, 0)
return 1.0, 1.0
def test_check_distribution_suggest_float(
storage_init_func: Callable[[], storages.BaseStorage]) -> None:
sampler = samplers.RandomSampler()
study = create_study(storage_init_func(), sampler=sampler)
trial = Trial(study, study._storage.create_new_trial(study._study_id))
x1 = trial.suggest_float("x1", 10, 20)
x2 = trial.suggest_uniform("x1", 10, 20)
assert x1 == x2
x3 = trial.suggest_float("x2", 1e-5, 1e-3, log=True)
x4 = trial.suggest_loguniform("x2", 1e-5, 1e-3)
assert x3 == x4
x5 = trial.suggest_float("x3", 10, 20, step=1.0)
x6 = trial.suggest_discrete_uniform("x3", 10, 20, 1.0)
assert x5 == x6
with pytest.raises(ValueError):
trial.suggest_float("x4", 1e-5, 1e-2, step=1e-5, log=True)
with pytest.raises(ValueError):
trial.suggest_int("x1", 10, 20)
trial = Trial(study, study._storage.create_new_trial(study._study_id))
with pytest.raises(ValueError):
trial.suggest_int("x1", 10, 20)
def objective(trial: Trial) -> float:
trial.suggest_float("a", 0, 10)
trial.suggest_float("b", 0.1, 10, log=True)
trial.suggest_float("c", 0, 10, step=1)
trial.suggest_int("d", 0, 10)
trial.suggest_categorical("e", ["foo", "bar", "baz"])
trial.suggest_int("f", 1, 10, log=True)
return 1.0
def objective_func_multi_objective(trial: Trial) -> Tuple[float, float]:
x = trial.suggest_float("x", -10, 10)
return (x + 5) ** 2, (x - 5) ** 2
def objective(trial: Trial) -> float:
trial.suggest_uniform("a", 0, 10)
trial.suggest_loguniform("b", 0.1, 10)
trial.suggest_discrete_uniform("c", 0, 10, 1)
trial.suggest_int("d", 0, 10)
trial.suggest_categorical("e", ["foo", "bar", "baz"])
trial.suggest_int("f", 1, 10, log=True)
return 1.0
def __call__(self, name: str, trial: Trial):
return trial.suggest_categorical(name, self.choices)
def __call__(self, name: str, trial: Trial):
return trial.suggest_discrete_uniform(name, self.low, self.high,
self.q)
def __call__(self, name: str, trial: Trial):
return trial.suggest_loguniform(name, self.low, self.high)
def __call__(self, name: str, trial: Trial):
return trial.suggest_int(name, self.low, self.high)
def objective(trial: Trial) -> float:
x1 = trial.suggest_float("x1", 0.1, 3)
return x1**2
def test_check_distribution_suggest_categorical(
storage_init_func: Callable[[], storages.BaseStorage]) -> None:
sampler = samplers.RandomSampler()
study = create_study(storage_init_func(), sampler=sampler)
trial = Trial(study, study._storage.create_new_trial(study._study_id))
trial.suggest_categorical("x", [10, 20, 30])
with pytest.raises(ValueError):
trial.suggest_categorical("x", [10, 20])
with pytest.raises(ValueError):
trial.suggest_int("x", 10, 20)
trial = Trial(study, study._storage.create_new_trial(study._study_id))
with pytest.raises(ValueError):
trial.suggest_int("x", 10, 20)
def objective(trial: Trial, exception: Exception) -> float:
trial.suggest_float("a", 0, 1)
raise exception
def test_suggest_low_equals_high(
storage_init_func: Callable[[], storages.BaseStorage]) -> None:
study = create_study(storage_init_func(),
sampler=samplers.TPESampler(n_startup_trials=0))
trial = Trial(study, study._storage.create_new_trial(study._study_id))
with patch.object(distributions,
"_get_single_value",
wraps=distributions._get_single_value) as mock_object:
assert trial.suggest_uniform("a", 1.0,
1.0) == 1.0 # Suggesting a param.
assert mock_object.call_count == 1
assert trial.suggest_uniform("a", 1.0,
1.0) == 1.0 # Suggesting the same param.
assert mock_object.call_count == 1
assert trial.suggest_loguniform("b", 1.0,
1.0) == 1.0 # Suggesting a param.
assert mock_object.call_count == 2
assert trial.suggest_loguniform(
"b", 1.0, 1.0) == 1.0 # Suggesting the same param.
assert mock_object.call_count == 2
assert trial.suggest_discrete_uniform(
"c", 1.0, 1.0, 1.0) == 1.0 # Suggesting a param.
assert mock_object.call_count == 3
assert (trial.suggest_discrete_uniform("c", 1.0, 1.0, 1.0) == 1.0
) # Suggesting the same param.
assert mock_object.call_count == 3
assert trial.suggest_int("d", 1, 1) == 1 # Suggesting a param.
assert mock_object.call_count == 4
assert trial.suggest_int("d", 1, 1) == 1 # Suggesting the same param.
assert mock_object.call_count == 4
assert trial.suggest_float("e", 1.0, 1.0) == 1.0 # Suggesting a param.
assert mock_object.call_count == 5
assert trial.suggest_float("e", 1.0,
1.0) == 1.0 # Suggesting the same param.
assert mock_object.call_count == 5
assert trial.suggest_float("f", 0.5, 0.5,
log=True) == 0.5 # Suggesting a param.
assert mock_object.call_count == 6
assert trial.suggest_float(
"f", 0.5, 0.5, log=True) == 0.5 # Suggesting the same param.
assert mock_object.call_count == 6
assert trial.suggest_float("g", 0.5, 0.5,
log=False) == 0.5 # Suggesting a param.
assert mock_object.call_count == 7
assert trial.suggest_float(
"g", 0.5, 0.5, log=False) == 0.5 # Suggesting the same param.
assert mock_object.call_count == 7
assert trial.suggest_float("h", 0.5, 0.5,
step=1.0) == 0.5 # Suggesting a param.
assert mock_object.call_count == 8
assert trial.suggest_float(
"h", 0.5, 0.5, step=1.0) == 0.5 # Suggesting the same param.
assert mock_object.call_count == 8
assert trial.suggest_int("i", 1, 1,
log=True) == 1 # Suggesting a param.
assert mock_object.call_count == 9
assert trial.suggest_int("i", 1, 1,
log=True) == 1 # Suggesting the same param.
assert mock_object.call_count == 9
def test_check_distribution_suggest_categorical(storage_mode: str) -> None:
sampler = samplers.RandomSampler()
with StorageSupplier(storage_mode) as storage:
study = create_study(storage=storage, sampler=sampler)
trial = Trial(study, study._storage.create_new_trial(study._study_id))
trial.suggest_categorical("x", [10, 20, 30])
with pytest.raises(ValueError):
trial.suggest_categorical("x", [10, 20])
with pytest.raises(ValueError):
trial.suggest_int("x", 10, 20)
trial = Trial(study, study._storage.create_new_trial(study._study_id))
with pytest.raises(ValueError):
trial.suggest_int("x", 10, 20)
def test_suggest_int_log(
storage_init_func: Callable[[], storages.BaseStorage]) -> None:
mock = Mock()
mock.side_effect = [1, 2]
sampler = samplers.RandomSampler()
study = create_study(storage_init_func(), sampler=sampler)
trial = Trial(study, study._storage.create_new_trial(study._study_id))
distribution = IntLogUniformDistribution(low=1, high=3)
with patch.object(sampler, "sample_independent", mock) as mock_object:
assert trial._suggest("x",
distribution) == 1 # Test suggesting a param.
assert trial._suggest(
"x", distribution) == 1 # Test suggesting the same param.
assert trial._suggest(
"y", distribution) == 2 # Test suggesting a different param.
assert trial.params == {"x": 1, "y": 2}
assert mock_object.call_count == 2
study = create_study(storage_init_func(), sampler=sampler)
trial = Trial(study, study._storage.create_new_trial(study._study_id))
with warnings.catch_warnings():
# UserWarning will be raised since [0.5, 10] is not divisible by 1.
warnings.simplefilter("ignore", category=UserWarning)
with pytest.raises(ValueError):
trial.suggest_int("z", 0.5, 10, log=True) # type: ignore
study = create_study(storage_init_func(), sampler=sampler)
trial = Trial(study, study._storage.create_new_trial(study._study_id))
with pytest.raises(ValueError):
trial.suggest_int("w", 1, 3, step=2, log=True)
def test_check_distribution_suggest_loguniform(storage_mode: str) -> None:
sampler = samplers.RandomSampler()
with StorageSupplier(storage_mode) as storage:
study = create_study(storage=storage, sampler=sampler)
trial = Trial(study, study._storage.create_new_trial(study._study_id))
with pytest.warns(None) as record:
trial.suggest_loguniform("x", 10, 20)
trial.suggest_loguniform("x", 10, 20)
trial.suggest_loguniform("x", 10, 30)
# we expect exactly one warning (not counting ones caused by deprecation)
record = [r for r in record if r.category != FutureWarning]
assert len(record) == 1
with pytest.raises(ValueError):
trial.suggest_int("x", 10, 20)
trial = Trial(study, study._storage.create_new_trial(study._study_id))
with pytest.raises(ValueError):
trial.suggest_int("x", 10, 20)
def create_trial() -> Trial:
return Trial(study, study._storage.create_new_trial(study._study_id))
def objective(trial: Trial, exception: Exception) -> float:
trial.suggest_uniform("z", 0, 1)
raise exception
def test_study_id() -> None:
study = create_study()
trial = Trial(study, study._storage.create_new_trial(study._study_id))
assert trial._study_id == trial.study._study_id
def objective(trial: Trial, base_value: float) -> float:
return trial.suggest_uniform("x", 0.1, 0.2) + base_value
def objective(trial: Trial) -> Tuple[float, float]:
with pytest.raises(NotImplementedError):
trial.should_prune()
return 1.0, 1.0
def objective(trial: Trial) -> float:
x = trial.suggest_float("x", -1, 1)
y = trial.suggest_int("y", -1, 1)
z = trial.suggest_float("z", -1, 1)
return x + y + z
def objective(trial: Trial) -> float:
a = trial.suggest_int("a", 0, 100)
b = trial.suggest_float("b", -100, 100)
return a * b
def test_check_distribution_suggest_int(storage_init_func: Callable[
[], storages.BaseStorage], enable_log: bool) -> None:
sampler = samplers.RandomSampler()
study = create_study(storage_init_func(), sampler=sampler)
trial = Trial(study, study._storage.create_new_trial(study._study_id))
with pytest.warns(None) as record:
trial.suggest_int("x", 10, 20, log=enable_log)
trial.suggest_int("x", 10, 20, log=enable_log)
trial.suggest_int("x", 10, 22, log=enable_log)
# We expect exactly one warning.
assert len(record) == 1
with pytest.raises(ValueError):
trial.suggest_float("x", 10, 20, log=enable_log)
trial = Trial(study, study._storage.create_new_trial(study._study_id))
with pytest.raises(ValueError):
trial.suggest_float("x", 10, 20, log=enable_log)
def objective_func(trial: Trial) -> float:
x = trial.suggest_float("x", -10, 10)
return (x + 5) ** 2
def objective(trial: Trial) -> float:
x = trial.suggest_float("x", 0, 5)
y = trial.suggest_float("y", 1, 1)
return 4 * x ** 2 + 4 * y ** 2
