def test_categorical_distribution_different_sequence_types():
# type: () -> None
c1 = distributions.CategoricalDistribution(choices=("Roppongi", "Azabu"))
c2 = distributions.CategoricalDistribution(choices=["Roppongi", "Azabu"])
assert c1 == c2
def test_single() -> None:
with warnings.catch_warnings():
# UserWarning will be raised since the range is not divisible by step.
warnings.simplefilter("ignore", category=UserWarning)
single_distributions: List[distributions.BaseDistribution] = [
distributions.UniformDistribution(low=1.0, high=1.0),
distributions.LogUniformDistribution(low=7.3, high=7.3),
distributions.DiscreteUniformDistribution(low=2.22, high=2.22, q=0.1),
distributions.DiscreteUniformDistribution(low=2.22, high=2.24, q=0.3),
distributions.IntUniformDistribution(low=-123, high=-123),
distributions.IntUniformDistribution(low=-123, high=-120, step=4),
distributions.CategoricalDistribution(choices=("foo",)),
distributions.IntLogUniformDistribution(low=2, high=2),
]
for distribution in single_distributions:
assert distribution.single()
nonsingle_distributions: List[distributions.BaseDistribution] = [
distributions.UniformDistribution(low=1.0, high=1.001),
distributions.LogUniformDistribution(low=7.3, high=10),
distributions.DiscreteUniformDistribution(low=-30, high=-20, q=2),
distributions.DiscreteUniformDistribution(low=-30, high=-20, q=10),
# In Python, "0.3 - 0.2 != 0.1" is True.
distributions.DiscreteUniformDistribution(low=0.2, high=0.3, q=0.1),
distributions.DiscreteUniformDistribution(low=0.7, high=0.8, q=0.1),
distributions.IntUniformDistribution(low=-123, high=0),
distributions.IntUniformDistribution(low=-123, high=0, step=123),
distributions.CategoricalDistribution(choices=("foo", "bar")),
distributions.IntLogUniformDistribution(low=2, high=4),
]
for distribution in nonsingle_distributions:
assert not distribution.single()
def test_single():
# type: () -> None
single_distributions = [
distributions.UniformDistribution(low=1.0, high=1.0),
distributions.LogUniformDistribution(low=7.3, high=7.3),
distributions.DiscreteUniformDistribution(low=2.22, high=2.22, q=0.1),
distributions.DiscreteUniformDistribution(low=2.22, high=2.24, q=0.3),
distributions.IntUniformDistribution(low=-123, high=-123),
distributions.IntUniformDistribution(low=-123, high=-120, step=4),
distributions.CategoricalDistribution(choices=("foo", )),
] # type: List[distributions.BaseDistribution]
for distribution in single_distributions:
assert distribution.single()
nonsingle_distributions = [
distributions.UniformDistribution(low=1.0, high=1.001),
distributions.LogUniformDistribution(low=7.3, high=10),
distributions.DiscreteUniformDistribution(low=-30, high=-20, q=2),
distributions.DiscreteUniformDistribution(low=-30, high=-20, q=10),
# In Python, "0.3 - 0.2 != 0.1" is True.
distributions.DiscreteUniformDistribution(low=0.2, high=0.3, q=0.1),
distributions.DiscreteUniformDistribution(low=0.7, high=0.8, q=0.1),
distributions.IntUniformDistribution(low=-123, high=0),
distributions.IntUniformDistribution(low=-123, high=0, step=123),
distributions.CategoricalDistribution(choices=("foo", "bar")),
] # type: List[distributions.BaseDistribution]
for distribution in nonsingle_distributions:
assert not distribution.single()
def test_single():
# type: () -> None
single_distributions = [
distributions.UniformDistribution(low=1.0, high=1.0),
distributions.LogUniformDistribution(low=7.3, high=7.3),
distributions.DiscreteUniformDistribution(low=2.22, high=2.22, q=0.1),
distributions.IntUniformDistribution(low=-123, high=-123),
distributions.CategoricalDistribution(choices=('foo', ))
] # type: List[distributions.BaseDistribution]
for distribution in single_distributions:
assert distribution.single()
nonsingle_distributions = [
distributions.UniformDistribution(low=0.0, high=-100.0),
distributions.UniformDistribution(low=1.0, high=1.001),
distributions.LogUniformDistribution(low=7.3, high=7.2),
distributions.LogUniformDistribution(low=7.3, high=10),
distributions.DiscreteUniformDistribution(low=-30, high=-40, q=3),
distributions.DiscreteUniformDistribution(low=-30, high=-20, q=2),
distributions.IntUniformDistribution(low=123, high=100),
distributions.IntUniformDistribution(low=-123, high=0),
distributions.CategoricalDistribution(choices=()),
distributions.CategoricalDistribution(choices=('foo', 'bar'))
] # type: List[distributions.BaseDistribution]
for distribution in nonsingle_distributions:
assert not distribution.single()
def test_distributions(storage_init_func):
# type: (typing.Callable[[], storages.BaseStorage]) -> None
def objective(trial):
# type: (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'])
return 1.0
study = create_study(storage_init_func())
study.optimize(objective, n_trials=1)
assert study.best_trial.distributions == {
'a': distributions.UniformDistribution(low=0, high=10),
'b': distributions.LogUniformDistribution(low=0.1, high=10),
'c': distributions.DiscreteUniformDistribution(low=0, high=10, q=1),
'd': distributions.IntUniformDistribution(low=0, high=10),
'e':
distributions.CategoricalDistribution(choices=('foo', 'bar', 'baz'))
}
def test_convert_old_distribution_to_new_distribution_noop() -> None:
# No conversion happens for CategoricalDistribution.
cd = distributions.CategoricalDistribution(choices=["a", "b", "c"])
assert distributions._convert_old_distribution_to_new_distribution(
cd) == cd
# No conversion happens for new distributions.
fd = distributions.FloatDistribution(low=0, high=10, log=False, step=None)
assert distributions._convert_old_distribution_to_new_distribution(
fd) == fd
dfd = distributions.FloatDistribution(low=0, high=10, log=False, step=2)
assert distributions._convert_old_distribution_to_new_distribution(
dfd) == dfd
lfd = distributions.FloatDistribution(low=1, high=10, log=True, step=None)
assert distributions._convert_old_distribution_to_new_distribution(
lfd) == lfd
id = distributions.IntDistribution(low=0, high=10)
assert distributions._convert_old_distribution_to_new_distribution(
id) == id
idd = distributions.IntDistribution(low=0, high=10, step=2)
assert distributions._convert_old_distribution_to_new_distribution(
idd) == idd
ild = distributions.IntDistribution(low=1, high=10, log=True)
assert distributions._convert_old_distribution_to_new_distribution(
ild) == ild
def test_check_distribution_compatibility():
# type: () -> None
# test the same distribution
for key in EXAMPLE_JSONS.keys():
distributions.check_distribution_compatibility(EXAMPLE_DISTRIBUTIONS[key],
EXAMPLE_DISTRIBUTIONS[key])
# test different distribution classes
pytest.raises(
ValueError, lambda: distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS['u'], EXAMPLE_DISTRIBUTIONS['l']))
# test dynamic value range (CategoricalDistribution)
pytest.raises(
ValueError, lambda: distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS['c2'],
distributions.CategoricalDistribution(choices=('Roppongi', 'Akasaka'))))
# test dynamic value range (except CategoricalDistribution)
distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS['u'], distributions.UniformDistribution(low=-3.0, high=-2.0))
distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS['l'], distributions.LogUniformDistribution(low=-0.1, high=1.0))
distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS['du'],
distributions.DiscreteUniformDistribution(low=-1.0, high=10.0, q=3.))
distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS['iu'], distributions.IntUniformDistribution(low=-1, high=1))
def test_empty_distribution():
# type: () -> None
# Empty distributions cannot be instantiated.
with pytest.raises(ValueError):
distributions.UniformDistribution(low=0.0, high=-100.0)
with pytest.raises(ValueError):
distributions.LogUniformDistribution(low=7.3, high=7.2)
with pytest.raises(ValueError):
distributions.DiscreteUniformDistribution(low=-30, high=-40, q=3)
with pytest.raises(ValueError):
distributions.IntUniformDistribution(low=123, high=100)
with pytest.raises(ValueError):
distributions.IntUniformDistribution(low=123, high=100, step=2)
with pytest.raises(ValueError):
distributions.CategoricalDistribution(choices=())
with pytest.raises(ValueError):
distributions.IntLogUniformDistribution(low=123, high=100)
with pytest.raises(ValueError):
distributions.IntLogUniformDistribution(low=123, high=100, step=2)
def test_relative_sampling(storage_mode: str,
comm: CommunicatorBase) -> None:
relative_search_space = {
"x": distributions.UniformDistribution(low=-10, high=10),
"y": distributions.LogUniformDistribution(low=20, high=30),
"z": distributions.CategoricalDistribution(choices=(-1.0, 1.0)),
}
relative_params = {"x": 1.0, "y": 25.0, "z": -1.0}
sampler = DeterministicRelativeSampler(
relative_search_space,
relative_params # type: ignore
)
with MultiNodeStorageSupplier(storage_mode, comm) as storage:
study = TestChainerMNStudy._create_shared_study(storage,
comm,
sampler=sampler)
mn_study = ChainerMNStudy(study, comm)
# Invoke optimize.
n_trials = 20
func = Func()
mn_study.optimize(func, n_trials=n_trials)
# Assert trial counts.
assert len(mn_study.trials) == n_trials
# Assert the parameters in `relative_params` have been suggested among all nodes.
for trial in mn_study.trials:
assert trial.params == relative_params
def suggest_categorical(self, name, choices):
# type: (str, Sequence[T]) -> T
"""Suggest a value for the categorical parameter.
The value is sampled from ``choices``.
Example:
Suggest a kernel function of `SVC <https://scikit-learn.org/stable/modules/generated/
sklearn.svm.SVC.html>`_.
.. code::
>>> def objective(trial):
>>> ...
>>> kernel = trial.suggest_categorical('kernel', ['linear', 'poly', 'rbf'])
>>> clf = sklearn.svm.SVC(kernel=kernel)
>>> ...
Args:
name:
A parameter name.
choices:
Candidates of parameter values.
Returns:
A suggested value.
"""
choices = tuple(choices)
return self._suggest(name, distributions.CategoricalDistribution(choices=choices))
def test_ask_distribution_conversion_noop() -> None:
fixed_distributions = {
"ud": distributions.FloatDistribution(low=0,
high=10,
log=False,
step=None),
"dud": distributions.FloatDistribution(low=0,
high=10,
log=False,
step=2),
"lud": distributions.FloatDistribution(low=1,
high=10,
log=True,
step=None),
"id": distributions.IntDistribution(low=0, high=10, log=False, step=1),
"idd": distributions.IntDistribution(low=0, high=10, log=False,
step=2),
"ild": distributions.IntDistribution(low=1, high=10, log=True, step=1),
"cd": distributions.CategoricalDistribution(choices=["a", "b", "c"]),
}
study = create_study()
trial = study.ask(fixed_distributions=fixed_distributions)
# Check fixed_distributions doesn't change.
assert trial.distributions == fixed_distributions
def test_invalid_distribution():
# type: () -> None
with pytest.warns(UserWarning):
distributions.CategoricalDistribution(choices=({
"foo": "bar"
}, )) # type: ignore
def test_distributions(storage_init_func):
# type: (typing.Callable[[], storages.BaseStorage]) -> None
def objective(trial):
# type: (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"])
return 1.0
study = create_study(storage_init_func())
study.optimize(objective, n_trials=1)
assert study.best_trial.distributions == {
"a": distributions.UniformDistribution(low=0, high=10),
"b": distributions.LogUniformDistribution(low=0.1, high=10),
"c": distributions.DiscreteUniformDistribution(low=0, high=10, q=1),
"d": distributions.IntUniformDistribution(low=0, high=10),
"e":
distributions.CategoricalDistribution(choices=("foo", "bar", "baz")),
}
def test_categorical_contains() -> None:
c = distributions.CategoricalDistribution(choices=("Roppongi", "Azabu"))
assert not c._contains(-1)
assert c._contains(0)
assert c._contains(1)
assert c._contains(1.5)
assert not c._contains(3)
def test_group() -> None:
with warnings.catch_warnings():
warnings.simplefilter("ignore", optuna.exceptions.ExperimentalWarning)
sampler = TPESampler(multivariate=True, group=True)
study = optuna.create_study(sampler=sampler)
with patch.object(sampler, "_sample_relative", wraps=sampler._sample_relative) as mock:
study.optimize(lambda t: t.suggest_int("x", 0, 10), n_trials=2)
assert mock.call_count == 1
assert study.trials[-1].distributions == {"x": distributions.IntDistribution(low=0, high=10)}
with patch.object(sampler, "_sample_relative", wraps=sampler._sample_relative) as mock:
study.optimize(
lambda t: t.suggest_int("y", 0, 10) + t.suggest_float("z", -3, 3), n_trials=1
)
assert mock.call_count == 1
assert study.trials[-1].distributions == {
"y": distributions.IntDistribution(low=0, high=10),
"z": distributions.FloatDistribution(low=-3, high=3),
}
with patch.object(sampler, "_sample_relative", wraps=sampler._sample_relative) as mock:
study.optimize(
lambda t: t.suggest_int("y", 0, 10)
+ t.suggest_float("z", -3, 3)
+ t.suggest_float("u", 1e-2, 1e2, log=True)
+ bool(t.suggest_categorical("v", ["A", "B", "C"])),
n_trials=1,
)
assert mock.call_count == 2
assert study.trials[-1].distributions == {
"u": distributions.FloatDistribution(low=1e-2, high=1e2, log=True),
"v": distributions.CategoricalDistribution(choices=["A", "B", "C"]),
"y": distributions.IntDistribution(low=0, high=10),
"z": distributions.FloatDistribution(low=-3, high=3),
}
with patch.object(sampler, "_sample_relative", wraps=sampler._sample_relative) as mock:
study.optimize(lambda t: t.suggest_float("u", 1e-2, 1e2, log=True), n_trials=1)
assert mock.call_count == 3
assert study.trials[-1].distributions == {
"u": distributions.FloatDistribution(low=1e-2, high=1e2, log=True)
}
with patch.object(sampler, "_sample_relative", wraps=sampler._sample_relative) as mock:
study.optimize(
lambda t: t.suggest_int("y", 0, 10) + t.suggest_int("w", 2, 8, log=True), n_trials=1
)
assert mock.call_count == 4
assert study.trials[-1].distributions == {
"y": distributions.IntDistribution(low=0, high=10),
"w": distributions.IntDistribution(low=2, high=8, log=True),
}
with patch.object(sampler, "_sample_relative", wraps=sampler._sample_relative) as mock:
study.optimize(lambda t: t.suggest_int("x", 0, 10), n_trials=1)
assert mock.call_count == 6
assert study.trials[-1].distributions == {"x": distributions.IntDistribution(low=0, high=10)}
def suggest_categorical(self, name, choices):
# type: (str, Sequence[CategoricalChoiceType]) -> CategoricalChoiceType
"""Suggest a value for the categorical parameter.
The value is sampled from ``choices``.
Example:
Suggest a kernel function of `SVC <https://scikit-learn.org/stable/modules/generated/
sklearn.svm.SVC.html>`_.
.. testsetup::
import numpy as np
from sklearn.model_selection import train_test_split
np.random.seed(seed=0)
X = np.random.randn(50).reshape(-1, 1)
y = np.random.randint(0, 2, 50)
X_train, X_valid, y_train, y_valid = train_test_split(X, y, random_state=0)
.. testcode::
import optuna
from sklearn.svm import SVC
def objective(trial):
kernel = trial.suggest_categorical('kernel', ['linear', 'poly', 'rbf'])
clf = SVC(kernel=kernel, gamma='scale', random_state=0)
clf.fit(X_train, y_train)
return clf.score(X_valid, y_valid)
study = optuna.create_study(direction='maximize')
study.optimize(objective, n_trials=3)
Args:
name:
A parameter name.
choices:
Parameter value candidates.
.. seealso::
:class:`~optuna.distributions.CategoricalDistribution`.
Returns:
A suggested value.
"""
choices = tuple(choices)
# There is no need to call self._check_distribution because
# CategoricalDistribution does not support dynamic value space.
return self._suggest(
name, distributions.CategoricalDistribution(choices=choices))
def test_distributions(storage_mode: str, comm: CommunicatorBase) -> None:
with MultiNodeStorageSupplier(storage_mode, comm) as storage:
study = TestChainerMNStudy._create_shared_study(storage, comm)
mn_trial = _create_new_chainermn_trial(study, comm)
mn_trial.suggest_categorical("x", [1])
assert mn_trial.distributions == {
"x": distributions.CategoricalDistribution(choices=(1, ))
}
def test_ask_fixed_search_space() -> None:
fixed_distributions = {
"x": distributions.UniformDistribution(0, 1),
"y": distributions.CategoricalDistribution(["bacon", "spam"]),
}
study = create_study()
trial = study.ask(fixed_distributions=fixed_distributions)
params = trial.params
assert len(trial.params) == 2
assert 0 <= params["x"] < 1
assert params["y"] in ["bacon", "spam"]
def test_distributions(storage_mode, cache_mode, comm):
# type: (str, bool, CommunicatorBase) -> None
with MultiNodeStorageSupplier(storage_mode, cache_mode,
comm) as storage:
study = TestChainerMNStudy._create_shared_study(storage, comm)
trial_id = storage.create_new_trial_id(study.study_id)
trial = Trial(study, trial_id)
mn_trial = integration.chainermn._ChainerMNTrial(trial, comm)
mn_trial.suggest_categorical('x', [1])
assert mn_trial.distributions == {
'x': distributions.CategoricalDistribution(choices=(1, ))
}
def test_optuna_search_invalid_param_dist() -> None:
X, y = make_blobs(n_samples=10)
est = KernelDensity()
param_dist = ["kernel", distributions.CategoricalDistribution(("gaussian", "linear"))]
with pytest.raises(TypeError, match="param_distributions must be a dictionary."):
integration.OptunaSearchCV(
est,
param_dist, # type: ignore
cv=3,
error_score="raise",
random_state=0,
return_train_score=True,
)
def test_contains():
# type: () -> None
u = distributions.UniformDistribution(low=1.0, high=2.0)
assert not u._contains(0.9)
assert u._contains(1)
assert u._contains(1.5)
assert not u._contains(2)
lu = distributions.LogUniformDistribution(low=0.001, high=100)
assert not lu._contains(0.0)
assert lu._contains(0.001)
assert lu._contains(12.3)
assert not lu._contains(100)
du = distributions.DiscreteUniformDistribution(low=1.0, high=10.0, q=2.0)
assert not du._contains(0.9)
assert du._contains(1.0)
assert du._contains(3.5)
assert du._contains(6)
assert du._contains(10)
assert not du._contains(10.1)
iu = distributions.IntUniformDistribution(low=1, high=10)
assert not iu._contains(0.9)
assert iu._contains(1)
assert iu._contains(4)
assert iu._contains(6)
assert iu._contains(10)
assert not iu._contains(10.1)
assert not iu._contains(11)
# IntUniformDistribution with a 'q' parameter.
iuq = distributions.IntUniformDistribution(low=1, high=10, step=2)
assert not iuq._contains(0.9)
assert iuq._contains(1)
assert iuq._contains(4)
assert iuq._contains(6)
assert iuq._contains(10)
assert not iuq._contains(10.1)
assert not iuq._contains(11)
c = distributions.CategoricalDistribution(choices=("Roppongi", "Azabu"))
assert not c._contains(-1)
assert c._contains(0)
assert c._contains(1)
assert c._contains(1.5)
assert not c._contains(3)
def test_check_distribution_compatibility():
# type: () -> None
# test the same distribution
for key in EXAMPLE_JSONS.keys():
distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS[key], EXAMPLE_DISTRIBUTIONS[key])
# test different distribution classes
pytest.raises(
ValueError,
lambda: distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS["u"], EXAMPLE_DISTRIBUTIONS["l"]),
)
# test dynamic value range (CategoricalDistribution)
pytest.raises(
ValueError,
lambda: distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS["c2"],
distributions.CategoricalDistribution(choices=("Roppongi",
"Akasaka")),
),
)
# test dynamic value range (except CategoricalDistribution)
distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS["u"],
distributions.UniformDistribution(low=-3.0, high=-2.0))
distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS["l"],
distributions.LogUniformDistribution(low=0.1, high=1.0))
distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS["du"],
distributions.DiscreteUniformDistribution(low=-1.0, high=11.0, q=3.0),
)
distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS["iu"],
distributions.IntUniformDistribution(low=-1, high=1))
distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS["ilu"],
distributions.IntLogUniformDistribution(low=1, high=13),
)
def test_optuna_search_invalid_param_dist():
# type: () -> None
X, y = make_blobs(n_samples=10)
est = KernelDensity()
param_dist = [
'kernel',
distributions.CategoricalDistribution(('gaussian', 'linear'))
]
optuna_search = integration.OptunaSearchCV(
est,
param_dist, # type: ignore
cv=3,
error_score='raise',
random_state=0,
return_train_score=True)
with pytest.raises(ValueError,
match='param_distributions must be a dictionary.'):
optuna_search.fit(X)
def suggest_categorical(self, name, choices):
# type: (str, Sequence[CategoricalChoiceType]) -> CategoricalChoiceType
"""Suggest a value for the categorical parameter.
The value is sampled from ``choices``.
Example:
Suggest a kernel function of `SVC <https://scikit-learn.org/stable/modules/generated/
sklearn.svm.SVC.html>`_.
.. code::
>>> def objective(trial):
>>> ...
>>> kernel = trial.suggest_categorical('kernel', ['linear', 'poly', 'rbf'])
>>> clf = sklearn.svm.SVC(kernel=kernel)
>>> ...
Args:
name:
A parameter name.
choices:
Parameter value candidates.
.. seealso::
:class:`~optuna.distributions.CategoricalDistribution`.
Returns:
A suggested value.
"""
choices = tuple(choices)
# There is no need to call self._check_distribution because
# CategoricalDistribution does not support dynamic value space.
return self._suggest(
name, distributions.CategoricalDistribution(choices=choices))
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_contains():
# type: () -> None
u = distributions.UniformDistribution(low=1., high=2.)
assert not u._contains(0.9)
assert u._contains(1)
assert u._contains(1.5)
assert not u._contains(2)
lu = distributions.LogUniformDistribution(low=0.001, high=100)
assert not lu._contains(0.0)
assert lu._contains(0.001)
assert lu._contains(12.3)
assert not lu._contains(100)
du = distributions.DiscreteUniformDistribution(low=1., high=10., q=2.)
assert not du._contains(0.9)
assert du._contains(1.0)
assert du._contains(3.5)
assert du._contains(6)
assert du._contains(10)
assert not du._contains(10.1)
iu = distributions.IntUniformDistribution(low=1, high=10)
assert not iu._contains(0.9)
assert iu._contains(1)
assert iu._contains(3.5)
assert iu._contains(6)
assert iu._contains(10)
assert iu._contains(10.1)
assert not iu._contains(11)
c = distributions.CategoricalDistribution(choices=('Roppongi', 'Azabu'))
assert not c._contains(-1)
assert c._contains(0)
assert c._contains(1)
assert c._contains(1.5)
assert not c._contains(3)
def test_create_trial_distribution_conversion_noop() -> None:
fixed_params = {
"ud": 0,
"dud": 2,
"lud": 1,
"id": 0,
"idd": 2,
"ild": 1,
"cd": "a",
}
fixed_distributions = {
"ud": distributions.FloatDistribution(low=0,
high=10,
log=False,
step=None),
"dud": distributions.FloatDistribution(low=0,
high=10,
log=False,
step=2),
"lud": distributions.FloatDistribution(low=1,
high=10,
log=True,
step=None),
"id": distributions.IntDistribution(low=0, high=10, log=False, step=1),
"idd": distributions.IntDistribution(low=0, high=10, log=False,
step=2),
"ild": distributions.IntDistribution(low=1, high=10, log=True, step=1),
"cd": distributions.CategoricalDistribution(choices=["a", "b", "c"]),
}
trial = create_trial(params=fixed_params,
distributions=fixed_distributions,
value=1)
# Check fixed_distributions doesn't change.
assert trial.distributions == fixed_distributions
def suggest_categorical(self, name, choices):
# type: (str, Sequence[T]) -> T
choices = tuple(choices)
return self._suggest(name, distributions.CategoricalDistribution(choices=choices))
def test_contains() -> None:
u = distributions.UniformDistribution(low=1.0, high=2.0)
assert not u._contains(0.9)
assert u._contains(1)
assert u._contains(1.5)
assert not u._contains(2)
lu = distributions.LogUniformDistribution(low=0.001, high=100)
assert not lu._contains(0.0)
assert lu._contains(0.001)
assert lu._contains(12.3)
assert not lu._contains(100)
with warnings.catch_warnings():
# UserWarning will be raised since the range is not divisible by 2.
# The range will be replaced with [1.0, 9.0].
warnings.simplefilter("ignore", category=UserWarning)
du = distributions.DiscreteUniformDistribution(low=1.0,
high=10.0,
q=2.0)
assert not du._contains(0.9)
assert du._contains(1.0)
assert du._contains(3.5)
assert du._contains(6)
assert du._contains(9)
assert not du._contains(9.1)
assert not du._contains(10)
iu = distributions.IntUniformDistribution(low=1, high=10)
assert not iu._contains(0.9)
assert iu._contains(1)
assert iu._contains(4)
assert iu._contains(6)
assert iu._contains(10)
assert not iu._contains(10.1)
assert not iu._contains(11)
# IntUniformDistribution with a 'step' parameter.
with warnings.catch_warnings():
# UserWarning will be raised since the range is not divisible by 2.
# The range will be replaced with [1, 9].
warnings.simplefilter("ignore", category=UserWarning)
iuq = distributions.IntUniformDistribution(low=1, high=10, step=2)
assert not iuq._contains(0.9)
assert iuq._contains(1)
assert iuq._contains(4)
assert iuq._contains(6)
assert iuq._contains(9)
assert not iuq._contains(9.1)
assert not iuq._contains(10)
c = distributions.CategoricalDistribution(choices=("Roppongi", "Azabu"))
assert not c._contains(-1)
assert c._contains(0)
assert c._contains(1)
assert c._contains(1.5)
assert not c._contains(3)
ilu = distributions.IntUniformDistribution(low=2, high=12)
assert not ilu._contains(0.9)
assert ilu._contains(2)
assert ilu._contains(4)
assert ilu._contains(6)
assert ilu._contains(12)
assert not ilu._contains(12.1)
assert not ilu._contains(13)
iluq = distributions.IntLogUniformDistribution(low=2, high=7)
assert not iluq._contains(0.9)
assert iluq._contains(2)
assert iluq._contains(4)
assert iluq._contains(5)
assert iluq._contains(6)
assert not iluq._contains(7.1)
assert not iluq._contains(8)
import json
from typing import Any
from typing import Dict
from typing import List
import warnings
import pytest
from optuna import distributions
EXAMPLE_DISTRIBUTIONS = {
"u": distributions.UniformDistribution(low=1.0, high=2.0),
"l": distributions.LogUniformDistribution(low=0.001, high=100),
"du": distributions.DiscreteUniformDistribution(low=1.0, high=9.0, q=2.0),
"iu": distributions.IntUniformDistribution(low=1, high=9, step=2),
"c1": distributions.CategoricalDistribution(choices=(2.71, -float("inf"))),
"c2": distributions.CategoricalDistribution(choices=("Roppongi", "Azabu")),
"c3": distributions.CategoricalDistribution(choices=["Roppongi", "Azabu"]),
"ilu": distributions.IntLogUniformDistribution(low=2, high=12, step=2),
} # type: Dict[str, Any]
EXAMPLE_JSONS = {
"u":
'{"name": "UniformDistribution", "attributes": {"low": 1.0, "high": 2.0}}',
"l":
'{"name": "LogUniformDistribution", "attributes": {"low": 0.001, "high": 100}}',
"du":
'{"name": "DiscreteUniformDistribution",'
'"attributes": {"low": 1.0, "high": 9.0, "q": 2.0}}',
"iu":
'{"name": "IntUniformDistribution", "attributes": {"low": 1, "high": 9, "step": 2}}',
