def test_sample_independent_ignored_states() -> None:
"""Tests FAIL, RUNNING, and WAITING states are equally."""
study = optuna.multi_objective.create_study(directions=["minimize", "maximize"])
dist = optuna.distributions.UniformDistribution(1.0, 100.0)
suggestions = []
for state in [
optuna.trial.TrialState.FAIL,
optuna.trial.TrialState.RUNNING,
optuna.trial.TrialState.WAITING,
]:
random.seed(128)
state_fn = build_state_fn(state)
past_trials = [
frozen_trial_factory(i, [random.random(), random.random()], state_fn=state_fn)
for i in range(32)
]
trial = multi_objective.trial.FrozenMultiObjectiveTrial(
2, frozen_trial_factory(32, [0, 0])
)
sampler = MOTPEMultiObjectiveSampler(seed=0)
attrs = MockSystemAttr()
with patch.object(study._storage, "get_all_trials", return_value=past_trials), patch.object(
study._storage, "set_trial_system_attr", side_effect=attrs.set_trial_system_attr
), patch.object(study._storage, "get_trial", return_value=trial), patch(
"optuna.multi_objective.trial.MultiObjectiveTrial.system_attrs", new_callable=PropertyMock
) as mock1, patch(
"optuna.multi_objective.trial.FrozenMultiObjectiveTrial.system_attrs",
new_callable=PropertyMock,
) as mock2:
mock1.return_value = attrs.value
mock2.return_value = attrs.value
suggestions.append(sampler.sample_independent(study, trial, "param-a", dist))
assert len(set(suggestions)) == 1
def test_sample_independent_uniform_distributions() -> None:
# Prepare sample from uniform distribution for cheking other distributions.
study = optuna.multi_objective.create_study(
directions=["minimize", "maximize"])
random.seed(128)
past_trials = [
frozen_trial_factory(
i, [random.random(), random.random()]) for i in range(16)
]
uni_dist = optuna.distributions.UniformDistribution(1.0, 100.0)
trial = multi_objective.trial.FrozenMultiObjectiveTrial(
2, frozen_trial_factory(16, [0, 0]))
sampler = MOTPEMultiObjectiveSampler(seed=0)
attrs = MockSystemAttr()
with patch.object(
study._storage, "get_all_trials", return_value=past_trials
), patch.object(
study._storage,
"set_trial_system_attr",
side_effect=attrs.set_trial_system_attr
), patch.object(study._storage, "get_trial", return_value=trial), patch(
"optuna.multi_objective.trial.MultiObjectiveTrial.system_attrs",
new_callable=PropertyMock
) as mock1, patch(
"optuna.multi_objective.trial.FrozenMultiObjectiveTrial.system_attrs",
new_callable=PropertyMock,
) as mock2:
mock1.return_value = attrs.value
mock2.return_value = attrs.value
uniform_suggestion = sampler.sample_independent(
study, trial, "param-a", uni_dist)
assert 1.0 <= uniform_suggestion < 100.0
def test_sample_int_uniform_distributions() -> None:
"""Test sampling from int distribution returns integer."""
study = optuna.multi_objective.create_study(directions=["minimize", "maximize"])
random.seed(128)
def int_value_fn(idx: int) -> float:
return random.randint(0, 100)
int_dist = optuna.distributions.IntUniformDistribution(1, 100)
past_trials = [
frozen_trial_factory(
i, [random.random(), random.random()], dist=int_dist, value_fn=int_value_fn
)
for i in range(16)
]
trial = multi_objective.trial.FrozenMultiObjectiveTrial(2, frozen_trial_factory(16, [0, 0]))
sampler = MOTPEMultiObjectiveSampler(seed=0)
attrs = MockSystemAttr()
with patch.object(study._storage, "get_all_trials", return_value=past_trials), patch.object(
study._storage, "set_trial_system_attr", side_effect=attrs.set_trial_system_attr
), patch.object(study._storage, "get_trial", return_value=trial), patch(
"optuna.multi_objective.trial.MultiObjectiveTrial.system_attrs", new_callable=PropertyMock
) as mock1, patch(
"optuna.multi_objective.trial.FrozenMultiObjectiveTrial.system_attrs",
new_callable=PropertyMock,
) as mock2:
mock1.return_value = attrs.value
mock2.return_value = attrs.value
int_suggestion = sampler.sample_independent(study, trial, "param-a", int_dist)
assert 1 <= int_suggestion <= 100
assert isinstance(int_suggestion, int)
def test_sample_independent_categorical_distributions() -> None:
"""Test samples are drawn from the specified category."""
study = optuna.multi_objective.create_study(directions=["minimize", "maximize"])
random.seed(128)
categories = [i * 0.3 + 1.0 for i in range(330)]
def cat_value_fn(idx: int) -> float:
return categories[random.randint(0, len(categories) - 1)]
cat_dist = optuna.distributions.CategoricalDistribution(categories)
past_trials = [
frozen_trial_factory(
i, [random.random(), random.random()], dist=cat_dist, value_fn=cat_value_fn
)
for i in range(16)
]
trial = multi_objective.trial.FrozenMultiObjectiveTrial(2, frozen_trial_factory(16, [0, 0]))
sampler = MOTPEMultiObjectiveSampler(seed=0)
attrs = MockSystemAttr()
with patch.object(study._storage, "get_all_trials", return_value=past_trials), patch.object(
study._storage, "set_trial_system_attr", side_effect=attrs.set_trial_system_attr
), patch.object(study._storage, "get_trial", return_value=trial), patch(
"optuna.multi_objective.trial.MultiObjectiveTrial.system_attrs", new_callable=PropertyMock
) as mock1, patch(
"optuna.multi_objective.trial.FrozenMultiObjectiveTrial.system_attrs",
new_callable=PropertyMock,
) as mock2:
mock1.return_value = attrs.value
mock2.return_value = attrs.value
categorical_suggestion = sampler.sample_independent(study, trial, "param-a", cat_dist)
assert categorical_suggestion in categories
def test_reseed_rng() -> None:
sampler = MOTPEMultiObjectiveSampler()
original_seed = sampler._motpe_sampler._rng.seed
with patch.object(
sampler._motpe_sampler, "reseed_rng", wraps=sampler._motpe_sampler.reseed_rng
) as mock_object:
sampler.reseed_rng()
assert mock_object.call_count == 1
assert original_seed != sampler._motpe_sampler._rng.seed
def test_reseed_rng() -> None:
sampler = MOTPEMultiObjectiveSampler()
original_random_state = sampler._motpe_sampler._rng.get_state()
with patch.object(sampler._motpe_sampler,
"reseed_rng",
wraps=sampler._motpe_sampler.reseed_rng) as mock_object:
sampler.reseed_rng()
assert mock_object.call_count == 1
assert str(original_random_state) != str(
sampler._motpe_sampler._rng.get_state())
def test_solve_hssp() -> None:
sampler = MOTPEMultiObjectiveSampler(seed=0)
random.seed(128)
# Two dimensions
for i in range(8):
subset_size = int(random.random() * i) + 1
test_case = np.asarray([[random.random(), random.random()] for _ in range(8)])
r = 1.1 * np.max(test_case, axis=0)
truth = 0.0
for subset in itertools.permutations(test_case, subset_size):
truth = max(truth, sampler._compute_hypervolume(np.asarray(subset), r))
indices = sampler._solve_hssp(test_case, list(range(len(test_case))), subset_size, r)
approx = sampler._compute_hypervolume(test_case[indices], r)
assert approx / truth > 0.6321 # 1 - 1/e
# Three dimensions
for i in range(8):
subset_size = int(random.random() * i) + 1
test_case = np.asarray(
[[random.random(), random.random(), random.random()] for _ in range(8)]
)
r = 1.1 * np.max(test_case, axis=0)
truth = 0
for subset in itertools.permutations(test_case, subset_size):
truth = max(truth, sampler._compute_hypervolume(np.asarray(subset), r))
indices = sampler._solve_hssp(test_case, list(range(len(test_case))), subset_size, r)
approx = sampler._compute_hypervolume(test_case[indices], r)
assert approx / truth > 0.6321 # 1 - 1/e
def test_sample_independent_n_startup_trial() -> None:
study = optuna.multi_objective.create_study(
directions=["minimize", "maximize"])
dist = optuna.distributions.UniformDistribution(1.0, 100.0)
random.seed(128)
past_trials = [
frozen_trial_factory(
i, [random.random(), random.random()]) for i in range(16)
]
trial = multi_objective.trial.FrozenMultiObjectiveTrial(
2, frozen_trial_factory(16, [0, 0]))
sampler = MOTPEMultiObjectiveSampler(n_startup_trials=16, seed=0)
attrs = MockSystemAttr()
with patch.object(
study._storage, "get_all_trials", return_value=past_trials[:15]
), patch.object(
study._storage,
"set_trial_system_attr",
side_effect=attrs.set_trial_system_attr
), patch.object(study._storage, "get_trial", return_value=trial), patch(
"optuna.multi_objective.trial.MultiObjectiveTrial.system_attrs",
new_callable=PropertyMock
) as mock1, patch(
"optuna.multi_objective.trial.FrozenMultiObjectiveTrial.system_attrs",
new_callable=PropertyMock,
) as mock2, patch.object(
optuna.multi_objective.samplers.RandomMultiObjectiveSampler,
"sample_independent",
return_value=1.0,
) as sample_method:
mock1.return_value = attrs.value
mock2.return_value = attrs.value
sampler.sample_independent(study, trial, "param-a", dist)
assert sample_method.call_count == 1
sampler = MOTPEMultiObjectiveSampler(n_startup_trials=16, seed=0)
attrs = MockSystemAttr()
with patch.object(
study._storage, "get_all_trials", return_value=past_trials
), patch.object(
study._storage,
"set_trial_system_attr",
side_effect=attrs.set_trial_system_attr
), patch.object(study._storage, "get_trial", return_value=trial), patch(
"optuna.multi_objective.trial.MultiObjectiveTrial.system_attrs",
new_callable=PropertyMock
) as mock1, patch(
"optuna.multi_objective.trial.FrozenMultiObjectiveTrial.system_attrs",
new_callable=PropertyMock,
) as mock2, patch.object(
optuna.multi_objective.samplers.RandomMultiObjectiveSampler,
"sample_independent",
return_value=1.0,
) as sample_method:
mock1.return_value = attrs.value
mock2.return_value = attrs.value
sampler.sample_independent(study, trial, "param-a", dist)
assert sample_method.call_count == 0
def test_sample_independent_disrete_uniform_distributions() -> None:
"""Test samples from discrete have expected intervals."""
study = optuna.multi_objective.create_study(
directions=["minimize", "maximize"])
random.seed(128)
disc_dist = optuna.distributions.DiscreteUniformDistribution(
1.0, 100.0, 0.1)
def value_fn(idx: int) -> float:
return int(random.random() * 1000) * 0.1
past_trials = [
frozen_trial_factory(
i, [random.random(), random.random()],
dist=disc_dist,
value_fn=value_fn) for i in range(16)
]
trial = multi_objective.trial.FrozenMultiObjectiveTrial(
2, frozen_trial_factory(16, [0, 0]))
sampler = MOTPEMultiObjectiveSampler(seed=0)
attrs = MockSystemAttr()
with patch.object(
study._storage, "get_all_trials", return_value=past_trials
), patch.object(
study._storage,
"set_trial_system_attr",
side_effect=attrs.set_trial_system_attr
), patch.object(study._storage, "get_trial", return_value=trial), patch(
"optuna.multi_objective.trial.MultiObjectiveTrial.system_attrs",
new_callable=PropertyMock
) as mock1, patch(
"optuna.multi_objective.trial.FrozenMultiObjectiveTrial.system_attrs",
new_callable=PropertyMock,
) as mock2:
mock1.return_value = attrs.value
mock2.return_value = attrs.value
discrete_uniform_suggestion = sampler.sample_independent(
study, trial, "param-a", disc_dist)
assert 1.0 <= discrete_uniform_suggestion <= 100.0
assert abs(
int(discrete_uniform_suggestion * 10) -
discrete_uniform_suggestion * 10) < 1e-3
def test_sample_independent_prior() -> None:
study = optuna.multi_objective.create_study(directions=["minimize", "maximize"])
dist = optuna.distributions.UniformDistribution(1.0, 100.0)
random.seed(128)
past_trials = [frozen_trial_factory(i, [random.random(), random.random()]) for i in range(16)]
# Prepare a trial and a sample for later checks.
trial = multi_objective.trial.FrozenMultiObjectiveTrial(2, frozen_trial_factory(16, [0, 0]))
sampler = MOTPEMultiObjectiveSampler(seed=0)
attrs = MockSystemAttr()
with patch.object(study._storage, "get_all_trials", return_value=past_trials), patch.object(
study._storage, "set_trial_system_attr", side_effect=attrs.set_trial_system_attr
), patch.object(study._storage, "get_trial", return_value=trial), patch(
"optuna.multi_objective.trial.MultiObjectiveTrial.system_attrs", new_callable=PropertyMock
) as mock1, patch(
"optuna.multi_objective.trial.FrozenMultiObjectiveTrial.system_attrs",
new_callable=PropertyMock,
) as mock2:
mock1.return_value = attrs.value
mock2.return_value = attrs.value
suggestion = sampler.sample_independent(study, trial, "param-a", dist)
sampler = MOTPEMultiObjectiveSampler(consider_prior=False, seed=0)
attrs = MockSystemAttr()
with patch.object(study._storage, "get_all_trials", return_value=past_trials), patch.object(
study._storage, "set_trial_system_attr", side_effect=attrs.set_trial_system_attr
), patch.object(study._storage, "get_trial", return_value=trial), patch(
"optuna.multi_objective.trial.MultiObjectiveTrial.system_attrs", new_callable=PropertyMock
) as mock1, patch(
"optuna.multi_objective.trial.FrozenMultiObjectiveTrial.system_attrs",
new_callable=PropertyMock,
) as mock2:
mock1.return_value = attrs.value
mock2.return_value = attrs.value
assert sampler.sample_independent(study, trial, "param-a", dist) != suggestion
sampler = MOTPEMultiObjectiveSampler(prior_weight=0.5, seed=0)
attrs = MockSystemAttr()
with patch.object(study._storage, "get_all_trials", return_value=past_trials), patch.object(
study._storage, "set_trial_system_attr", side_effect=attrs.set_trial_system_attr
), patch.object(study._storage, "get_trial", return_value=trial), patch(
"optuna.multi_objective.trial.MultiObjectiveTrial.system_attrs", new_callable=PropertyMock
) as mock1, patch(
"optuna.multi_objective.trial.FrozenMultiObjectiveTrial.system_attrs",
new_callable=PropertyMock,
) as mock2:
mock1.return_value = attrs.value
mock2.return_value = attrs.value
assert sampler.sample_independent(study, trial, "param-a", dist) != suggestion
def test_sample_independent() -> None:
sampler = MOTPEMultiObjectiveSampler()
study = optuna.multi_objective.create_study(
directions=["minimize", "maximize"], sampler=sampler
)
def _objective(trial: multi_objective.trial.MultiObjectiveTrial) -> Tuple[float, float]:
x = trial.suggest_float("x", 0, 1)
y = trial.suggest_float("y", 0, 1)
return x, y
with patch.object(sampler, "sample_independent", wraps=sampler.sample_independent) as mock:
study.optimize(_objective, n_trials=10)
assert mock.call_count == 20
def test_get_observation_pairs() -> None:
def objective(
trial: optuna.multi_objective.trial.Trial) -> Tuple[float, float]:
trial.suggest_int("x", 5, 5)
return 5.0, 5.0
sampler = MOTPEMultiObjectiveSampler(seed=0)
study = optuna.multi_objective.create_study(
directions=["minimize", "maximize"], sampler=sampler)
study.optimize(objective, n_trials=5)
assert _motpe._get_observation_pairs(study, "x") == (
[5.0, 5.0, 5.0, 5.0, 5.0],
[[5.0, -5.0], [5.0, -5.0], [5.0, -5.0], [5.0, -5.0], [5.0, -5.0]],
)
assert _motpe._get_observation_pairs(study, "y") == (
[None, None, None, None, None],
[[5.0, -5.0], [5.0, -5.0], [5.0, -5.0], [5.0, -5.0], [5.0, -5.0]],
)
def test_sample_independent_handle_unsuccessful_states(state: optuna.trial.TrialState) -> None:
study = optuna.multi_objective.create_study(directions=["minimize", "maximize"])
dist = optuna.distributions.UniformDistribution(1.0, 100.0)
random.seed(128)
# Prepare sampling result for later tests.
past_trials = [frozen_trial_factory(i, [random.random(), random.random()]) for i in range(32)]
trial = multi_objective.trial.FrozenMultiObjectiveTrial(2, frozen_trial_factory(32, [0, 0]))
sampler = MOTPEMultiObjectiveSampler(seed=0)
attrs = MockSystemAttr()
with patch.object(study._storage, "get_all_trials", return_value=past_trials), patch.object(
study._storage, "set_trial_system_attr", side_effect=attrs.set_trial_system_attr
), patch.object(study._storage, "get_trial", return_value=trial), patch(
"optuna.multi_objective.trial.MultiObjectiveTrial.system_attrs", new_callable=PropertyMock
) as mock1, patch(
"optuna.multi_objective.trial.FrozenMultiObjectiveTrial.system_attrs",
new_callable=PropertyMock,
) as mock2:
mock1.return_value = attrs.value
mock2.return_value = attrs.value
all_success_suggestion = sampler.sample_independent(study, trial, "param-a", dist)
# Test unsuccessful trials are handled differently.
state_fn = build_state_fn(state)
past_trials = [
frozen_trial_factory(i, [random.random(), random.random()], state_fn=state_fn)
for i in range(32)
]
trial = multi_objective.trial.FrozenMultiObjectiveTrial(2, frozen_trial_factory(32, [0, 0]))
sampler = MOTPEMultiObjectiveSampler(seed=0)
attrs = MockSystemAttr()
with patch.object(study._storage, "get_all_trials", return_value=past_trials), patch.object(
study._storage, "set_trial_system_attr", side_effect=attrs.set_trial_system_attr
), patch.object(study._storage, "get_trial", return_value=trial), patch(
"optuna.multi_objective.trial.MultiObjectiveTrial.system_attrs", new_callable=PropertyMock
) as mock1, patch(
"optuna.multi_objective.trial.FrozenMultiObjectiveTrial.system_attrs",
new_callable=PropertyMock,
) as mock2:
mock1.return_value = attrs.value
mock2.return_value = attrs.value
partial_unsuccessful_suggestion = sampler.sample_independent(study, trial, "param-a", dist)
assert partial_unsuccessful_suggestion != all_success_suggestion
def test_infer_relative_search_space() -> None:
sampler = MOTPEMultiObjectiveSampler()
# 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) == {}
def test_sample_relative() -> None:
sampler = MOTPEMultiObjectiveSampler()
# Study and frozen-trial are not supposed to be accessed.
study = Mock(spec=[])
frozen_trial = Mock(spec=[])
assert sampler.sample_relative(study, frozen_trial, {}) == {}
def test_sample_independent_misc_arguments() -> None:
study = optuna.multi_objective.create_study(
directions=["minimize", "maximize"])
dist = optuna.distributions.UniformDistribution(1.0, 100.0)
random.seed(128)
past_trials = [
frozen_trial_factory(
i, [random.random(), random.random()]) for i in range(32)
]
# Prepare a trial and a sample for later checks.
trial = multi_objective.trial.FrozenMultiObjectiveTrial(
2, frozen_trial_factory(16, [0, 0]))
sampler = MOTPEMultiObjectiveSampler(seed=0)
attrs = MockSystemAttr()
with patch.object(
study._storage, "get_all_trials", return_value=past_trials
), patch.object(
study._storage,
"set_trial_system_attr",
side_effect=attrs.set_trial_system_attr
), patch.object(study._storage, "get_trial", return_value=trial), patch(
"optuna.multi_objective.trial.MultiObjectiveTrial.system_attrs",
new_callable=PropertyMock
) as mock1, patch(
"optuna.multi_objective.trial.FrozenMultiObjectiveTrial.system_attrs",
new_callable=PropertyMock,
) as mock2:
mock1.return_value = attrs.value
mock2.return_value = attrs.value
suggestion = sampler.sample_independent(study, trial, "param-a", dist)
# Test misc. parameters.
sampler = MOTPEMultiObjectiveSampler(n_ehvi_candidates=13, seed=0)
attrs = MockSystemAttr()
with patch.object(
study._storage, "get_all_trials", return_value=past_trials
), patch.object(
study._storage,
"set_trial_system_attr",
side_effect=attrs.set_trial_system_attr
), patch.object(study._storage, "get_trial", return_value=trial), patch(
"optuna.multi_objective.trial.MultiObjectiveTrial.system_attrs",
new_callable=PropertyMock
) as mock1, patch(
"optuna.multi_objective.trial.FrozenMultiObjectiveTrial.system_attrs",
new_callable=PropertyMock,
) as mock2:
mock1.return_value = attrs.value
mock2.return_value = attrs.value
assert sampler.sample_independent(study, trial, "param-a",
dist) != suggestion
sampler = MOTPEMultiObjectiveSampler(gamma=lambda _: 5, seed=0)
attrs = MockSystemAttr()
with patch.object(
study._storage, "get_all_trials", return_value=past_trials
), patch.object(
study._storage,
"set_trial_system_attr",
side_effect=attrs.set_trial_system_attr
), patch.object(study._storage, "get_trial", return_value=trial), patch(
"optuna.multi_objective.trial.MultiObjectiveTrial.system_attrs",
new_callable=PropertyMock
) as mock1, patch(
"optuna.multi_objective.trial.FrozenMultiObjectiveTrial.system_attrs",
new_callable=PropertyMock,
) as mock2:
mock1.return_value = attrs.value
mock2.return_value = attrs.value
assert sampler.sample_independent(study, trial, "param-a",
dist) != suggestion
sampler = MOTPEMultiObjectiveSampler(weights_above=lambda n: np.zeros(n),
seed=0)
attrs = MockSystemAttr()
with patch.object(
study._storage, "get_all_trials", return_value=past_trials
), patch.object(
study._storage,
"set_trial_system_attr",
side_effect=attrs.set_trial_system_attr
), patch.object(study._storage, "get_trial", return_value=trial), patch(
"optuna.multi_objective.trial.MultiObjectiveTrial.system_attrs",
new_callable=PropertyMock
) as mock1, patch(
"optuna.multi_objective.trial.FrozenMultiObjectiveTrial.system_attrs",
new_callable=PropertyMock,
) as mock2:
mock1.return_value = attrs.value
mock2.return_value = attrs.value
assert sampler.sample_independent(study, trial, "param-a",
dist) != suggestion