def test_get_observation_pairs():
# type: () -> None
def objective(trial):
# type: (Trial) -> float
x = trial.suggest_int('x', 5, 5)
if trial.number == 0:
return x
elif trial.number == 1:
trial.report(1, 4)
trial.report(2, 7)
raise TrialPruned()
elif trial.number == 2:
trial.report(float('nan'), 3)
raise TrialPruned()
elif trial.number == 3:
raise TrialPruned()
else:
raise RuntimeError()
# direction=minimize.
study = optuna.create_study(direction='minimize')
study.optimize(objective, n_trials=5)
study.storage.create_new_trial_id(
study.study_id) # Create a running trial.
in_trial_study = InTrialStudy(study)
assert tpe.sampler._get_observation_pairs(in_trial_study, 'x') == (
[5.0, 5.0, 5.0, 5.0],
[
(-float('inf'), 5.0), # COMPLETE
(-7, 2), # PRUNED (with intermediate values)
(
-3, float('inf')
), # PRUNED (with a NaN intermediate value; it's treated as infinity)
(float('inf'), 0.0) # PRUNED (without intermediate values)
])
assert tpe.sampler._get_observation_pairs(in_trial_study, 'y') == ([], [])
# direction=maximize.
study = optuna.create_study(direction='maximize')
study.optimize(objective, n_trials=4)
study.storage.create_new_trial_id(
study.study_id) # Create a running trial.
in_trial_study = InTrialStudy(study)
assert tpe.sampler._get_observation_pairs(in_trial_study, 'x') == (
[5.0, 5.0, 5.0, 5.0],
[
(-float('inf'), -5.0), # COMPLETE
(-7, -2), # PRUNED (with intermediate values)
(
-3, float('inf')
), # PRUNED (with a NaN intermediate value; it's treated as infinity)
(float('inf'), 0.0) # PRUNED (without intermediate values)
])
assert tpe.sampler._get_observation_pairs(in_trial_study, 'y') == ([], [])
def test_log_uniform(sampler_class, distribution):
# type: (typing.Callable[[], BaseSampler], LogUniformDistribution) -> None
study = optuna.study.create_study(sampler=sampler_class())
in_trial_study = InTrialStudy(study)
points = np.array([
study.sampler.sample_independent(in_trial_study, _create_new_trial(study), 'x',
distribution) for _ in range(100)
])
assert np.all(points >= distribution.low)
assert np.all(points < distribution.high)
assert not isinstance(
study.sampler.sample_independent(in_trial_study, _create_new_trial(study), 'x',
distribution), np.floating)
def test_categorical(sampler_class, choices):
# type: (typing.Callable[[], BaseSampler], typing.Tuple[T, ...]) -> None
distribution = CategoricalDistribution(choices)
study = optuna.study.create_study(sampler=sampler_class())
in_trial_study = InTrialStudy(study)
points = np.array([
study.sampler.sample_independent(in_trial_study,
_create_new_trial(study), 'x',
distribution) for _ in range(100)
])
# 'x' value is corresponding to an index of distribution.choices.
assert np.all(points >= 0)
assert np.all(points <= len(distribution.choices) - 1)
round_points = np.round(points)
np.testing.assert_almost_equal(round_points, points)
def test_in_trial_study(storage_mode):
# type: (str) -> None
with StorageSupplier(storage_mode) as storage:
study = optuna.create_study(storage=storage)
# Run ten trials.
study.optimize(lambda t: t.suggest_int('x', 0, 10), n_trials=10)
# Create an `InTrialStudy` instance.
in_trial_study = InTrialStudy(study)
# Test best trial and trials.
assert in_trial_study.best_params == study.best_params
assert in_trial_study.best_value == study.best_value
assert in_trial_study.best_trial == study.best_trial
assert in_trial_study.trials == study.trials
# Test study direction.
assert in_trial_study.direction == study.direction
def test_discrete_uniform(sampler_class, distribution):
# type: (typing.Callable[[], BaseSampler], DiscreteUniformDistribution) -> None
study = optuna.study.create_study(sampler=sampler_class())
in_trial_study = InTrialStudy(study)
points = np.array([
study.sampler.sample_independent(in_trial_study, _create_new_trial(study), 'x',
distribution) for _ in range(100)
])
assert np.all(points >= distribution.low)
assert np.all(points <= distribution.high)
assert not isinstance(
study.sampler.sample_independent(in_trial_study, _create_new_trial(study), 'x',
distribution), np.floating)
# Check all points are multiples of distribution.q.
points = points
points -= distribution.low
points /= distribution.q
round_points = np.round(points)
np.testing.assert_almost_equal(round_points, points)
