def test_reseed_rng() -> None:
sampler = MOTPESampler()
original_seed = sampler._rng.seed
with patch.object(
sampler._mo_random_sampler, "reseed_rng", wraps=sampler._mo_random_sampler.reseed_rng
) as mock_object:
sampler.reseed_rng()
assert mock_object.call_count == 1
assert original_seed != sampler._rng.seed
class MOTPEMultiObjectiveSampler(BaseMultiObjectiveSampler):
"""Multi-objective sampler using the MOTPE algorithm.
This sampler is a multiobjective version of :class:`~optuna.samplers.TPESampler`.
For further information about MOTPE algorithm, please refer to the following paper:
- `Multiobjective tree-structured parzen estimator for computationally expensive optimization
problems <https://dl.acm.org/doi/abs/10.1145/3377930.3389817>`_
Args:
consider_prior:
Enhance the stability of Parzen estimator by imposing a Gaussian prior when
:obj:`True`. The prior is only effective if the sampling distribution is
either :class:`~optuna.distributions.UniformDistribution`,
:class:`~optuna.distributions.DiscreteUniformDistribution`,
:class:`~optuna.distributions.LogUniformDistribution`,
:class:`~optuna.distributions.IntUniformDistribution`,
or :class:`~optuna.distributions.IntLogUniformDistribution`.
prior_weight:
The weight of the prior. This argument is used in
:class:`~optuna.distributions.UniformDistribution`,
:class:`~optuna.distributions.DiscreteUniformDistribution`,
:class:`~optuna.distributions.LogUniformDistribution`,
:class:`~optuna.distributions.IntUniformDistribution`,
:class:`~optuna.distributions.IntLogUniformDistribution`, and
:class:`~optuna.distributions.CategoricalDistribution`.
consider_magic_clip:
Enable a heuristic to limit the smallest variances of Gaussians used in
the Parzen estimator.
consider_endpoints:
Take endpoints of domains into account when calculating variances of Gaussians
in Parzen estimator. See the original paper for details on the heuristics
to calculate the variances.
n_startup_trials:
The random sampling is used instead of the MOTPE algorithm until the given number
of trials finish in the same study. 11 * number of variables - 1 is recommended in the
original paper.
n_ehvi_candidates:
Number of candidate samples used to calculate the expected hypervolume improvement.
gamma:
A function that takes the number of finished trials and returns the number of trials to
form a density function for samples with low grains. See the original paper for more
details.
weights_above:
A function that takes the number of finished trials and returns a weight for them. As
default, weights are automatically calculated by the MOTPE's default strategy.
seed:
Seed for random number generator.
.. note::
Initialization with Latin hypercube sampling may improve optimization performance.
However, the current implementation only supports initialization with random sampling.
Example:
.. testcode::
import optuna
seed = 128
num_variables = 9
n_startup_trials = 11 * num_variables - 1
def objective(trial):
x = []
for i in range(1, num_variables + 1):
x.append(trial.suggest_float(f"x{i}", 0.0, 2.0 * i))
return x
sampler = optuna.multi_objective.samplers.MOTPEMultiObjectiveSampler(
n_startup_trials=n_startup_trials, n_ehvi_candidates=24, seed=seed
)
study = optuna.multi_objective.create_study(
["minimize"] * num_variables, sampler=sampler
)
study.optimize(objective, n_trials=250)
"""
def __init__(
self,
consider_prior: bool = True,
prior_weight: float = 1.0,
consider_magic_clip: bool = True,
consider_endpoints: bool = True,
n_startup_trials: int = 10,
n_ehvi_candidates: int = 24,
gamma: Callable[[int], int] = default_gamma,
weights_above: Callable[[int], np.ndarray] = _default_weights_above,
seed: Optional[int] = None,
) -> None:
with warnings.catch_warnings():
warnings.simplefilter("ignore", ExperimentalWarning)
self._motpe_sampler = MOTPESampler(
consider_prior=consider_prior,
prior_weight=prior_weight,
consider_magic_clip=consider_magic_clip,
consider_endpoints=consider_endpoints,
n_startup_trials=n_startup_trials,
n_ehvi_candidates=n_ehvi_candidates,
gamma=gamma,
weights_above=weights_above,
seed=seed,
)
def reseed_rng(self) -> None:
self._motpe_sampler.reseed_rng()
def infer_relative_search_space(
self,
study: "multi_objective.study.MultiObjectiveStudy",
trial: "multi_objective.trial.FrozenMultiObjectiveTrial",
) -> Dict[str, BaseDistribution]:
return {}
def sample_relative(
self,
study: "multi_objective.study.MultiObjectiveStudy",
trial: "multi_objective.trial.FrozenMultiObjectiveTrial",
search_space: Dict[str, BaseDistribution],
) -> Dict[str, Any]:
return {}
def sample_independent(
self,
study: "multi_objective.study.MultiObjectiveStudy",
trial: "multi_objective.trial.FrozenMultiObjectiveTrial",
param_name: str,
param_distribution: BaseDistribution,
) -> Any:
return self._motpe_sampler.sample_independent(_create_study(study),
_create_trial(trial),
param_name,
param_distribution)
