Source code for optuna.multi_objective.samplers._motpe

from typing import Any
from typing import Callable
from typing import Dict
from typing import Optional
import warnings

import numpy as np

import optuna
from optuna import multi_objective
from optuna._deprecated import deprecated
from optuna.distributions import BaseDistribution
from optuna.exceptions import ExperimentalWarning
from optuna.multi_objective.samplers import _MultiObjectiveSamplerAdapter
from optuna.multi_objective.samplers import BaseMultiObjectiveSampler
from optuna.pruners import NopPruner
from optuna.samplers import MOTPESampler
from optuna.samplers._tpe.multi_objective_sampler import _default_weights_above
from optuna.samplers._tpe.multi_objective_sampler import default_gamma
from import create_study
from optuna.trial import create_trial
from optuna.trial import FrozenTrial

[docs]@deprecated("2.4.0", "4.0.0") 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 <>`_ 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, )
[docs] def reseed_rng(self) -> None: self._motpe_sampler.reseed_rng()
[docs] def infer_relative_search_space( self, study: "", trial: "multi_objective.trial.FrozenMultiObjectiveTrial", ) -> Dict[str, BaseDistribution]: return {}
[docs] def sample_relative( self, study: "", trial: "multi_objective.trial.FrozenMultiObjectiveTrial", search_space: Dict[str, BaseDistribution], ) -> Dict[str, Any]: return {}
[docs] def sample_independent( self, study: "", 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 )
def _create_study(mo_study: "") -> "optuna.Study": study = create_study( storage=mo_study._storage, sampler=_MultiObjectiveSamplerAdapter(mo_study.sampler), pruner=NopPruner(), study_name="_motpe-" + mo_study._storage.get_study_name_from_id(mo_study._study_id), directions=mo_study.directions, load_if_exists=True, ) for mo_trial in mo_study.trials: with warnings.catch_warnings(): warnings.simplefilter("ignore", ExperimentalWarning) study.add_trial(_create_trial(mo_trial)) return study def _create_trial(mo_trial: "multi_objective.trial.FrozenMultiObjectiveTrial") -> FrozenTrial: with warnings.catch_warnings(): warnings.simplefilter("ignore", ExperimentalWarning) trial = create_trial( state=mo_trial.state, values=mo_trial.values, params=mo_trial.params, distributions=mo_trial.distributions, user_attrs=mo_trial.user_attrs, system_attrs=mo_trial.system_attrs, ) return trial