import abc
from typing import Any
from typing import Dict
from optuna._experimental import experimental
from optuna.distributions import BaseDistribution
from optuna import multi_objective
[docs]@experimental("1.4.0")
class BaseMultiObjectiveSampler(object, metaclass=abc.ABCMeta):
"""Base class for multi-objective samplers.
The abstract methods of this class are the same as ones defined by
:class:`~optuna.samplers.BaseSampler` except for taking
multi-objective versions of study and trial instances as the arguments.
"""
[docs] @abc.abstractmethod
def infer_relative_search_space(
self,
study: "multi_objective.study.MultiObjectiveStudy",
trial: "multi_objective.trial.FrozenMultiObjectiveTrial",
) -> Dict[str, BaseDistribution]:
"""Infer the search space that will be used by relative sampling in the target trial.
This method is called right before
:func:`~optuna.multi_objective.samplers.BaseMultiObjectiveSampler.sample_relative`
method, and the search space returned by this method is passed to it. The parameters not
contained in the search space will be sampled by using
:func:`~optuna.multi_objective.samplers.BaseMultiObjectiveSampler.sample_independent`
method.
Args:
study:
Target study object.
trial:
Target trial object.
Returns:
A dictionary containing the parameter names and parameter's distributions.
.. seealso::
Please refer to :func:`~optuna.samplers.intersection_search_space` as an
implementation of
:func:`~optuna.multi_objective.samplers.BaseMultiObjectiveSampler.infer_relative_search_space`.
"""
raise NotImplementedError
[docs] @abc.abstractmethod
def sample_relative(
self,
study: "multi_objective.study.MultiObjectiveStudy",
trial: "multi_objective.trial.FrozenMultiObjectiveTrial",
search_space: Dict[str, BaseDistribution],
) -> Dict[str, Any]:
"""Sample parameters in a given search space.
This method is called once at the beginning of each trial, i.e., right before the
evaluation of the objective function. This method is suitable for sampling algorithms
that use the relationship between parameters.
Args:
study:
Target study object.
trial:
Target trial object.
search_space:
The search space returned by
:func:`~optuna.multi_objective.samplers.BaseMultiObjectiveSampler.infer_relative_search_space`.
Returns:
A dictionary containing the parameter names and the values.
"""
raise NotImplementedError
[docs] @abc.abstractmethod
def sample_independent(
self,
study: "multi_objective.study.MultiObjectiveStudy",
trial: "multi_objective.trial.FrozenMultiObjectiveTrial",
param_name: str,
param_distribution: BaseDistribution,
) -> Any:
"""Sample a parameter for a given distribution.
This method is called only for the parameters not contained in the search space returned
by :func:`~optuna.multi_objective.samplers.MultiObjectiveBaseSampler.sample_relative`
method. This method is suitable for sampling algorithms that do not use the relationship
between parameters such as random sampling.
Args:
study:
Target study object.
trial:
Target trial object.
param_name:
Name of the sampled parameter.
param_distribution:
Distribution object that specifies a prior and/or scale of the sampling algorithm.
Returns:
A parameter value.
"""
raise NotImplementedError