Source code for optuna.samplers.base

import abc
import six

from optuna import type_checking

if type_checking.TYPE_CHECKING:
    from typing import Any  # NOQA
    from typing import Dict  # NOQA

    from optuna.distributions import BaseDistribution  # NOQA
    from optuna.structs import FrozenTrial  # NOQA
    from optuna.study import Study  # NOQA


[docs]@six.add_metaclass(abc.ABCMeta) class BaseSampler(object): """Base class for samplers. Optuna combines two types of sampling strategies, which are called *relative sampling* and *independent sampling*. *The relative sampling* determines values of multiple parameters simultaneously so that sampling algorithms can use relationship between parameters (e.g., correlation). Target parameters of the relative sampling are described in a relative search space, which is determined by :func:`~optuna.samplers.BaseSampler.infer_relative_search_space`. *The independent sampling* determines a value of a single parameter without considering any relationship between parameters. Target parameters of the independent sampling are the parameters not described in the relative search space. More specifically, parameters are sampled by the following procedure. At the beginning of a trial, :meth:`~optuna.samplers.BaseSampler.infer_relative_search_space` is called to determine the relative search space for the trial. Then, :meth:`~optuna.samplers.BaseSampler.sample_relative` is invoked to sample parameters from the relative search space. During the execution of the objective function, :meth:`~optuna.samplers.BaseSampler.sample_independent` is used to sample parameters that don't belong to the relative search space. The following figure depicts the lifetime of a trial and how the above three methods are called in the trial. .. image:: ../../image/sampling-sequence.png | """
[docs] @abc.abstractmethod def infer_relative_search_space(self, study, trial): # type: (Study, FrozenTrial) -> 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.samplers.BaseSampler.sample_relative` method, and the search space returned by this method is pass to it. The parameters not contained in the search space will be sampled by using :func:`~optuna.samplers.BaseSampler.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.samplers.BaseSampler.infer_relative_search_space`. """ raise NotImplementedError
[docs] @abc.abstractmethod def sample_relative(self, study, trial, search_space): # type: (Study, FrozenTrial, 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 relationship between parameters such as Gaussian Process and CMA-ES. Args: study: Target study object. trial: Target trial object. search_space: The search space returned by :func:`~optuna.samplers.BaseSampler.infer_relative_search_space`. Returns: A dictionary containing the parameter names and the values. """ raise NotImplementedError
[docs] @abc.abstractmethod def sample_independent(self, study, trial, param_name, param_distribution): # type: (Study, FrozenTrial, str, 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.samplers.BaseSampler.sample_relative` method. This method is suitable for sampling algorithms that do not use relationship between parameters such as random sampling and TPE. 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