optuna.samplers.BaseSampler

class optuna.samplers.BaseSampler[源代码]

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 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, infer_relative_search_space() is called to determine the relative search space for the trial. Then, sample_relative() is invoked to sample parameters from the relative search space. During the execution of the objective function, 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.

../../_images/sampling-sequence.png

Methods

after_trial(study, trial, state, values)

Trial post-processing.

infer_relative_search_space(study, trial)

Infer the search space that will be used by relative sampling in the target trial.

reseed_rng()

Reseed sampler’s random number generator.

sample_independent(study, trial, param_name, …)

Sample a parameter for a given distribution.

sample_relative(study, trial, search_space)

Sample parameters in a given search space.

after_trial(study, trial, state, values)[源代码]

Trial post-processing.

This method is called after the objective function returns and right before the trials is finished and its state is stored.

注解

Added in v2.4.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v2.4.0.

参数
返回类型

None

abstract infer_relative_search_space(study, trial)[源代码]

Infer the search space that will be used by relative sampling in the target trial.

This method is called right before 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 sample_independent() method.

参数
返回

A dictionary containing the parameter names and parameter’s distributions.

返回类型

Dict[str, optuna.distributions.BaseDistribution]

参见

Please refer to intersection_search_space() as an implementation of infer_relative_search_space().

reseed_rng()[源代码]

Reseed sampler’s random number generator.

This method is called by the Study instance if trials are executed in parallel with the option n_jobs>1. In that case, the sampler instance will be replicated including the state of the random number generator, and they may suggest the same values. To prevent this issue, this method assigns a different seed to each random number generator.

返回类型

None

abstract sample_independent(study, trial, param_name, param_distribution)[源代码]

Sample a parameter for a given distribution.

This method is called only for the parameters not contained in the search space returned by sample_relative() method. This method is suitable for sampling algorithms that do not use relationship between parameters such as random sampling and TPE.

注解

The failed trials are ignored by any build-in samplers when they sample new parameters. Thus, failed trials are regarded as deleted in the samplers’ perspective.

参数
  • study (optuna.study.Study) – Target study object.

  • trial (optuna.trial._frozen.FrozenTrial) – Target trial object. Take a copy before modifying this object.

  • param_name (str) – Name of the sampled parameter.

  • param_distribution (optuna.distributions.BaseDistribution) – Distribution object that specifies a prior and/or scale of the sampling algorithm.

返回

A parameter value.

返回类型

Any

abstract sample_relative(study, trial, search_space)[源代码]

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.

注解

The failed trials are ignored by any build-in samplers when they sample new parameters. Thus, failed trials are regarded as deleted in the samplers’ perspective.

参数
返回

A dictionary containing the parameter names and the values.

返回类型

Dict[str, Any]