optuna.integration.PyCmaSampler

class optuna.integration.PyCmaSampler(x0=None, sigma0=None, cma_stds=None, seed=None, cma_opts=None, n_startup_trials=1, independent_sampler=None, warn_independent_sampling=True)[源代码]

A Sampler using cma library as the backend.

示例

Optimize a simple quadratic function by using PyCmaSampler.

import optuna


def objective(trial):
    x = trial.suggest_float("x", -1, 1)
    y = trial.suggest_int("y", -1, 1)
    return x ** 2 + y


sampler = optuna.integration.PyCmaSampler()
study = optuna.create_study(sampler=sampler)
study.optimize(objective, n_trials=20)

Note that parallel execution of trials may affect the optimization performance of CMA-ES, especially if the number of trials running in parallel exceeds the population size.

备注

CmaEsSampler is deprecated and renamed to PyCmaSampler in v2.0.0. Please use PyCmaSampler instead of CmaEsSampler.

参数

  • x0 (Optional[Dict[str, Any]]) – A dictionary of an initial parameter values for CMA-ES. By default, the mean of low and high for each distribution is used. Please refer to cma.CMAEvolutionStrategy for further details of x0.

  • sigma0 (Optional[float]) – Initial standard deviation of CMA-ES. By default, sigma0 is set to min_range / 6, where min_range denotes the minimum range of the distributions in the search space. If distribution is categorical, min_range is len(choices) - 1. Please refer to cma.CMAEvolutionStrategy for further details of sigma0.

  • cma_stds (Optional[Dict[str, float]]) – A dictionary of multipliers of sigma0 for each parameters. The default value is 1.0. Please refer to cma.CMAEvolutionStrategy for further details of cma_stds.

  • seed (Optional[int]) – A random seed for CMA-ES.

  • cma_opts (Optional[Dict[str, Any]]) –

    Options passed to the constructor of cma.CMAEvolutionStrategy class.

    Note that BoundaryHandler, bounds, CMA_stds and seed arguments in cma_opts will be ignored because it is added by PyCmaSampler automatically.

  • n_startup_trials (int) – The independent sampling is used instead of the CMA-ES algorithm until the given number of trials finish in the same study.

  • independent_sampler (Optional[optuna.samplers._base.BaseSampler]) –

    A BaseSampler instance that is used for independent sampling. The parameters not contained in the relative search space are sampled by this sampler. The search space for PyCmaSampler is determined by intersection_search_space().

    If None is specified, RandomSampler is used as the default.

    参见

    optuna.samplers module provides built-in independent samplers such as RandomSampler and TPESampler.

  • warn_independent_sampling (bool) –

    If this is True, a warning message is emitted when the value of a parameter is sampled by using an independent sampler.

    Note that the parameters of the first trial in a study are always sampled via an independent sampler, so no warning messages are emitted in this case.

返回类型

None

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.

备注

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

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

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.

备注

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

float

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.

备注

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, float]