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)[source]

A Sampler using cma library as the backend.

Example

Optimize a simple quadratic function by using PyCmaSampler.

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.

Parameters:

  • x0 (dict[str, Any] | None) – 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 (float | None) – 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 (dict[str, float] | None) – 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 (int | None) – A random seed for CMA-ES.

  • cma_opts (dict[str, Any] | None) –

    Options passed to the constructor of cma.CMAEvolutionStrategy class.

    Note that default option is cma_default_options, but 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 (BaseSampler | None) –

    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.

    See also

    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.

Methods

after_trial(study, trial, state, values)

Trial post-processing.

before_trial(study, trial)

Trial pre-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)[source]

Trial post-processing.

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

Note

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.

Parameters:

  • study (Study) – Target study object.

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

  • state (TrialState) – Resulting trial state.

  • values (Sequence[float] | None) – Resulting trial values. Guaranteed to not be None if trial succeeded.

Return type:

None

before_trial(study, trial)[source]

Trial pre-processing.

This method is called before the objective function is called and right after the trial is instantiated. More precisely, this method is called during trial initialization, just before the infer_relative_search_space() call. In other words, it is responsible for pre-processing that should be done before inferring the search space.

Note

Added in v3.3.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v3.3.0.

Parameters:

  • study (Study) – Target study object.

  • trial (FrozenTrial) – Target trial object.

Return type:

None

infer_relative_search_space(study, trial)[source]

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.

Parameters:

  • study (Study) – Target study object.

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

Returns:

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

Return type:

dict[str, BaseDistribution]

See also

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

reseed_rng()[source]

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.

Return type:

None

sample_independent(study, trial, param_name, param_distribution)[source]

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.

Note

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.

Parameters:

  • study (Study) – Target study object.

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

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

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

Returns:

A parameter value.

Return type:

float

sample_relative(study, trial, search_space)[source]

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.

Note

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.

Parameters:

  • study (Study) – Target study object.

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

  • search_space (dict[str, BaseDistribution]) – The search space returned by infer_relative_search_space().

Returns:

A dictionary containing the parameter names and the values.

Return type:

dict[str, float]