optuna.samplers.CmaEsSampler

class optuna.samplers.CmaEsSampler(x0: Optional[Dict[str, Any]] = None, sigma0: Optional[float] = None, n_startup_trials: int = 1, independent_sampler: Optional[optuna.samplers._base.BaseSampler] = None, warn_independent_sampling: bool = True, seed: Optional[int] = None, *, consider_pruned_trials: bool = False, restart_strategy: Optional[str] = None, inc_popsize: int = 2)[source]

A Sampler using CMA-ES algorithm.

Example

Optimize a simple quadratic function by using CmaEsSampler.

import optuna

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

sampler = optuna.samplers.CmaEsSampler()
study = optuna.create_study(sampler=sampler)
study.optimize(objective, n_trials=20)

Please note that this sampler does not support CategoricalDistribution. If your search space contains categorical parameters, I recommend you to use TPESampler instead. Furthermore, there is room for performance improvements in parallel optimization settings. This sampler cannot use some trials for updating the parameters of multivariate normal distribution.

For further information about CMA-ES algorithm and its restarting strategy algorithm, please refer to the following papers:

See also

You can also use optuna.integration.CmaEsSampler which is a sampler using cma library as the backend.

Parameters
  • x0 – A dictionary of an initial parameter values for CMA-ES. By default, the mean of low and high for each distribution is used. Note that x0 is sampled uniformly within the search space domain for each restart if you specify restart_strategy argument.

  • sigma0 – 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.

  • seed – A random seed for CMA-ES.

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

  • independent_sampler

    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 CmaEsSampler 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

    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.

  • restart_strategy

    Strategy for restarting CMA-ES optimization when converges to a local minimum. If given None, CMA-ES will not restart (default). If given ‘ipop’, CMA-ES will restart with increasing population size. Please see also inc_popsize parameter.

    Note

    Added in v2.1.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v2.1.0.

  • inc_popsize – Multiplier for increasing population size before each restart. This argument will be used when setting restart_strategy = 'ipop'.

  • consider_pruned_trials

    If this is True, the PRUNED trials are considered for sampling.

    Note

    Added in v2.0.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v2.0.0.

    Note

    It is suggested to set this flag False when the MedianPruner is used. On the other hand, it is suggested to set this flag True when the HyperbandPruner is used. Please see the benchmark result for the details.

__init__(x0: Optional[Dict[str, Any]] = None, sigma0: Optional[float] = None, n_startup_trials: int = 1, independent_sampler: Optional[optuna.samplers._base.BaseSampler] = None, warn_independent_sampling: bool = True, seed: Optional[int] = None, *, consider_pruned_trials: bool = False, restart_strategy: Optional[str] = None, inc_popsize: int = 2)None[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([x0, sigma0, n_startup_trials, …])

Initialize self.

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.

infer_relative_search_space(study: optuna.study.Study, trial: optuna.trial._frozen.FrozenTrial) → Dict[str, optuna.distributions.BaseDistribution][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 pass to it. The parameters not contained in the search space will be sampled by using sample_independent() method.

Parameters
  • study – Target study object.

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

Returns

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

See also

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

reseed_rng()None[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.

sample_independent(study: optuna.study.Study, trial: optuna.trial._frozen.FrozenTrial, param_name: str, param_distribution: optuna.distributions.BaseDistribution) → Any[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 – Target study object.

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

sample_relative(study: optuna.study.Study, trial: optuna.trial._frozen.FrozenTrial, search_space: Dict[str, optuna.distributions.BaseDistribution]) → Dict[str, Any][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 – Target study object.

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

  • search_space – The search space returned by infer_relative_search_space().

Returns

A dictionary containing the parameter names and the values.