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 CMAES 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 CMAES 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 CMAES. By default, the mean of
low
andhigh
for each distribution is used. Note thatx0
is sampled uniformly within the search space domain for each restart if you specifyrestart_strategy
argument.sigma0 – Initial standard deviation of CMAES. By default,
sigma0
is set tomin_range / 6
, wheremin_range
denotes the minimum range of the distributions in the search space.seed – A random seed for CMAES.
n_startup_trials – The independent sampling is used instead of the CMAES 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 forCmaEsSampler
is determined byintersection_search_space()
.If
None
is specified,RandomSampler
is used as the default.See also
optuna.samplers
module provides builtin independent samplers such asRandomSampler
andTPESampler
.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 CMAES optimization when converges to a local minimum. If given
None
, CMAES will not restart (default). If given ‘ipop’, CMAES will restart with increasing population size. Please see alsoinc_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 theMedianPruner
is used. On the other hand, it is suggested to set this flagTrue
when theHyperbandPruner
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 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 usingsample_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 ofinfer_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 optionn_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 buildin 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 CMAES.
Note
The failed trials are ignored by any buildin 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.