optuna.samplers.CmaEsSampler
- class optuna.samplers.CmaEsSampler(x0=None, sigma0=None, n_startup_trials=1, independent_sampler=None, warn_independent_sampling=True, seed=None, *, consider_pruned_trials=False, restart_strategy=None, popsize=None, inc_popsize=2, use_separable_cma=False, source_trials=None)[source]
A sampler using cmaes as the backend.
Example
Optimize a simple quadratic function by using
CmaEsSampler
.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.samplers.CmaEsSampler() study = optuna.create_study(sampler=sampler) study.optimize(objective, n_trials=20)
Please note that this sampler does not support CategoricalDistribution. However,
FloatDistribution
withstep
, (suggest_float()
) andIntDistribution
(suggest_int()
) are supported.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, please refer to the following papers:
See also
You can also use
optuna.integration.PyCmaSampler
which is a sampler using cma library as the backend.- Parameters
x0 (Optional[Dict[str, Any]]) – A dictionary of an initial parameter values for CMA-ES. 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 (Optional[float]) – Initial standard deviation of CMA-ES. By default,
sigma0
is set tomin_range / 6
, wheremin_range
denotes the minimum range of the distributions in the search space.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[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 forCmaEsSampler
is determined byintersection_search_space()
.If
None
is specified,RandomSampler
is used as the default.See also
optuna.samplers
module provides built-in independent samplers such asRandomSampler
andTPESampler
.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.
restart_strategy (Optional[str]) –
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 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.
popsize (Optional[int]) – A population size of CMA-ES. When set
restart_strategy = 'ipop'
, this is used as the initial population size.inc_popsize (int) – Multiplier for increasing population size before each restart. This argument will be used when setting
restart_strategy = 'ipop'
.consider_pruned_trials (bool) –
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.use_separable_cma (bool) –
If this is
True
, the covariance matrix is constrained to be diagonal. Due to reduce the model complexity, the learning rate for the covariance matrix is increased. Consequently, this algorithm outperforms CMA-ES on separable functions.Note
Added in v2.6.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v2.6.0.
source_trials (Optional[List[FrozenTrial]]) –
This option is for Warm Starting CMA-ES, a method to transfer prior knowledge on similar HPO tasks through the initialization of CMA-ES. This method estimates a promising distribution from
source_trials
and generates the parameter of multivariate gaussian distribution. Please note that it is prohibited to usex0
,sigma0
, oruse_separable_cma
argument together.Note
Added in v2.6.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v2.6.0.
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 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 trials 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 (Optional[Sequence[float]]) – Resulting trial values. Guaranteed to not be
None
if trial succeeded.
- 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 usingsample_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
See also
Please refer to
intersection_search_space()
as an implementation ofinfer_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 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.- 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
- 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