optuna.integration.SkoptSampler¶

class
optuna.integration.
SkoptSampler
(independent_sampler: Optional[optuna.samplers._base.BaseSampler] = None, warn_independent_sampling: bool = True, skopt_kwargs: Optional[Dict[str, Any]] = None, n_startup_trials: int = 1, *, consider_pruned_trials: bool = False)[source]¶ Sampler using ScikitOptimize as the backend.
Example
Optimize a simple quadratic function by using
SkoptSampler
.import optuna def objective(trial): x = trial.suggest_uniform('x', 10, 10) y = trial.suggest_int('y', 0, 10) return x**2 + y sampler = optuna.integration.SkoptSampler() study = optuna.create_study(sampler=sampler) study.optimize(objective, n_trials=10)
 Parameters
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 forSkoptSampler
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.
skopt_kwargs –
Keyword arguments passed to the constructor of skopt.Optimizer class.
Note that
dimensions
argument inskopt_kwargs
will be ignored because it is added bySkoptSampler
automatically.n_startup_trials – The independent sampling is used until the given number of trials finish in the same study.
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
As the number of trials \(n\) increases, each sampling takes longer and longer on a scale of \(O(n^3)\). And, if this is
True
, the number of trials will increase. So, it is suggested to set this flagFalse
when each evaluation of the objective function is relatively faster than each sampling. On the other hand, it is suggested to set this flagTrue
when each evaluation of the objective function is relatively slower than each sampling.

__init__
(independent_sampler: Optional[optuna.samplers._base.BaseSampler] = None, warn_independent_sampling: bool = True, skopt_kwargs: Optional[Dict[str, Any]] = None, n_startup_trials: int = 1, *, consider_pruned_trials: bool = False) → None[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
([independent_sampler, …])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.