optuna.samplers.GPSampler

class optuna.samplers.GPSampler(*, seed=None, independent_sampler=None, n_startup_trials=10, deterministic_objective=False)

Sampler using Gaussian process-based Bayesian optimization.

This sampler fits a Gaussian process (GP) to the objective function and optimizes the acquisition function to suggest the next parameters.

The current implementation uses:

• Matern kernel with nu=2.5 (twice differentiable),

• Automatic relevance determination (ARD) for the length scale of each parameter,

• Gamma prior for inverse squared lengthscales, kernel scale, and noise variance,

• Log Expected Improvement (logEI) as the acquisition function, and

• Quasi-Monte Carlo (QMC) sampling to optimize the acquisition function.

Note

This sampler requires scipy and torch. You can install these dependencies with pip install scipy torch.

Parameters:

• seed (int | None) – Random seed to initialize internal random number generator. Defaults to None (a seed is picked randomly).

• independent_sampler (BaseSampler | None) – Sampler used for initial sampling (for the first n_startup_trials trials) and for conditional parameters. Defaults to None (a random sampler with the same seed is used).

• n_startup_trials (int) – Number of initial trials. Defaults to 10.

• deterministic_objective (bool) – Whether the objective function is deterministic or not. If True, the sampler will fix the noise variance of the surrogate model to the minimum value (slightly above 0 to ensure numerical stability). Defaults to False.

Note

Added in v3.6.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v3.6.0.

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)

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)

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)

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()

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)

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:

Any

sample_relative(study, trial, search_space)

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, Any]