optuna_integration.BoTorchSampler
- class optuna_integration.BoTorchSampler(*, candidates_func=None, constraints_func=None, n_startup_trials=10, consider_running_trials=False, independent_sampler=None, seed=None, device=None)[source]
A sampler that uses BoTorch, a Bayesian optimization library built on top of PyTorch.
This sampler allows using BoTorch’s optimization algorithms from Optuna to suggest parameter configurations. Parameters are transformed to continuous space and passed to BoTorch, and then transformed back to Optuna’s representations. Categorical parameters are one-hot encoded.
See also
See an example how to use the sampler.
See also
See the BoTorch homepage for details and for how to implement your own
candidates_func
.Note
An instance of this sampler should not be used with different studies when used with constraints. Instead, a new instance should be created for each new study. The reason for this is that the sampler is stateful keeping all the computed constraints.
- Parameters:
candidates_func (Callable[[torch.Tensor, torch.Tensor, torch.Tensor | None, torch.Tensor, torch.Tensor | None], torch.Tensor] | None) –
An optional function that suggests the next candidates. It must take the training data, the objectives, the constraints, the search space bounds and return the next candidates. The arguments are of type
torch.Tensor
. The return value must be atorch.Tensor
. However, ifconstraints_func
is omitted, constraints will beNone
. For any constraints that failed to compute, the tensor will contain NaN.If omitted, it is determined automatically based on the number of objectives and whether a constraint is specified. If the number of objectives is one and no constraint is specified, log-Expected Improvement is used. If constraints are specified, quasi MC-based batch Expected Improvement (qEI) is used. If the number of objectives is either two or three, Quasi MC-based batch Expected Hypervolume Improvement (qEHVI) is used. Otherwise, for a larger number of objectives, analytic Expected Hypervolume Improvement is used if no constraints are specified, or the faster Quasi MC-based extended ParEGO (qParEGO) is used if constraints are present.
The function should assume maximization of the objective.
See also
See
optuna_integration.botorch.qei_candidates_func()
for an example.constraints_func (Callable[[FrozenTrial], Sequence[float]] | None) –
An optional function that computes the objective constraints. It must take a
FrozenTrial
and return the constraints. The return value must be a sequence offloat
s. A value strictly larger than 0 means that a constraint is violated. A value equal to or smaller than 0 is considered feasible.If omitted, no constraints will be passed to
candidates_func
nor taken into account during suggestion.n_startup_trials (int) – Number of initial trials, that is the number of trials to resort to independent sampling.
consider_running_trials (bool) –
If True, the acquisition function takes into consideration the running parameters whose evaluation has not completed. Enabling this option is considered to improve the performance of parallel optimization.
Note
Added in v3.2.0 as an experimental argument.
independent_sampler (BaseSampler | None) – An independent sampler to use for the initial trials and for parameters that are conditional.
seed (int | None) – Seed for random number generator.
device (torch.device | None) – A
torch.device
to store input and output data of BoTorch. Please set a CUDA device if you fasten sampling.
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.
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 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 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.
- before_trial(study, trial)[source]
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)[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:
- Returns:
A dictionary containing the parameter names and the values.
- Return type: