optuna.integration.BoTorchSampler

class optuna.integration.BoTorchSampler(*, candidates_func=None, constraints_func=None, n_startup_trials=10, independent_sampler=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 be not 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

    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 a torch.Tensor. However, if constraints_func is omitted, constraints will be None. For any constraints that failed to compute, the tensor will contain NaN.

    If omitted, is determined automatically based on the number of objectives. If the number of objectives is one, Quasi MC-based batch Expected Improvement (qEI) is used. If the number of objectives is larger than one but smaller than four, Quasi MC-based batch Expected Hypervolume Improvement (qEHVI) is used. Otherwise, for larger number of objectives, the faster Quasi MC-based extended ParEGO (qParEGO) is used.

    The function should assume maximization of the objective.

  • constraints_func

    An optional function that computes the objective constraints. It must take a FrozenTrial and return the constraints. The return value must be a sequence of float s. A value strictly larger than 0 means that a constraints 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 if candidates_func is omitted.

  • n_startup_trials – Number of initial trials, that is the number of trials to resort to independent sampling.

  • independent_sampler – An independent sampler to use for the initial trials and for parameters that are conditional.

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.

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)[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 (optuna.study.Study) – Target study object.

  • trial (optuna.trial._frozen.FrozenTrial) – Target trial object. Take a copy before modifying this object.

  • state (optuna.trial._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 using sample_independent() method.

Parameters
  • study (optuna.study.Study) – Target study object.

  • trial (optuna.trial._frozen.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, optuna.distributions.BaseDistribution]

See also

Please refer to intersection_search_space() as an implementation of infer_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 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)[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 (optuna.study.Study) – Target study object.

  • trial (optuna.trial._frozen.FrozenTrial) – Target trial object. Take a copy before modifying this object.

  • param_name (str) – Name of the sampled parameter.

  • param_distribution (optuna.distributions.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)[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 (optuna.study.Study) – Target study object.

  • trial (optuna.trial._frozen.FrozenTrial) – Target trial object. Take a copy before modifying this object.

  • search_space (Dict[str, optuna.distributions.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]