bayes_opt.acquisition.GPHedge

class bayes_opt.acquisition.GPHedge(base_acquisitions: Sequence[AcquisitionFunction], random_state: int | RandomState | None = None) → None

GPHedge acquisition function.

At each suggestion step, GPHedge samples suggestions from each base acquisition function acq_i. Then a candidate is selected from the suggestions based on the on the cumulative rewards of each acq_i. After evaluating the candidate, the gains are updated (in the next iteration) based on the updated expectation value of the candidates.

For more information, see:

Brochu et al., “Portfolio Allocation for Bayesian Optimization”, https://arxiv.org/abs/1009.5419

Parameters:

• base_acquisitions (Sequence[AcquisitionFunction]) – Sequence of base acquisition functions.

• random_state (int | RandomState | None) – Set the random state for reproducibility.

base_acq(*args: Any, **kwargs: Any) → NoReturn

Raise an error, since the base acquisition function is ambiguous.

Return type:

NoReturn

Parameters:

• args (Any)

• kwargs (Any)

get_acquisition_params() → dict[str, Any]

Get the current acquisition function parameters.

Return type:

dict[str, Any]

Returns:

dict – Dictionary containing the current acquisition function parameters.

set_acquisition_params(params: dict[str, Any]) → None

Set the acquisition function parameters.

Parameters:

params (dict[str, Any]) – Dictionary containing the acquisition function parameters.

Return type:

None

suggest(gp: GaussianProcessRegressor, target_space: TargetSpace, n_random: int = 10000, n_smart: int = 10, fit_gp: bool = True, random_state: int | RandomState | None = None) → ndarray[tuple[Any, ...], dtype[floating[Any]]]

Suggest a promising point to probe next.

Parameters:

• gp (GaussianProcessRegressor) – A fitted Gaussian Process.

• target_space (TargetSpace) – The target space to probe.

• n_random (int) – Number of random samples to use.

• n_smart (int) – Number of starting points for the L-BFGS-B optimizer.

• fit_gp (bool) – Whether to fit the Gaussian Process to the target space. Set to False if the GP is already fitted.

• random_state (int | RandomState | None) – Random state to use for the optimization.

Return type:

ndarray[tuple[Any, ...], dtype[floating[Any]]]

Returns:

np.ndarray – Suggested point to probe next.