bayes_opt.acquisition.UpperConfidenceBound¶

class bayes_opt.acquisition.UpperConfidenceBound(kappa: float = 2.576, exploration_decay: float | None = None, exploration_decay_delay: int | None = None, random_state: int | RandomState | None = None) → None¶

Upper Confidence Bound acquisition function.

The upper confidence bound is calculated as

\[\text{UCB}(x) = \mu(x) + \kappa \sigma(x).\]

Parameters:¶

kappa : float, default 2.576¶

Governs the exploration/exploitation tradeoff. Lower prefers exploitation, higher prefers exploration.

exploration_decay : float, default None¶

Decay rate for kappa. If None, no decay is applied.

exploration_decay_delay : int, default None¶

Delay for decay. If None, decay is applied from the start.

random_state : int | RandomState | None¶

base_acq(mean: ndarray[tuple[int, ...], dtype[floating[Any]]], std: ndarray[tuple[int, ...], dtype[floating[Any]]]) → ndarray[tuple[int, ...], dtype[floating[Any]]]¶

Calculate the upper confidence bound.

Parameters:¶

mean : np.ndarray¶

Mean of the predictive distribution.

std : np.ndarray¶

Standard deviation of the predictive distribution.

Return type:¶

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

Returns:¶

np.ndarray – Acquisition function value.

decay_exploration() → None¶

Decay kappa by a constant rate.

Adjust exploration/exploitation trade-off by reducing kappa. :rtype: None

Note

This method is called automatically at the end of each suggest() call.

Return type:¶

None

get_acquisition_params() → dict¶

Get the current acquisition function parameters.

Return type:¶

dict

Returns:¶

dict – Dictionary containing the current acquisition function parameters.

set_acquisition_params(params: dict) → None¶

Set the acquisition function parameters.

Parameters:¶

params : dict¶

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[int, ...], 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, default 10_000¶

Number of random samples to use.

n_smart : int, default 10¶

Number of starting points for the L-BFGS-B optimizer.

fit_gp : bool, default True¶

Whether to fit the Gaussian Process to the target space. Set to False if the GP is already fitted.

random_state : int, RandomState, default None¶

Random state to use for the optimization.

Return type:¶

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

Returns:¶

np.ndarray – Suggested point to probe next.