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, default None

Set the random state for reproducibility.

base_acq(mean: ndarray[Any, dtype[floating[Any]]], std: ndarray[Any, dtype[floating[Any]]]) ndarray[Any, 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[Any, 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

suggest(gp: GaussianProcessRegressor, target_space: TargetSpace, n_random: int = 10000, n_l_bfgs_b: int = 10, fit_gp: bool = True) ndarray[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, default 10_000

Number of random samples to use.

n_l_bfgs_b : 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.

Return type:

ndarray[Any, dtype[floating[Any]]]

Returns:

np.ndarray – Suggested point to probe next.