bayes_opt.acquisition.UpperConfidenceBound

class bayes_opt.acquisition.UpperConfidenceBound(kappa=2.576, exploration_decay=None, exploration_decay_delay=None, random_state=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, std)

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[Float]

Returns:

np.ndarray – Acquisition function value.

decay_exploration()

Decay kappa by a constant rate.

Adjust exploration/exploitation trade-off by reducing kappa.

Note

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

Return type:

None

suggest(gp, target_space, n_random=10000, n_l_bfgs_b=10, fit_gp=True)

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[Float]

Returns:

np.ndarray – Suggested point to probe next.