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.