bayeso.acquisition

It defines acquisition functions, each of which is employed to determine where next to evaluate.

bayeso.acquisition.aei(pred_mean: ndarray, pred_std: ndarray, Y_train: ndarray, noise: float, jitter: float = 1e-05) → ndarray

It is an augmented expected improvement criterion.

Parameters:

pred_mean (numpy.ndarray) – posterior predictive mean function over X_test. Shape: (l, ).
pred_std (numpy.ndarray) – posterior predictive standard deviation function over X_test. Shape: (l, ).
Y_train (numpy.ndarray) – outputs of X_train. Shape: (n, 1).
noise (float) – noise for augmenting exploration.
jitter (float, optional) – jitter for pred_std.

Returns:

acquisition function values. Shape: (l, ).

Return type:

numpy.ndarray

Raises:

AssertionError

bayeso.acquisition.ei(pred_mean: ndarray, pred_std: ndarray, Y_train: ndarray, jitter: float = 1e-05) → ndarray

It is an expected improvement criterion.

Parameters:

pred_mean (numpy.ndarray) – posterior predictive mean function over X_test. Shape: (l, ).
pred_std (numpy.ndarray) – posterior predictive standard deviation function over X_test. Shape: (l, ).
Y_train (numpy.ndarray) – outputs of X_train. Shape: (n, 1).
jitter (float, optional) – jitter for pred_std.

Returns:

acquisition function values. Shape: (l, ).

Return type:

numpy.ndarray

Raises:

AssertionError

bayeso.acquisition.pi(pred_mean: ndarray, pred_std: ndarray, Y_train: ndarray, jitter: float = 1e-05) → ndarray

It is a probability of improvement criterion.

Parameters:

pred_mean (numpy.ndarray) – posterior predictive mean function over X_test. Shape: (l, ).
pred_std (numpy.ndarray) – posterior predictive standard deviation function over X_test. Shape: (l, ).
Y_train (numpy.ndarray) – outputs of X_train. Shape: (n, 1).
jitter (float, optional) – jitter for pred_std.

Returns:

acquisition function values. Shape: (l, ).

Return type:

numpy.ndarray

Raises:

AssertionError

bayeso.acquisition.pure_exploit(pred_mean: ndarray) → ndarray

It is a pure exploitation criterion.

Parameters:: pred_mean (numpy.ndarray) – posterior predictive mean function over X_test. Shape: (l, ).
Returns:: acquisition function values. Shape: (l, ).
Return type:: numpy.ndarray
Raises:: AssertionError

bayeso.acquisition.pure_explore(pred_std: ndarray) → ndarray

It is a pure exploration criterion.

Parameters:: pred_std (numpy.ndarray) – posterior predictive standard deviation function over X_test. Shape: (l, ).
Returns:: acquisition function values. Shape: (l, ).
Return type:: numpy.ndarray
Raises:: AssertionError

bayeso.acquisition.ucb(pred_mean: ndarray, pred_std: ndarray, Y_train: ndarray | None = None, kappa: float = 2.0, increase_kappa: bool = True) → ndarray

It is a Gaussian process upper confidence bound criterion.

Parameters:

pred_mean (numpy.ndarray) – posterior predictive mean function over X_test. Shape: (l, ).
pred_std (numpy.ndarray) – posterior predictive standard deviation function over X_test. Shape: (l, ).
Y_train (numpy.ndarray, optional) – outputs of X_train. Shape: (n, 1).
kappa (float, optional) – trade-off hyperparameter between exploration and exploitation.
increase_kappa (bool., optional) – flag for increasing a kappa value as Y_train grows. If Y_train is None, it is ignored, which means kappa is fixed.

Returns:

acquisition function values. Shape: (l, ).

Return type:

numpy.ndarray

Raises:

AssertionError