bayeso.acquisition

It defines acquisition functions.

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

It is a probability 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: numpy.ndarray) → numpy.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: numpy.ndarray) → numpy.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: numpy.ndarray, pred_std: numpy.ndarray, Y_train: Optional[numpy.ndarray] = None, kappa: float = 2.0, increase_kappa: bool = True) → numpy.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