bayeso.acquisition

bayeso.acquisition.aei(pred_mean, pred_std, Y_train, noise, jitter=1e-05)

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, pred_std, Y_train, jitter=1e-05)

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, pred_std, Y_train, jitter=1e-05)

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, pred_std=None, Y_train=None)

It is a pure exploitation criterion.

Parameters:
  • pred_mean (numpy.ndarray) – posterior predictive mean function over X_test. Shape: (l, ).
  • pred_std (numpy.ndarray, optional) – posterior predictive standard deviation function over X_test. Shape: (l, ). It can be given, but it is ignored when it works.
  • Y_train (numpy.ndarray, optional) – outputs of X_train. Shape: (n, 1). It can be given, but it is ignored when it works.
Returns:

acquisition function values. Shape: (l, ).
Return type:

numpy.ndarray
Raises:

AssertionError
bayeso.acquisition.pure_explore(pred_std, pred_mean=None, Y_train=None)

It is a pure exploration criterion.

Parameters:
  • pred_std (numpy.ndarray) – posterior predictive standard deviation function over X_test. Shape: (l, ).
  • pred_mean (numpy.ndarray, optional) – posterior predictive mean function over X_test. Shape: (l, ). It can be given, but it is ignored when it works.
  • Y_train (numpy.ndarray, optional) – outputs of X_train. Shape: (n, 1). It can be given, but it is ignored when it works.
Returns:

acquisition function values. Shape: (l, ).
Return type:

numpy.ndarray
Raises:

AssertionError
bayeso.acquisition.ucb(pred_mean, pred_std, Y_train=None, kappa=2.0, is_increased=True)

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.
  • is_increased (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