bayeso.acquisitions.augmented_expected_improvement

It defines an augmented expected improvement acquisition function.

  1. Huang, D., Allen, T. T., Notz, W. I., & Zeng, N. (2006). Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models. Journal of Global Optimization, 34(3), pp. 441–466.

bayeso.acquisitions.augmented_expected_improvement.acq_fun(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