bayeso.acquisitions.log_expected_improvement

It defines a log expected improvement acquisition function.

  1. Ament, S., Daulton, S., Eriksson, D., Balandat, M., & Bakshy, E. (2023). Unexpected Improvements to Expected Improvement for Bayesian Optimization. In Advances in Neural Information Processing Systems, 36, pp. 20577–20612.

bayeso.acquisitions.log_expected_improvement.acq_fun(pred_mean: ndarray, pred_std: ndarray, Y_train: ndarray, jitter: float = 1e-05) → ndarray

It is a log 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.acquisitions.log_expected_improvement.erfcx(x)
bayeso.acquisitions.log_expected_improvement.log1mexp(x)