deephyper.skopt.utils.expected_minimum#

deephyper.skopt.utils.expected_minimum(res, n_random_starts=20, random_state=None)[source]#

Compute the minimum over the predictions of the last surrogate model. Uses expected_minimum_random_sampling with n_random_starts = 100000, when the space contains any categorical values.

Note

The returned minimum may not necessarily be an accurate prediction of the minimum of the true objective function.

Parameters:

  • res (OptimizeResult, scipy object) – The optimization result returned by a skopt minimizer.

  • n_random_starts (int, default=20) – The number of random starts for the minimization of the surrogate model.

  • random_state (int, RandomState instance, or None (default)) – Set random state to something other than None for reproducible results.

Returns:

  • x (list) – location of the minimum.

  • fun (float) – the surrogate function value at the minimum.