skopt.utils.expected_minimum_random_sampling¶

skopt.utils.expected_minimum_random_sampling(res, n_random_starts=100000, random_state=None)[source][source]¶

Minimum search by doing naive random sampling, Returns the parameters that gave the minimum function value. Can be used 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

resOptimizeResult, scipy object

The optimization result returned by a skopt minimizer.

n_random_startsint, default=100000

The number of random starts for the minimization of the surrogate model.

random_stateint, RandomState instance, or None (default)

Set random state to something other than None for reproducible results.

Returns

xlist

location of the minimum.

funfloat

the surrogate function value at the minimum.