Speedup of the acquisition function optimization #14
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… points the minimum of 100k random points is selected.
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I replaced the calls to scipy's minimize with tests of random points within the parameter space defined by pbounds. This results in speedups in most cases I have used it with. (I normally optimize 2-10 parameters)
The setting can be controlled with the test_random_points parameters. It's default is "False" (previous behaviour - using scipy's minimize). Setting it to an integer makes it test n random points instead.
The number of tested points depends on the number of parameters to optimize. I normally set it to 100k...
Example notebook:
https://github.com/stmax82/BayesianOptimization/blob/acq_opt/examples/acquisition%20function%20optimization.ipynb