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Source code for recreating the results of "Correcting boundary over-exploration deficiencies in Bayesian optimization with virtual derivative sign observations" featured in MLSP 2018

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vdsobo

A simple Python package for virtual derivative sign observation Bayesian optimization.

See our paper for details.

For GPy, one needs to use this version

Minimum working example for simple 1 d gaussian:

import optimization
import util
import acquisitions
l = util.get_gaussian_functions(1,1,1)
l_new = util.normalize_functions(l)
bo = DerivativeBayesianOptimization(func = l_new[0][0], X=initial_x(1), acquisition_function=acquisitions.EI, max_iter=10, print_progress=1, adaptive=True)
X,Y = bo.optimize()
bo.plot_model("test.png")

This example also runs by 'python optimization.py'

Fore more complicated cases, see the documentation in source code (Start from init of optimization.BayesianOptimization class)

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Source code for recreating the results of "Correcting boundary over-exploration deficiencies in Bayesian optimization with virtual derivative sign observations" featured in MLSP 2018

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