Comparison with other software

pyGPGO is not the only available Python package for bayesian optimization. To the best of our knowledge, we believe that it is one of the most comprehensive ones in terms of features available to the user. We show a table comparing some of the most common features here:

	pyGPGO	Spearmint	fmfn/BayesianOptimization	pyBO	MOE	GPyOpt	scikit-optimize
GP implementation	Native	Native	via scikit-learn	via Reggie	Native	via GPy	via scikit-learn
Modular	Yes	No	No	No	No	Yes	No
Surrogates	{GP, tSP, RF, ET, GBM}	{GP}	{GP}	{GP}	{GP}	{GP, RF, WGP}	{GP, RF, GBM}
Type II ML optimization	Yes	No	No	No	Yes	Yes	Yes
MCMC inference	Yes (via pyMC3)	Yes	No	Yes	No	Yes	No
Choice of MCMC sampler	Yes	Yes	No	Yes	No	No	No
Acquisition functions	{PI, EI, UCB, Entropy}	{EI}	{PI, EI, UCB}	{PI, EI, UCB, Thompson sampling}	{EI}	{PI, EI, UCB}	{PI, EI, UCB}
Integrated acq. function	Yes	Yes	No	Yes	No	Yes	No
License	MIT	Academic	MIT	BSD-2	Apache	BSD-3	BSD
Last update (as of Apr. 2017)		Apr 2016	Mar 2017	Sept 2015	Apr 2016	Apr 2017	Apr 2017
Python version	> 3.5	2.7	2/3	2/3	2.7	2/3	2/3

If you like some other feature implemented into pyGPGO or think this table is outdated or incorrect, please let us know by opening an issue on the Github repository of the package!