Gpyopt: Discrete and categorical data?

Created on 18 Nov 2015 · 7Comments · Source: SheffieldML/GPyOpt

Can GPyOpt currently deal with categorical and discrete inputs? I couldn't find any tutorial on this aspect

Source

CarrKnight

Most helpful comment

Are discrete variables also fitted with a GP? Or are they assumed to be categorical / having no local correlation?

1Reinier on 5 Dec 2016

👍10

All 7 comments

Hi CarrKnight
Still it is not possible to deal with discrete inputs but that is something we have in mind to implement. Soon, we will have ready a module for bandits optimization in the devel brach.
Javier

javiergonzalezh on 1 Dec 2015

Cheers, thanks!

CarrKnight on 1 Dec 2015

Back again! Have a look to the notebook in the devel branch

https://github.com/SheffieldML/GPyOpt/blob/devel/manual/GPyOpt_mixed_domain.ipynb

We have tested it some examples and it seems to work well but we are still testing it.

javiergonzalezh on 31 Mar 2016

this was perfect, thank you!

CarrKnight on 16 May 2016

Are discrete variables also fitted with a GP? Or are they assumed to be categorical / having no local correlation?

1Reinier on 5 Dec 2016

👍10

@1Reinier This depends on the choice of kernel, no? I'm interested in the anyone's take on implementing custom kernels on mixed type domains (e.g. Zhou et al. 2011)

ekalosak on 19 Sep 2019

@ekalosak

Could you elaborate more? What are the commonly used kernels? What are the advantages and disadvantages of each of them? Is there somewhere to read more about them? Thanks in advance!