incorporate into symbolic regression benchmarks

[lacava]

hello, thanks for providing a python implementation of GSGP! If you are interested, it would be great to get a sklearn-compatible interface for GSGP so we can benchmark it easily with other methods as part of our benchmark repo. Let me know if you are interested in collaborating on this!

[amoraglio]

Hello William, Thank you for your message and for creating a GP benchmark compatible with sklearn, which sounds like a very nice way to make GP available to the masses! :) In principle I would be interested to provide a GSGP implementation for this project. However I am afraid I am not going to have time until April after the frenzy teaching term-time is over. So, hopefully this is fine. Also, perhaps I should mention that my GSGP implementation is an early minimal prototype to illustrate how the system works, and how we could address the problem of exponential growth using a neat functional programming approach, rather than an optimised robust implementation suitable for benchmarking, but perhaps being an open source project that everybody can contribute to it I could add my implementation and hope for somebody else to optimise it? :) Thanks, Alberto

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On Wed, 24 Feb 2021 at 15:07, William La Cava ***@***.***> wrote: hello, thanks for providing a python implementation of GSGP! If you are interested, it would be great to get a sklearn-compatible interface for GSGP so we can benchmark it easily with other methods as part of our benchmark repo <https://github.com/EpistasisLab/regression-benchmark>. Let me know if you are interested in collaborating on this! — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#3>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ABCZ473YQZ4RAGH2QK3CEDDTAUIZRANCNFSM4YEVVQXQ> .