Benchmark / GPlearn

[eladmw]

Can you do a basic comparison between this and gplearn with regards to speed and flexibility?

[MilesCranmer]

Great idea. Any specific tests you would be interested in?

I used to actually use gplearn, but the pure-Python/numpy performance was never enough for any of the problems I work on in my research. The whole reason I wrote PySR was so I could get the SR performance I need for my projects. (Eureqa is still the fastest GA-based option out there, but it's commercialized and online-only now, and without a Python API, which makes experiments hard to run, so I've actually stopped using it in favor of PySR).

I think a lot of the difference between the packages comes from that DEAP (backend of gplearn) is pure-Python, whereas the entire search here is compiled end-to-end, and is asynchronously distributed. And also a few other optimizations introduced here that are specific to symbolic regression (like the constant tuning), which I'm not sure is available in DEAP.

I can start by putting this in the README (or a separate repo for benchmarks?), but I'd also eventually like to write this up somewhere!

Cheers, Miles

[eladmw]

For a benchmark, I would say : speed, performance, features, and simplicity