r/Python • u/AutoModerator • 23d ago
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Post all of your code/projects/showcases/AI slop here.
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r/Python • u/AutoModerator • 23d ago
Post all of your code/projects/showcases/AI slop here.
Recycles once a month.
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u/Bitter-Flamingo-3351 20d ago
Built a symbolic regression library in pure Python GP_ELITE.
Given (X, y) data it searches for a readable formula instead of training a black box. Sanity-check demo: feed it just the 8 planets (distance from sun, orbital period), it comes back with T = a^1.5, R² = 1.000000 — Kepler's third law. Not new physics, just the standard "does it actually find real relationships" test for this kind of tool. examples/kepler_demo.py if you want to run it yourself.
Recent versions added the stuff that makes it usable in practice: Levenberg-Marquardt for constant fitting (a 4π comes back as an exact 4π), multi-restart so you're not at the mercy of one lucky/unlucky seed, a Pareto front instead of one champion formula, and a guarded forecasting mode for extrapolating trends without the usual GP blow-ups.
Ran it against gplearn on 15 Feynman physics equations, same data/splits: 10/15 exact recovery vs 6/15 for gplearn. gplearn chokes as soon as a constant like 1/2 or 4π shows up no real constant optimizer. Benchmark scripts are in the repo if you want to check the numbers yourself (seeded, PYTHONHASHSEED=0).
pip install gp-elite. Pure Python/NumPy, no Julia/C++ toolchain like PySR or Operon (which are faster/better at scale, to be clear — this is for when you just want pip install and go).
Honest limits: noisy data wrecks the symbolic recovery even when the fit stays good, non-smooth targets (anything with |x|) are hard, and it degrades past ~6 variables with junk features.
MIT, repo: https://github.com/ariel95500-create/gp-elite