Add GP_ELITE (pure-Python GP with Levenberg-Marquardt constants)#212
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ariel95500-create wants to merge 10 commits into
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Add GP_ELITE (pure-Python GP with Levenberg-Marquardt constants)#212ariel95500-create wants to merge 10 commits into
ariel95500-create wants to merge 10 commits into
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Implement GPEliteSRBench for symbolic regression with max time constraint.
Added metadata for GP_ELITE project including authors, description, and URL.
Added metadata for GP_ELITE project including authors, description, and URL.
Implement GPEliteSRBench class for symbolic regression with time constraints and multiple restarts. Includes methods for fitting the model, generating symbolic expressions, and evaluating complexity.
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This PR adds GP_ELITE, a pure-Python symbolic regression library
(pip install gp-elite, MIT).
Method: genetic programming with island parallelism, Levenberg-Marquardt
constant optimization, native multi-restart with merged archives, and a
complexity/accuracy Pareto front. The regressor passes scikit-learn's
check_estimator, implements max_time via time-boxed sequential restarts,
and model() returns a sympy-compatible string using the dataset column
names, with input normalization folded in (the string reproduces
est.predict on raw features to ~1e-12).
Internal run of the SRBench ground-truth protocol on the 119 Feynman
datasets (10k rows, 75/25 split, 3 seeds, R2_test>0.999): 60.2% of runs
solved, 69% of datasets solved by at least one seed, median R2 0.99973.
Raw jsonl logs and the runner are available in the project repo.
Repo: https://github.com/ariel95500-create/gp-elite
Happy to adjust anything to fit the harness.