Add PIR (Physics Intermediate Representation) method#202
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Symbolic regression with dimensional analysis, RANSAC consensus, residual refinement, and sparse coefficient selection. Torch-free. - experiment/methods/PIRRegressor.py: sklearn-compatible estimator with est, hyper_params, complexity(est), model(est, X) - experiment/methods/src/PIR_install.sh: installs physics-engine@v0.1.0 Engine: https://github.com/Qazi-pk/physics-engine (MIT, v0.1.0, commit 736a89c)
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CI is failing in Set up job with Happy to wait while it's addressed, or to open a small follow-up PR bumping the cache action version if that would help. |
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Method: PIR (Physics Intermediate Representation)
A torch-free symbolic regression method combining dimensional analysis,
RANSAC consensus filtering, residual refinement, and sparse coefficient
selection. Sklearn-compatible.
736a89c)Files added
experiment/methods/PIRRegressor.py— exportsest,hyper_params,complexity(est),model(est, X)experiment/methods/src/PIR_install.sh— pip-installsphysics-engine@v0.1.0from the public source repo (no source code vendored in this PR)
Configuration
Blind-sweep settings (SEED=0):
enforce_dimensions=False,allowed_powers=[1, 2],include_pairwise_products=True,use_ransac=True,use_residual=True,use_sparse=True,use_ot_loss=False,add_physics_features=False.Other parameters at defaults.
Honest scope
Blind Tier A (Feynman, SRBench-compatible protocol, 5 seeds):
7/44 solved (≈7.6 mean) under the configuration above. An earlier
27.3% figure in the project's own notes was formula-peeking and is not
the blind result.
The current classical engine is architecturally limited to ≤ 2-variable
monomial structures (pairwise structure detector); extension to ≥ 3-variable
laws is future work and not part of this PR.
Open question for reviewers
model(est, X)returns a sympy-parseable string using the column names ofthe input
pd.DataFrame X. If SRBench's symbolic-equivalence checkexpects a specific naming convention (e.g.
x_0..x_mvs Feynman'sq1,Ef, …), please flag — happy to adjust the variable mapping inmodel()to match.Checklist
devbranchfit,predict,random_state)install.shpulls from stable tagmodel(est, X)returns a sympy-compatible string