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physo

physo

Use PhySO for symbolic regression, class symbolic regression, toolkit-level expression workflows, and benchmark problem loaders.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill-Dokumentation

GitHub

name: physo description: "Use PhySO for symbolic regression, class symbolic regression, toolkit-level expression workflows, and benchmark problem loaders." read_when: "Use when the user names physo, PhySO, symbolic regression, ClassSR, toolkit expression helpers, or benchmark problem loaders." disable-model-invocation: true metadata: disco-role: operating license: MIT

PhySO

Use this skill when working with the physo package, the scientific Python symbolic-regression library.

Install and Import Check

Run scripts/physo_skill_doctor.py to confirm the installed package, key public exports, and the CPU scientific stack used by the verified baseline. The checked baseline was PhySO 1.2.0 on CPU; this skill does not claim CUDA verification. Import-time LaTeX warnings are expected when optional display tools are missing.

Route by Task

  • One dataset with X, y, units, y_weights, candidate_wrapper, run presets, logging, or Pareto inspection: use sub-skills/sr/SKILL.md.
  • Multiple realizations with shared class constants or realization-specific constants: use sub-skills/class-sr/SKILL.md.
  • Prefix expressions, libraries, tokens, Program/VectPrograms, random sampling, constant optimization, display, or result reloads: use sub-skills/toolkit/SKILL.md.
  • Feynman/Class benchmark problems, sample generation, metadata, or symbolic equivalence checks: use sub-skills/benchmarks/SKILL.md.

Shared References

Common Starting Points

  • Start with the doctor script when the environment or package import is uncertain.
  • Route to a sub-skill before drafting commands or code for a user workflow.
  • Use the sub-skill smoke scripts for route-specific tiny checks after the environment is known to be usable.
  • If the user asks for benchmark reproduction, training-scale sweeps, or maintainer jobfiles, stop at the boundary and keep this runtime skill focused on day-to-day package workflows.

Boundaries

This skill is self-contained. It does not depend on opening the source checkout at runtime, and it does not cover repo maintenance, release automation, or long benchmark campaigns.

Gespeicherte Skills