srbf: Symbolic Regression Benchmark Framework
srbf evaluates symbolic-regression models on shared benchmarks with shared metrics. It is the
evaluation framework carved out of flash-ansr: the
evaluation engine, model adapters, benchmarks, and metrics. It depends one-way on flash-ansr
(srbf imports flash-ansr; flash-ansr never imports srbf).
Built for contributions. Developers of SR methods add their model by opening a PR with an
adapter (two methods) plus install instructions. The built-in adapters (flash_ansr, pysr,
nesymres, e2e, skeleton_pool, brute_force) are reference examples, not a closed set. See the
adapter contribution guide.
Status: 0.1, cleanly-carved eval. The engine seam (
srbf.eval.coreProtocols +srbf.eval.engine) is model-agnostic, but every built-in adapter importsflash-ansrand the adapter set is a registry edited by PR. A pluginregister_adapter()entry-point and raw-dataset ((X, y)CSV/parquet) ingestion are planned follow-ons.
Install
pip install srbf # engine + metrics + the pip-installable adapters (flash-ansr, PySR)
pip install "srbf[baselines]" # + pip baseline deps (sympy, pysr, omegaconf)
srbf pulls in flash-ansr and simplipy automatically. The unpackaged research baselines
(NeSymReS, E2E) are provisioned out-of-band; see docs/models.md.
Quickstart
# 1. point srbf at a tree holding configs/, data/, and models/ (your srbf checkout works)
export FLASH_ANSR_ROOT=$(pwd)
# 2. get a model to evaluate (flash-ansr's CLI ships with srbf)
flash_ansr install psaegert/flash-ansr-v23.0-3M
# 3. fetch a benchmark + build its skeleton pool (see docs/benchmarks.md)
# ...then run an evaluation:
srbf run -c configs/evaluation/scaling/v23.0-3M_val.yaml --limit 50 -v
Outputs land under results/evaluation/.../*.pkl, one row per evaluated dataset with flat metric
columns. Run programmatically instead:
from srbf import build_evaluation_run
plan = build_evaluation_run(config="configs/evaluation/scaling/v23.0-3M_val.yaml")
plan.engine.run(limit=plan.remaining, output_path=plan.output_path)
Documentation
| Guide | What it covers |
|---|---|
| Running evaluations | the srbf run CLI, config anatomy (data_source / model_adapter / runner / experiments), outputs, resume |
| Benchmarks & datasets | fetching FastSRB, building skeleton pools with flash_ansr import-data, custom sets |
| Models & provisioning | installing/patching the built-in models; the model_adapter block per type |
| Adding your model | the adapter protocol + registry, and the PR flow to contribute a new SR method |
Development
pip install -e ".[dev]"
pre-commit run --all-files
pytest tests
License
MIT (see LICENSE). Third-party attributions in THIRD_PARTY_LICENSES.
1 maintainer
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