---

name: fit-and-diagnose description: "Fit ordinary PySR regressors, inspect Pareto fronts, select equations, and diagnose slow or poor searches." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

PySR fit-and-diagnose

Use this sub-skill when the task is an ordinary PySR symbolic-regression fit: prepare tabular data, choose a small operator set, run PySRRegressor.fit, inspect model.equations_, pick equations from the Pareto front, call predict/sympy/latex, or debug a slow or low-quality search.

Route first

User need	Use here?	Route
Plain PySRRegressor fit, data shape, feature names, weights, denoising, feature selection, Pareto selection	Yes	Continue below.
Custom Julia operators, custom elementwise/full losses, units, operator constraints, required/forbidden structure by loss penalties	No	../customization-and-constraints/SKILL.md
Known expression templates, parametric expressions, shared/vector-valued templates, guesses for templates	No	../structured-expressions/SKILL.md
Saved hall-of-fame files, reload/checkpoints, SymPy/LaTeX/JAX/Torch export details, TensorBoard artifacts	No	../export-and-artifacts/SKILL.md
Installation, first Julia startup, threading, multiprocessing, Slurm, long-run scaling, reproducibility environment	Usually no	../runtime-and-scaling/SKILL.md

Minimal operating loop

1. Validate data shape. Use X as (n_samples, n_features) and y as (n_samples,) for one target or (n_samples, n_outputs) for independent multi-output fits. Prefer a pandas DataFrame when feature names matter; otherwise pass safe variable_names to fit.
2. Start with a small operator set. Use only operators that are plausible for the domain. For a polynomial-like first pass, start with binary_operators=["+", "-", "*"]; add /, trig, exp, or other operators only when justified.
3. Bound the first run. Start short to validate the setup, then run longer. Use niterations, timeout_in_seconds, or max_evals for budget control. Avoid interpreting the first Julia compile as a search failure.
4. Fit and inspect the whole front. model.fit(X, y, weights=..., variable_names=...) stores a Pareto front in model.equations_. Print it and inspect loss versus complexity rather than reporting only the auto-selected row.
5. Select explicitly when needed. model_selection="best" balances loss and simplicity; "accuracy" chooses minimum loss; "score" chooses the largest score. Pass index= to predict, sympy, or latex to evaluate a specific row.
6. Iterate scientifically. If results are trivial or slow, simplify features/operators, adjust maxsize, use select_k_features for high-dimensional tabular data, consider denoise=True or weights for noisy data, and only then add more compute.

See references/workflows.md for recipes, references/api-reference.md for focused signatures and option semantics, and references/troubleshooting.md for failure modes.

Safe bundled smoke helper

scripts/pysr_quickstart_smoke.py is a small, deterministic helper for agents. It is help/dry-run oriented by default and imports PySR only when --run-fit is supplied.

python scripts/pysr_quickstart_smoke.py --help
python scripts/pysr_quickstart_smoke.py --run-fit --niterations 1 --timeout-seconds 20

The helper is a smoke check, not a benchmark. A successful run proves that import, a tiny fit, equations_, predict, sympy, and latex are usable in the current environment; it does not prove convergence on the user's dataset.