---

name: class-sr description: "Multi-dataset class symbolic regression with shared and realization-specific free constants." read_when: "Use when multiple related realizations must share one symbolic law with class and realization-specific constants." disable-model-invocation: true metadata: disco-role: operating license: MIT

Class SR

Role

Use this sub-skill when the user has multiple realizations of the same phenomenon and wants one shared symbolic form with:

• class free constants shared by every realization
• realization-specific free constants (spe_free_consts)
• ClassSR-specific data-shape validation and quick-start guidance

If the user has only one dataset, route to the sibling sr sub-skill instead. If the user mainly needs expression-tree or constant-table manipulation, hand off to the sibling toolkit sub-skill.

Included

• physo.ClassSR
• multi_X, multi_y, multi_y_weights
• class_free_consts_* and spe_free_consts_*
• ClassSR config selection (config0b, config1b, config2b)
• safe quick-start execution on CPU
• loading and inspecting Pareto logs
• constant inspection handoff to toolkit-style workflows

Excluded

• single-dataset physo.SR
• benchmark equivalence utilities and benchmark runner automation
• generated jobfile helpers and maintainer-only benchmark orchestration
• generic multi-task ML workflows that are not ClassSR

Verified entry points

• physo.ClassSR
• physo.config.config0b.config0b
• physo.config.config1b.config1b
• physo.config.config2b.config2b
• physo.read_pareto_pkl
• physo.load_expr
• run_logger.get_pareto_front()
• best_expr.free_consts.class_values
• best_expr.free_consts.spe_values
• best_expr.get_infix_sympy(evaluate_consts=True)

Canonical workflow

1. Build multi_X and multi_y as lists with one element per realization.
   • Each X_i must have shape (n_dim, n_samples_i).
   • Each y_i must have shape (n_samples_i,).
   • Different realizations may have different sample counts.
2. Provide weights with multi_y_weights when you need to bias one realization or one region of a realization.
3. Define constants and units.
   • class_free_consts_* are shared across realizations.
   • spe_free_consts_* may vary by realization.
4. Pick a ClassSR config.
   • config0b: quick smoke / demos
   • config1b: dimensional-analysis-heavy ClassSR and Milky Way streams
   • config2b: class benchmark style runs
   • prefer the b variants for ClassSR because they give more free-constant optimization steps
5. Run physo.ClassSR(...) on CPU for a smoke test, or on the target device if the environment is already verified.
6. Inspect the result with best_expr.get_infix_pretty(), run_logger.get_pareto_front(), and best_expr.free_consts.
7. If you need deeper program or constant-table work, switch to the sibling toolkit sub-skill instead of expanding this one.

Input / output contract

• Input multi_X: list-like of length n_realizations; each item is array-like with shape (n_dim, n_samples_i).
• Input multi_y: list-like of length n_realizations; each item is array-like with shape (n_samples_i,).
• Input multi_y_weights: one of
  • a single scalar, broadcast to every realization
  • a list/array of length n_realizations containing one scalar per realization
  • a list/array of length n_realizations containing one weight array per realization with shape (n_samples_i,)
• Input class_free_consts_names / class_free_consts_units / class_free_consts_init_val: same length, one entry per shared constant.
  • In the ClassSR wrapper, pass init values in name order as a list/array-like container.
• Input spe_free_consts_names / spe_free_consts_units / spe_free_consts_init_val: same length, one entry per realization-specific constant.
  • In the ClassSR wrapper, pass init values in name order as a list/array-like container.
  • spe_free_consts_init_val may use a scalar or a length-n_realizations vector per constant.
• Output best_expression: physo.physym.program.Program
• Output run_logger: logger object with Pareto-front access
• Output best_expression.free_consts.class_values: shape (1, n_class_free_consts)
• Output best_expression.free_consts.spe_values: shape (1, n_spe_free_consts, n_realizations)
• Output best_expression.get_infix_sympy(evaluate_consts=True): array with one expression per realization

Quick safe run

Use the bundled smoke helper for a CPU-safe sanity check:

python skills/disco/physo/sub-skills/class-sr/scripts/smoke_class_sr_quick_start.py

The helper uses tiny deterministic data, short epochs, CPU execution, and temporary log files.

Troubleshooting

• If imports fail, check the environment first: physo, torch, numpy, sympy, pandas, matplotlib, scikit-learn.
• If a realization has a different sample count, that is allowed; only each X_i / y_i pair must match.
• If multi_y_weights length does not match multi_y, or any per-point weight array has the wrong length, fix the list shape first.
• If class/spe constant unit arrays do not match the name counts, fix the name/unit lists before running.
• If spe_free_consts_init_val is a vector, its length must match n_realizations.
• If parallel_mode or CUDA behavior is unclear, keep the smoke run on device='cpu' with parallel_mode=False.
• CUDA-capable torch builds may still print parallel-mode warnings during a CPU smoke run; treat them as expected noise unless the run actually fails.
• If LaTeX or display extras are missing, prefer get_infix_pretty() or get_infix_sympy(); pretty LaTeX rendering is optional.
• If you need saved logs or Pareto reloads, use run_logger.get_pareto_front() first and then physo.read_pareto_pkl(...) on the saved _pareto.pkl file.

Related native tests and examples

• physo/task/tests/class_sr_UnitTest.py — canonical ClassSR task check
• physo/physym/tests/free_const_UnitTest.py — class/spe constant table behavior
• physo/physym/tests/dataset_UnitTest.py — realization and weight-shape validation
• physo/benchmark/ClassDataset/tests/ClassProblem_UnitTest.py — benchmark-shaped multi-dataset loading
• demos/class_sr_quick_start.py — source quick-start recipe adapted by the smoke script
• demos/class_sr/demo_free_fall/demo_free_fall.py — reference ClassSR example with class and spe constants
• demos/class_sr/demo_milky_way_streams/MW_streams_run.py — reference-only, longer benchmark-style run

See also

• Root physo skill for package-level routing and shared troubleshooting
• Sibling toolkit skill for program inspection, constant-table work, and log loading
• Sibling benchmarks skill for benchmark problem loaders and symbolic comparison