MountainSort5: expose `pca_seed` / `random_seed` sorter param (pair with MS5 configurable PCA seed)

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Stack tecnologico
python
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data

Direzione di ricerca

Start with src/spikeinterface/sorters/external/mountainsort5.py, especially the whitening_seed parameter and _params_description; check the current MountainSort5 Scheme parameter objects and issue #69 to confirm seed support before forwarding it. Then review the sorter documentation and existing MountainSort5 tests to identify where to document and check reproducibility. Done means a seed parameter is passed through, its distinction from whitening_seed and threading caveats are documented, and a small seeded-run test is added.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

Summary

si.run_sorter('mountainsort5', ...) already accepts whitening_seed and forwards it to whiten(seed=...) (#4600). There is no analogous parameter for MountainSort5's internal PCA / clustering RNG.

Callers who need deterministic, seed-controlled sorts currently set process-global np.random.seed / random.seed before run_sorter, force n_jobs=1 + single-threaded BLAS, and hope MS5 / sklearn honor the ambient RNG. That is fragile:

  • On mountainsort5 < 0.5.9, unseeded PCA() can follow the ambient NumPy RNG.
  • On mountainsort5 ≥ 0.5.9, PCA hardcodes random_state=0, so ambient seeding no longer changes PCA output.

Proposal

Depends on exposing a seed in MountainSort5 itself (https://github.com/flatironinstitute/mountainsort5/issues/69).

  1. Add a sorter param next to whitening_seed in src/spikeinterface/sorters/external/mountainsort5.py (suggested name: pca_seed or random_seed; default matching MS5, likely 0).
  2. Forward it into the Scheme1/Scheme2(/3) parameter objects once MS5 supports it.
  3. Document in _params_description and the sorter docs that:
    • this controls MS5 PCA / classifier PCA RNG;
    • whitening_seed is separate (SI whitening before MS5);
    • n_jobs > 1 / multi-threaded BLAS can void bit-identical reproducibility.
  4. Add a small test: two runs with the same seed agree on spike trains (or unit count + spike counts on a tiny recording); a different seed can differ (if feasible in CI time).
Optional follow-ups (not required for this PR)
  • Persist the effective seed into sorting-folder / provenance metadata.
  • A top-level seed= on run_sorter that fans out to sorter-specific kwargs when supported.
  • A short “deterministic sorting” recipe in the docs (seed + spawn/mp_context + threadpool_limits(1)).

I can open a PR once the MS5 parameter lands (or against a known MS5 commit if maintainers prefer).

Context

We rely on reproducible MS5 sorts for parameter sweeps and for re-running a chosen configuration with a content fingerprint of the spike trains. An explicit sorter kwarg would make that a supported SpikeInterface path instead of ambient RNG.

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