pp_arpls — Ar P L S¶
Group: Baseline correction · Binding: n4m.sklearn.ArPLS · C ABI: n4m_pp_arpls_*
Description¶
Asymmetrically reweighted penalized least squares.
Parameters¶
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Explanations¶
Bibliographic source¶
Standard spectroscopic operator — see the nirs4all preprocessing / augmentation handbook and the cited literature within the binding docstring.
Mathematical principle¶
Asymmetrically reweighted penalized least squares.
Implementation¶
C ABI n4m_pp_arpls_* in libn4m (create / apply / destroy lifecycle), wrapped by n4m.sklearn.ArPLS. The same numerical kernel backs every language binding.
Usage¶
from n4m.sklearn import ArPLS
op = ArPLS()
X_transformed = op.fit_transform(X)
Benchmarks¶
Adaptive wall-clock per cell measured against full_matrix.csv. Only backends that implement this method are listed; libraries without the method are omitted.
Verdict · ✓ ref / ≈ ref / ~ shape mark a reference-gate pass at strict / relaxed / qualitative tolerance · ✓ bind = pls4all binding agrees with the C++ baseline · ⇄ cross-check = documented by-design selector/RNG/model, noncanonical API/facade convention, or secondary oracle · ✗ divergent · ⚠ error · — not run. The fastest backend per column is marked 🏆.
Reference gate: strict — numeric equivalence (rmse_rel_tol ≤ 1e-12).
| Backend | Parity | 50×250 (ms) | 250×50 (ms) |
|---|---|---|---|
| C++ native · libn4m | |||
pls4all.cpp.blas | ✓ ref | — | — |
pls4all.cpp.blas+omp | ✓ ref | 3.96 ms | 3.35 ms |
pls4all.cpp.omp | ✓ ref | — | — |
pls4all.cpp.ref | ✓ ref | 3.34 ms | 3.67 ms |
| Python · pls4all | |||
pls4all.python | ✓ bind | 2.38 ms | 2.64 ms |
pls4all.sklearn | ✓ bind | 2.36 ms🏆 | 2.62 ms🏆 |
| Python · external | |||
nirs4all | source | 20.0 ms | 87.2 ms |
ref.python_numpy | source | — | — |
See also: methods index · interactive dashboard