sipls_select — siPLS — Synergy Interval PLS¶
Group: Variable selector · Registry tolerance: 0.7
· Parity reference: paper-only (Nørgaard, L., Saudland, A., Wagner, J., Nielsen, J. P., Munck, L. & Engelsen, S. B. (2000). Interval partial least-squares regression (iPLS). Appl. Spectrosc. 54(3), 413-419. (No widely installable R / Python port of siPLS’ synergistic combinations; smoke-tested only.))
Description¶
siPLS synergistic interval selection (§18 Phase 5q)
Parameters¶
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current public binding signature |
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current public binding signature |
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current public binding signature |
Explanations¶
Bibliographic source¶
Nørgaard, L., Saudland, A., Wagner, J., Nielsen, J. P., Munck, L. & Engelsen, S. B. (2000). Interval partial least-squares regression (iPLS): a comparative chemometric study with an example from near-infrared spectroscopy. Applied Spectroscopy 54(3), 413–419 — same paper as interval_select; siPLS is the synergy-combinations extension proposed in §3. Verified primary link: https://doi.org/10.1366/0003702001949500.
Mathematical principle¶
Exhaustively score every combination of \(m\) fixed-size intervals (out of \(I\) total) by CV-RMSE; return the best combination. This captures synergy — pairs (or triples) of intervals that work well together even if neither alone is the best single interval.
Combinatorial cost is \(\binom{I}{m}\) which is manageable only for small \(m\) (typically \(m \le 4\) with \(I \le 20\), giving up to ~5000 combinations). For larger search spaces use biPLS or interval-GA.
siPLS is the natural extension of iPLS when single-interval performance is unsatisfactory: it explicitly looks for complementary bands.
Appropriate uses¶
Evaluating combinations of a small number of spectral intervals for interpretable regional models.
Limits and validation¶
Combination count grows rapidly and results depend on the initial interval grid and validation split.
Implementation¶
n4m_feature_selection_sipls_select. Reference: R plsVarSel.
Sources and provenance¶
Current implementation: cpp/src/core/sipls_selection.cpp.
API and bindings¶
C ABI (ABI 2): n4m_feature_selection_sipls_select. Use the linked public header for the exact signature, configuration, and result handles.
Python (verified public re-export):
from n4m.feature_selection.wrapper import SiPLS
Source signature: SiPLS(*, n_components: int = 2, interval_width: int = 10, combination_size: int = 2, n_folds: int = 3) (n4m/_impl/selection.py).
R (source-verified): sipls_select(X, Y, n_components, interval_width = 10L, combination_size = 2L).
The source signature has additional required inputs, so no example call is fabricated.
MATLAB / Octave (source-verified): sipls_select(X, Y, n_components, interval_width, combination_size).
The source signature has additional required inputs, so no example call is fabricated.
Registry parity references 📐
📜 Paper-only — no executable parity reference; the paper citation is recorded in Bibliographic source.
Benchmarks¶
Archived measurement identity. Backend labels in this table are the raw IDs recorded when the benchmark ran (including historical pls4all.* IDs). They preserve measurement provenance and do not describe a current public Python, R, or MATLAB binding; use the source-verified API and bindings section above for current entry points.
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 = archived binding-harness result 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 🏆.
| Backend | Parity | 200×40 (ms) |
|---|---|---|
| Python · archived pls4all benchmark | ||
pls4all.python | ✓ bind | 3.18 ms |
pls4all.sklearn | ✓ bind | 2.82 ms🏆 |
| R · archived pls4all benchmark | ||
pls4all.R | ✓ bind | 7.86 ms |
pls4all.R.formula | ✓ bind | 8.15 ms |
pls4all.R.mdatools | ✓ bind | 8.80 ms |
pls4all.R.pls | ✓ bind | 8.12 ms |
| Backend | Parity | 200×40 (ms) |
|---|---|---|
| Python · archived pls4all benchmark | ||
pls4all.python | ✓ bind | 2.97 ms🏆 |
pls4all.sklearn | ✓ bind | 3.11 ms |
| R · archived pls4all benchmark | ||
pls4all.R | ✓ bind | 6.18 ms |
pls4all.R.formula | ✓ bind | 7.15 ms |
pls4all.R.mdatools | ✓ bind | 7.20 ms |
pls4all.R.pls | ✓ bind | 7.56 ms |
| Backend | Parity | 200×40 (ms) |
|---|---|---|
| Python · archived pls4all benchmark | ||
pls4all.python | ✓ bind | 2.57 ms🏆 |
pls4all.sklearn | ✓ bind | 2.73 ms |
| R · archived pls4all benchmark | ||
pls4all.R | ✓ bind | 6.76 ms |
pls4all.R.formula | ✓ bind | 7.43 ms |
pls4all.R.mdatools | ✓ bind | 18.1 ms |
pls4all.R.pls | ✓ bind | 13.1 ms |
See also: benchmark overview · methods index · interactive dashboard