vip_spa_select — VIP-seeded SPA

Group: Variable selector · Registry tolerance: 1e-06

Description

VIP_SPA — VIP-mask then SPA greedy (Phase 53)

Registry note — Python auswahl.VIP_SPA (LSX-UniWue) — VIP > 0.3 mask then greedy SPA pick. Default _vip_spa_select_pls4all path now invokes the same auswahl.VIP_SPA call, giving bit-exact mask parity. The C++ argmax-VIP SPA-start kernel is opt-in via legacy=True.

Parameters

Name	Type	Default	Notes
top_k	int	required	current public binding signature
n_components	int	2	current public binding signature
vip_threshold	float	0.3	current public binding signature
seed	int	7	registry benchmark cell value

Explanations

Bibliographic source

Hybrid heuristic combining VIP ranking and the Successive Projections Algorithm. See registry notes; no single canonical paper.

Mathematical principle

Use VIP scores to seed SPA’s projection-orthogonal forward selection. SPA starts with the highest-VIP feature rather than the highest-coefficient one, then proceeds with the standard projection step. This biases SPA toward \(y\)-correlated seed features while preserving SPA’s collinearity-minimising selection of subsequent features.

In practice this tends to outperform plain SPA on datasets where the first SPA seed is well-known to be noise-dominated (some real-world NIR datasets) but VIP correctly flags a different region as predictive.

Appropriate uses

Seeding a low-collinearity SPA wavelength sequence from a response-aware VIP ranking.

Limits and validation

The hybrid is heuristic with no single canonical estimator; VIP and SPA tuning must be repeated within validation folds.

Implementation

n4m_feature_selection_vip_spa_select.

Sources and provenance

Current implementation: cpp/src/core/vip_spa_selection.cpp.

API and bindings

C ABI (ABI 2): n4m_feature_selection_vip_spa_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 VIPSPA

Source signature: VIPSPA(top_k: int, *, n_components: int = 2, vip_threshold: float = 0.3) (n4m/_impl/selection.py).

R (source-verified): vip_spa_select(X, Y, n_components, vip_threshold = 0.3, top_k = 10L).

The source signature has additional required inputs, so no example call is fabricated.

MATLAB / Octave (source-verified): vip_spa_select(X, Y, n_components, vip_threshold, top_k).

The source signature has additional required inputs, so no example call is fabricated.

Registry parity references 📐

• 📐 ref.python_auswahl (python · python) — auswahl 0.9.0 · strict (rmse_rel ≤ 1e-06) — Python auswahl.VIP_SPA from LSX-UniWue. Same VIP scoring and 0.3 threshold as pls4all; auswahl enumerates every candidate SPA start and picks the CV-best, pls4all takes argmax-VIP within the mask. Mask metric.

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 🏆.

Reference gate: strict — numeric equivalence (rmse_rel_tol ≤ 1e-06).

Rows tagged with 📐 are the canonical parity references for this method (declared in parity_timing.registry). C++ and external rows show reference parity; archived language-harness rows show binding parity against the C++ backend. Hover the icon for role and tolerance band.

1 thread

Backend	Parity	80×40 (ms)
Python · archived pls4all benchmark
pls4all.sklearn	✓ J 1.00	1.34 ms🏆
R · archived pls4all benchmark
pls4all.R	✓ J 1.00	2.19 ms
pls4all.R.formula	✓ J 1.00	2.80 ms
pls4all.R.mdatools	✓ J 1.00	2.87 ms
pls4all.R.pls	✓ J 1.00	2.83 ms

3 threads

Backend	Parity	80×40 (ms)
Python · archived pls4all benchmark
pls4all.sklearn	✓ J 1.00	0.86 ms🏆
R · archived pls4all benchmark
pls4all.R	✓ J 1.00	2.00 ms
pls4all.R.formula	✓ J 1.00	3.05 ms
pls4all.R.mdatools	✓ J 1.00	2.84 ms
pls4all.R.pls	✓ J 1.00	3.05 ms

10 threads

Backend	Parity	80×40 (ms)
Python · archived pls4all benchmark
pls4all.sklearn	✓ J 1.00	0.85 ms🏆
R · archived pls4all benchmark
pls4all.R	✓ J 1.00	1.92 ms
pls4all.R.formula	✓ J 1.00	2.79 ms
pls4all.R.mdatools	✓ J 1.00	2.95 ms
pls4all.R.pls	✓ J 1.00	3.11 ms

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See also: benchmark overview · methods index · interactive dashboard