# `vip_spa_select` — VIP-seeded SPA _Group_: **Variable selector** · _Registry tolerance_: `1e-06` ## Description VIP_SPA — VIP-mask then SPA greedy (Phase 53) From the `pls4all.sklearn.VIPSPASelector` docstring: > VIP_SPA — VIP-mask + 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` | `None` | Number of features to retain. | | `n_components` | `int` | `2` | Number of latent components extracted (k). | | `vip_threshold` | `float` | `0.3` | Minimum VIP score required to enter the SPA candidate pool. | | `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. ### Implementation `n4m_feature_selection_vip_spa_select`. R roxygen note (`methods_extra.R::vip_spa_select`): > VIP-SPA hybrid selector. > @param n_components Integer. Number of latent components. > @param vip_threshold Method-specific parameter. See the underlying `*_fit()` function for the exact semantics. > @param top_k Method-specific parameter. See the underlying `*_fit()` function for the exact semantics. > @param X Numeric matrix of predictors (rows = samples, cols = features). > @param Y Numeric matrix or vector of responses, with one row per sample. > @export MATLAB header (`bindings/matlab/+pls4all/vip_spa_select.m`): ```text pls4all.vip_spa_select VIP-then-SPA hybrid (Phase 53). ``` ### Usage Every pls4all binding tab dispatches into the same C kernel; the external libraries listed at the bottom of the page are the parity references registered in `benchmarks.parity_timing.registry`. Switch tabs to read the same fit in your language. The R package now ships drop-in-compatible facades for the CRAN `pls` package (`plsr`, `pcr`, `mvr`) and for the `mdatools::pls(x, y, ...)` matrix idiom — those tabs appear only on the methods that have a meaningful equivalence. **pls4all bindings** ::::{tab-set} :class: pls4all-bindings :::{tab-item} C ABI · libn4m :sync: c :class-label: lang-c ```c /* C ABI — libn4m */ n4m_context_t* ctx = n4m_context_create(); n4m_config_t* cfg = n4m_config_create(); n4m_method_result_t* res = NULL; n4m_feature_selection_vip_spa_select(ctx, cfg, &x_view, &y_view, /* hyperparams */, &res); /* … read coefficients / mask / scores via */ /* n4m_method_result_get_double_matrix / vector / scalar … */ n4m_method_result_destroy(res); n4m_config_destroy(cfg); n4m_context_destroy(ctx); ``` ::: :::{tab-item} Python · pls4all (raw) :sync: python-raw :class-label: lang-python ```python import pls4all from pls4all._methods import vip_spa_select_fit with pls4all.Context() as ctx, pls4all.Config() as cfg: res = vip_spa_select_fit(ctx, cfg, X, y, n_components=4) # then: res.matrix("predictions"), res.matrix("coefficients"), # res.vector("mask"), res.scalar("intercept"), … ``` ::: :::{tab-item} Python · pls4all.sklearn :sync: python-sklearn :class-label: lang-python ```python from pls4all.sklearn import VIPSPASelector mdl = VIPSPASelector(top_k, n_components=2, vip_threshold=0.3) mdl.fit(X, y) y_hat = mdl.predict(X_test) ``` ::: :::{tab-item} R · pls4all_method() :sync: r-dispatcher :class-label: lang-r ```r library(pls4all) # Unified low-level dispatcher (May 2026 R cleanup): res <- pls4all_method("vip_spa_select", X, y, n_components = 4L, params = list(vip_threshold = 0.3, top_k = 6L, seed = 7L)) # res is a named list with MethodResult arrays/scalars. # selected_indices / top_k_intervals are 1-based. ``` ::: :::{tab-item} R · pls4all (raw fn) :sync: r-raw :class-label: lang-r ```r library(pls4all) res <- vip_spa_select(X, Y, n_components, vip_threshold = 0.3, top_k = 10L) yhat <- pls4all_predict(res, X_test) ``` ::: :::{tab-item} MATLAB · pls4all (MEX) :sync: matlab-mex :class-label: lang-matlab ```matlab res = pls4all.vip_spa_select(X, y, 4); % see header of bindings/matlab/+pls4all/vip_spa_select.m for full % parameter surface: % res = vip_spa_select(X, Y, n_components, vip_threshold, top_k) yhat = predict(res, Xtest); ``` ::: :::{tab-item} MATLAB · pls4all (classdef) :sync: matlab-classdef :class-label: lang-matlab _No idiomatic classdef wrapper — invoke `pls4all.fit("vip_spa_select", X, y, …)` directly from the unified MEX factory._ ::: :::: **Registry parity references** 📐 :::{card} :class-card: external-refs - 📐 **`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 Adaptive wall-clock per cell measured against [`full_matrix.csv`](../benchmarks/overview.md). 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-06`). Rows tagged with **📐** are the canonical parity references for this method (declared in [`parity_timing.registry`](../benchmarks/methodology.md)). C++ and external rows show reference parity; pls4all language bindings show binding parity against the C++ backend. Hover the icon for role and tolerance band. ::::{tab-set} :class: parity-tabs :::{tab-item} 1 thread :sync: threads-1
BackendParity80×40 (ms)
Python · pls4all
pls4all.sklearn✓ J 1.001.34 ms🏆
R · pls4all
pls4all.R✓ J 1.002.19 ms
pls4all.R.formula✓ J 1.002.80 ms
pls4all.R.mdatools✓ J 1.002.87 ms
pls4all.R.pls✓ J 1.002.83 ms
::: :::{tab-item} 3 threads :sync: threads-3
BackendParity80×40 (ms)
Python · pls4all
pls4all.sklearn✓ J 1.000.86 ms🏆
R · pls4all
pls4all.R✓ J 1.002.00 ms
pls4all.R.formula✓ J 1.003.05 ms
pls4all.R.mdatools✓ J 1.002.84 ms
pls4all.R.pls✓ J 1.003.05 ms
::: :::{tab-item} 10 threads :sync: threads-10
BackendParity80×40 (ms)
Python · pls4all
pls4all.sklearn✓ J 1.000.85 ms🏆
R · pls4all
pls4all.R✓ J 1.001.92 ms
pls4all.R.formula✓ J 1.002.79 ms
pls4all.R.mdatools✓ J 1.002.95 ms
pls4all.R.pls✓ J 1.003.11 ms
::: :::: --- _See also_: [benchmark overview](../benchmarks/overview.md) · [methods index](index.md) · [interactive dashboard](../landing/dashboard.md)