variable_select_vip — VIP (Variable Importance in Projection)

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

Description

VIP top-k variable selection (§18 Phase 5a, method=0)

From the pls4all.sklearn.VIPSelector docstring:

Variable Importance in Projection top-k selector (Favilla 2013).

Full Python sklearn-wrapper docstring 
Variable Importance in Projection top-k selector (Favilla 2013).

Parameters
----------
top_k : int
    Number of features to keep.
n_components, solver, center_x, scale_x, tol, max_iter
    Underlying PLS hyperparameters used for VIP scoring.

Notes
-----
Exposes ``vip_scores_`` as an alias for the generic ``scores_``
attribute, for callers used to the chemometrics naming convention.

Registry note — R plsVarSel::VIP on pls::plsr(method='kernelpls', scale=FALSE). pls4all pins the matching solver (Solver.KERNEL_ALGORITHM, scale_x=False, scale_y=False) and compute_vip_scores implements the same column-normalised W formula, so the selected-index masks agree bit-for-bit (max_abs=0).

Parameters

Name

Type

Default

Notes

top_k

int

None

Number of features to retain.

n_components

int

2

Number of latent components extracted (k).

solver

str

'simpls'

Inner algorithm: ‘nipals’, ‘simpls’, ‘svd’, ‘kernel’, ‘orthogonal-scores’, ‘power’, ‘randomized-svd’, ‘wide-kernel’.

center_x

bool

True

Subtract the column mean of X before fitting.

scale_x

bool

True

Standardize X columns to unit variance before fitting.

tol

float

1e-06

Convergence tolerance for iterative solvers (NIPALS / power-iteration).

max_iter

int

500

Maximum iterations for iterative solvers.

Explanations

Bibliographic source

Wold, S., Sjöström, M. & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 58(2), 109–130.

Mathematical principle

VIP scores quantify each feature’s contribution across all \(k\) latent components of a PLS model, weighted by how much each component explains of \(\mathbf{y}\): \(\mathrm{VIP}_j = \sqrt{\frac{p}{\mathrm{SSY}} \sum_{a=1}^{k} w_{ja}^2 \, \mathrm{SSY}_a}\), where \(w_{ja}\) is the loading weight of feature \(j\) in component \(a\) and \(\mathrm{SSY}_a\) is the explained sum of squares of \(\mathbf{y}\) in component \(a\).

The normalisation guarantees \(\sum_j \mathrm{VIP}_j^2 = p\), so the heuristic \(\mathrm{VIP}_j > 1\) identifies features contributing more than their fair share. VIP is the workhorse of spectroscopic variable selection — simple, deterministic, fast, and well understood.

Implementation

n4m_variable_select_rank with metric=VIP. Reference: R plsVarSel 0.10.0.

MATLAB header (bindings/matlab/+pls4all/vip_select.m):

pls4all.vip_select  VIP-based feature ranking.

   res = pls4all.vip_select(X, Y, n_components, top_k)

 Fits an internal SIMPLS model (store_scores=1) and ranks features by
 their Variable Importance in Projection (VIP) scores.

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

/* C ABI — libn4m */
n4m_context_t* ctx = n4m_context_create();
n4m_config_t*  cfg = n4m_config_create();
n4m_method_result_t* res = NULL;
n4m_variable_select_rank(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);

import pls4all
from pls4all._methods import variable_select_rank
with pls4all.Context() as ctx, pls4all.Config() as cfg:
    res = variable_select_rank(ctx, cfg, X, y, n_components=4)
# then: res.matrix("predictions"), res.matrix("coefficients"),
# res.vector("mask"), res.scalar("intercept"), …

from pls4all.sklearn import VIPSelector
mdl = VIPSelector(top_k, n_components=2, solver='simpls', center_x=True, scale_x=True, tol=1e-06, max_iter=500)
mdl.fit(X, y)
y_hat = mdl.predict(X_test)

library(pls4all)
# Unified low-level dispatcher (May 2026 R cleanup):
res <- pls4all_method("variable_select_vip", X, y,
                      n_components = 4L, params = list(top_k = 10L))
# res is a named list with MethodResult arrays/scalars.
# selected_indices / top_k_intervals are 1-based.

res = pls4all.vip_select(X, y, 4);
% see header of bindings/matlab/+pls4all/vip_select.m for full
% parameter surface:
%   res = vip_select(X, Y, n_components, top_k)
yhat = predict(res, Xtest);

No idiomatic classdef wrapper — invoke pls4all.fit("variable_select_vip", X, y, …) directly from the unified MEX factory.

Registry parity references 📐

  • 📐 ref.r_plsvarsel (R · r) — plsVarSel 0.10.0 · strict (rmse_rel ≤ 1e-06) — R plsVarSel::VIP ranking on pls::plsr(method='kernelpls', scale=FALSE) — matches the pls4all kernel-PLS path used by _variable_select_rank_pls4all(rank_method=0). The top-k indices are returned (1-based -> 0-based in the loader).

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-06).

Rows tagged with 📐 are the canonical parity references for this method (declared in parity_timing.registry). 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.

BackendParity200×40 (ms)
C++ native · libn4m
pls4all.cpp.blas+omp✓ J 1.002.55 ms
Python · pls4all
pls4all.python✓ J 1.002.47 ms
pls4all.sklearn✓ J 1.001.97 ms🏆
R · pls4all
pls4all.R✓ J 1.006.19 ms
pls4all.R.formula✓ J 1.006.51 ms
pls4all.R.mdatools✓ J 1.006.61 ms
pls4all.R.pls✓ J 1.005.86 ms
R · external
📐ref.r_plsvarselsource16.2 ms
BackendParity200×40 (ms)
C++ native · libn4m
pls4all.cpp.blas+omp✓ J 1.008.22 ms
Python · pls4all
pls4all.python✓ J 1.007.47 ms
pls4all.sklearn✓ J 1.002.74 ms🏆
R · pls4all
pls4all.R✓ J 1.0025.1 ms
pls4all.R.formula✓ J 1.0011.1 ms
pls4all.R.mdatools✓ J 1.009.03 ms
pls4all.R.pls✓ J 1.0017.2 ms
R · external
📐ref.r_plsvarselsource18.5 ms
BackendParity200×40 (ms)
C++ native · libn4m
pls4all.cpp.blas+omp✓ J 1.004.22 ms
Python · pls4all
pls4all.python✓ J 1.004.15 ms
pls4all.sklearn✓ J 1.003.66 ms🏆
R · pls4all
pls4all.R✓ J 1.005.24 ms
pls4all.R.formula✓ J 1.006.31 ms
pls4all.R.mdatools✓ J 1.006.76 ms
pls4all.R.pls✓ J 1.006.29 ms
R · external
📐ref.r_plsvarselsource12.5 ms

See also: benchmark overview · methods index · interactive dashboard