stability_select — MC-UVE (Monte-Carlo coefficient stability)

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

Description

Stability/MCUVE selection (§18 Phase 5c)

From the pls4all.sklearn.StabilitySelector docstring:

MCUVE-style stability selector via Monte-Carlo subsampling.

Registry note — R plsVarSel::mcuve_pls Monte-Carlo UVE stability ranking with top-k truncation. Default _stability_select_pls4all path mirrors the same R call with seed=11, giving bit-exact mask parity. The C++ splitmix64 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).

n_iterations

int

50

Number of selection iterations or Monte-Carlo passes.

seed

int

0

Random seed for reproducible sampling/initialization.

n_folds

int

3

Number of cross-validation folds used inside the selector.

Explanations

Bibliographic source

Cai, W., Li, Y. & Shao, X. (2008). A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra. Chemometrics and Intelligent Laboratory Systems 90(2), 188–194.

Mathematical principle

MC-UVE evaluates the stability of each feature’s PLS coefficient across Monte-Carlo subsamples of the calibration set: \(\mathrm{stab}_j = |\bar{b}_j| / s(b_j)\), where \(\bar{b}_j\) and \(s(b_j)\) are the mean and standard deviation of \(b_j\) across \(B\) bootstrap fits. Features with high stability ratio are reliably predictive; those with low ratio are noise-driven and discarded.

Conceptually a univariate analogue of stability selection (Meinshausen & Bühlmann 2010). The interaction with collinearity in the spectrum is benign: collinear features tend to share the contribution across bootstraps in a stable way, so their joint stability is high.

Typical Monte-Carlo budget: \(B = 50\)–\(200\) subsamples, each at 80 % of the calibration size.

Implementation

n4m_stability_select. Reference: R plsVarSel.

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

pls4all.stability_select  Coefficient-stability selector (MCUVE-style).

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

from pls4all.sklearn import StabilitySelector
mdl = StabilitySelector(top_k, n_components=2, n_iterations=50, seed=0, n_folds=3)
mdl.fit(X, y)
y_hat = mdl.predict(X_test)

library(pls4all)
# Unified low-level dispatcher (May 2026 R cleanup):
res <- pls4all_method("stability_select", 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.stability_select(X, y, 4);
% see header of bindings/matlab/+pls4all/stability_select.m for full
% parameter surface:
%   res = stability_select(X, Y, n_components, top_k)
yhat = predict(res, Xtest);

No idiomatic classdef wrapper — invoke pls4all.fit("stability_select", 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::mcuve_pls Monte-Carlo UVE. Returns the selected indices (no separate score buffer is exposed by the package; we just use the survivor list).

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 1.00690.4 ms
Python · pls4all
pls4all.python✓ J 1.001.7 s
pls4all.sklearn⇄ J 0.273.70 ms🏆
R · pls4all
pls4all.R⇄ J 0.2715.9 ms
pls4all.R.formula⇄ J 0.276.82 ms
pls4all.R.mdatools⇄ J 0.278.71 ms
pls4all.R.pls⇄ J 0.278.38 ms
R · external
📐ref.r_plsvarselsource492.8 ms
BackendParity200×40 (ms)
C++ native · libn4m
pls4all.cpp.blas+omp✓ J 1.00727.7 ms
Python · pls4all
pls4all.python✓ J 1.00698.1 ms
pls4all.sklearn⇄ J 0.271.79 ms🏆
R · pls4all
pls4all.R⇄ J 0.276.01 ms
pls4all.R.formula⇄ J 0.275.91 ms
pls4all.R.mdatools⇄ J 0.276.92 ms
pls4all.R.pls⇄ J 0.278.89 ms
R · external
📐ref.r_plsvarselsource259.7 ms
BackendParity200×40 (ms)
C++ native · libn4m
pls4all.cpp.blas+omp✓ J 1.00727.3 ms
Python · pls4all
pls4all.python✓ J 1.001.5 s
pls4all.sklearn⇄ J 0.271.92 ms🏆
R · pls4all
pls4all.R⇄ J 0.275.39 ms
pls4all.R.formula⇄ J 0.276.52 ms
pls4all.R.mdatools⇄ J 0.277.32 ms
pls4all.R.pls⇄ J 0.276.46 ms
R · external
📐ref.r_plsvarselsource230.9 ms

See also: benchmark overview · methods index · interactive dashboard