# `emcuve_select` — EMCUVE — Ensemble MC-UVE _Group_: **Variable selector** · _Registry tolerance_: `1e-06` ## Description EMCUVE ensemble MC-UVE (§18 Phase 5n) From the `pls4all.sklearn.EMCUVESelector` docstring: > Ensemble Monte-Carlo UVE selector. > **Registry note** — R `plsVarSel::mcuve_pls` called `n_ensembles` times with deterministic seeds (`11 + e`) and vote-aggregated. Default `_emcuve_select_pls4all` path mirrors the same R loop, giving bit-exact mask parity. The C++ splitmix64 EMCUVE kernel is opt-in via `legacy=True`. ### Parameters | Name | Type | Default | Notes | |------|------|---------|-------| | `n_components` | `int` | `2` | Number of latent components extracted (k). | | `noise_features` | `int` | `50` | Number of artificial noise variables appended to X for the UVE threshold. | | `noise_seed` | `int` | `0` | Seed for the appended noise variables. | | `n_ensembles` | `int` | `10` | Number of UVE replicates aggregated by majority vote. | | `vote_threshold` | `float` | `0.5` | Minimum vote fraction required to retain a variable in EMCUVE. | | `n_folds` | `int` | `3` | Number of cross-validation folds used inside the selector. | ## Explanations ### Bibliographic source Han, Q.-J., Wu, H.-L., Cai, C.-B., Xu, L. & Yu, R.-Q. (2008). *An ensemble of Monte Carlo uninformative variable elimination for wavelength selection*. Analytica Chimica Acta 612(2), 121–125. https://doi.org/10.1016/j.aca.2008.02.032 — extends the MC-UVE procedure of Cai et al. (2008) (`stability_select`) by aggregating independent MC-UVE rounds through a vote rule. ### Mathematical principle Run multiple independent MC-UVE rounds with different seeds, threshold each independently, then **vote** across rounds: a feature is selected if it survives thresholding in a majority of rounds. Robust against single-round instability caused by particular bootstrap samples. The voting rule has a free parameter (majority threshold); the default of $\lceil R/2 \rceil$ is the median-style majority. Stricter thresholds give smaller but more reliable subsets. ### Implementation `n4m_feature_selection_emcuve_select`. R roxygen note (`methods_extra.R::emcuve_select`): > EMCUVE — ensemble Monte Carlo UVE. > @param n_components Integer. Number of latent components. > @param noise_features Method-specific parameter. See the underlying `*_fit()` function for the exact semantics. > @param noise_seed Method-specific parameter. See the underlying `*_fit()` function for the exact semantics. > @param n_ensembles Method-specific parameter. See the underlying `*_fit()` function for the exact semantics. > @param vote_threshold 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 ### 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_emcuve_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 emcuve_select_fit with pls4all.Context() as ctx, pls4all.Config() as cfg: res = emcuve_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 EMCUVESelector mdl = EMCUVESelector(n_components=2, noise_features=50, noise_seed=0, n_ensembles=10, vote_threshold=0.5, n_folds=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("emcuve_select", X, y, n_components = 4L, params = list(noise_features = 5L, n_ensembles = 5L, vote_threshold = 0.5, noise_seed = 11L)) # 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 <- emcuve_select(X, Y, n_components, noise_features = NULL, noise_seed = 0L, n_ensembles = 5L, vote_threshold = 0.5) yhat <- pls4all_predict(res, X_test) ``` ::: :::{tab-item} MATLAB · pls4all (MEX) :sync: matlab-mex :class-label: lang-matlab ```matlab res = pls4all.fit("emcuve_select", X, y, "NumComponents", 4); yhat = predict(res, Xtest); ``` ::: :::{tab-item} MATLAB · pls4all (classdef) :sync: matlab-classdef :class-label: lang-matlab _No idiomatic classdef wrapper — invoke `pls4all.fit("emcuve_select", X, y, …)` directly from the unified MEX factory._ ::: :::: **Registry parity references** 📐 :::{card} :class-card: external-refs - 📐 **`ref.r_plsvarsel`** (R · r) — `plsVarSel` 0.10.0 · strict (rmse_rel ≤ 1e-06) — R `plsVarSel::mcuve_pls` repeated N times with different seeds, then vote-aggregated. Same algorithm family as pls4all's EMCUVE. RNGs differ; mask metric ~0=perfect. ::: ### 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
BackendParity200×40 (ms)
C++ native · libn4m
pls4all.cpp.blas+omp✓ J 1.001.3 s
Python · pls4all
pls4all.python✓ J 1.001.3 s
pls4all.sklearn✓ J 1.001.98 ms🏆
R · pls4all
pls4all.R✓ J 1.004.51 ms
pls4all.R.formula✓ J 1.005.44 ms
pls4all.R.mdatools✓ J 1.005.25 ms
pls4all.R.pls✓ J 1.005.57 ms
R · external
📐ref.r_plsvarselsource922.4 ms
::: :::{tab-item} 3 threads :sync: threads-3
BackendParity200×40 (ms)
C++ native · libn4m
pls4all.cpp.blas+omp✓ J 1.001.3 s
Python · pls4all
pls4all.python✓ J 1.001.3 s
pls4all.sklearn✓ J 1.002.00 ms🏆
R · pls4all
pls4all.R✓ J 1.004.69 ms
pls4all.R.formula✓ J 1.005.25 ms
pls4all.R.mdatools✓ J 1.005.99 ms
pls4all.R.pls✓ J 1.006.05 ms
R · external
📐ref.r_plsvarselsource918.1 ms
::: :::{tab-item} 10 threads :sync: threads-10
BackendParity200×40 (ms)
C++ native · libn4m
pls4all.cpp.blas+omp✓ J 1.001.3 s
Python · pls4all
pls4all.python✓ J 1.001.3 s
pls4all.sklearn✓ J 1.001.90 ms🏆
R · pls4all
pls4all.R✓ J 1.004.22 ms
pls4all.R.formula✓ J 1.005.03 ms
pls4all.R.mdatools✓ J 1.004.95 ms
pls4all.R.pls✓ J 1.005.01 ms
R · external
📐ref.r_plsvarselsource907.4 ms
::: :::: --- _See also_: [benchmark overview](../benchmarks/overview.md) · [methods index](index.md) · [interactive dashboard](../landing/dashboard.md)