# `vissa_select` — VISSA — Variable Iterative Space-Shrinkage _Group_: **Variable selector** · _Registry tolerance_: `1e-06` ## Description VISSA-PLS — Variable Iterative Space Shrinkage (§49) From the `pls4all.sklearn.VISSASelector` docstring: > Variable Iterative Subspace Shrinkage Approach (Deng 2014). > **Registry note** — Python `auswahl.VISSA 0.9.0` (LSX-UniWue) — canonical Deng 2014 implementation via weighted binary matrix sampling. Default `_vissa_select_pls4all` path mirrors the same auswahl call with seed=11, giving bit-exact mask parity. The C++ splitmix64 VISSA kernel is opt-in via `legacy=True`. ### Parameters | Name | Type | Default | Notes | |------|------|---------|-------| | `n_components` | `int` | `2` | Number of latent components extracted (k). | | `n_iterations` | `int` | `10` | Number of selection iterations or Monte-Carlo passes. | | `n_submodels` | `int` | `60` | Number of bootstrap sub-models drawn per VISSA iteration. | | `ratio_kept` | `float` | `0.1` | Fraction of top-scoring features retained at each VISSA shrinkage step. | | `threshold` | `float` | `0.5` | Inclusion-probability cut-off below which features are dropped. | | `floor_probability` | `float` | `0.05` | Lower bound applied to per-feature inclusion probabilities to avoid premature pruning. | | `n_folds` | `int` | `3` | Number of cross-validation folds used inside the selector. | | `seed` | `int` | `0` | Random seed for reproducible sampling/initialization. | ## Explanations ### Bibliographic source Deng, B. C., Yun, Y. H., Liang, Y. Z. & Yi, L. Z. (2014). *A new strategy to prevent over-fitting in partial least squares models based on model population analysis*. Analytica Chimica Acta 880, 32–41. ### Mathematical principle VISSA evaluates a **population of random subsets** of the same size, refines the population by selecting the best by CV-RMSE, and iteratively shrinks the search space toward features that survive in many high-performing subsets. Features that appear in many top subsets are deemed important; the search converges to a consensus subset. Different from CARS in that the search space is shrunken **by consensus over a population** rather than by exponential decay over iterations. This gives smoother convergence and less sensitivity to single high-leverage subsets. ### Implementation `n4m_feature_selection_vissa_select`. R roxygen note (`methods_extra.R::vissa_select`): > VISSA — Variable Iterative Space Shrinkage Approach. > @param n_components Integer. Number of latent components. > @param n_iterations Integer >= 1. Number of outer-loop iterations. > @param n_submodels Integer >= 1. Number of inner sub-models per VISSA-style iteration. > @param ratio_kept Numeric in (0, 1]. Fraction of features kept per iteration. > @param threshold Numeric. Convergence / pruning threshold. > @param floor_probability Numeric in [0, 0.5). Lower bound on per-feature retention probability. > @param seed Integer. Random seed for reproducibility. > @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_vissa_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 vissa_select_fit with pls4all.Context() as ctx, pls4all.Config() as cfg: res = vissa_select_fit(ctx, cfg, X, y, n_components=3) # 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 VISSASelector mdl = VISSASelector(n_components=2, n_iterations=10, n_submodels=60, ratio_kept=0.1, threshold=0.5, floor_probability=0.05, n_folds=3, seed=0) 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("vissa_select", X, y, n_components = 3L, params = list(n_iterations = 10L, n_submodels = 60L, ratio_kept = 0.1, threshold = 0.5, floor_probability = 0.05, seed = 42L)) # 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 <- vissa_select(X, Y, n_components, n_iterations = 20L, n_submodels = 100L, ratio_kept = 0.1, threshold = 0.5, floor_probability = 0.01, seed = 0L) yhat <- pls4all_predict(res, X_test) ``` ::: :::{tab-item} MATLAB · pls4all (MEX) :sync: matlab-mex :class-label: lang-matlab ```matlab res = pls4all.fit("vissa_select", X, y, "NumComponents", 3); yhat = predict(res, Xtest); ``` ::: :::{tab-item} MATLAB · pls4all (classdef) :sync: matlab-classdef :class-label: lang-matlab _No idiomatic classdef wrapper — invoke `pls4all.fit("vissa_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.VISSA` from LSX-UniWue with deterministic seed=11; the pls4all default path calls the same helper with the same seed, so masks coincide bit-for-bit. The C++ splitmix64 VISSA kernel is opt-in via legacy=True. ::: ### 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
BackendParity80×25 (ms)
Python · pls4all
pls4all.sklearn✓ J 0.6010.8 ms🏆
R · pls4all
pls4all.R✓ J 0.6011.2 ms
pls4all.R.formula✓ J 0.6011.1 ms
pls4all.R.mdatools✓ J 0.6049.1 ms
pls4all.R.pls✓ J 0.6021.4 ms
::: :::{tab-item} 3 threads :sync: threads-3
BackendParity80×25 (ms)
Python · pls4all
pls4all.sklearn✓ J 0.6013.0 ms
R · pls4all
pls4all.R✓ J 0.6013.9 ms
pls4all.R.formula✓ J 0.6014.6 ms
pls4all.R.mdatools✓ J 0.6011.4 ms🏆
pls4all.R.pls✓ J 0.6011.4 ms
::: :::{tab-item} 10 threads :sync: threads-10
BackendParity80×25 (ms)
Python · pls4all
pls4all.sklearn✓ J 0.608.85 ms🏆
R · pls4all
pls4all.R✓ J 0.6011.8 ms
pls4all.R.formula✓ J 0.6012.5 ms
pls4all.R.mdatools✓ J 0.6013.1 ms
pls4all.R.pls✓ J 0.6011.2 ms
::: :::: --- _See also_: [benchmark overview](../benchmarks/overview.md) · [methods index](index.md) · [interactive dashboard](../landing/dashboard.md)