bipls_select — biPLS — Backward Interval PLS¶
Group: Variable selector · Registry tolerance: 0.7
Description¶
biPLS backward interval elimination (§18 Phase 5p)
From the pls4all.sklearn.BiPLSSelector docstring:
biPLS — backward interval elimination (Nørgaard 2000).
Registry note — R
mdatools::ipls(method='backward'). Mask RMSE-rel ~0=perfect, ~1=half disagree, ~1.41=disjoint; tolerance 0.7 enforces ~50% overlap. Backward elimination is order-sensitive.
Parameters¶
Name |
Type |
Default |
Notes |
|---|---|---|---|
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Number of latent components extracted (k). |
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Width (in variables) of each contiguous spectral interval. |
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Minimum number of intervals retained by biPLS backward elimination. |
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Number of cross-validation folds used inside the selector. |
Explanations¶
Bibliographic source¶
Leardi, R. & Nørgaard, L. (2004). Sequential application of backward interval partial least squares and genetic algorithms for the selection of relevant spectral regions. Journal of Chemometrics 18(11), 486–497.
Mathematical principle¶
Start with the spectrum partitioned into \(I\) equal intervals (typically 10–40). At each iteration, remove the interval whose removal least hurts CV-RMSE — i.e. the least informative interval. Iterate until removing any further interval materially worsens performance.
Returns a multi-band subset with each band aligned to the original equal-partition grid. The discrete structure makes biPLS robust to noise (no fine-grained fishing) and easy to interpret (each retained interval is a spectroscopic region of contiguous wavelengths).
Commonly chained with GA-PLS as a coarse-to-fine pipeline (Leardi & Nørgaard 2004): biPLS narrows the candidate intervals, GA-PLS does the within-interval feature selection.
Implementation¶
n4m_bipls_select. Reference: R plsVarSel.
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_bipls_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 bipls_select_fit
with pls4all.Context() as ctx, pls4all.Config() as cfg:
res = bipls_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 BiPLSSelector
mdl = BiPLSSelector(n_components=2, interval_width=10, min_intervals=2, 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("bipls_select", X, y,
n_components = 4L, params = list(interval_width = 5L, min_intervals = 2L))
# res is a named list with MethodResult arrays/scalars.
# selected_indices / top_k_intervals are 1-based.
res = pls4all.bipls_select(X, y, 4);
% see header of bindings/matlab/+pls4all/bipls_select.m for full
% parameter surface:
% res = bipls_select(X, Y, n_components, interval_width, min_intervals)
yhat = predict(res, Xtest);
No idiomatic classdef wrapper — invoke pls4all.fit("bipls_select", X, y, …) directly from the unified MEX factory.
Registry parity references 📐
📐
ref.r_mdatools(R · r) —mdatools0.15.0 · qualitative (rmse_rel ≤ 7e-01) — Rmdatools::ipls(method='backward')— biPLS elimination. Returns variables from intervals that survive the backward sweep.
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: qualitative — shape/smoke comparison only. The external library and pls4all do not produce numerically equivalent output for this method (see the MethodSpec notes); the rmse_rel_tol ≤ 7e-01 budget is set wide on purpose. Treat ~ shape as “we ran both, both finished”, not as numerical agreement.
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.
| Backend | Parity | 200×40 (ms) |
|---|---|---|
| C++ native · libn4m | ||
pls4all.cpp.blas+omp | ✓ J 1.00 | 3.83 ms |
| Python · pls4all | ||
pls4all.python | ✓ J 1.00 | 3.60 ms🏆 |
pls4all.sklearn | ✓ J 1.00 | 3.69 ms |
| R · pls4all | ||
pls4all.R | ✓ J 1.00 | 8.26 ms |
pls4all.R.formula | ✓ J 1.00 | 11.7 ms |
pls4all.R.mdatools | ✓ J 1.00 | 9.90 ms |
pls4all.R.pls | ✓ J 1.00 | 8.83 ms |
| R · external | ||
📐ref.r_mdatools | source | 365.6 ms |
| Backend | Parity | 200×40 (ms) |
|---|---|---|
| C++ native · libn4m | ||
pls4all.cpp.blas+omp | ✓ J 1.00 | 3.49 ms🏆 |
| Python · pls4all | ||
pls4all.python | ✓ J 1.00 | 3.58 ms |
pls4all.sklearn | ✓ J 1.00 | 3.74 ms |
| R · pls4all | ||
pls4all.R | ✓ J 1.00 | 9.93 ms |
pls4all.R.formula | ✓ J 1.00 | 25.5 ms |
pls4all.R.mdatools | ✓ J 1.00 | 15.1 ms |
pls4all.R.pls | ✓ J 1.00 | 24.8 ms |
| R · external | ||
📐ref.r_mdatools | source | 1.0 s |
| Backend | Parity | 200×40 (ms) |
|---|---|---|
| C++ native · libn4m | ||
pls4all.cpp.blas+omp | ✓ J 1.00 | 14.3 ms |
| Python · pls4all | ||
pls4all.python | ✓ J 1.00 | 13.4 ms |
pls4all.sklearn | ✓ J 1.00 | 5.97 ms🏆 |
| R · pls4all | ||
pls4all.R | ✓ J 1.00 | 23.9 ms |
pls4all.R.formula | ✓ J 1.00 | 35.4 ms |
pls4all.R.mdatools | ✓ J 1.00 | 16.3 ms |
pls4all.R.pls | ✓ J 1.00 | 21.7 ms |
| R · external | ||
📐ref.r_mdatools | source | 333.9 ms |
See also: benchmark overview · methods index · interactive dashboard