`pso_select` — PSO-PLS — Particle Swarm Optimisation¶

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

Description¶

PSO-PLS — Binary Particle Swarm variable selection (§48)

From the pls4all.sklearn.PSOSelector docstring:

Binary Particle Swarm Optimization selector.

Registry note — Python pyswarms 1.3.0 Binary PSO with the same PSO coefficients, velocity clamp and contiguous 3-fold PLS-CV-RMSE fitness. Default _pso_select_pls4all path mirrors the same pyswarms call with seed=11, giving bit-exact mask parity. The C++ splitmix64 PSO kernel is opt-in via legacy=True.

Parameters¶

Name	Type	Default	Notes
`n_components`	`int`	`2`	Number of latent components extracted (k).
`n_swarm`	`int`	`30`	Number of particles in the binary PSO swarm.
`n_iterations`	`int`	`50`	Number of selection iterations or Monte-Carlo passes.
`w`	`float`	`0.729`	PSO inertia weight on the previous-velocity term.
`c1`	`float`	`1.494`	PSO cognitive (personal-best) acceleration coefficient.
`c2`	`float`	`1.494`	PSO social (global-best) acceleration coefficient.
`v_max`	`float`	`4.0`	Velocity clipping bound for binary PSO.
`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¶

Kennedy, J. & Eberhart, R. (1995). Particle swarm optimization. IEEE ICNN 1995, vol. 4, 1942–1948. — binary PSO variant used for variable selection.

Mathematical principle¶

Binary PSO maintains a swarm of particles where each particle’s position is a \(p\)-bit feature mask. Velocity updates blend the particle’s personal best, the swarm’s global best, and an inertia term; positions are stochastically rounded to 0/1 via a sigmoid.

Compared to GA-PLS, PSO converges faster on smooth fitness landscapes but is more susceptible to premature convergence on multi-modal ones. The two methods are complementary: PSO for quick reconnaissance, GA for final polish.

Recommended swarm size: 20–50. The fitness is again PLS CV-RMSE on the masked subset.

Implementation¶

n4m_pso_select. Reference: Python pyswarms for the PSO core, wrapped against PLS CV-RMSE.

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

/* C ABI — libn4m */
n4m_context_t* ctx = n4m_context_create();
n4m_config_t*  cfg = n4m_config_create();
n4m_method_result_t* res = NULL;
n4m_pso_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);

Python · pls4all (raw)

import pls4all
from pls4all._methods import pso_select_fit
with pls4all.Context() as ctx, pls4all.Config() as cfg:
    res = pso_select_fit(ctx, cfg, X, y, n_components=3)
# then: res.matrix("predictions"), res.matrix("coefficients"),
# res.vector("mask"), res.scalar("intercept"), …

Python · pls4all.sklearn

from pls4all.sklearn import PSOSelector
mdl = PSOSelector(n_components=2, n_swarm=30, n_iterations=50, w=0.729, c1=1.494, c2=1.494, v_max=4.0, n_folds=3, seed=0)
mdl.fit(X, y)
y_hat = mdl.predict(X_test)

R · pls4all_method()

library(pls4all)
# Unified low-level dispatcher (May 2026 R cleanup):
res <- pls4all_method("pso_select", X, y,
                      n_components = 3L, params = list(n_swarm = 10L, n_iterations = 12L, w = 0.729, c1 = 1.494, c2 = 1.494, v_max = 4.0, seed = 42L))
# res is a named list with MethodResult arrays/scalars.
# selected_indices / top_k_intervals are 1-based.

MATLAB · pls4all (MEX)

res  = pls4all.fit("pso_select", X, y, "NumComponents", 3);
yhat = predict(res, Xtest);

MATLAB · pls4all (classdef)

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

Registry parity references 📐

📐 ref.python_pyswarms (python · python) — pyswarms 1.3.0 · strict (rmse_rel ≤ 1e-06) — Python pyswarms.discrete.BinaryPSO 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 PSO kernel is opt-in via legacy=True.

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.

1 thread

Backend	Parity	80×25 (ms)
C++ native · libn4m
`pls4all.cpp.blas+omp`	✓ J 1.00	219.8 ms
Python · pls4all
`pls4all.python`	✓ J 1.00	219.3 ms
`pls4all.sklearn`	⇄ J 0.46	4.05 ms🏆
R · pls4all
`pls4all.R`	⇄ J 0.46	6.11 ms
`pls4all.R.formula`	⇄ J 0.46	6.29 ms
`pls4all.R.mdatools`	⇄ J 0.46	7.33 ms
`pls4all.R.pls`	⇄ J 0.46	7.12 ms
Python · external
📐`ref.python_pyswarms`	source	163.8 ms

3 threads

Backend	Parity	80×25 (ms)
C++ native · libn4m
`pls4all.cpp.blas+omp`	✓ J 1.00	169.0 ms
Python · pls4all
`pls4all.python`	✓ J 1.00	298.6 ms
`pls4all.sklearn`	⇄ J 0.46	3.17 ms🏆
R · pls4all
`pls4all.R`	⇄ J 0.46	20.9 ms
`pls4all.R.formula`	⇄ J 0.46	20.3 ms
`pls4all.R.mdatools`	⇄ J 0.46	16.0 ms
`pls4all.R.pls`	⇄ J 0.46	24.6 ms
Python · external
📐`ref.python_pyswarms`	source	217.8 ms

10 threads

Backend	Parity	80×25 (ms)
C++ native · libn4m
`pls4all.cpp.blas+omp`	✓ J 1.00	165.6 ms
Python · pls4all
`pls4all.python`	✓ J 1.00	181.1 ms
`pls4all.sklearn`	⇄ J 0.46	2.80 ms🏆
R · pls4all
`pls4all.R`	⇄ J 0.46	4.19 ms
`pls4all.R.formula`	⇄ J 0.46	4.78 ms
`pls4all.R.mdatools`	⇄ J 0.46	4.76 ms
`pls4all.R.pls`	⇄ J 0.46	4.74 ms
Python · external
📐`ref.python_pyswarms`	source	152.3 ms

nirs4all-methods

Navigation

`pso_select` — PSO-PLS — Particle Swarm Optimisation¶

Description¶

Parameters¶

Explanations¶

Bibliographic source¶

Mathematical principle¶

Implementation¶

Usage¶

Benchmarks¶

pso_select — PSO-PLS — Particle Swarm Optimisation¶

Description¶

Parameters¶

Explanations¶

Bibliographic source¶

Mathematical principle¶

Implementation¶

Usage¶

Benchmarks¶

`pso_select` — PSO-PLS — Particle Swarm Optimisation¶