irf_select — IRF — Iterative Random Forest

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

Description

Interval Random Frog (Phase 52)

From the pls4all.sklearn.IRFSelector docstring:

IRF — Interval Random Frog (Yun 2013).

Registry note — Python auswahl.IntervalRandomFrog (LSX-UniWue; Yun 2013). Same algorithm as libPLS irf. Default _irf_select_pls4all path mirrors the same auswahl call with random_state=seed, 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	100	Number of selection iterations or Monte-Carlo passes.
window_size	int	5	Length of the moving window for recursive / interval-random-frog models.
initial_intervals	int	5	Number of seed intervals for the interval-random-frog walk.
seed	int	0	Random seed for reproducible sampling/initialization.

Explanations

Bibliographic source

Basu, S., Kumbier, K., Brown, J. B. & Yu, B. (2018). Iterative random forests to discover predictive and stable high-order interactions. Proceedings of the National Academy of Sciences 115(8), 1943–1948.

Mathematical principle

IRF iteratively re-weights random forest feature importances and refits. At each iteration, features with high feature-importance get oversampled in the bootstrap of the next forest; the loop converges to a stable ranking of features by their interaction-aware importance.

Adapted for PLS prediction: the IRF importance ranking is used to select the top-\(k\) features, then PLS is fit on the selected subset. The RF importance is non-linear so this catches predictive features that interact rather than contributing additively — typically missed by linear selectors like VIP.

Implementation

n4m_irf_select.

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_irf_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 irf_select_fit
with pls4all.Context() as ctx, pls4all.Config() as cfg:
    res = irf_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 IRFSelector
mdl = IRFSelector(top_k, n_components=2, n_iterations=100, window_size=5, initial_intervals=5, 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("irf_select", X, y,
                      n_components = 3L, params = list(n_iterations = 30L, window_size = 4L, initial_intervals = 5L, top_k = 5L, seed = 11L))
# res is a named list with MethodResult arrays/scalars.
# selected_indices / top_k_intervals are 1-based.

MATLAB · pls4all (MEX)

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

MATLAB · pls4all (classdef)

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

Registry parity references 📐

• 📐 ref.python_auswahl (python · python) — auswahl 0.9.0 · strict (rmse_rel ≤ 1e-06) — Python auswahl.IntervalRandomFrog (LSX-UniWue; Yun 2013). Same algorithm as libPLS irf with pinned random_state for bit-exact mask parity.

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	120×30 (ms)
Python · pls4all
pls4all.sklearn	✓ J 0.41	2.02 ms🏆
R · pls4all
pls4all.R	✓ J 0.41	2.92 ms
pls4all.R.formula	✓ J 0.41	3.44 ms
pls4all.R.mdatools	✓ J 0.41	3.37 ms
pls4all.R.pls	✓ J 0.41	3.91 ms

3 threads

Backend	Parity	120×30 (ms)
Python · pls4all
pls4all.sklearn	✓ J 0.41	1.53 ms🏆
R · pls4all
pls4all.R	✓ J 0.41	2.93 ms
pls4all.R.formula	✓ J 0.41	3.29 ms
pls4all.R.mdatools	✓ J 0.41	3.58 ms
pls4all.R.pls	✓ J 0.41	3.55 ms

10 threads

Backend	Parity	120×30 (ms)
Python · pls4all
pls4all.sklearn	✓ J 0.41	1.53 ms🏆
R · pls4all
pls4all.R	✓ J 0.41	2.56 ms
pls4all.R.formula	✓ J 0.41	3.34 ms
pls4all.R.mdatools	✓ J 0.41	3.55 ms
pls4all.R.pls	✓ J 0.41	3.57 ms

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See also: benchmark overview · methods index · interactive dashboard