# `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_feature_selection_irf_select`. R roxygen note (`methods_extra.R::irf_select`): > IRF — Interval Random Frog. > @param n_components Integer. Number of latent components. > @param n_iterations Integer >= 1. Number of outer-loop iterations. > @param window_size Method-specific parameter. See the underlying `*_fit()` function for the exact semantics. > @param initial_intervals Method-specific parameter. See the underlying `*_fit()` function for the exact semantics. > @param top_k Method-specific parameter. See the underlying `*_fit()` function for the exact semantics. > @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_irf_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 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"), … ``` ::: :::{tab-item} Python · pls4all.sklearn :sync: python-sklearn :class-label: lang-python ```python 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) ``` ::: :::{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("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. ``` ::: :::{tab-item} R · pls4all (raw fn) :sync: r-raw :class-label: lang-r ```r library(pls4all) res <- irf_select(X, Y, n_components, n_iterations = 100L, window_size = 10L, initial_intervals = 10L, top_k = 5L, seed = 0L) yhat <- pls4all_predict(res, X_test) ``` ::: :::{tab-item} MATLAB · pls4all (MEX) :sync: matlab-mex :class-label: lang-matlab ```matlab res = pls4all.fit("irf_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("irf_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.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`](../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
BackendParity120×30 (ms)
Python · pls4all
pls4all.sklearn✓ J 0.412.02 ms🏆
R · pls4all
pls4all.R✓ J 0.412.92 ms
pls4all.R.formula✓ J 0.413.44 ms
pls4all.R.mdatools✓ J 0.413.37 ms
pls4all.R.pls✓ J 0.413.91 ms
::: :::{tab-item} 3 threads :sync: threads-3
BackendParity120×30 (ms)
Python · pls4all
pls4all.sklearn✓ J 0.411.53 ms🏆
R · pls4all
pls4all.R✓ J 0.412.93 ms
pls4all.R.formula✓ J 0.413.29 ms
pls4all.R.mdatools✓ J 0.413.58 ms
pls4all.R.pls✓ J 0.413.55 ms
::: :::{tab-item} 10 threads :sync: threads-10
BackendParity120×30 (ms)
Python · pls4all
pls4all.sklearn✓ J 0.411.53 ms🏆
R · pls4all
pls4all.R✓ J 0.412.56 ms
pls4all.R.formula✓ J 0.413.34 ms
pls4all.R.mdatools✓ J 0.413.55 ms
pls4all.R.pls✓ J 0.413.57 ms
::: :::: --- _See also_: [benchmark overview](../benchmarks/overview.md) · [methods index](index.md) · [interactive dashboard](../landing/dashboard.md)