random_frog_select — Random Frog

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

Description

Random Frog selection (§18 Phase 5g)

Registry note — Python auswahl.RandomFrog (LSX-UniWue; Li 2012). Same algorithm as libPLS randomfrog_pls. Default _random_frog_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	required	current public binding signature
n_components	int	2	current public binding signature
n_iterations	int	100	current public binding signature
initial_size	int	20	current public binding signature
min_size	int | None	None	current public binding signature
max_size	int | None	None	current public binding signature
n_folds	int	3	current public binding signature
seed	int	0	current public binding signature

Explanations

Bibliographic source

Li, H., Xu, Q. & Liang, Y. (2012). Random frog: an efficient reversible jump Markov chain Monte Carlo-like approach for variable selection. Analytica Chimica Acta 740, 20–26. Verified primary link: https://doi.org/10.1016/j.aca.2012.06.031.

Mathematical principle

Random Frog runs a random walk over feature subsets: at each step it proposes a transition to a neighbouring subset (add / remove / swap a feature) and accepts the transition with a Metropolis-style probability based on the improvement in CV-RMSE. Features that appear frequently in the visited subsets are deemed important.

Output: the inclusion frequency vector — fraction of iterations in which each feature was selected. Sort by frequency and take the top-\(k\) for the final subset.

Random Frog is sample-efficient compared to GA-PLS (no population of full subsets to maintain) but slower to mix on very high-dimensional data. Recommended on spectra of moderate size (a few hundred wavelengths).

Appropriate uses

Sampling variable subsets to estimate inclusion probabilities under a PLS validation objective.

Limits and validation

Finite-chain inclusion frequencies depend on initialization, proposal settings, and seed; they are not Bayesian posterior probabilities.

Implementation

n4m_feature_selection_random_frog_select.

Sources and provenance

Current implementation: cpp/src/core/random_frog_selection.cpp.

API and bindings

C ABI (ABI 2): n4m_feature_selection_random_frog_select. Use the linked public header for the exact signature, configuration, and result handles.

Python (verified public re-export):

from n4m.feature_selection.wrapper import RandomFrog

Source signature: RandomFrog(top_k: int, *, n_components: int = 2, n_iterations: int = 100, initial_size: int = 20, min_size: int | None = None, max_size: int | None = None, n_folds: int = 3, seed: int = 0) (n4m/_impl/selection.py).

R (source-verified): random_frog_select(X, Y, n_components, n_iterations = 100L, initial_size = 30L, min_size = NULL, max_size = NULL, top_k = 10L, seed = 0L).

The source signature has additional required inputs, so no example call is fabricated.

MATLAB / Octave (source-verified): random_frog_select(X, Y, n_components, n_iterations, ... initial_size, min_size, max_size, top_k, seed).

The source signature has additional required inputs, so no example call is fabricated.

Registry parity references 📐

• 📐 ref.python_auswahl (python · python) — auswahl 0.9.0 · strict (rmse_rel ≤ 1e-06) — Python auswahl.RandomFrog (LSX-UniWue; Li 2012). Same algorithm as libPLS randomfrog_pls with pinned random_state for bit-exact mask parity.

Benchmarks

Archived measurement identity. Backend labels in this table are the raw IDs recorded when the benchmark ran (including historical pls4all.* IDs). They preserve measurement provenance and do not describe a current public Python, R, or MATLAB binding; use the source-verified API and bindings section above for current entry points.

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 = archived binding-harness result 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; archived language-harness rows show binding parity against the C++ backend. Hover the icon for role and tolerance band.

1 thread

Backend	Parity	200×40 (ms)
Python · archived pls4all benchmark
pls4all.sklearn	✓ J 0.18	2.19 ms🏆
R · archived pls4all benchmark
pls4all.R	✓ J 0.18	4.98 ms
pls4all.R.formula	✓ J 0.18	5.67 ms
pls4all.R.mdatools	✓ J 0.18	5.96 ms
pls4all.R.pls	✓ J 0.18	5.94 ms

3 threads

Backend	Parity	200×40 (ms)
Python · archived pls4all benchmark
pls4all.sklearn	✓ J 0.18	2.19 ms🏆
R · archived pls4all benchmark
pls4all.R	✓ J 0.18	4.83 ms
pls4all.R.formula	✓ J 0.18	5.64 ms
pls4all.R.mdatools	✓ J 0.18	5.77 ms
pls4all.R.pls	✓ J 0.18	5.73 ms

10 threads

Backend	Parity	200×40 (ms)
Python · archived pls4all benchmark
pls4all.sklearn	✓ J 0.18	2.19 ms🏆
R · archived pls4all benchmark
pls4all.R	✓ J 0.18	5.15 ms
pls4all.R.formula	✓ J 0.18	5.73 ms
pls4all.R.mdatools	✓ J 0.18	5.76 ms
pls4all.R.pls	✓ J 0.18	5.71 ms

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See also: benchmark overview · methods index · interactive dashboard