ridge_global — n4m.model_selection.aom_search.ridge_global

Namespace: n4m.model_selection.aom_search · Fully-qualified: n4m.model_selection.aom_search.ridge_global · Catalog id: aom_pop.ridge_global

API surface

C ABI: no standalone exported symbol is declared for this method.

Python (verified public re-export): from n4m.model_selection.aom_search import aom_ridge_global

Signature: aom_ridge_global(X, y, *, operators = None, cv: int = 5, fold_ids = None, ridge_lambdas = (0.0001, 0.01, 1.0, 100.0), center_x: bool | None = None, scale_x: bool | None = None, center_y: bool | None = None, scale_y: bool | None = None, moment_policy: str | int = 'auto')

R: no current source-verified entry point was found for this catalog method.

MATLAB / Octave: no current source-verified entry point was found for this catalog method.

Parameters

Name

Type

Default

X

—

required

y

—

required

operators

—

None

cv

int

5

fold_ids

—

None

ridge_lambdas

—

(0.0001, 0.01, 1.0, 100.0)

center_x

bool | None

None

scale_x

bool | None

None

center_y

bool | None

None

scale_y

bool | None

None

moment_policy

str | int

'auto'

Explanations

Bibliographic source

No canonical paper defines this strict-linear Ridge route. Beurier, G. et al. (2026). AOM-PLS / POP-PLS paper companion, arXiv:2605.13587, https://arxiv.org/abs/2605.13587. The product variants below are implementation-specific extensions; the paper does not by itself specify their ABI-2 orchestration.

Mathematical principle

Treat each declared strict AOM operator as a one-step chain, choose the operator and positive Ridge penalty by CV RMSE, then export the selected linear predictor in original input coordinates.

Appropriate uses

A concise, auditable global preprocessing choice for Ridge calibration models.

Limits and validation

It cannot model interactions among sequential operators. A low CV score after selection is optimistic unless evaluated in an outer split; only strict linear transformations can be folded back.

Implementation

n4m.model_selection.aom_search.aom_ridge_global and AOMRidgeGlobalRegressor; no standalone C ABI symbol.

Sources and provenance

https://github.com/GBeurier/nirs4all-methods/blob/main/bindings/python/src/n4m/_impl/native.py; https://github.com/GBeurier/nirs4all-methods/blob/main/bindings/python/src/n4m/model_selection/aom_search.py

Catalog note

Python-backed donor-style AOM Ridge global selector constrained to strict-linear single-operator AOM views. It delegates scoring and final fit to the native aom_chain_sweep_run Ridge-only path, selects one operator plus one positive Ridge alpha by train CV, and returns folded input_coefficients plus intercept for replay through NativeAOMRidgeGlobalRegressor. It intentionally excludes donor branch_global, MKL/kernel, row-reference-dependent preprocessing and nonlinear AOM Ridge modes; native v1 builds in CUDA-enabled configurations but this is not yet a fused GPU grinder.

Timing benchmark: benchmarks/cross_binding/bench_aom_ridge_global_timing.py

See also: methods index.