aom_chain_fixed_fit — n4m.model_selection.aom_search.aom_chain_fixed_fit

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

API surface

C ABI (ABI 2): n4m_model_selection_aom_chain_fixed_fit_run. Use the linked public header for the exact signature, configuration, and result handles.

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

Signature: aom_chain_fixed_fit_run(X, y, chain, *, head: str | int = 'ridge', param: float = 0.1, 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', cuda_pls_parallel_folds: bool | None = None, cuda_pls_min_device_features: int | None = None, cuda_pls_many_batched: bool | None = None)

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

chain

—

required

head

str | int

'ridge'

param

float

0.1

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'

cuda_pls_parallel_folds

bool | None

None

cuda_pls_min_device_features

int | None

None

cuda_pls_many_batched

bool | None

None

Explanations

Bibliographic source

No single canonical paper defines this fixed-candidate ABI wrapper. 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

Fit one already-selected strict AOM chain and head on the supplied calibration data. The operation separates candidate choice from the final refit, preserving the selected chain’s configuration and prediction surface.

Appropriate uses

Materializing a model selected by a prior, recorded search or by an external validation protocol.

Limits and validation

This operation performs no independent model selection and cannot repair leakage in the upstream choice. Its result is valid only for the recorded chain/head parameters and compatible input wavelength layout.

Implementation

n4m.model_selection.aom_search.aom_chain_fixed_fit_run; C ABI n4m_model_selection_aom_chain_fixed_fit_run.

Sources and provenance

https://github.com/GBeurier/nirs4all-methods/blob/main/cpp/src/c_api/c_api_method_result.cpp

Catalog note

Native final-only reusable fit for one already-selected caller-provided strict-linear AOM chain/head/parameter. This is the individual winner reuse surface behind NativeAOMFixedCandidateRegressor(fit_mode="final_only") and the final model-building step of NativeAOMScreenRefitRegressor: it fits the selected Ridge lambda or PLS component count on all training rows, folds transformed coefficients back into original input space, and returns final predictions, input_coefficients and intercept without running CV. It is not a ranking endpoint: OOF/fold outputs are empty and CV score fields are NaN unless a higher-level wrapper injects an exact-CV score that was already verified by aom_refit_candidates.

Timing benchmark: benchmarks/cross_binding/bench_aom_sweep_timing.py

See also: methods index.