aom_chain_screen_refit — n4m.model_selection.aom_campaign.aom_chain_screen_refit

Namespace: n4m.model_selection.aom_campaign · Fully-qualified: n4m.model_selection.aom_campaign.aom_chain_screen_refit · Catalog id: aom_pop.aom_chain_screen_refit

API surface

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

Python (verified public re-export): from n4m.model_selection.aom_campaign import aom_chain_screen_refit_campaign

Signature: aom_chain_screen_refit_campaign(X, y, chains = None, *, profile: str = 'lab', families: dict | None = None, templates: Sequence[Sequence[str]] | None = None, max_chains: int | None = None, chain_chunk_size: int = 4096, top_k: int = 50, refit_top_k: int | None = None, refit_per_head_top_k: int | None = None, cv: int = 5, fold_ids = None, ridge_lambdas = (0.01, 0.1, 1.0, 10.0), pls_components = (1, 2, 4), heads = ('ridge', 'pls'), 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', refit_moment_policy: str | int | None = None, pls_score_mode: str | int = 'cv', chain_ordering: str = 'input', split_head_scoring: str = 'off', cuda_pls_parallel_folds: bool | None = None, cuda_pls_min_device_features: int | None = None, cuda_pls_many_batched: bool | None = None, backend_cuda_available: bool | None = None, backend_min_cuda_product: int | None = None, checkpoint_path: str | Path | None = None, resume: bool = True, max_chunks_per_run: int | None = None, refit_sort_by: str | None = 'refit_cv_rmse', refit_execution: str = 'auto', refit_auto_max_extra_fraction: float = 1.0, return_predictions: bool = False)

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
chains	—	None
profile	str	'lab'
families	dict | None	None
templates	Sequence[Sequence[str]] | None	None
max_chains	int | None	None
chain_chunk_size	int	4096
top_k	int	50
refit_top_k	int | None	None
refit_per_head_top_k	int | None	None
cv	int	5
fold_ids	—	None
ridge_lambdas	—	(0.01, 0.1, 1.0, 10.0)
pls_components	—	(1, 2, 4)
heads	—	('ridge', 'pls')
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'
refit_moment_policy	str | int | None	None
pls_score_mode	str | int	'cv'
chain_ordering	str	'input'
split_head_scoring	str	'off'
cuda_pls_parallel_folds	bool | None	None
cuda_pls_min_device_features	int | None	None
cuda_pls_many_batched	bool | None	None
backend_cuda_available	bool | None	None
backend_min_cuda_product	int | None	None
checkpoint_path	str | Path | None	None
resume	bool	True
max_chunks_per_run	int | None	None
refit_sort_by	str | None	'refit_cv_rmse'
refit_execution	str	'auto'
refit_auto_max_extra_fraction	float	1.0
return_predictions	bool	False

Explanations

Bibliographic source

No single canonical paper defines this screen/refit orchestration surface. 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

Screen a declared strict-linear candidate pool using the configured inexpensive criterion, retain the candidates prescribed by the policy, then refit those candidates with exact CV before choosing the final model.

Appropriate uses

Large but finite strict-chain searches where an auditable two-stage reduction is needed before costly exact validation.

Limits and validation

A screening score is not the final selection metric. The retained-pool policy changes the chance of losing the optimum and must be reported; outer validation remains necessary.

Implementation

n4m.model_selection.aom_campaign.aom_chain_screen_refit_campaign; no standalone C ABI symbol.

Sources and provenance

https://github.com/GBeurier/nirs4all-methods/blob/main/bindings/python/src/n4m/model_selection/aom_campaign.py

Catalog note

Python-backed two-pass AOM preprocessing campaign over the native n4m_model_selection_aom_chain_sweep_run ABI, with final model reuse delegated to the catalogued final-only aom_pop.aom_chain_fixed_fit surface. The first pass runs a chunked score-only strict-linear chain screen, including optional pls_score_mode=gcv_proxy for explicit moment-only PLS proxy ranking, checkpoint/resume and global/per-head/per-route top-k aggregation from full chunk score tables. The second pass refits retained candidates by exact native CV with pls_score_mode=cv, preserving screen and refit reports for audit; refit_per_head_top_k can add each head’s best rows to the exact-refit pool before deduplication, while score-only refit can group by chain/head, batch retained chains sharing a head/parameter signature, or explicitly union-batch retained parameters per head while keeping the same returned exact-CV scores. Union batching exposes scored and extra-scored candidate counters so its surplus cost is auditable, and aom_refit_execution_plan previews those counters for each mode without touching X/y. NativeAOMScreenRefitRegressor exposes the same workflow as a sklearn-style reusable regressor: fit runs the two-pass campaign, then fits the selected verified row through NativeAOMFixedCandidateRegressor in final_only mode so predict uses folded input-space coefficients on new X without replaying CV after exact refit. NativeAOMMomentScreenRefitRegressor, NativeAOMMomentPLSScreenRefitRegressor, NativeAOMMomentPLSExactScreenRefitRegressor and NativeAOMMomentRidgeScreenRefitRegressor are end-user presets over the same workflow for mixed Ridge/PLS GCV-proxy->exact-refit, PLS GCV-proxy->exact-refit, PLS exact-screen->exact-refit and Ridge exact-screen->exact-refit paths. This is intended for ultra-configurable preprocessing selection experiments and reusable selected models without dataset-name routing; it is not the fused CUDA/IKPLS grinder.

Timing benchmark: benchmarks/cross_binding/bench_aom_screen_refit_scaling.py

See also: methods index.