AOM Models

AOM models are spectroscopy-specific estimators that search over linear preprocessing operators inside the calibration model. AOMPLSRegressor and POPPLSRegressor are provided directly by the native nirs4all-methods (n4m) binding; nirs4all no longer carries duplicate Python implementations. They can use the same split protocol as the outer pipeline, including user-defined splitters such as KFold, GroupKFold, StratifiedGroupKFold, or custom splitters.

Use the runnable example at examples/user/04_models/U07_aom_panoply.py for a complete comparison of AOM-PLS, AOM-Ridge, AOMRidgeAutoSelector, AOMRidgeBlender, and FastAOM.

Which AOM Model To Use

Model	Best Use	Split-Aware Parameter
AOMRidgeBlender	Best general AOM-Ridge recipe; convex blend of several candidate variants	outer_cv and inner_cv
AOMRidgeAutoSelector	Pick one best AOM-Ridge variant instead of blending	outer_cv and inner_cv
AOMRidgeRegressor	Fast single AOM-Ridge model, good first production baseline	cv
AOMPLSRegressor	Native n4m PLS-compatible AOM model with operator-bank selection	fold_ids and cv
POPPLSRegressor	Native n4m per-component operator-selection PLS	fold_ids and cv
FastAOMPLSRidge	Faster chain-screened AOM family when runtime matters	no pipeline-fold injection required

Pipeline Split Reuse

For AOM models with internal validation, set train_params.use_pipeline_folds_for_aom on the model step:

from sklearn.model_selection import KFold
from nirs4all.operators.models import AOMRidgeBlender

pipeline = [
    KFold(n_splits=4, shuffle=True, random_state=42),
    {
        "model": AOMRidgeBlender(outer_cv=4, inner_cv=4, random_state=42),
        "name": "AOMRidge-blender",
        "train_params": {"use_pipeline_folds_for_aom": "required"},
    },
]

The same pattern works with grouped splits:

from sklearn.model_selection import GroupKFold
from nirs4all.operators.models import AOMRidgeAutoSelector

pipeline = [
    {"split": GroupKFold(n_splits=4), "group_by": "batch_id"},
    {
        "model": AOMRidgeAutoSelector(outer_cv=4, inner_cv=4, random_state=42),
        "name": "AOMRidge-auto",
        "train_params": {"use_pipeline_folds_for_aom": "required"},
    },
]

required is recommended for grouped or stratified protocols. It raises if the pipeline folds cannot be mapped into the estimator. The default auto policy uses pipeline folds when possible and silently falls back to the estimator’s own CV when no compatible folds exist.

What nirs4all Injects

When the model is recognised as an AOM estimator, nirs4all builds a precomputed splitter in the coordinate system of the current training slice and injects it as follows:

Estimator Capability	Injection
exposes fold_ids	sets one validated native fold id per training row and cv=n_splits
exposes cv_splitter	sets cv_splitter=<pipeline splitter> and cv=n_splits
exposes outer_cv	sets outer_cv=<pipeline splitter>
exposes both outer_cv and inner_cv	sets both to the pipeline splitter
exposes cv only	sets cv=<pipeline splitter>
exposes external_folds	sets AOM-lib style validation folds and selection="external"

For AOMRidgeAutoSelector and AOMRidgeBlender, each candidate sees only the candidate-local subset of the pipeline folds. This keeps nested AOM tuning inside the same user-defined protocol without letting candidate validation rows leak into candidate training.

Practical Candidate Set

The default Blender candidates are the strongest paper-style set, but they are heavier. For a fast user example, start with a compact candidate list:

QUICK_AOM_RIDGE_CANDIDATES = [
    {
        "label": "Ridge-identity",
        "selection": "superblock",
        "operator_bank": "identity",
        "block_scaling": "none",
    },
    {
        "label": "AOMRidge-global-compact",
        "selection": "global",
        "operator_bank": "compact",
        "block_scaling": "none",
    },
    {
        "label": "AOMRidge-global-compact-snv",
        "selection": "global",
        "operator_bank": "compact",
        "block_scaling": "none",
        "branch_preproc": "snv",
    },
]

Then pass it to the aggregators:

from nirs4all.operators.models import AOMRidgeBlender

model = AOMRidgeBlender(
    candidates=QUICK_AOM_RIDGE_CANDIDATES,
    outer_cv=4,
    inner_cv=4,
    outer_cv_kind="kfold",
    regularizer=0.01,
    random_state=42,
)

For final benchmark runs, omit candidates to use the full headline set.

Limitations

Pipeline-fold forwarding is disabled when a fit-influence policy resamples the training rows before model fitting, because the original fold indices no longer align with X_train. With use_pipeline_folds_for_aom="required", this raises instead of falling back.

The native regressors accept max_components, operators, cv, fold_ids, center_x, scale_x, center_y, and scale_y. Older Python-only arguments such as n_components="auto", operator_bank, criterion, auto_select, bank, repeats, and backend are not accepted and are not silently ignored.