AOM Ridge Active Superblock

n4m.aom_ridge_active_superblock is a strict-linear donor-style AOM Ridge method. It starts from a bank of AOM preprocessing operators, screens a small active subset on each training fold, then fits one Ridge model on the concatenated active operator outputs.

The fitted model is replayable from the original input spectra:

pred = X @ result["input_coefficients"] + result["intercept"]

The sklearn wrapper is n4m.sklearn.NativeAOMRidgeActiveSuperblockRegressor. The same surface is available from n4m.aom and n4m.moment.

Selection Protocol

  • Alpha selection is train-CV only.

  • For every CV fold, active operators are screened from that fold’s training rows only.

  • The final model screens active operators once on the full calibration set, then fits Ridge with the selected alpha.

  • Test/eval rows are never used for production selection.

The active score is defined on the strict-linear outputs produced by n4m.aom_preprocess, not on donor-private covariance helpers:

score_b = || scale_b * Z_b.T @ y_centered ||_F^2

where Z_b is the centered output of operator b. Optional kta and blend score modes use the same train-only operator outputs.

Scope

This method intentionally excludes donor AOM Ridge modes that are outside the strict moment porting scope:

  • branch_global row-reference preprocessing

  • MKL/kernel weighting

  • nonlinear lifts

  • dataset/source/name routing

CUDA builds can run this method through the same native preprocessing and Ridge bindings, but this is not a fused GPU active-superblock grinder.