sweep — n4m.model_selection.sweep.sweep

Namespace: n4m.model_selection.sweep · Fully-qualified: n4m.model_selection.sweep.sweep · Catalog id: utilities.sweep

API surface

C ABI (ABI 2): n4m_model_selection_sweep_run. Use the linked public header for the exact signature, configuration, and result handles.

Python (verified public re-export): from n4m.model_selection.sweep import sweep_run

Signature: sweep_run(X, y, *, cv: int = 5, fold_ids = None, ridge_lambdas = (0.01, 0.1, 1.0, 10.0), pls_components = None, heads = ('ridge',), center_x: bool | None = None, scale_x: bool | None = None, center_y: bool | None = None, scale_y: bool | None = None, score_only: bool = False, 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
cv	int	5
fold_ids	—	None
ridge_lambdas	—	(0.01, 0.1, 1.0, 10.0)
pls_components	—	None
heads	—	('ridge',)
center_x	bool | None	None
scale_x	bool | None	None
center_y	bool | None	None
scale_y	bool | None	None
score_only	bool	False
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 canonical scientific paper defines this orchestration utility; it is a reproducible enumeration and scoring surface.

Mathematical principle

Evaluate every configuration in a declared finite grid under the supplied validation plan, collect scores and diagnostics, then expose a deterministic ranked table rather than silently choosing a configuration.

Appropriate uses

Small, explicit hyperparameter or method grids where the complete candidate record is needed for audit and comparison.

Limits and validation

A sweep increases selection-induced optimism; use nested or held-out evaluation. It cannot make an under-specified grid represent an untested method family and should not be confused with Bayesian optimization.

Implementation

n4m.model_selection.sweep; C ABI n4m_model_selection_sweep_run.

Sources and provenance

https://github.com/GBeurier/nirs4all-methods/blob/main/bindings/python/src/n4m/model_selection/sweep.py; https://github.com/GBeurier/nirs4all-methods/blob/main/cpp/include/n4m/model_selection.h

Catalog note

Native Ridge/PLS candidate screen. Ridge is exact over row-additive moments when efficient and uses native dual kernels in wide regimes; compatible single-target NIPALS/regression PLS1 grids score from train/held-out moments, with a scalar host loop for medium-width CUDA builds and a device-resident cuBLAS component loop plus reused exact-CV fold workspace for very wide p>=1024 CUDA moment screens; materialized prefix scoring remains for other PLS regimes. PLS fit-cost counters are exposed as n_pls_moment_cv_fits, n_pls_moment_host_cv_fits, n_pls_moment_cuda_device_cv_fits, n_pls_materialized_cv_fits, n_pls_moment_final_fits, n_pls_moment_host_final_fits, n_pls_moment_cuda_device_final_fits and n_pls_materialized_final_fits so timing screens can distinguish moment-prefix fits, host/device PLS1 moment execution, and materialized fallback fits. Python exposes score_only=True for ranking passes that keep scores and selected ids while skipping selected-model buffers; score-only materialized fallback cells compute held-out SSE directly from linear fits when possible, and wide dual cross-kernel Ridge cells score directly from K_cross, to avoid held-out prediction-buffer allocation. This is not stream-parallel or fused batched IKPLS yet.

Timing benchmark: benchmarks/cross_binding/bench_moment_sweep_timing.py

See also: methods index.