spectoprep 1.0.2

Overview

SpectoPrep searches over combinations of scatter correction, smoothing, baseline correction, scaling and related transforms. Each candidate pipeline is scored with group-aware cross-validation and fitted with OptimizedRidgeCV, which selects the ridge penalty by CV. The goal is a reproducible, leakage-aware alternative to hand-tuned preprocessing recipes.

Features

• Bayesian pipeline search over structure and transform hyperparameters

• Group-aware CV: group_kfold, group_shuffle_split, leave_p_group_out

• RidgeCV downstream model (no manual ridge_alpha in the search space)

• Broad preprocessing catalogue (MSC, EMSC, SNV, Savitzky–Golay, ALS, scalers, PCA, …)

• Structured logging via structlog and a Typer CLI (spectoprep info)

• Visualization helpers for spectra and optimization summaries

Installation

pip install spectoprep

Or:

conda install -c habeebest spectoprep

PyPI and the habeebest conda channel are both published automatically from tagged releases (see updated_package_deployment.md).

Requires Python 3.10+.

Quick start

import numpy as np
from spectoprep import PipelineOptimizer

rng = np.random.default_rng(0)
X_train = rng.normal(size=(80, 200))
y_train = rng.normal(size=80)
groups = np.arange(80)

optimizer = PipelineOptimizer(
    X_train=X_train,
    y_train=y_train,
    groups=groups,
    preprocessing_steps=["msc", "savgol", "detrend", "scaler", "snv"],
    cv_method="group_kfold",
    n_splits=5,
    max_pipeline_length=2,
    allowed_preprocess_combinations=[1, 2],
)

best_params, best_pipeline = optimizer.bayesian_optimize(
    init_points=25,
    n_iter=200,
)

summary = optimizer.summarize_optimization()
predictions, rmse, r2 = optimizer.get_best_pipeline_predictions(best_pipeline)

CLI

spectoprep version
spectoprep info

Selected preprocessing methods

• msc / emsc: multiplicative (extended) scatter correction

• snv / lsnv / rnv: (localized / robust) standard normal variate

• savgol: Savitzky–Golay filtering / derivatives

• detrend / als: linear detrend / asymmetric least squares baseline

• scaler / robust_scaler / meancn: column scaling and mean centering

• pca / select_k_best: dimensionality reduction and feature selection

Run spectoprep info for the full catalogue.

Documentation

• Read the Docs: https://spectoprep.readthedocs.io

• GitHub Pages: https://habeeb3579.github.io/Spectoprep/

Limitations

SpectoPrep currently supports regression only. Classification pipelines are out of scope.

Contributing

Contributions are welcome. See CONTRIBUTING.rst and open a pull request.

License

MIT — see LICENSE.

Citation

If you use SpectoPrep in research, please cite:

@article{babatunde2025automated,
  title={Automated Spectral Preprocessing via Bayesian Optimization for Chemometric Analysis of Milk Constituents},
  author={Babatunde, Habeeb Abolaji and McDougal, Owen M and Andersen, Timothy},
  journal={Foods},
  volume={14},
  number={17},
  pages={2996},
  year={2025},
  publisher={MDPI}
}