SpectoPrep documentation#

SpectoPrep finds effective spectroscopic preprocessing pipelines with Bayesian optimization. It searches over scatter correction, smoothing, baseline, scaling and related transforms, then fits a ridge model whose penalty is selected by cross-validation.

from spectoprep import PipelineOptimizer
import numpy as np

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

optimizer = PipelineOptimizer(
    X_train=X,
    y_train=y,
    groups=groups,
    preprocessing_steps=["msc", "savgol", "snv", "scaler"],
    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=5, n_iter=20)

Why SpectoPrep?#

  • Joint search — pipeline structure and transform hyperparameters in one Bayesian loop.

  • Group-aware CV — group_kfold, group_shuffle_split, or leave_p_group_out so replicate spectra stay together.

  • Modern ridge backend — downstream OptimizedRidgeCV selects alpha by CV (no manual ridge_alpha search).

  • Production tooling — structured logging (structlog), a Typer CLI, and a full test suite.

Scope#

SpectoPrep targets regression chemometric workflows (NIR, MIR, Raman and similar). Classification is not supported.

User guide

Reference

Project

Indices and tables#