ferrolearn-core 0.1.3

Core traits, error types, and pipeline for the ferrolearn ML framework
Documentation

ferrolearn-core

Core traits, error types, dataset abstractions, pipeline infrastructure, and pluggable linear algebra backend for the ferrolearn machine learning framework.

Every other ferrolearn crate depends on this one.

Key types

ML traits

  • Fit<X, Y> — train a model, producing a fitted type
  • Predict<X> — generate predictions from a fitted model
  • Transform<X> — transform data (scalers, PCA, etc.)
  • PartialFit<X, Y> — incremental / online learning
  • FitTransform<X> — fit and transform in one step

Compile-time safety

Unfitted models implement Fit but not Predict. Calling fit() returns a new fitted type that implements Predict. Attempting to call predict() on an unfitted model is a compile error.

use ferrolearn_core::{Fit, Predict};

// model.predict(&x);  // compile error — Ridge does not implement Predict
let fitted = model.fit(&x, &y)?;
let y_hat = fitted.predict(&x_test)?;  // OK — FittedRidge implements Predict

Error handling

All public functions return Result<T, FerroError>. Library code never panics.

Pipeline

Pipeline composes transformers and a final estimator using dynamic dispatch:

use ferrolearn_core::pipeline::Pipeline;

let pipeline = Pipeline::new()
    .add_transformer(scaler)
    .add_estimator(model);

Backend trait

The Backend trait abstracts linear algebra operations (SVD, QR, Cholesky, eigendecomposition). The default NdarrayFaerBackend delegates to the faer crate.

Introspection

  • HasCoefficients — access fitted model coefficients
  • HasFeatureImportances — access feature importance scores
  • HasClasses — access class labels from classifiers

License

Licensed under either of Apache License, Version 2.0 or MIT License at your option.