Release 0.3.1

In this release of Linfa the documentation is extended, new examples are added and the functionality of datasets improved. No new algorithms were added.

The meta-issue #82 gives a good overview of the necessary documentation improvements and testing/documentation/examples were considerably extended in this release.

Further new functionality was added to datasets and multi-target datasets are introduced. Bootstrapping is now possible for features and samples and you can cross-validate your model with k-folding. We polished various bits in the kernel machines and simplified the interface there.

The trait structure of regression metrics are simplified and the silhouette score introduced for easier testing of K-Means and other algorithms.

Changes

• improve documentation in all algorithms, various commits
• add a website to the infrastructure (c8acc785b)
• add k-folding with and without copying (b0af80546f8)
• add feature naming and pearson's cross correlation (71989627f)
• improve ergonomics when handling kernels (1a7982b973)
• improve TikZ generator in linfa-trees (9d71f603bbe)
• introduce multi-target datasets (b231118629)
• simplify regression metrics and add cluster metrics (d0363a1fa8ef)

Example

You can now perform cross-validation with k-folding. @Sauro98 actually implemented two versions, one which copies the dataset into k folds and one which avoid excessive memory operations by copying only the validation dataset around. For example to test a model with 8-folding:

// perform cross-validation with the F1 score
let f1_runs = dataset
    .iter_fold(8, |v| params.fit(&v).unwrap())
    .map(|(model, valid)| {
        let cm = model
            .predict(&valid)
            .mapv(|x| x > Pr::even())
            .confusion_matrix(&valid).unwrap();
  
          cm.f1_score()
    })  
    .collect::<Array1<_>>();
  
// calculate mean and standard deviation
println!("F1 score: {}±{}",
    f1_runs.mean().unwrap(),
    f1_runs.std_axis(Axis(0), 0.0),
);