What & Why

automl automates model selection and training on top of the smartcore machine learning library, helping Rust developers quickly build regression, classification, and clustering models.

Quickstart

Install from crates.io or use the GitHub repository for the latest changes:

# Cargo.toml
[dependencies]
automl = "0.2.9"

# Cargo.toml
[dependencies]
automl = { git = "https://github.com/cmccomb/rust-automl" }

use automl::{RegressionModel, RegressionSettings};
use smartcore::linalg::basic::matrix::DenseMatrix;

let x = DenseMatrix::from_2d_vec(&vec![
    vec![1.0_f64, 2.0, 3.0],
    vec![2.0, 3.0, 4.0],
    vec![3.0, 4.0, 5.0],
]).unwrap();
let y = vec![1.0_f64, 2.0, 3.0];
let _model = RegressionModel::new(x, y, RegressionSettings::default());

Loading data from CSV

Use load_labeled_csv to read a dataset and separate the target column:

use automl::{RegressionModel, RegressionSettings};
use automl::utils::load_labeled_csv;

let (x, y) = load_labeled_csv("tests/fixtures/supervised_sample.csv", 2).unwrap();
let mut model = RegressionModel::new(x, y, RegressionSettings::default());

Use load_csv_features to read unlabeled data for clustering:

use automl::{ClusteringModel};
use automl::settings::ClusteringSettings;
use automl::utils::load_csv_features;

let x = load_csv_features("tests/fixtures/clustering_points.csv").unwrap();
let mut model = ClusteringModel::new(x.clone(), ClusteringSettings::default().with_k(2));
model.train();
let clusters: Vec<u8> = model.predict(&x).unwrap();

Examples

Classification

use automl::{ClassificationModel};
use automl::settings::{ClassificationSettings, RandomForestClassifierParameters};
use smartcore::linalg::basic::matrix::DenseMatrix;

let x = DenseMatrix::from_2d_vec(&vec![
    vec![0.0_f64, 0.0],
    vec![1.0, 1.0],
    vec![1.0, 0.0],
    vec![0.0, 1.0],
]).unwrap();
let y = vec![0_u32, 1, 1, 0];
let settings = ClassificationSettings::default()
    .with_random_forest_classifier_settings(
        RandomForestClassifierParameters::default().with_n_trees(10),
    );
let _model = ClassificationModel::new(x, y, settings);

Clustering

use automl::ClusteringModel;
use automl::settings::ClusteringSettings;
use smartcore::linalg::basic::matrix::DenseMatrix;

let x = DenseMatrix::from_2d_vec(&vec![
    vec![1.0_f64, 1.0],
    vec![1.2, 0.8],
    vec![8.0, 8.0],
    vec![8.2, 8.2],
]).unwrap();
let mut model = ClusteringModel::new(x.clone(), ClusteringSettings::default().with_k(2));
model.train();
let truth = vec![1_u8, 1, 2, 2];
model.evaluate(&truth);
println!("{model}");
let _clusters: Vec<u8> = model.predict(&x).expect("prediction");

Additional runnable examples are available in the examples/ directory, including minimal_classification.rs, maximal_classification.rs, minimal_regression.rs, maximal_regression.rs, minimal_clustering.rs, and maximal_clustering.rs.

Model comparison:

┌───────────────────────────────┬─────────────────────┬───────────────────┬──────────────────┐
│ Model                         │ Time                │ Training Accuracy │ Testing Accuracy │
╞═══════════════════════════════╪═════════════════════╪═══════════════════╪══════════════════╡
│ Random Forest Classifier      │ 835ms 393us 583ns   │ 1.00              │ 0.96             │
├───────────────────────────────┼─────────────────────┼───────────────────┼──────────────────┤
│ Decision Tree Classifier      │ 15ms 404us 750ns    │ 1.00              │ 0.93             │
├───────────────────────────────┼─────────────────────┼───────────────────┼──────────────────┤
│ KNN Classifier                │ 28ms 874us 208ns    │ 0.96              │ 0.92             │
└───────────────────────────────┴─────────────────────┴───────────────────┴──────────────────┘

Capabilities

• Feature Engineering: PCA, SVD, interaction terms, polynomial terms
• Regression: Decision Tree, KNN, Random Forest, Linear, Ridge, LASSO, Elastic Net, Support Vector Regression
• Classification: Random Forest, Decision Tree, KNN, Logistic Regression, Gaussian Naive Bayes
• Clustering: K-Means, Agglomerative, DBSCAN
• Meta-learning: Blending (experimental)
• Persistence: Save/load settings and models

Development

Before submitting changes, run:

cargo fmt --all -- --check
cargo clippy --all-targets -- -D warnings
cargo test
cargo audit
cargo test --doc

Security audits run weekly via a scheduled workflow, but running cargo audit locally before submitting changes helps catch issues earlier.

Pull requests are welcome!

License

Licensed under the MIT OR Apache-2.0 license.