User guide | API | Notebooks

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Machine Learning in Rust

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To start getting familiar with the new smartcore v0.4 API, there is now available a Jupyter Notebook environment repository. Please see instructions there, contributions welcome see CONTRIBUTING.

smartcore is a fast, ergonomic machine learning library for Rust, covering classical supervised and unsupervised methods with a modular linear algebra abstraction and optional ndarray support. It aims to provide production-friendly APIs, strong typing, and good defaults while remaining flexible for research and experimentation.

Highlights

• Broad algorithm coverage: linear models, tree-based methods, ensembles, SVMs, neighbors, clustering, decomposition, and preprocessing.
• Strong linear algebra traits with optional ndarray integration for users who prefer array-first workflows.
• WASM-first defaults with attention to portability; features such as serde and datasets are opt-in.
• Practical utilities for model selection, evaluation, readers (CSV), dataset generators, and built-in sample datasets.

Install

Add to Cargo.toml:

[dependencies]
smartcore = "^0.4.3"

For the latest development branch:

[dependencies]
smartcore = { git = "https://github.com/smartcorelib/smartcore", branch = "development" }

Optional features (examples):

• datasets
• serde
• ndarray-bindings (deprecated in favor of ndarray-only support per recent changes)

Check Cargo.toml for available features and compatibility notes.

Quick start

Here is a minimal example fitting a KNN classifier from native Rust vectors using DenseMatrix:

use smartcore::linalg::basic::matrix::DenseMatrix;
use smartcore::neighbors::knn_classifier::KNNClassifier;

// Turn vector slices into a matrix
let x = DenseMatrix::from_2d_array(&[
    &[1., 2.],
    &[3., 4.],
    &[5., 6.],
    &[7., 8.],
    &[9., 10.],
]).unwrap;

// Class labels
let y = vec![2, 2, 2, 3, 3];

// Train classifier
let knn = KNNClassifier::fit(&x, &y, Default::default()).unwrap();

// Predict
let yhat = knn.predict(&x).unwrap();

This example mirrors the “First Example” section of the crate docs and demonstrates smartcore’s ergonomic API surface.

Algorithms

smartcore organizes algorithms into clear modules with consistent traits:

• Clustering: K-Means, DBSCAN, agglomerative (including single-linkage), with K-Means++ initialization and utilities.
• Matrix decomposition: SVD, EVD, Cholesky, LU, QR, plus related linear algebra helpers.
• Linear models: OLS, Ridge, Lasso, ElasticNet, Logistic Regression.
• Ensemble and tree-based: Random Forest (classifier and regressor), Extra Trees, shared reusable components across trees and forests.
• SVM: SVC/SVR with kernel enum support and multiclass extensions.
• Neighbors: KNN classification and regression with distance metrics and fast selection helpers.
• Naive Bayes: Gaussian, Bernoulli, Categorical, Multinomial.
• Preprocessing: encoders, split utilities, and common transforms.
• Model selection and metrics: K-fold, search parameters, and evaluation utilities.

Recent refactors emphasize reusable components in trees/forests and expanded multiclass SVM capabilities. XGBoost-style regression and single-linkage clustering have been added. See CHANGELOG for API changes and migration notes.

Data access and readers

• CSV readers: Read matrices from CSV with configurable delimiter and header rows, with helpful error messages and testing utilities (including non-IO reader abstractions).
• Dataset generators: make_blobs, make_circles, make_moons for quick experiments.
• Built-in datasets (feature-gated): digits, diabetes, breast cancer, boston, with serialization utilities to persist or refresh .xy bundles.

WebAssembly and portability

smartcore adopts a WASM/WASI-first posture in defaults to ease browser and embedded deployments. Some file-system operations are restricted in wasm targets; tests and IO utilities are structured to avoid unsupported calls where possible. Enable features like serde selectively to minimize footprint. Consult module-level docs and CHANGELOG for target-specific caveats.

Notebooks

A curated set of Jupyter notebooks is available via the companion repository to explore smartcore interactively. To run locally, use EVCXR to enable Rust notebooks. This is the recommended path to quickly experiment with the v0.4 API.

Roadmap and recent changes

• Trait-system refactor, fewer structs and more object-safe traits, large codebase reorganization.
• Move to Rust 2021 edition and cleanup of duplicate code paths.
• Seeds and deterministic controls across algorithms using RNG plumbing.
• Search parameter API for hyperparameter exploration in K-Means and SVM families.
• Tree and forest components refactored for reuse; Extra Trees added.
• SVM multiclass support; SVR kernel enum and related improvements.
• XGBoost-style regression introduced; single-linkage clustering implemented.

See CHANGELOG.md for precise details, deprecations, and breaking changes. Some features like nalgebra-bindings have been dropped in favor of ndarray-only paths. Default features are tuned for WASM/WASI builds; enable serde/datasets as needed.

Contributing

Contributions are welcome:

• Open an issue describing the change and link it in the PR.
• Keep PRs in sync with the development branch and ensure tests pass on stable Rust.
• Provide or update tests; run clippy and apply formatting. Coverage and linting are part of the workflow.
• Use the provided PR and issue templates to describe behavior changes, new features, and expectations.

If adding IO, prefer abstractions that make non-IO testing straightforward (see readers/iotesting). For datasets, keep serialization helpers in tests gated appropriately to avoid unintended file writes in wasm targets.

License

smartcore is open source under a permissive license; see Cargo.toml and LICENSE for details. The crate metadata identifies “smartcore Developers” as authors; community contributions are credited via Git history and releases.

Acknowledgments

smartcore’s design incorporates well-known ML patterns while staying idiomatic to Rust. Thanks to all contributors who have helped expand algorithms, improve docs, modernize traits, and harden the codebase for production.