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#8 in Machine learning

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Linfa mascot icon

Linfa

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linfa (Italian) / sap (English):

The vital circulating fluid of a plant.

linfa aims to provide a comprehensive toolkit to build Machine Learning applications with Rust.

Kin in spirit to Python's scikit-learn, it focuses on common preprocessing tasks and classical ML algorithms for your everyday ML tasks.

Website | Community chat

Current state

Where does linfa stand right now? Are we learning yet?

linfa currently provides sub-packages with the following algorithms:

Name Purpose Status Category Notes
clustering Data clustering Tested / Benchmarked Unsupervised learning Clustering of unlabeled data; contains K-Means, Gaussian-Mixture-Model and DBSCAN
kernel Kernel methods for data transformation Tested Pre-processing Maps feature vector into higher-dimensional space
linear Linear regression Tested Partial fit Contains Ordinary Least Squares (OLS), Generalized Linear Models (GLM)
elasticnet Elastic Net Tested Supervised learning Linear regression with elastic net constraints
logistic Logistic regression Tested Partial fit Builds two-class logistic regression models
reduction Dimensionality reduction Tested Pre-processing Diffusion mapping and Principal Component Analysis (PCA)
trees Decision trees Experimental Supervised learning Linear decision trees
svm Support Vector Machines Tested Supervised learning Classification or regression analysis of labeled datasets
hierarchical Agglomerative hierarchical clustering Tested Unsupervised learning Cluster and build hierarchy of clusters
bayes Naive Bayes Tested Supervised learning Contains Gaussian Naive Bayes
ica Independent component analysis Tested Unsupervised learning Contains FastICA implementation
pls Partial Least Squares Tested Supervised learning Contains PLS estimators for dimensionality reduction and regression
tsne Dimensionality reduction Tested Unsupervised learning Contains exact solution and Barnes-Hut approximation t-SNE
preprocessing Normalization & Vectorization Tested Pre-processing Contains data normalization/whitening and count vectorization/tf-idf

We believe that only a significant community effort can nurture, build, and sustain a machine learning ecosystem in Rust - there is no other way forward.

If this strikes a chord with you, please take a look at the roadmap and get involved!

BLAS/Lapack backend

At the moment you can choose between the following BLAS/LAPACK backends: openblas, netblas or intel-mkl

Backend Linux Windows macOS
OpenBLAS ✔️ - -
Netlib ✔️ - -
Intel MKL ✔️ ✔️ ✔️

For example if you want to use the system IntelMKL library for the PCA example, then pass the corresponding feature:

cd linfa-reduction && cargo run --release --example pca --features linfa/intel-mkl-system

This selects the intel-mkl system library as BLAS/LAPACK backend. On the other hand if you want to compile the library and link it with the generated artifacts, pass intel-mkl-static.

License

Dual-licensed to be compatible with the Rust project.

Licensed under the Apache License, Version 2.0 http://www.apache.org/licenses/LICENSE-2.0 or the MIT license http://opensource.org/licenses/MIT, at your option. This file may not be copied, modified, or distributed except according to those terms.

Dependencies

~2–42MB
~760K SLoC