Crate linfa

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.

Current state

Such bold ambitions! Where are we now? Are we learning yet?

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.

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
nn	Nearest Neighbours & Distances	Tested / Benchmarked	Pre-processing	Spatial index structures and distance functions
ftrl	Follow The Reguralized Leader - proximal	Tested / Benchmarked	Partial fit	Contains L1 and L2 regularization. Possible incremental update

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!

Re-exports

pub use dataset::Dataset;

pub use dataset::DatasetBase;

pub use dataset::DatasetPr;

pub use dataset::DatasetView;

pub use dataset::Float;

pub use dataset::Label;

pub use error::Error;

pub use param_guard::ParamGuard;

pub use composing::*;

Modules

composing

Composition models

correlation

Correlation analysis for dataset features

dataset

Datasets

error

Error types in Linfa

metrics

Common metrics functions for classification and regression

param_guard

prelude

Linfa prelude.

traits

Provide traits for different classes of algorithms