Machine learning crate for Rust
A machine learning package for Rust.
For full usage details, see the API documentation.
This crate contains reasonably effective implementations of a number of common machine learning algorithms.
At the moment,
rustlearnuses its own basic dense and sparse array types, but I will be happy to use something more robust once a clear winner in that space emerges.
libsvmlibrary,
All the models support fitting and prediction on both dense and sparse data, and the implementations should be roughly competitive with Python
sklearnimplementations, both in accuracy and performance.
A number of models support both parallel model fitting and prediction.
Model serialization is supported via
serde.
rustlearn
Usage should be straightforward.
use rustlearn::prelude::*;
use rustlearn::prelude::*;use rustlearn::linear_models::sgdclassifier::Hyperparameters; // more imports
use rustlearn::prelude::*; use rustlearn::datasets::iris; use rustlearn::cross_validation::CrossValidation; use rustlearn::linear_models::sgdclassifier::Hyperparameters; use rustlearn::metrics::accuracy_score;let (X, y) = iris::load_data();
let num_splits = 10; let num_epochs = 5;
let mut accuracy = 0.0;
for (train_idx, test_idx) in CrossValidation::new(X.rows(), num_splits) {
let X_train = X.get_rows(&train_idx); let y_train = y.get_rows(&train_idx); let X_test = X.get_rows(&test_idx); let y_test = y.get_rows(&test_idx); let mut model = Hyperparameters::new(X.cols()) .learning_rate(0.5) .l2_penalty(0.0) .l1_penalty(0.0) .one_vs_rest(); for _ in 0..num_epochs { model.fit(&X_train, &y_train).unwrap(); } let prediction = model.predict(&X_test).unwrap(); accuracy += accuracy_score(&y_test, &prediction);
}
accuracy /= num_splits as f32;
use rustlearn::prelude::*;use rustlearn::ensemble::random_forest::Hyperparameters; use rustlearn::datasets::iris; use rustlearn::trees::decision_tree;
let (data, target) = iris::load_data();
let mut tree_params = decision_tree::Hyperparameters::new(data.cols()); tree_params.min_samples_split(10) .max_features(4);
let mut model = Hyperparameters::new(tree_params, 10) .one_vs_rest();
model.fit(&data, &target).unwrap();
// Optionally serialize and deserialize the model
// let encoded = bincode::serialize(&model).unwrap(); // let decoded: OneVsRestWrapper = bincode::deserialize(&encoded).unwrap();
let prediction = model.predict(&data).unwrap();
Pull requests are welcome.
To run basic tests, run
cargo test.
Running
cargo test --features "all_tests" --releaseruns all tests, including generated and slow tests. Running
cargo bench --features bench(only on the nightly branch) runs benchmarks.