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Machine learning crate for Rust
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maciejkula/rustlearn
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A machine learning package for Rust.
For full usage details, see theAPI documentation.
This crate contains reasonably effectiveimplementations of a number of common machine learning algorithms.
At the moment,rustlearn uses its own basic dense and sparse array types, but I will be happyto use something more robust once a clear winner in that space emerges.
- logistic regression using stochastic gradient descent,
- support vector machines using the
libsvmlibrary, - decision trees using the CART algorithm,
- random forests using CART decision trees, and
- factorization machines.
All the models support fitting and prediction on both dense and sparse data, and the implementationsshould be roughly competitive with Pythonsklearn implementations, both in accuracy and performance.
A number of models support both parallel model fitting and prediction.
Model serialization is supported viaserde.
Usage should be straightforward.
- import the prelude for all the linear algebra primitives and common traits:
use rustlearn::prelude::*;
- import individual models and utilities from submodules:
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;letmut accuracy =0.0;for(train_idx, test_idx)inCrossValidation::new(X.rows(), num_splits){letX_train =X.get_rows(&train_idx);let y_train = y.get_rows(&train_idx);letX_test =X.get_rows(&test_idx);let y_test = y.get_rows(&test_idx);letmut model =Hyperparameters::new(X.cols()).learning_rate(0.5).l2_penalty(0.0).l1_penalty(0.0).one_vs_rest();for _in0..num_epochs{ model.fit(&X_train,&y_train).unwrap();}let prediction = model.predict(&X_test).unwrap(); accuracy +=accuracy_score(&y_test,&prediction);}accuracy /= num_splitsasf32;
use rustlearn::prelude::*;use rustlearn::ensemble::random_forest::Hyperparameters;use rustlearn::datasets::iris;use rustlearn::trees::decision_tree;let(data, target) = iris::load_data();letmut tree_params = decision_tree::Hyperparameters::new(data.cols());tree_params.min_samples_split(10).max_features(4);letmut 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<RandomForest> = bincode::deserialize(&encoded).unwrap();let prediction = model.predict(&data).unwrap();
Pull requests are welcome.
To run basic tests, runcargo test.
Runningcargo test --features "all_tests" --release runs all tests, including generated and slow tests.Runningcargo bench --features bench (only on the nightly branch) runs benchmarks.
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Machine learning crate for Rust
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