LIBMF Rust

LIBMF - large-scale sparse matrix factorization - for Rust

Installation

Add this line to your application’s Cargo.toml under [dependencies]:

toml libmf = "0.2"

Getting Started

Prep your data in the format row_index, column_index, value

rust let mut data = libmf::Matrix::new(); data.push(0, 0, 5.0); data.push(0, 2, 3.5); data.push(1, 1, 4.0);

Fit a model

rust let model = libmf::Model::params().fit(&data).unwrap();

Make predictions

rust model.predict(row_index, column_index);

Get the latent factors (these approximate the training matrix)

rust model.p_factors(); model.q_factors();

Get the bias (average of all elements in the training matrix)

rust model.bias();

Save the model to a file

rust model.save("model.txt").unwrap();

Load the model from a file

rust let model = libmf::Model::load("model.txt").unwrap();

Pass a validation set

rust let model = libmf::Model::params().fit_eval(&train_set, &eval_set).unwrap();

Cross-Validation

Perform cross-validation

rust let avg_error = libmf::Model::params().cv(&data, 5).unwrap();

Parameters

Set parameters - default values below

rust libmf::Model::params() .loss(libmf::Loss::RealL2) // loss function .factors(8) // number of latent factors .threads(12) // number of threads used .bins(25) // number of bins .iterations(20) // number of iterations .lambda_p1(0.0) // coefficient of L1-norm regularization on P .lambda_p2(0.1) // coefficient of L2-norm regularization on P .lambda_q1(0.0) // coefficient of L1-norm regularization on Q .lambda_q2(0.1) // coefficient of L2-norm regularization on Q .learning_rate(0.1) // learning rate .alpha(0.1) // importance of negative entries .c(0.0001) // desired value of negative entries .nmf(false) // perform non-negative MF (NMF) .quiet(false); // no outputs to stdout

Loss Functions

For real-valued matrix factorization

• Loss::RealL2 - squared error (L2-norm)
• Loss::RealL1 - absolute error (L1-norm)
• Loss::RealKL - generalized KL-divergence

For binary matrix factorization

• Loss::BinaryLog - logarithmic error
• Loss::BinaryL2 - squared hinge loss
• Loss::BinaryL1 - hinge loss

For one-class matrix factorization

• Loss::OneClassRow - row-oriented pair-wise logarithmic loss
• Loss::OneClassCol - column-oriented pair-wise logarithmic loss
• Loss::OneClassL2 - squared error (L2-norm)

Metrics

Calculate RMSE (for real-valued MF)

rust model.rmse(&data);

Calculate MAE (for real-valued MF)

rust model.mae(&data);

Calculate generalized KL-divergence (for non-negative real-valued MF)

rust model.gkl(&data);

Calculate logarithmic loss (for binary MF)

rust model.logloss(&data);

Calculate accuracy (for binary MF)

rust model.accuracy(&data);

Calculate MPR (for one-class MF)

rust model.mpr(&data, transpose);

Calculate AUC (for one-class MF)

rust model.auc(&data, transpose);

Reference

Specify the initial capacity for a matrix

rust let mut data = libmf::Matrix::with_capacity(3);

Resources

• LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems

Upgrading

0.2.0

Use

rust let model = libmf::Model::params().factors(20).fit(&data).unwrap();

instead of

rust let mut model = libmf::Model::new(); model.factors = 20; model.fit(&data);

History

View the changelog

Contributing

Everyone is encouraged to help improve this project. Here are a few ways you can help:

• Report bugs
• Fix bugs and submit pull requests
• Write, clarify, or fix documentation
• Suggest or add new features

To get started with development:

sh git clone --recursive https://github.com/ankane/libmf-rust.git cd libmf-rust cargo test