LIBMF Rust

LIBMF - large-scale sparse matrix factorization - for Rust

Installation

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

toml libmf = "0.1"

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);

Create a model

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

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");

Load the model from a file

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

Pass a validation set

rust model.fit_eval(&train_set, &eval_set);

Cross-Validation

Perform cross-validation

rust model.cv(&data, 5);

Parameters

Set parameters - default values below

rust model.loss = 0; // loss function model.factors = 8; // number of latent factors model.threads = 12; // number of threads used model.bins = 25; // number of bins model.iterations = 20; // number of iterations model.lambda_p1 = 0; // coefficient of L1-norm regularization on P model.lambda_p2 = 0.1; // coefficient of L2-norm regularization on P model.lambda_q1 = 0; // coefficient of L1-norm regularization on Q model.lambda_q2 = 0.1; // coefficient of L2-norm regularization on Q model.learning_rate = 0.1; // learning rate model.alpha = 0.1; // importance of negative entries model.c = 0.0001; // desired value of negative entries model.nmf = false; // perform non-negative MF (NMF) model.quiet = false; // no outputs to stdout

Loss Functions

For real-valued matrix factorization

• 0 - squared error (L2-norm)
• 1 - absolute error (L1-norm)
• 2 - generalized KL-divergence

For binary matrix factorization

• 5 - logarithmic error
• 6 - squared hinge loss
• 7 - hinge loss

For one-class matrix factorization

• 10 - row-oriented pair-wise logarithmic loss
• 11 - column-oriented pair-wise logarithmic loss
• 12 - 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

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