LIBMF Rust

LIBMF - large-scale sparse matrix factorization - for Rust

Check out Disco for higher-level collaborative filtering

Installation

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

libmf = "0.3"

Getting Started

Prep your data in the format row_index, column_index, value

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

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

Make predictions

model.predict(row_index, column_index);

Get the latent factors (these approximate the training matrix)

model.p(row_index);
model.q(column_index);
// or
model.p_iter();
model.q_iter();

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

model.bias();

Save the model to a file

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

Load a model from a file

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

Pass a validation set

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

Cross-Validation

Perform cross-validation

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

Parameters

Set parameters - default values below

libmf::Model::params()
    .loss(libmf::Loss::RealL2)     // loss function
    .factors(8)                    // number of latent factors
    .threads(12)                   // number of threads
    .bins(25)                      // number of bins
    .iterations(20)                // number of iterations
    .lambda_p1(0.0)                // L1-regularization parameter for P
    .lambda_p2(0.1)                // L2-regularization parameter for P
    .lambda_q1(0.0)                // L1-regularization parameter for Q
    .lambda_q2(0.1)                // L2-regularization parameter for Q
    .learning_rate(0.1)            // learning rate
    .alpha(1.0)                    // 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)

model.rmse(&data);

Calculate MAE (for real-valued MF)

model.mae(&data);

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

model.gkl(&data);

Calculate logarithmic loss (for binary MF)

model.logloss(&data);

Calculate accuracy (for binary MF)

model.accuracy(&data);

Calculate MPR (for one-class MF)

model.mpr(&data, transpose);

Calculate AUC (for one-class MF)

model.auc(&data, transpose);

Example

Download the MovieLens 100K dataset.

Add these lines to your application’s Cargo.toml under [dependencies]:

csv = "1"
serde = { version = "1", features = ["derive"] }

And use:

use csv::ReaderBuilder;
use serde::Deserialize;
use std::fs::File;

#[derive(Debug, Deserialize)]
struct Row {
    user_id: i32,
    item_id: i32,
    rating: f32,
    time: i32,
}

fn main() {
    let mut train_set = libmf::Matrix::new();
    let mut valid_set = libmf::Matrix::new();

    let file = File::open("u.data").unwrap();
    let mut rdr = ReaderBuilder::new()
        .has_headers(false)
        .delimiter(b'\t')
        .from_reader(file);
    for (i, record) in rdr.records().enumerate() {
        let row: Row = record.unwrap().deserialize(None).unwrap();
        let matrix = if i < 80000 { &mut train_set } else { &mut valid_set };
        matrix.push(row.user_id, row.item_id, row.rating);
    }

    let model = libmf::Model::params().fit_eval(&train_set, &valid_set).unwrap();
    println!("RMSE: {:?}", model.rmse(&valid_set));
}

Reference

Specify the initial capacity for a matrix

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:

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