libmf v0.1.0

LIBMF Node

LIBMF - large-scale sparse matrix factorization - for Node.js

Installation

Run:

npm install libmf

Getting Started

Prep your data in the format rowIndex, columnIndex, value

import { Matrix } from 'libmf';

const data = new Matrix();
data.push(0, 0, 5.0);
data.push(0, 2, 3.5);
data.push(1, 1, 4.0);

Create a model

import { Model } from 'libmf';

const model = new Model();
model.fit(data);

Make predictions

model.predict(rowIndex, columnIndex);

Get the latent factors (these approximate the training matrix)

model.p();
model.q();

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

model.bias();

Save the model to a file

model.save('model.txt');

Load the model from a file

const model = Model.load('model.txt');

Pass a validation set

model.fit(data, evalSet);

Destroy the model

model.destroy();

Cross-Validation

Perform cross-validation

model.cv(data);

Specify the number of folds

model.cv(data, 5);

Parameters

Pass parameters - default values below

import { Loss } from 'libmf';

new Model({
  loss: Loss.REAL_L2,     // loss function
  factors: 8,             // number of latent factors
  threads: 12,            // number of threads used
  bins: 25,               // number of bins
  iterations: 20,         // number of iterations
  lambdaP1: 0,            // coefficient of L1-norm regularization on P
  lambdaP2: 0.1,          // coefficient of L2-norm regularization on P
  lambdaQ1: 0,            // coefficient of L1-norm regularization on Q
  lambdaQ2: 0.1,          // coefficient of L2-norm regularization on Q
  learningRate: 0.1,      // learning rate
  alpha: 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.REAL_L2 - squared error (L2-norm)
• Loss.REAL_L1 - absolute error (L1-norm)
• Loss.REAL_KL - generalized KL-divergence

For binary matrix factorization

• Loss.BINARY_LOG - logarithmic error
• Loss.BINARY_L2 - squared hinge loss
• Loss.BINARY_L1 - hinge loss

For one-class matrix factorization

• Loss.ONE_CLASS_ROW - row-oriented pair-wise logarithmic loss
• Loss.ONE_CLASS_COL - column-oriented pair-wise logarithmic loss
• Loss.ONE_CLASS_L2 - 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);

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 https://github.com/ankane/libmf-node.git
cd libmf-node
npm install
npm run vendor
npm test