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LIBMF - large-scale sparse matrix factorization - for Ruby
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README.md

LIBMF

LIBMF - large-scale sparse matrix factorization - for Ruby

Check out Disco for higher-level collaborative filtering

Build Status

Installation

Add this line to your application’s Gemfile:

gem 'libmf'

Getting Started

Prep your data in the format [row_index, column_index, value]

data = [
  [0, 0, 5.0],
  [0, 2, 3.5],
  [1, 1, 4.0]
]

Create a model

model = Libmf::Model.new
model.fit(data)

Make predictions

model.predict(row_index, column_index)

Get the bias and latent factors

model.bias
model.p_factors
model.q_factors

Save the model to a file

model.save_model("model.txt")

Load the model from a file

model.load_model("model.txt")

Pass a validation set

model.fit(data, eval_set: eval_set)

Cross-Validation

Perform cross-validation

model.cv(data)

Specify the number of folds

model.cv(data, folds: 5)

Parameters

Pass parameters - default values below

Libmf::Model.new(
  loss: 0,                # 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,           # coefficient of L1-norm regularization on P
  lambda_p2: 0.1,         # coefficient of L2-norm regularization on P
  lambda_q1: 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
  copy_data: true         # copy data in training procedure
)

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)

Resources

History

View the changelog

Contributing

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

To get started with development:

git clone --recursive https://github.com/ankane/libmf.git
cd libmf
bundle install
bundle exec rake compile
bundle exec rake test
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