High performance factorization machines for Ruby

xLearn

xLearn - the high performance machine learning library - for Ruby

🔥 Uses the C API for blazing performance

Supports:

Linear models
Factorization machines
Field-aware factorization machines

Installation

First, install xLearn. On Mac, copy build/lib/libxlearn_api.dylib to /usr/local/lib.

Add this line to your application’s Gemfile:

gem 'xlearn'

Getting Started

Prep your data

x = [[1, 2], [3, 4], [5, 6], [7, 8]]
y = [1, 2, 3, 4]

Train a model

model = XLearn::Linear.new(task: "reg")
model.fit(x, y)

Use XLearn::FM for factorization machines and XLearn::FFM for field-aware factorization machines

Make predictions

model.predict(x)

Save the model to a file

model.save_model("model.bin")

Load the model from a file

model.load_model("model.bin")

Save a text version of the model

model.save_txt("model.txt")

Pass a validation set

model.fit(x_train, y_train, eval_set: [x_val, y_val])

Train online

model.partial_fit(x_train, y_train)

Get the bias term, linear term, and latent factors

model.bias_term
model.linear_term
model.latent_factors # fm and ffm only

Parameters

Specify parameters

model = XLearn::Linear.new(k: 20, epoch: 50)

Supports the same parameters as Python

Cross-Validation

Cross-validation

model.cv(x, y)

Specify the number of folds

model.cv(x, y, folds: 5)

Data

Data can be an array of arrays

[[1, 2, 3], [4, 5, 6]]

Or a Daru data frame

Daru::DataFrame.from_csv("houses.csv")

Or a Numo NArray

Numo::DFloat.new(3, 2).seq

Performance

For large datasets, read data directly from files

model.fit("train.txt", eval_set: "validate.txt")
model.predict("test.txt")
model.cv("train.txt")

For linear models and factorization machines, use CSV:

label,value_1,value_2,...,value_n

Or the libsvm format (better for sparse data):

label index_1:value_1 index_2:value_2 ... index_n:value_n

You can also use commas instead of spaces for separators

For field-aware factorization machines, use the libffm format:

label field_1:index_1:value_1 field_2:index_2:value_2 ...

You can also use commas instead of spaces for separators

You can also write predictions directly to a file

model.predict("test.txt", out_path: "predictions.txt")

xLearn Installation

There’s an experimental branch that includes xLearn with the gem for easiest installation.

gem 'xlearn', github: 'ankane/xlearn', branch: 'vendor', submodules: true

Please file an issue if it doesn’t work for you.

You can also specify the path to xLearn in an initializer:

XLearn.ffi_lib << "/path/to/xlearn/lib/libxlearn_api.so"

Use libxlearn_api.dylib for Mac and xlearn_api.dll for Windows

Credits

This library is modeled after xLearn’s Scikit-learn API.

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