Vowpal Wabbit

Vowpal Wabbit - fast online machine learning - for Ruby

Installation

First, install the Vowpal Wabbit C++ library. For Homebrew, use:

brew install vowpal-wabbit

Add this line to your application’s Gemfile:

gem 'vowpalwabbit'

Getting Started

Prep your data

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

Train a model

model = VowpalWabbit::Regressor.new(learning_rate: 100)
model.fit(x, y)

Use VowpalWabbit::Classifier for classification and VowpalWabbit::Model for other models

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")

Train online

model.partial_fit(x, y)

Get the intercept and coefficients

model.intercept
model.coefs

Score - R-squared for regression and accuracy for classification

model.score(x, y)

Parameters

Specify parameters

model = VowpalWabbit::Model.new(cb: 4)

Supports the same parameters as the CLI

Data

Data can be an array of arrays

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

Or a Numo NArray

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

Or an array of strings

[
  "0 | price:.23 sqft:.25 age:.05 2006",
  "1 2 'second_house | price:.18 sqft:.15 age:.35 1976",
  "0 1 0.5 'third_house | price:.53 sqft:.32 age:.87 1924"
]

Or a path to a file

model.fit("train.txt")
model.partial_fit("train.txt")
model.predict("train.txt")
model.score("train.txt")

Files can be compressed

model.fit("train.txt.gz")

Read more about the input format

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/vowpalwabbit.git
cd vowpalwabbit
bundle install
bundle exec rake test