ankane/lightgbm-ruby

High performance gradient boosting for Ruby

LightGBM Ruby

LightGBM - high performance gradient boosting - for Ruby

Installation

Add this line to your application’s Gemfile:

gem "lightgbm"

On Mac, also install OpenMP:

brew install libomp

Training API

Prep your data

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

Train a model

params = {objective: "regression"}
train_set = LightGBM::Dataset.new(x, label: y)
booster = LightGBM.train(params, train_set)

Predict

booster.predict(x)

Save the model to a file

booster.save_model("model.txt")

Load the model from a file

booster = LightGBM::Booster.new(model_file: "model.txt")

Get the importance of features

booster.feature_importance

Early stopping

LightGBM.train(params, train_set, valid_sets: [train_set, test_set], early_stopping_rounds: 5)

CV

LightGBM.cv(params, train_set, nfold: 5, verbose_eval: true)

Scikit-Learn API

Prep your data

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

Train a model

model = LightGBM::Regressor.new
model.fit(x, y)

For classification, use LightGBM::Classifier

Predict

model.predict(x)

For classification, use predict_proba for probabilities

Save the model to a file

model.save_model("model.txt")

Load the model from a file

model.load_model("model.txt")

Get the importance of features

model.feature_importances

Early stopping

model.fit(x, y, eval_set: [[x_test, y_test]], early_stopping_rounds: 5)

Data

Data can be an array of arrays

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

Or a Numo array

Numo::NArray.cast([[1, 2, 3], [4, 5, 6]])

Or a Rover data frame

Rover.read_csv("houses.csv")

Or a Daru data frame

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

Helpful Resources

• Parameters
• Parameter Tuning

Related Projects

• XGBoost - XGBoost for Ruby
• Eps - Machine learning for Ruby

Credits

This library follows the Python API. A few differences are:

• The get_ and set_ prefixes are removed from methods
• The default verbosity is -1
• With the cv method, stratified is set to false

Thanks to the xgboost gem for showing how to use FFI.

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/lightgbm-ruby.git
cd lightgbm-ruby
bundle install
bundle exec rake vendor:all
bundle exec rake test