XGBoost

XGBoost - high performance gradient boosting - for Ruby

Installation

Add this line to your application’s Gemfile:

gem 'xgb'

On Mac, also install OpenMP:

brew install libomp

Learning API

Prep your data

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

Train a model

params = {objective: "reg:squarederror"}
dtrain = XGBoost::DMatrix.new(x, label: y)
booster = XGBoost.train(params, dtrain)

Predict

dtest = XGBoost::DMatrix.new(x)
booster.predict(dtest)

Save the model to a file

booster.save_model("my.model")

Load the model from a file

booster = XGBoost::Booster.new(model_file: "my.model")

Get the importance of features

booster.score

Early stopping

XGBoost.train(params, dtrain, evals: [[dtrain, "train"], [dtest, "eval"]], early_stopping_rounds: 5)

CV

XGBoost.cv(params, dtrain, nfold: 3, verbose_eval: true)

Set metadata about a model

booster["key"] = "value"
booster.attributes

Scikit-Learn API

Prep your data

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

Train a model

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

For classification, use XGBoost::Classifier

Predict

model.predict(x)

For classification, use predict_proba for probabilities

Save the model to a file

model.save_model("my.model")

Load the model from a file

model.load_model("my.model")

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

• LightGBM - LightGBM for Ruby
• Eps - Machine learning for Ruby

Credits

This library follows the Python API, with the get_ and set_ prefixes removed from methods to make it more Ruby-like.

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/xgboost.git
cd xgboost
bundle install
bundle exec rake vendor:all
bundle exec rake test