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XGBoost - high performance gradient boosting - for Ruby
Add this line to your application’s Gemfile:
gem "xgb"
On Mac, also install OpenMP:
brew install libomp
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
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 = XGBoost::Regressor.new(early_stopping_rounds: 5)
model.fit(x, y, eval_set: [[x_test, y_test]])
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")
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.
View the changelog
Everyone is encouraged to help improve this project. Here are a few ways you can help:
To get started with development:
git clone https://github.com/ankane/xgboost-ruby.git
cd xgboost-ruby
bundle install
bundle exec rake vendor:all
bundle exec rake test
FAQs
Unknown package
We found that xgb demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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