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https://github.com/ankane/lightgbm-ruby
High performance gradient boosting for Ruby
https://github.com/ankane/lightgbm-ruby
lightgbm machine-learning rubyml
Last synced: about 1 month ago
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High performance gradient boosting for Ruby
- Host: GitHub
- URL: https://github.com/ankane/lightgbm-ruby
- Owner: ankane
- License: mit
- Created: 2019-08-14T03:42:26.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2024-01-26T05:08:25.000Z (8 months ago)
- Last Synced: 2024-06-12T08:24:27.984Z (3 months ago)
- Topics: lightgbm, machine-learning, rubyml
- Language: Ruby
- Homepage:
- Size: 3.67 MB
- Stars: 66
- Watchers: 5
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE.txt
Awesome Lists containing this project
README
# LightGBM Ruby
[LightGBM](https://github.com/microsoft/LightGBM) - high performance gradient boosting - for Ruby
[](https://github.com/ankane/lightgbm-ruby/actions)
## Installation
Add this line to your application’s Gemfile:
```ruby
gem "lightgbm"
```
On Mac, also install OpenMP:
```sh
brew install libomp
```
## Training API
Prep your data
```ruby
x = [[1, 2], [3, 4], [5, 6], [7, 8]]
y = [1, 2, 3, 4]
```
Train a model
```ruby
params = {objective: "regression"}
train_set = LightGBM::Dataset.new(x, label: y)
booster = LightGBM.train(params, train_set)
```
Predict
```ruby
booster.predict(x)
```
Save the model to a file
```ruby
booster.save_model("model.txt")
```
Load the model from a file
```ruby
booster = LightGBM::Booster.new(model_file: "model.txt")
```
Get the importance of features
```ruby
booster.feature_importance
```
Early stopping
```ruby
LightGBM.train(params, train_set, valid_sets: [train_set, test_set], early_stopping_rounds: 5)
```
CV
```ruby
LightGBM.cv(params, train_set, nfold: 5, verbose_eval: true)
```
## Scikit-Learn API
Prep your data
```ruby
x = [[1, 2], [3, 4], [5, 6], [7, 8]]
y = [1, 2, 3, 4]
```
Train a model
```ruby
model = LightGBM::Regressor.new
model.fit(x, y)
```
> For classification, use `LightGBM::Classifier`
Predict
```ruby
model.predict(x)
```
> For classification, use `predict_proba` for probabilities
Save the model to a file
```ruby
model.save_model("model.txt")
```
Load the model from a file
```ruby
model.load_model("model.txt")
```
Get the importance of features
```ruby
model.feature_importances
```
Early stopping
```ruby
model.fit(x, y, eval_set: [[x_test, y_test]], early_stopping_rounds: 5)
```
## Data
Data can be an array of arrays
```ruby
[[1, 2, 3], [4, 5, 6]]
```
Or a Numo array
```ruby
Numo::NArray.cast([[1, 2, 3], [4, 5, 6]])
```
Or a Rover data frame
```ruby
Rover.read_csv("houses.csv")
```
Or a Daru data frame
```ruby
Daru::DataFrame.from_csv("houses.csv")
```
## Helpful Resources
- [Parameters](https://lightgbm.readthedocs.io/en/latest/Parameters.html)
- [Parameter Tuning](https://lightgbm.readthedocs.io/en/latest/Parameters-Tuning.html)
## Related Projects
- [XGBoost](https://github.com/ankane/xgboost-ruby) - XGBoost for Ruby
- [Eps](https://github.com/ankane/eps) - Machine learning for Ruby
## Credits
This library follows the [Python API](https://lightgbm.readthedocs.io/en/latest/Python-API.html). 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](https://github.com/PairOnAir/xgboost-ruby) gem for showing how to use FFI.
## History
View the [changelog](https://github.com/ankane/lightgbm-ruby/blob/master/CHANGELOG.md)
## Contributing
Everyone is encouraged to help improve this project. Here are a few ways you can help:
- [Report bugs](https://github.com/ankane/lightgbm-ruby/issues)
- Fix bugs and [submit pull requests](https://github.com/ankane/lightgbm-ruby/pulls)
- Write, clarify, or fix documentation
- Suggest or add new features
To get started with development:
```sh
git clone https://github.com/ankane/lightgbm-ruby.git
cd lightgbm-ruby
bundle install
bundle exec rake vendor:all
bundle exec rake test
```