Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/ankane/xgboost-ruby
High performance gradient boosting for Ruby
https://github.com/ankane/xgboost-ruby
machine-learning rubyml xgboost
Last synced: 4 days ago
JSON representation
High performance gradient boosting for Ruby
- Host: GitHub
- URL: https://github.com/ankane/xgboost-ruby
- Owner: ankane
- License: apache-2.0
- Created: 2019-08-15T10:56:06.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2024-10-17T20:53:50.000Z (17 days ago)
- Last Synced: 2024-10-29T11:13:07.499Z (5 days ago)
- Topics: machine-learning, rubyml, xgboost
- Language: Ruby
- Homepage:
- Size: 281 KB
- Stars: 106
- Watchers: 5
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE.txt
Awesome Lists containing this project
README
# XGBoost Ruby
[XGBoost](https://github.com/dmlc/xgboost) - high performance gradient boosting - for Ruby
[](https://github.com/ankane/xgboost-ruby/actions)
## Installation
Add this line to your application’s Gemfile:
```ruby
gem "xgb"
```
On Mac, also install OpenMP:
```sh
brew install libomp
```
## Learning 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: "reg:squarederror"}
dtrain = XGBoost::DMatrix.new(x, label: y)
booster = XGBoost.train(params, dtrain)
```
Predict
```ruby
dtest = XGBoost::DMatrix.new(x)
booster.predict(dtest)
```
Save the model to a file
```ruby
booster.save_model("my.model")
```
Load the model from a file
```ruby
booster = XGBoost::Booster.new(model_file: "my.model")
```
Get the importance of features
```ruby
booster.score
```
Early stopping
```ruby
XGBoost.train(params, dtrain, evals: [[dtrain, "train"], [dtest, "eval"]], early_stopping_rounds: 5)
```
CV
```ruby
XGBoost.cv(params, dtrain, nfold: 3, verbose_eval: true)
```
Set metadata about a model
```ruby
booster["key"] = "value"
booster.attributes
```
## 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 = XGBoost::Regressor.new
model.fit(x, y)
```
> For classification, use `XGBoost::Classifier`
Predict
```ruby
model.predict(x)
```
> For classification, use `predict_proba` for probabilities
Save the model to a file
```ruby
model.save_model("my.model")
```
Load the model from a file
```ruby
model.load_model("my.model")
```
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://xgboost.readthedocs.io/en/latest/parameter.html)
- [Parameter Tuning](https://xgboost.readthedocs.io/en/latest/tutorials/param_tuning.html)
## Related Projects
- [LightGBM](https://github.com/ankane/lightgbm) - LightGBM for Ruby
- [Eps](https://github.com/ankane/eps) - Machine learning for Ruby
## Credits
This library follows the [Python API](https://xgboost.readthedocs.io/en/latest/python/python_api.html), with the `get_` and `set_` prefixes removed from methods to make it more Ruby-like.
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/xgboost-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/xgboost-ruby/issues)
- Fix bugs and [submit pull requests](https://github.com/ankane/xgboost-ruby/pulls)
- Write, clarify, or fix documentation
- Suggest or add new features
To get started with development:
```sh
git clone https://github.com/ankane/xgboost-ruby.git
cd xgboost-ruby
bundle install
bundle exec rake vendor:all
bundle exec rake test
```