README.md in xgb-0.1.2 vs README.md in xgb-0.1.3
- old
+ new
@@ -6,36 +6,47 @@
[](https://travis-ci.org/ankane/xgb)
## Installation
-First, [install XGBoost](https://xgboost.readthedocs.io/en/latest/build.html). On Mac, copy `lib/libxgboost.dylib` to `/usr/local/lib`.
+First, [install XGBoost](https://xgboost.readthedocs.io/en/latest/build.html). For Homebrew, use:
+```sh
+brew install xgboost
+```
+
Add this line to your application’s Gemfile:
```ruby
gem 'xgb'
```
## Getting Started
-This library follows the [Python API](https://xgboost.readthedocs.io/en/latest/python/python_api.html). Some methods and options are missing at the moment. PRs welcome!
+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. Some methods and options are missing at the moment. PRs welcome!
## 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 = Xgb::DMatrix.new(x_train, label: y_train)
+dtrain = Xgb::DMatrix.new(x, label: y)
booster = Xgb.train(params, dtrain)
```
Predict
```ruby
-dtest = Xgb::DMatrix.new(x_test)
+dtest = Xgb::DMatrix.new(x)
booster.predict(dtest)
```
Save the model to a file
@@ -135,10 +146,28 @@
```ruby
Numo::DFloat.new(3, 2).seq
```
+## XGBoost Installation
+
+There’s an experimental branch that includes XGBoost with the gem for easiest installation.
+
+```ruby
+gem 'xgb', github: 'ankane/xgb', branch: 'vendor', submodules: true
+```
+
+Please file an issue if it doesn’t work for you.
+
+You can also specify the path to XGBoost in an initializer:
+
+```ruby
+Xgb.ffi_lib << "/path/to/xgboost/lib/libxgboost.so"
+```
+
+> Use `libxgboost.dylib` for Mac and `xgboost.dll` for Windows
+
## Helpful Resources
- [Parameters](https://xgboost.readthedocs.io/en/latest/parameter.html)
- [Parameter Tuning](https://xgboost.readthedocs.io/en/latest/tutorials/param_tuning.html)
@@ -147,10 +176,10 @@
- [LightGBM](https://github.com/ankane/lightgbm) - LightGBM for Ruby
- [Eps](https://github.com/ankane/eps) - Machine Learning for Ruby
## Credits
-Thanks to the [xgboost](https://github.com/PairOnAir/xgboost-ruby) gem for serving as an initial reference, and Selva Prabhakaran for the [test datasets](https://github.com/selva86/datasets).
+Thanks to the [xgboost](https://github.com/PairOnAir/xgboost-ruby) gem for serving as an initial reference.
## History
View the [changelog](https://github.com/ankane/xgb/blob/master/CHANGELOG.md)