https://github.com/kongzii/swiftxgboost
Swift wrapper for XGBoost gradient boosting machine learning framework with Numpy and TensorFlow support.
https://github.com/kongzii/swiftxgboost
boosting-tree machine-learning swift swift-for-tensorflow xgboost
Last synced: 10 months ago
JSON representation
Swift wrapper for XGBoost gradient boosting machine learning framework with Numpy and TensorFlow support.
- Host: GitHub
- URL: https://github.com/kongzii/swiftxgboost
- Owner: kongzii
- License: apache-2.0
- Created: 2020-05-23T09:43:55.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2020-08-29T07:01:35.000Z (almost 6 years ago)
- Last Synced: 2025-08-21T02:23:45.836Z (11 months ago)
- Topics: boosting-tree, machine-learning, swift, swift-for-tensorflow, xgboost
- Language: Swift
- Homepage:
- Size: 5.29 MB
- Stars: 26
- Watchers: 3
- Forks: 2
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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# XGBoost for Swift
Bindings for [the XGBoost system library](https://en.wikipedia.org/wiki/XGBoost).
The aim of this package is to mimic [XGBoost Python bindings](https://xgboost.readthedocs.io/en/latest/python/python_intro.html) but, at the same time, utilize the power of Swift and C compatibility. Some things thus behave differently but should provide you maximum flexibility over XGBoost.
Check out:
- [Examples](https://github.com/kongzii/SwiftXGBoost/tree/master/Examples)
- [Code documentation](https://kongzii.github.io/SwiftXGBoost/)
- [ReadTheDocs](https://swiftxgboost.readthedocs.io/)
## Installation
### System library dependency
#### Linux
Install XGBoost from sources
```
git clone https://github.com/dmlc/xgboost
cd xgboost
git checkout tags/v1.1.1
git submodule update --init --recursive
mkdir build
cd build
cmake ..
make
make install
ldconfig
```
Or you can use provided installation script
```
./install.sh
```
#### macOS
You can build and install similarly as on Linux, or just use brew
```
brew install xgboost
```
##### Note
Before version 1.1.1, XGBoost did not create pkg-config. This was fixed with PR [Add pkgconfig to cmake #5744](https://github.com/dmlc/xgboost/pull/5744).
If you are using for some reason older versions, you may need to specify path to the XGBoost libraries while building, e.g.:
```
swift build -Xcc -I/usr/local/include -Xlinker -L/usr/local/lib
```
or create pkg-config file manualy. Example of it for `macOS 10.15` and `XGBoost 1.1.0` is
```
prefix=/usr/local/Cellar/xgboost/1.1.0
exec_prefix=${prefix}/bin
libdir=${prefix}/lib
includedir=${prefix}/include
Name: xgboost
Description: XGBoost machine learning libarary.
Version: 1.1.0
Cflags: -I${includedir}
Libs: -L${libdir} -lxgboost
```
and needs to be placed at `/usr/local/lib/pkgconfig/xgboost.pc`
### Package
Add a dependency in your your `Package.swift`
```swift
.package(url: "https://github.com/kongzii/SwiftXGBoost.git", from: "0.0.0"),
```
Import Swifty XGBoost
```swift
import XGBoost
```
or directly C library
```swift
import CXGBoost
```
both `Booster` and `DMatrix` classes are exposing pointers to the underlying C,
so you can utilize C-API directly for more advanced usage.
As the library is still evolving, there can be incompatible changes between updates,
the releases before version 1.0.0 doesn't follow [Semantic Versioning](https://semver.org/).
Please use the exact version if you do not want to worry about updating your packages.
```swift
.package(url: "https://github.com/kongzii/SwiftXGBoost.git", .exact("0.1.0")),
```
## Python compatibility
DMatrix can be created from numpy array just like in Python
```swift
let pandas = Python.import("pandas")
let dataFrame = pandas.read_csv("data.csv")
let data = try DMatrix(
name: "training",
from: dataFrame.values
)
```
and the swift array can be converted back to numpy
```swift
let predicted = try booster.predict(
from: validationData
)
let compare = pandas.DataFrame([
"Label lower bound": yLowerBound[validIndex],
"Label upper bound": yUpperBound[validIndex],
"Prediced": predicted.makeNumpyArray(),
])
print(compare)
```
This is possible thanks to the [PythonKit](https://github.com/pvieito/PythonKit.git).
For more detailed usage and workarounds for known issues, check out [examples](https://github.com/kongzii/SwiftXGBoost/tree/master/Examples).
## TensorFlow compability
[Swift4TensorFlow](https://github.com/tensorflow/swift) is a great project from Google.
If you are using one of the S4TF swift toolchains, you can combine its power directly with XGBoost.
```swift
let tensor = Tensor(shape: TensorShape([2, 3]), scalars: [1, 2, 3, 4, 5, 6])
let data = try DMatrix(name: "training", from: tensor)
```
### Note
[Swift4TensorFlow](https://github.com/tensorflow/swift) toolchains ships with preinstalled [PythonKit](https://github.com/pvieito/PythonKit.git) and you may run into a problem when using package with extra [PythonKit](https://github.com/pvieito/PythonKit.git) dependency. If so, please just add package version with `-tensorflow` suffix, where [PythonKit](https://github.com/pvieito/PythonKit.git) dependency is removed.
```swift
.package(url: "https://github.com/kongzii/SwiftXGBoost.git", .exact("0.7.0-tensorflow")),
```
This bug is known and hopefully will be resolved soon.
## Examples
More examples can be found in [Examples directory](https://github.com/kongzii/SwiftXGBoost/tree/master/Examples)
and run inside docker
```
docker-compose run swiftxgboost swift run exampleName
```
or on host
```
swift run exampleName
```
### Basic functionality
```swift
import XGBoost
// Register your own callback function for log(info) messages
try XGBoost.registerLogCallback {
print("Swifty log:", String(cString: $0!))
}
// Create some random features and labels
let randomArray = (0 ..< 1000).map { _ in Float.random(in: 0 ..< 2) }
let labels = (0 ..< 100).map { _ in Float([0, 1].randomElement()!) }
// Initialize data, DMatrixHandle in the background
let data = try DMatrix(
name: "data",
from: randomArray,
shape: Shape(100, 10),
label: labels,
threads: 1
)
// Slice array into train and test
let train = try data.slice(indexes: 0 ..< 90, newName: "train")
let test = try data.slice(indexes: 90 ..< 100, newName: "test")
// Parameters for Booster, check https://xgboost.readthedocs.io/en/latest/parameter.html
let parameters = [
Parameter("verbosity", "2"),
Parameter("seed", "0"),
]
// Create Booster model, `with` data will be cached
let booster = try Booster(
with: [train, test],
parameters: parameters
)
// Train booster, optionally provide callback functions called before and after each iteration
try booster.train(
iterations: 10,
trainingData: train,
evaluationData: [train, test]
)
// Predict from test data
let predictions = try booster.predict(from: test)
// Save
try booster.save(to: "model.xgboost")
```
## Development
### Documentation
[Jazzy](https://github.com/realm/jazzy) is used for the generation of documentation.
You can generate documentation locally using
```
make documentation
```
Github pages will be updated automatically when merged into master.
### Tests
Where possible, Swift implementation is tested against reference implementation in Python via PythonKit. For example, test of `score` method in `scoreEmptyFeatureMapTest`
```swift
let pyFMap = [String: Int](pyXgboost.get_score(
fmap: "", importance_type: "weight"))!
let (fMap, _) = try booster.score(featureMap: "", importance: .weight)
XCTAssertEqual(fMap, pyFMap)
```
#### Run locally
On ubuntu using docker
```
docker-compose run test
```
On host
```
swift test
```
### Code format
[SwiftFormat](https://github.com/nicklockwood/SwiftFormat) is used for code formatting.
```
make format
```