a448a8320c
The old NativeLibLoader had a short-circuit load path which modified java.library.path and attempted to load the xgboost library from outside the jar first, falling back to loading the library from inside the jar. This path is a no-op every time when using XGBoost outside of it's source tree. Additionally it triggers an illegal reflective access warning in the module system in 9, 10, and 11. On Java 12 the ClassLoader fields are not accessible via reflection (separately from the illegal reflective acces warning), and so it fails in a way that isn't caught by the code which falls back to loading the library from inside the jar. This commit removes that code path and always loads the xgboost library from inside the jar file as it's a valid technique across multiple JVM implementations and works with all versions of Java.
XGBoost4J: Distributed XGBoost for Scala/Java
Documentation | Resources | Release Notes
XGBoost4J is the JVM package of xgboost. It brings all the optimizations and power xgboost into JVM ecosystem.
- Train XGBoost models in scala and java with easy customizations.
- Run distributed xgboost natively on jvm frameworks such as Apache Flink and Apache Spark.
You can find more about XGBoost on Documentation and Resource Page.
Add Maven Dependency
XGBoost4J, XGBoost4J-Spark, etc. in maven repository is compiled with g++-4.8.5
Access release version
maven
<dependency>
<groupId>ml.dmlc</groupId>
<artifactId>xgboost4j</artifactId>
<version>latest_version_num</version>
</dependency>
sbt
"ml.dmlc" % "xgboost4j" % "latest_version_num"
For the latest release version number, please check here.
if you want to use xgboost4j-spark, you just need to replace xgboost4j with xgboost4j-spark
Access SNAPSHOT version
You need to add github as repo:
maven:
<repository>
<id>GitHub Repo</id>
<name>GitHub Repo</name>
<url>https://raw.githubusercontent.com/CodingCat/xgboost/maven-repo/</url>
</repository>
sbt:
resolvers += "GitHub Repo" at "https://raw.githubusercontent.com/CodingCat/xgboost/maven-repo/"
the add dependency as following:
maven
<dependency>
<groupId>ml.dmlc</groupId>
<artifactId>xgboost4j</artifactId>
<version>latest_version_num</version>
</dependency>
sbt
"ml.dmlc" % "xgboost4j" % "latest_version_num"
For the latest release version number, please check here.
if you want to use xgboost4j-spark, you just need to replace xgboost4j with xgboost4j-spark
Examples
Full code examples for Scala, Java, Apache Spark, and Apache Flink can be found in the examples package.
NOTE on LIBSVM Format:
There is an inconsistent issue between XGBoost4J-Spark and other language bindings of XGBoost.
When users use Spark to load trainingset/testset in LibSVM format with the following code snippet:
spark.read.format("libsvm").load("trainingset_libsvm")
Spark assumes that the dataset is 1-based indexed. However, when you do prediction with other bindings of XGBoost (e.g. Python API of XGBoost), XGBoost assumes that the dataset is 0-based indexed. It creates a pitfall for the users who train model with Spark but predict with the dataset in the same format in other bindings of XGBoost.