137 lines
4.6 KiB
Markdown
137 lines
4.6 KiB
Markdown
# XGBoost4J: Distributed XGBoost for Scala/Java
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[](https://travis-ci.org/dmlc/xgboost)
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[](https://xgboost.readthedocs.org/en/latest/jvm/index.html)
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[](../LICENSE)
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[Documentation](https://xgboost.readthedocs.org/en/latest/jvm/index.html) |
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[Resources](../demo/README.md) |
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[Release Notes](../NEWS.md)
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XGBoost4J is the JVM package of xgboost. It brings all the optimizations
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and power xgboost into JVM ecosystem.
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- Train XGBoost models in scala and java with easy customizations.
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- Run distributed xgboost natively on jvm frameworks such as
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Apache Flink and Apache Spark.
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You can find more about XGBoost on [Documentation](https://xgboost.readthedocs.org/en/latest/jvm/index.html) and [Resource Page](../demo/README.md).
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## Add Maven Dependency
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XGBoost4J, XGBoost4J-Spark, etc. in maven repository is compiled with g++-4.8.5.
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### Access release version
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<b>Maven</b>
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```
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<dependency>
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<groupId>ml.dmlc</groupId>
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<artifactId>xgboost4j_2.12</artifactId>
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<version>latest_version_num</version>
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</dependency>
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<dependency>
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<groupId>ml.dmlc</groupId>
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<artifactId>xgboost4j-spark_2.12</artifactId>
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<version>latest_version_num</version>
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</dependency>
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```
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<b>sbt</b>
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```sbt
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libraryDependencies ++= Seq(
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"ml.dmlc" %% "xgboost4j" % "latest_version_num",
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"ml.dmlc" %% "xgboost4j-spark" % "latest_version_num"
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)
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```
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For the latest release version number, please check [here](https://github.com/dmlc/xgboost/releases).
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To enable the GPU algorithm (`tree_method='gpu_hist'`), use artifacts `xgboost4j-gpu_2.12` and `xgboost4j-spark-gpu_2.12` instead.
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### Access SNAPSHOT version
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First add the following Maven repository hosted by the XGBoost project:
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<b>Maven</b>:
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```xml
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<repository>
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<id>XGBoost4J Snapshot Repo</id>
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<name>XGBoost4J Snapshot Repo</name>
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<url>https://s3-us-west-2.amazonaws.com/xgboost-maven-repo/snapshot/</url>
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</repository>
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```
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<b>sbt</b>:
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```sbt
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resolvers += "XGBoost4J Snapshot Repo" at "https://s3-us-west-2.amazonaws.com/xgboost-maven-repo/snapshot/"
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```
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Then add XGBoost4J as a dependency:
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<b>Maven</b>
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```
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<dependency>
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<groupId>ml.dmlc</groupId>
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<artifactId>xgboost4j_2.12</artifactId>
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<version>latest_version_num-SNAPSHOT</version>
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</dependency>
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<dependency>
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<groupId>ml.dmlc</groupId>
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<artifactId>xgboost4j-spark_2.12</artifactId>
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<version>latest_version_num-SNAPSHOT</version>
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</dependency>
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```
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<b>sbt</b>
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```sbt
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libraryDependencies ++= Seq(
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"ml.dmlc" %% "xgboost4j" % "latest_version_num-SNAPSHOT",
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"ml.dmlc" %% "xgboost4j-spark" % "latest_version_num-SNAPSHOT"
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)
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```
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For the latest release version number, please check [the repository listing](https://s3-us-west-2.amazonaws.com/xgboost-maven-repo/list.html).
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To enable the GPU algorithm (`tree_method='gpu_hist'`), use artifacts `xgboost4j-gpu_2.12` and `xgboost4j-spark-gpu_2.12` instead.
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## Examples
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Full code examples for Scala, Java, Apache Spark, and Apache Flink can
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be found in the [examples package](https://github.com/dmlc/xgboost/tree/master/jvm-packages/xgboost4j-example).
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**NOTE on LIBSVM Format**:
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There is an inconsistent issue between XGBoost4J-Spark and other language bindings of XGBoost.
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When users use Spark to load trainingset/testset in LIBSVM format with the following code snippet:
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```scala
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spark.read.format("libsvm").load("trainingset_libsvm")
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```
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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.
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## Development
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You can build/package xgboost4j locally with the following steps:
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**Linux:**
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1. Ensure [Docker for Linux](https://docs.docker.com/install/) is installed.
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2. Clone this repo: `git clone --recursive https://github.com/dmlc/xgboost.git`
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3. Run the following command:
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- With Tests: `./xgboost/jvm-packages/dev/build-linux.sh`
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- Skip Tests: `./xgboost/jvm-packages/dev/build-linux.sh --skip-tests`
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**Windows:**
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1. Ensure [Docker for Windows](https://docs.docker.com/docker-for-windows/install/) is installed.
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2. Clone this repo: `git clone --recursive https://github.com/dmlc/xgboost.git`
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3. Run the following command:
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- With Tests: `.\xgboost\jvm-packages\dev\build-linux.cmd`
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- Skip Tests: `.\xgboost\jvm-packages\dev\build-linux.cmd --skip-tests`
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*Note: this will create jars for deployment on Linux machines.*
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