e1f57b4417
* add back train method but mark as deprecated * add back train method but mark as deprecated * fix scalastyle error * fix scalastyle error * cross building files * update * build with docker * remove * temp * update build script * update pom * update * update version * upload build * fix path * update README.md * fix compiler version to 4.8.5
73 lines
2.3 KiB
Markdown
73 lines
2.3 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 SNAPSHOT version
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You need to add github as repo:
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<b>maven</b>:
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```xml
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<repository>
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<id>GitHub Repo</id>
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<name>GitHub Repo</name>
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<url>https://raw.githubusercontent.com/CodingCat/xgboost/maven-repo/</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 += "GitHub Repo" at "https://raw.githubusercontent.com/CodingCat/xgboost/maven-repo/"
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```
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the add dependency as following:
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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</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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"ml.dmlc" % "xgboost4j" % "latest_version_num"
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```
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if you want to use `xgboost4j-spark`, you just need to replace xgboost4j with `xgboost4j-spark`
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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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* Use *1-based* ascending indexes for the LIBSVM format in distributed training mode
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* Spark does the internal conversion, and does not accept formats that are 0-based
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* Whereas, use *0-based* indexes format when predicting in normal mode - for instance, while using the saved model in the Python package |