75 lines
2.5 KiB
Markdown
75 lines
2.5 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 on scala and java with easy customizations.
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- Run distributed xgboost natively on jvm frameworks such as Flink and 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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## Hello World
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### XGBoost Scala
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```scala
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import ml.dmlc.xgboost4j.scala.DMatrix
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import ml.dmlc.xgboost4j.scala.XGBoost
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object XGBoostScalaExample {
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def main(args: Array[String]) {
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// read trainining data, available at xgboost/demo/data
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val trainData =
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new DMatrix("/path/to/agaricus.txt.train")
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// define parameters
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val paramMap = List(
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"eta" -> 0.1,
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"max_depth" -> 2,
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"objective" -> "binary:logistic").toMap
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// number of iterations
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val round = 2
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// train the model
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val model = XGBoost.train(paramMap, trainData, round)
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// run prediction
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val predTrain = model.predict(trainData)
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// save model to the file.
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model.saveModel("/local/path/to/model")
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}
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}
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```
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### XGBoost Flink
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```scala
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import ml.dmlc.xgboost4j.scala.flink.XGBoost
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import org.apache.flink.api.scala._
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import org.apache.flink.api.scala.ExecutionEnvironment
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import org.apache.flink.ml.MLUtils
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object DistTrainWithFlink {
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def main(args: Array[String]) {
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val env: ExecutionEnvironment = ExecutionEnvironment.getExecutionEnvironment
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// read trainining data
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val trainData =
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MLUtils.readLibSVM(env, "/path/to/data/agaricus.txt.train")
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// define parameters
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val paramMap = List(
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"eta" -> 0.1,
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"max_depth" -> 2,
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"objective" -> "binary:logistic").toMap
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// number of iterations
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val round = 2
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// train the model
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val model = XGBoost.train(paramMap, trainData, round)
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val predTrain = model.predict(trainData.map{x => x.vector})
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model.saveModelToHadoop("file:///path/to/xgboost.model")
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}
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}
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```
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### XGBoost Spark
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