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update README for jvm-packages

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CodingCat 2016-03-11 15:28:55 -05:00
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commit a3b2e76230
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jvm-packages/README.md
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@ -34,7 +34,7 @@ object XGBoostScalaExample {
// number of iterations // number of iterations
val round = 2 val round = 2
// train the model // train the model
val model = XGBoost.train(paramMap, trainData, round) val model = XGBoost.train(trainData, paramMap, round)
// run prediction // run prediction
val predTrain = model.predict(trainData) val predTrain = model.predict(trainData)
// save model to the file. // save model to the file.
@ -43,34 +43,6 @@ object XGBoostScalaExample {
} }
``` ```
### XGBoost Flink
```scala
import ml.dmlc.xgboost4j.scala.flink.XGBoost
import org.apache.flink.api.scala._
import org.apache.flink.api.scala.ExecutionEnvironment
import org.apache.flink.ml.MLUtils
object DistTrainWithFlink {
def main(args: Array[String]) {
val env: ExecutionEnvironment = ExecutionEnvironment.getExecutionEnvironment
// read trainining data
val trainData =
MLUtils.readLibSVM(env, "/path/to/data/agaricus.txt.train")
// define parameters
val paramMap = List(
"eta" -> 0.1,
"max_depth" -> 2,
"objective" -> "binary:logistic").toMap
// number of iterations
val round = 2
// train the model
val model = XGBoost.train(paramMap, trainData, round)
val predTrain = model.predict(trainData.map{x => x.vector})
model.saveModelToHadoop("file:///path/to/xgboost.model")
}
}
```
### XGBoost Spark ### XGBoost Spark
```scala ```scala
import org.apache.spark.SparkContext import org.apache.spark.SparkContext
@ -101,3 +73,33 @@ object DistTrainWithSpark {
} }
} }
``` ```
### XGBoost Flink
```scala
import ml.dmlc.xgboost4j.scala.flink.XGBoost
import org.apache.flink.api.scala._
import org.apache.flink.api.scala.ExecutionEnvironment
import org.apache.flink.ml.MLUtils
object DistTrainWithFlink {
def main(args: Array[String]) {
val env: ExecutionEnvironment = ExecutionEnvironment.getExecutionEnvironment
// read trainining data
val trainData =
MLUtils.readLibSVM(env, "/path/to/data/agaricus.txt.train")
// define parameters
val paramMap = List(
"eta" -> 0.1,
"max_depth" -> 2,
"objective" -> "binary:logistic").toMap
// number of iterations
val round = 2
// train the model
val model = XGBoost.train(trainData, paramMap, round)
val predTrain = model.predict(trainData.map{x => x.vector})
model.saveModelToHadoop("file:///path/to/xgboost.model")
}
}
```