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[jvm-packages] Accept groupData in spark model eval (#2244)

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* Support model evaluation for ranking tasks by accepting
 groupData in XGBoostModel.eval
This commit is contained in:
ebernhardson
2017-05-02 10:03:20 -07:00
committed by Nan Zhu
parent a375ad2822
commit ccccf8a015
2 changed files with 11 additions and 2 deletions
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8
jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostModel.scala
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@@ -87,10 +87,13 @@ abstract class XGBoostModel(protected var _booster: Booster)
* @param evalFunc the customized evaluation function, null by default to use the default metric
* of model
* @param iter the current iteration, -1 to be null to use customized evaluation functions
* @param groupData group data specify each group size for ranking task. Top level corresponds
* to partition id, second level is the group sizes.
* @return the average metric over all partitions
*/
def eval(evalDataset: RDD[MLLabeledPoint], evalName: String, evalFunc: EvalTrait = null,
iter: Int = -1, useExternalCache: Boolean = false): String = {
iter: Int = -1, useExternalCache: Boolean = false,
groupData: Seq[Seq[Int]] = null): String = {
require(evalFunc != null || iter != -1, "you have to specify the value of either eval or iter")
val broadcastBooster = evalDataset.sparkContext.broadcast(_booster)
val broadcastUseExternalCache = evalDataset.sparkContext.broadcast($(useExternalMemory))
@@ -110,6 +113,9 @@ abstract class XGBoostModel(protected var _booster: Booster)
}
import DataUtils._
val dMatrix = new DMatrix(labeledPointsPartition, cacheFileName)
if (groupData != null) {
dMatrix.setGroup(groupData(TaskContext.getPartitionId()).toArray)
}
(evalFunc, iter) match {
case (null, _) => {
val predStr = broadcastBooster.value.evalSet(Array(dMatrix), Array(evalName), iter)