I am working some kind of Spark MLlib Test(Decision Tree) and I used IRIS
data from Cran-R package.
Original IRIS Data is not a good format for Spark MLlib. so I changed data
format(change data format and features's location)
When I ran sample Spark MLlib code for DT, I met the error like below
How can i solve this error?
==============================================================
14/12/15 14:27:30 ERROR TaskSetManager: Task 21.0:0 failed 4 times; aborting
job
14/12/15 14:27:30 INFO TaskSchedulerImpl: Cancelling stage 21
14/12/15 14:27:30 INFO DAGScheduler: Failed to run aggregate at
DecisionTree.scala:657
14/12/15 14:27:30 INFO TaskSchedulerImpl: Stage 21 was cancelled
14/12/15 14:27:30 WARN TaskSetManager: Loss was due to
org.apache.spark.TaskKilledException
org.apache.spark.TaskKilledException
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:178)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
14/12/15 14:27:30 INFO TaskSchedulerImpl: Removed TaskSet 21.0, whose tasks
have all completed, from pool
org.apache.spark.SparkException: Job aborted due to stage failure: Task
21.0:0 failed 4 times, most recent failure: Exception failure in TID 34 on
host krbda1anode01.kr.test.com: scala.MatchError: 2.0 (of class
java.lang.Double)
org.apache.spark.mllib.tree.DecisionTree$.classificationBinSeqOp$1(DecisionTree.scala:568)
org.apache.spark.mllib.tree.DecisionTree$.org$apache$spark$mllib$tree$DecisionTree$$binSeqOp$1(DecisionTree.scala:623)
org.apache.spark.mllib.tree.DecisionTree$$anonfun$4.apply(DecisionTree.scala:657)
org.apache.spark.mllib.tree.DecisionTree$$anonfun$4.apply(DecisionTree.scala:657)
scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:144)
scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:144)
scala.collection.Iterator$class.foreach(Iterator.scala:727)
scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
scala.collection.TraversableOnce$class.foldLeft(TraversableOnce.scala:144)
scala.collection.AbstractIterator.foldLeft(Iterator.scala:1157)
scala.collection.TraversableOnce$class.aggregate(TraversableOnce.scala:201)
scala.collection.AbstractIterator.aggregate(Iterator.scala:1157)
org.apache.spark.rdd.RDD$$anonfun$21.apply(RDD.scala:838)
org.apache.spark.rdd.RDD$$anonfun$21.apply(RDD.scala:838)
org.apache.spark.SparkContext$$anonfun$23.apply(SparkContext.scala:1116)
org.apache.spark.SparkContext$$anonfun$23.apply(SparkContext.scala:1116)
org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:111)
org.apache.spark.scheduler.Task.run(Task.scala:51)
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:187)
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
java.lang.Thread.run(Thread.java:745)
Driver stacktrace:
at
org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1033)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1017)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1015)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at
org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1015)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:633)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:633)
at scala.Option.foreach(Option.scala:236)
at
org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:633)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1207)
at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
at akka.actor.ActorCell.invoke(ActorCell.scala:456)
at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
at akka.dispatch.Mailbox.run(Mailbox.scala:219)
at
akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386)
at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
at
scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
at
scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
at
scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
==============================================================
input data is ( first column means species. 1 =setosa 2 => versicolor)
1,5.1,3.5,1.4,0.2
1,4.9,3,1.4,0.2
1,4.7,3.2,1.3,0.2
1,4.6,3.1,1.5,0.2
1,5,3.6,1.4,0.2
1,5.4,3.9,1.7,0.4
2,4.6,3.4,1.4,0.3
2,5,3.4,1.5,0.2
2,4.4,2.9,1.4,0.2
2,4.9,3.1,1.5,0.1
2,5.4,3.7,1.5,0.2
2,4.8,3.4,1.6,0.2
2,4.8,3,1.4,0.1
sample Spark MLlib Decision tree code is
import org.apache.spark.SparkContext
import org.apache.spark.mllib.tree.DecisionTree
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.tree.configuration.Algo._
import org.apache.spark.mllib.tree.impurity.Gini
// Load and parse the data file
val data = sc.textFile("dt_R.csv")
val parsedData = data.map { line =>
val parts = line.split(',').map(_.toDouble)
LabeledPoint(parts(0), Vectors.dense(parts.tail))
}
// Run training algorithm to build the model
val maxDepth = 3
val model = DecisionTree.train(parsedData, Classification, Gini, maxDepth)
--
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