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From "Joseph E. Gonzalez (JIRA)" <j...@apache.org>
Subject [jira] [Created] (SPARK-4130) loadLibSVMFile does not handle extra whitespace
Date Wed, 29 Oct 2014 07:06:33 GMT
Joseph E. Gonzalez created SPARK-4130:
-----------------------------------------

             Summary: loadLibSVMFile does not handle extra whitespace
                 Key: SPARK-4130
                 URL: https://issues.apache.org/jira/browse/SPARK-4130
             Project: Spark
          Issue Type: Bug
          Components: MLlib
            Reporter: Joseph E. Gonzalez


When testing MLlib on the splice site data (http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#splice-site)
the loadSVM.  To reproduce in spark shell:

import org.apache.spark.mllib.util.MLUtils
val data =  MLUtils.loadLibSVMFile(sc, "hdfs://ec2-54-200-69-227.us-west-2.compute.amazonaws.com:9000/splice_site.t")

generates the error:

org.apache.spark.SparkException: Job aborted due to stage failure: Task 0.0:73 failed 4 times,
most recent failure: Exception failure in TID 335 on host ip-172-31-31-54.us-west-2.compute.internal:
java.lang.NumberFormatException: For input string: ""
        java.lang.NumberFormatException.forInputString(NumberFormatException.java:65)
        java.lang.Integer.parseInt(Integer.java:504)
        java.lang.Integer.parseInt(Integer.java:527)
        scala.collection.immutable.StringLike$class.toInt(StringLike.scala:229)
        scala.collection.immutable.StringOps.toInt(StringOps.scala:31)
        org.apache.spark.mllib.util.MLUtils$$anonfun$4$$anonfun$5.apply(MLUtils.scala:81)
        org.apache.spark.mllib.util.MLUtils$$anonfun$4$$anonfun$5.apply(MLUtils.scala:79)
        scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
        scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
        scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
        scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
        scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
        scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108)
        org.apache.spark.mllib.util.MLUtils$$anonfun$4.apply(MLUtils.scala:79)
        org.apache.spark.mllib.util.MLUtils$$anonfun$4.apply(MLUtils.scala:76)
        scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
        scala.collection.Iterator$class.foreach(Iterator.scala:727)
        scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
        scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
        scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
        org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:107)
        org.apache.spark.rdd.RDD.iterator(RDD.scala:227)
        org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
        org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
        org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
        org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:111)
        org.apache.spark.scheduler.Task.run(Task.scala:51)
        org.apache.spark.executor.Executor$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)




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