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Posted to issues@spark.apache.org by "Xiangrui Meng (JIRA)" <ji...@apache.org> on 2014/10/30 08:07:33 UTC
[jira] [Resolved] (SPARK-4130) loadLibSVMFile does not handle extra
whitespace
[ https://issues.apache.org/jira/browse/SPARK-4130?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Xiangrui Meng resolved SPARK-4130.
----------------------------------
Resolution: Fixed
Fix Version/s: 1.2.0
Issue resolved by pull request 2996
[https://github.com/apache/spark/pull/2996]
> 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
> Assignee: Joseph E. Gonzalez
> Fix For: 1.2.0
>
>
> 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:
> {code:scala}
> 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")
> {code}
> generates the error:
> {code}
> 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)
> {code}
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