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From "Joseph E. Gonzalez (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-4130) loadLibSVMFile does not handle extra whitespace
Date Wed, 29 Oct 2014 07:09:33 GMT

     [ https://issues.apache.org/jira/browse/SPARK-4130?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Joseph E. Gonzalez updated SPARK-4130:
--------------------------------------
    Description: 
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:

```
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)
```

  was:
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)
```


> 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:
> {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:
> ```
> 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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