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Posted to issues@spark.apache.org by "Jeff Zhang (JIRA)" <ji...@apache.org> on 2015/12/03 01:34:11 UTC
[jira] [Closed] (SPARK-12092) StringIndexer failing with Unseen
label exception on test data
[ https://issues.apache.org/jira/browse/SPARK-12092?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Jeff Zhang closed SPARK-12092.
------------------------------
Resolution: Won't Fix
> StringIndexer failing with Unseen label exception on test data
> ---------------------------------------------------------------
>
> Key: SPARK-12092
> URL: https://issues.apache.org/jira/browse/SPARK-12092
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Affects Versions: 1.5.2
> Reporter: vishnu viswanath
> Priority: Minor
>
> StringIndexer fails with exception
> {code}
> Caused by: org.apache.spark.SparkException: Unseen label
> {code}
> StringIndexer which is *fit()* on train data, throws unseen label exception during *transform()* of test data, if the column in test data is having a value which was not seen during train stage.
> The workaround is to have the train data and test data before fitting the StringIndexer, which is not always practical.
> {code}
> org.apache.spark.SparkException: Unseen label: new_value
> at org.apache.spark.ml.feature.StringIndexerModel$$anonfun$4.apply(StringIndexer.scala:139)
> at org.apache.spark.ml.feature.StringIndexerModel$$anonfun$4.apply(StringIndexer.scala:134)
> at org.apache.spark.sql.catalyst.expressions.ScalaUDF$$anonfun$2.apply(ScalaUDF.scala:75)
> at org.apache.spark.sql.catalyst.expressions.ScalaUDF$$anonfun$2.apply(ScalaUDF.scala:74)
> at org.apache.spark.sql.catalyst.expressions.ScalaUDF.eval(ScalaUDF.scala:964)
> at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificMutableProjection.apply(Unknown Source)
> at org.apache.spark.sql.execution.Project$$anonfun$1$$anonfun$apply$2.apply(basicOperators.scala:55)
> at org.apache.spark.sql.execution.Project$$anonfun$1$$anonfun$apply$2.apply(basicOperators.scala:53)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
> at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
> at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
> at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
> at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
> at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
> at scala.collection.AbstractIterator.to(Iterator.scala:1157)
> at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
> at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
> at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
> at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
> at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:909)
> at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:909)
> at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1850)
> at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1850)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
> at org.apache.spark.scheduler.Task.run(Task.scala:88)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:745)
> {code}
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