Bigdata Machinelearning requirement failed: Invalid initial capacity

[Anonymous]

Hi friends,
plz help me, i am trying to create a logisticregression model, when i ran the job i am facing the error saying:
java.lang.IllegalArgumentException: requirement failed: Invalid initial capacity
at scala.Predef$.require(Predef.scala:233)
at org.apache.spark.util.collection.OpenHashSet.<init>(OpenHashSet.scala:50)
at org.apache.spark.util.collection.OpenHashSet.<init>(OpenHashSet.scala:56)
at org.apache.spark.util.collection.OpenHashMap$mcD$sp.<init>(OpenHashMap.scala:41)
at org.apache.spark.ml.feature.StringIndexerModel.<init>(StringIndexer.scala:127)
at org.apache.spark.ml.feature.StringIndexer.fit(StringIndexer.scala:88)
at org.apache.spark.ml.feature.StringIndexer.fit(StringIndexer.scala:67)
at org.apache.spark.ml.Pipeline$$anonfun$fit$2.apply(Pipeline.scala:144)
at org.apache.spark.ml.Pipeline$$anonfun$fit$2.apply(Pipeline.scala:140)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at scala.collection.IterableViewLike$Transformed$class.foreach(IterableViewLike.scala:42)
at scala.collection.SeqViewLike$AbstractTransformed.foreach(SeqViewLike.scala:43)
at org.apache.spark.ml.Pipeline.fit(Pipeline.scala:140)
at org.apache.spark.ml.Pipeline.fit(Pipeline.scala:91)
at org.apache.spark.ml.Pipeline$$anonfun$fit$2.apply(Pipeline.scala:144)
at org.apache.spark.ml.Pipeline$$anonfun$fit$2.apply(Pipeline.scala:140)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at scala.collection.IterableViewLike$Transformed$class.foreach(IterableViewLike.scala:42)
at scala.collection.SeqViewLike$AbstractTransformed.foreach(SeqViewLike.scala:43)
at org.apache.spark.ml.Pipeline.fit(Pipeline.scala:140)

  

[Anonymous]

Hi,
Could you please post your job setting screenshots into forum which will be helpful for us to address your issue?
Best regards
Sabrina

[Anonymous]

Here is the screenshot of Spark Configuration.