Is it possible that after filtering the feature dimension changed?
This may happen if you use LIBSVM format but didn't specify the number
of features. -Xiangrui
On Tue, Dec 9, 2014 at 4:54 AM, Sameer Tilak ssti...@live.com wrote:
Hi All,
I was able to run LinearRegressionwithSGD for a largeer dataset ( 2GB
sparse). I have now filtered the data and I am running regression on a
subset of it (~ 200 MB). I see this error, which is strange since it was
running fine with the superset data. Is this a formatting issue (which I
doubt) or is this some other issue in data preparation? I confirmed that
there is no empty line in my dataset. Any help with this will be highly
appreciated.
14/12/08 20:32:03 WARN TaskSetManager: Lost TID 5 (task 3.0:1)
14/12/08 20:32:03 WARN TaskSetManager: Loss was due to
java.lang.ArrayIndexOutOfBoundsException
java.lang.ArrayIndexOutOfBoundsException: 150323
at
breeze.linalg.operators.DenseVector_SparseVector_Ops$$anon$129.apply(SparseVectorOps.scala:231)
at
breeze.linalg.operators.DenseVector_SparseVector_Ops$$anon$129.apply(SparseVectorOps.scala:216)
at breeze.linalg.operators.BinaryRegistry$class.apply(BinaryOp.scala:60)
at breeze.linalg.VectorOps$$anon$178.apply(Vector.scala:391)
at breeze.linalg.NumericOps$class.dot(NumericOps.scala:83)
at breeze.linalg.DenseVector.dot(DenseVector.scala:47)
at
org.apache.spark.mllib.optimization.LeastSquaresGradient.compute(Gradient.scala:125)
at
org.apache.spark.mllib.optimization.GradientDescent$$anonfun$runMiniBatchSGD$1$$anonfun$1.apply(GradientDescent.scala:180)
at
org.apache.spark.mllib.optimization.GradientDescent$$anonfun$runMiniBatchSGD$1$$anonfun$1.apply(GradientDescent.scala:179)
at
scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:144)
at
scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:144)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at
scala.collection.TraversableOnce$class.foldLeft(TraversableOnce.scala:144)
at scala.collection.AbstractIterator.foldLeft(Iterator.scala:1157)
at
scala.collection.TraversableOnce$class.aggregate(TraversableOnce.scala:201)
at scala.collection.AbstractIterator.aggregate(Iterator.scala:1157)
at org.apache.spark.rdd.RDD$$anonfun$21.apply(RDD.scala:838)
at org.apache.spark.rdd.RDD$$anonfun$21.apply(RDD.scala:838)
at org.apache.spark.SparkContext$$anonfun$23.apply(SparkContext.scala:1116)
at org.apache.spark.SparkContext$$anonfun$23.apply(SparkContext.scala:1116)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:111)
at org.apache.spark.scheduler.Task.run(Task.scala:51)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:187)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
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