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From Xiangrui Meng <men...@gmail.com>
Subject Re: MLLIb: Linear regression: Loss was due to java.lang.ArrayIndexOutOfBoundsException
Date Mon, 15 Dec 2014 19:52:05 GMT
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 <sstilak@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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