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https://issues.apache.org/jira/browse/SPARK-9277?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Manoj Kumar updated SPARK-9277:
-------------------------------
Labels: starter (was: )
> SparseVector constructor must throw an error when declared number of elements
> less than array lenght
> ----------------------------------------------------------------------------------------------------
>
> Key: SPARK-9277
> URL: https://issues.apache.org/jira/browse/SPARK-9277
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Affects Versions: 1.3.1
> Reporter: Andrey Vykhodtsev
> Priority: Minor
> Labels: starter
> Attachments: SparseVector test.html, SparseVector test.ipynb
>
>
> I found that one can create SparseVector inconsistently and it will lead to
> an Java error in runtime, for example when training LogisticRegressionWithSGD.
> Here is the test case:
> In [2]:
> sc.version
> Out[2]:
> u'1.3.1'
> In [13]:
> from pyspark.mllib.linalg import SparseVector
> from pyspark.mllib.regression import LabeledPoint
> from pyspark.mllib.classification import LogisticRegressionWithSGD
> In [3]:
> x = SparseVector(2, {1:1, 2:2, 3:3, 4:4, 5:5})
> In [10]:
> l = LabeledPoint(0, x)
> In [12]:
> r = sc.parallelize([l])
> In [14]:
> m = LogisticRegressionWithSGD.train(r)
> Error:
> Py4JJavaError: An error occurred while calling
> o86.trainLogisticRegressionModelWithSGD.
> : org.apache.spark.SparkException: Job aborted due to stage failure: Task 7
> in stage 11.0 failed 1 times, most recent failure: Lost task 7.0 in stage
> 11.0 (TID 47, localhost): java.lang.ArrayIndexOutOfBoundsException: 2
> Attached is the notebook with the scenario and the full message
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