[jira] [Assigned] (SPARK-9277) SparseVector constructor must throw an error when declared number of elements less than array length

["Apache Spark (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-9277?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-9277:
-----------------------------------

    Assignee:     (was: Apache Spark)

> SparseVector constructor must throw an error when declared number of elements less than array length
> ----------------------------------------------------------------------------------------------------
>
>                 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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