[jira] [Resolved] (SPARK-17950) Match SparseVector behavior with DenseVector

["Hyukjin Kwon (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-17950?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon resolved SPARK-17950.
----------------------------------
    Resolution: Incomplete

> Match SparseVector behavior with DenseVector
> --------------------------------------------
>
>                 Key: SPARK-17950
>                 URL: https://issues.apache.org/jira/browse/SPARK-17950
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML, MLlib, PySpark
>    Affects Versions: 2.0.1
>            Reporter: AbderRahman Sobh
>            Priority: Minor
>              Labels: bulk-closed
>   Original Estimate: 0h
>  Remaining Estimate: 0h
>
> What changes were proposed in this pull request?
> Simply added the __getattr__ to SparseVector that DenseVector has, but calls to a SciPy sparse representation instead of storing a vector all the time in self.array
> This allows for use of functions on the values of an entire SparseVector in the same direct way that users interact with DenseVectors.
> i.e. you can simply call SparseVector.mean() to average the values in the entire vector.
> Note: The functions do have a slight bit of variance due to calling SciPy and not NumPy. However, the majority of useful functions (sums, means, max, etc.) are available to both packages anyways.
> How was this patch tested?
> Manual testing on local machine.
> Passed ./python/run-tests
> No UI changes.



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