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From "AbderRahman Sobh (JIRA)" <>
Subject [jira] [Commented] (SPARK-17950) Match SparseVector behavior with DenseVector
Date Mon, 17 Oct 2016 23:50:00 GMT


AbderRahman Sobh commented on SPARK-17950:

Yes, the full array needs to be expanded since the numpy functions potentially need to operate
on every value in the array. There is room for another implementation that instead simply
mimics the numpy functions (and their handles) and provides smarter implementations for solving
means and such when using a SparseVector. If that is preferable, I can modify the code to
do that instead.

I also just realized that I am not 100% sure if the garbage collection works as I am expecting.
My assumption was that Python would automatically clean up after using the array, but since
it is technically inside of the object it might need another line to explicitly clear the
array out?

> Match SparseVector behavior with DenseVector
> --------------------------------------------
>                 Key: SPARK-17950
>                 URL:
>             Project: Spark
>          Issue Type: Improvement
>          Components: MLlib, PySpark
>    Affects Versions: 2.0.1
>            Reporter: AbderRahman Sobh
>            Priority: Minor
>   Original Estimate: 0h
>  Remaining Estimate: 0h
> Simply added the `__getattr__` to SparseVector that DenseVector has, but calls self.toArray()
instead of storing a vector all the time in self.array
> This allows for use of numpy functions on the values of a 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.

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