Github user itg-abby commented on the issue:
https://github.com/apache/spark/pull/15496
I have applied the code change to both ML and MLLIB now. And, I added some
simple tests to check if the SciPy sparse functions are behaving correctly.
(Only MLLIB has tests for SciPy functions so I only added test cases there).
Additionally, I updated the implementation with a wrapper script which 1)
allows for functions with inputs to work correctly and 2) seamlessly allows for
SciPy's functions which generate a SciPy matrix output to be automatically
returned as a SparseVector object.
Example use case:
```
c = SparseVector(40, {1: 1, 3: 2, 23: 7, 25:9, 39:3})
c.power(4)
SparseVector(40, {1: 1.0, 3: 16.0, 23: 2401.0, 25: 6561.0, 39: 81.0})
```
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