On Sat, Oct 27, 2018 at 6:10 AM Hameer Abbasi <einstein.edison@gmail.com> wrote:
Hi Stefan!

PyData/Sparse is pretty far along, by January or so we should have a CSR/CSC replacement that is ND. It needs optimisation in a lot of cases but the API is compatible with NumPy and works pretty well already IMO.

PyData/Sparse is pretty much independent of any changes to scipy.sparse at this point. We build on top of NumPy, not scipy.sparse.

Feel free to use any or all of my code for sparray, although I think Ralf Gommers, Matthew Rocklin and others were of the opinion that the data structure should stay in PyData/Sparse and linear algebra and csgraph etc should go into SciPy.

Just to make sure we're talking about the same things here: Stefan, I think with "sparray" you mean "an n-D sparse array implementation that lives in SciPy", nothing more specific? In that case pydata/sparse is the one implementation, and including it in scipy.sparse would make it "sparray". I'm currently indeed leaning towards depending on pydata/sparse rather than including it in scipy.

Cheers,
Ralf



Best Regards,
Hameer Abbasi

On Friday, Oct 26, 2018 at 7:03 PM, Stefan van der Walt <stefanv@berkeley.edu> wrote:
Hi Hameer,

On Fri, 26 Oct 2018 10:47:09 +0200, Hameer Abbasi wrote:
The only core functionality dependent on scipy.sparse is matrix
multiplication and the like. Everything else is for inter-operability.

Thank you for commenting here.

As you know, I am enthusiastic about seeing an `sparray` equivalent to
`spmatrix`. When we last spoke, my recollection was that it would be
beneficial to `pydata/sparse`. Is this still correct?

If not, are we now in a situation where it would be more helpful to
build `sparray` based on `pydata/sparse`.

If we can have a good sparse array API in place in SciPy, it may
significantly simplify code in various other libraries (I'm thinking of
scikit-learn, e.g.).

Best regards,
Stéfan
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