[scikit-learn] Long term roadmap and moonshot goals

Piotr Szymański niedakh at gmail.com
Tue Jul 23 11:36:24 EDT 2019

If I could pitch in, it would be lovely, very lovely indeed, if
scikit-learn models could:

- operate on sparse data, both input and output by default
- implement some kind of sparse vector representation (as in
https://github.com/scikit-learn/scikit-learn/issues/8908 )
- perhaps have a unifiying numpy.array / scipy.sparse_matrix interface to
give people some slack on jumping betwen [] operator conventions

We would benefit from that strongly in scikit-multilearn, as when a
multi-output problem is transformed to a single-output problem based on
unique combinations, this representation has to be dense for scikit-learn
at the moment. We end up losing some speed there. I'm sure other libraries
like ex. imbalanced-learn, or scikit-multiflow would also see these as a
huge thing.


On Sun, Jul 14, 2019 at 8:44 PM Andreas Mueller <t3kcit at gmail.com> wrote:

> Hi all.
> At SciPy, Brian Granger raised a good point about their planning for the
> Jupyter Project, which is the importance of long-term goals.
> I think it's great that we now have a detailed short-term roadmap
> (https://scikit-learn.org/dev/roadmap.html).
> Given that we now have about 6(!) full time people (Oliver, Jeremy,
> Guillaume, Nicolas, Thomas, Adrin) on scikit-learn (GO TEAM!!), I think
> it's realistic
> to achieve most of these within a year or two. We have actually made
> some significant progress already.
> I think now would be a good time to start thinking about a longer-term
> roadmap, say 3-5 years out.
> What do we want to achieve? What are realistic goals, and what are
> moonshot goals?
> Having a common vision and shared goals might help us with funding, but
> might also help us with prioritization and motivation.
> What do you think? Do you think this is important and worth-while?
> And what should our goals be?
> Best,
> Andy
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Piotr Szymański
niedakh at gmail.com
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