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Can you give an example?<br>
<br>
I imagine that just supporting the data structure will not give you
any speed benefit unless the algorithms are reimplemented to take
advantage of the problem structure.<br>
Even if the output of logistic regression would be a sparse binary
vector, you'd still need to compute every entry, which would be the
slow part.<br>
<br>
<br>
<br>
<div class="moz-cite-prefix">On 7/23/19 10:36 AM, Piotr Szymański
wrote:<br>
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<blockquote type="cite"
cite="mid:CAOMdbFDM3thhjziccmTX0GGphHpz4svqF7fGGDZHd4QQgk5NTw@mail.gmail.com">
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<div>If I could pitch in, it would be lovely, very lovely
indeed, if scikit-learn models could:</div>
<div><br>
</div>
<div>- operate on sparse data, both input and output by default</div>
<div>- implement some kind of sparse vector representation (as
in <a
href="https://github.com/scikit-learn/scikit-learn/issues/8908"
moz-do-not-send="true">https://github.com/scikit-learn/scikit-learn/issues/8908</a>
)<br>
</div>
<div>- perhaps have a unifiying numpy.array /
scipy.sparse_matrix interface to give people some slack on
jumping betwen [] operator conventions</div>
<div><br>
</div>
<div>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.</div>
<div><br>
</div>
<div>Best,</div>
<div>Piotr<br>
</div>
<div><br>
</div>
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<div class="gmail_quote">
<div dir="ltr" class="gmail_attr">On Sun, Jul 14, 2019 at 8:44
PM Andreas Mueller <<a href="mailto:t3kcit@gmail.com"
moz-do-not-send="true">t3kcit@gmail.com</a>> wrote:<br>
</div>
<blockquote class="gmail_quote" style="margin:0px 0px 0px
0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">Hi
all.<br>
At SciPy, Brian Granger raised a good point about their
planning for the <br>
Jupyter Project, which is the importance of long-term goals.<br>
<br>
I think it's great that we now have a detailed short-term
roadmap <br>
(<a href="https://scikit-learn.org/dev/roadmap.html"
rel="noreferrer" target="_blank" moz-do-not-send="true">https://scikit-learn.org/dev/roadmap.html</a>).<br>
Given that we now have about 6(!) full time people (Oliver,
Jeremy, <br>
Guillaume, Nicolas, Thomas, Adrin) on scikit-learn (GO
TEAM!!), I think <br>
it's realistic<br>
to achieve most of these within a year or two. We have
actually made <br>
some significant progress already.<br>
<br>
I think now would be a good time to start thinking about a
longer-term <br>
roadmap, say 3-5 years out.<br>
What do we want to achieve? What are realistic goals, and what
are <br>
moonshot goals?<br>
Having a common vision and shared goals might help us with
funding, but <br>
might also help us with prioritization and motivation.<br>
<br>
What do you think? Do you think this is important and
worth-while?<br>
And what should our goals be?<br>
<br>
Best,<br>
Andy<br>
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<br>
-- <br>
<div dir="ltr" class="gmail_signature">
<div dir="ltr">
<div><font face="georgia, serif">Piotr Szymański<br>
<a href="mailto:niedakh@gmail.com" target="_blank"
moz-do-not-send="true">niedakh@gmail.com</a></font></div>
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