I really want to push this approach, and hope I have time to establish
it early next year.
And I think that a zero-intersection approach would be better than a
fork, as it avoids incompatible changes.
On 12/03/2014 10:31 AM, Mathieu Blondel wrote:
As you mentioned popular methods from scikit-learn-contrib could be
promoted to scikit-learn.
Conversely, methods which became obsolete in scikit-learn could move
to scikit-learn-contrib to lower the maintenance burden.
Mathieu
On Thu, Dec 4, 2014 at 12:26 AM, Mathieu Blondel <math...@mblondel.org
<mailto:math...@mblondel.org>> wrote:
Hi Satra,
I can't find the link but there was a discussion some time ago
about creating a scikit-learn-contrib organization on github for
this purpose.
Two differences with what you suggest:
1) this wouldn't be a fork, i.e., the intersection with
scikit-learn would be empty
2) we were thinking of creating repositories for different
sub-topics (multilabel classification, kernel approximations, etc)
2) might require too much work in terms of making releases so a
global scikit-learn-contrib might be more realistic.
scikit-learn-contrib would have its own website
http://contrib.scikit-learn.org.
There would still be some work involved for minimal reviewing and
releasing, though.
Mathieu
On Wed, Dec 3, 2014 at 11:56 PM, Satrajit Ghosh <sa...@mit.edu
<mailto:sa...@mit.edu>> wrote:
hi folks,
since this comes up from time to time and i completely
understand the needed focus and limited resources within
scikit-learn, how about the following approach:
- let the community (to put zero additional burden on the
current maintainers) maintain a fork of scikit-learn that
provides no guarantees other than it is kept upto date with
scikit-learn/master.
- people are welcome to add any algorithms to this (trivial,
non-trivial, recent)
- if things prove useful within this branch/fork/labs they can
be incorporated into the main stream through the current
standard PR mechanism
people will use it at their own discretion, but what it would
allow is for people to have a single place within which to toy
with things while still maintaining the core benefits of
scikit-learn.
with the different kinds of data (types and size) coming
online, algorithm development has gone in many different
directions. some variants are on speed/hardware, others on
generalizability, yet others on domain specific apps, etc.,..
what works in one domain/app may completely fail in another.
the hope here is that this fork would let interested people
toy with this developmental eco-system as opposed to the
stable maintained ecosystem. the key advantages of having a
fork are that:
- folks don't have to recreate packaging
- it brings all the folks who are forking anyway together
instead of splitting off into forks (multiple forks are harder
to use)
- it makes for increased availability of algorithms that may
be useful in practice but never makes it out because the world
is biased towards loudspeakers
- it doesn't add anything to the current maintainers plates,
nor take away anything from the main project. perhaps those
wishing to add things will take it upon themselves to maintain
this fork.
- and if you find that more people are using this fork rather
than the mainstream (that might tell you something about the
current culture of science and engineering in practice).
- there might be fixes that can be incorporated into master
coming into this fork because more people end up toying within it
- if this fork goes bust, nobody cares.
you could even call the fork:
scikit-learn-minefield
scikit-learn-teenage-mutants
...
scikit-learn-labs
cheers,
satra
On Wed, Dec 3, 2014 at 5:25 AM, Joel Nothman
<joel.noth...@gmail.com <mailto:joel.noth...@gmail.com>> wrote:
I agree. We should ammend this sentence to say that if
the paper is an
clear-cut improvement on top of a very used method, it
should be
examinded.
Done <http://scikit-learn.org/dev/faq.html>.
On 3 December 2014 at 20:07, Gael Varoquaux
<gael.varoqu...@normalesup.org
<mailto:gael.varoqu...@normalesup.org>> wrote:
On Wed, Dec 03, 2014 at 06:04:58PM +0900, Mathieu
Blondel wrote:
> I think 1000 citations is a bit too much to ask. We
should probably
> update the FAQ with something more reasonable, like
say 200 citations.
> That said, I agree that the citation threshold is
just an indicator.
> For example, SAG and AdaGrad, which are considerely
considered for
> inclusion, have around 75 and 250 citations currently.
I agree. We should ammend this sentence to say that if
the paper is an
clear-cut improvement on top of a very used method, it
should be
examinded.
> Perhaps scikit-learn needs to strengthen and
formalise its support
> for external related projects that adopt its API
design to
> implement less established techniques. The listing
> at https://github.com/scikit-learn/
>
scikit-learn/wiki/Third-party-projects-and-code-snippets
lacks
> glamour, and could be easier to find and navigate.
> +1
+1
> We need to bring this page to the main documentation
and make it more sexy.
Good with me.
G
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