Hi, all,
Congratulations on the acceptance of your GSoC proposal!
I'm a Master student from Peking University, and I have a great interest on
scikit-learn because of frequency use.
And I also want to contribute to the community, or try to fix some bugs.
Could you count me in?
By the way, I have met a problem, Unlike most other scores, R^2 score may
be negative (it need not actually be the square of a quantity R).
In my understanding, R^2 could be thought as the square of Correlation
coefficient thus R^2 can't be negative. Is there something wrong, who
can tell me?
Thanks,
Yao.
At 2015-05-04 19:02:36, "Michael Eickenberg" <michael.eickenb...@gmail.com>
wrote:
Dear Artem,
congratulations on the acceptance of your GSoC proposal! I am certain there
will be a very interesting summer ahead of us. Kyle and I are excited to be
mentors and will do our best to provide all the guidance necessary for your
project to succeed. It is very rich and will be a great addition to the
codebase.
Your blog post on the gists of the methods is written in a very understandable
way and permits a good overview of the topics you are going to address in
depth. It shows that you have the right intuitions, and are ready to delve into
the intricacies of the methods [1]. Take advantage of the next weeks to do so!
Let's make sure we hit the ground running at the end of this warm-up phase.
As for your next plans, sketching the algorithms in very high level pseudo-code
is of course an excellent idea and can be a next blog post.
After this, you can zoom in on the details of how each pseudo-code step can be
implemented. If you get the level of detail right, I recommend the Python
language to describe your algorithms ;) -- what I mean is that getting a
minimal version of the algorithm to work, just as a function, not a sklearn
estimator, is a valuable baseline to have, and it usually deepens the
understanding as well.
As for the API questions, it is of course quite essential to remain conscious
at all times of the issues that have been identified in prior discussion and to
think of ways to add a metric learning module without succumbing to excessive
feature creep. My hunch is that given some working minimal versions of the
algorithms, we can perhaps crystallize out what is absolutely necessary in
terms of additions, so I would prefer that order of priorities. There is also
some work to be done in identifying other parts of scikit-learn that already
deal with (dis-)similarity type data (cf eg the kernels defined in the PR for
gaussian processes) and see how these can be made to work in a consistent way.
A crucial aspect that we need to figure out is "what is a good outcome?": Of
course we would like to have some PRs merged at the end of summer, yes. But
what makes a concrete implementation good? Speed (with respect to what)?
Readability? Maintainability (yes please!)? Elegance (what does that even
mean?)?
It may be helpful if you could devise a more fine-grained timeline for the
community bonding period than what is currently stated on the wiki page. How
about blogging your progress in understanding? Writing things down for others
to understand is a very good way of identifying any doubts you may have on
particular aspects. A mini blog-entry at the end of each week simply recounting
what has been done and also what has been discussed will also yield an
effective overview of ongoing topics.
In the meantime, please don't hesitate to bombard us, the other mentors, and
the list with any questions you may have.
Michael
[1] http://i.imgur.com/at3vIHh.png
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