Hi, guys. My name is Milton Pividori and this is the first time I write to
this list. I'm a PhD student, working on clustering, particularly on
consensus clustering. I'm relatively new to Python, and I am migrating
legacy code from MATLAB. I plan to use scikit-learn as well as other
libraries.
After looking at the scikit code and the mailing list, I didn't found any
methods related to consensus clustering or cluster ensembles. I think the
main paper about it is the one from Strehl and Ghosh (2002, JMLR, link
<http://www.jmlr.org/papers/volume3/strehl02a/strehl02a.pdf>). I don't know
if you discussed about it before, but I think it could be a good idea to
have these consensus functions implemented in scikit-learn (the paper
proposes three, graph-based).
I was thinking on how to implement them. These three consensus functions
(CSPA, HGPA and MCLA) use METIS for graph partitioning. That could be an
obstacle for scikit-learn interests, as a new dependency would be needed (I
found python bindings for it). It would be also necessary to implement some
methods for ensemble generation with varying levels of diversity
(generating different clustering partitions by varying algorithms, changing
their parameters or manipulating data with projections, subsampling or
feature selection), but that's easier than implementing the consensus
functions.
Well, it's just an idea. I would be glad to help with coding if this is
interesting for the community.
Regards,
2015-02-12 13:38 GMT-03:00 Sebastian Raschka <se.rasc...@gmail.com>:
> What about adding multiclass support for the SVC "roc_auc" for grid search
> CV to the to do list?
>
> Best,
> Sebastian
>
> On Feb 12, 2015, at 10:12 AM, Ronnie Ghose <ronnie.gh...@gmail.com> wrote:
>
> +1 to partial fit -1 to gam and more probabilistic things in sklean
>
> On Thu, Feb 12, 2015, 9:22 AM ragv ragv <rag...@gmail.com> wrote:
>
>> Hi,
>>
>> Is there a good deal of interest in having GAMs implemented?
>>
>> The timeline for such a project would go something like :
>>
>> Before GSoC:
>> * Implement SpAM
>>
>> Before Midterm :
>> * Help merge pyearth into scikit learn
>> * Implement Additive Model -> `AdditiveClassifier` /
>> `AdditiveRegressor` ( Not sure if my wording here is correct )
>>
>> After Midterm :
>> * Implement GAMLSS
>> * Implement LISO
>>
>> Kindly also see
>> https://github.com/scikit-learn/scikit-learn/issues/3482 for
>> references with citation counts.
>>
>> The package mgcv by Simon Woods / GAM / BAM in CRAN is mature and
>> could be used as reference material too...
>>
>> On a scale of 0 to 100 could I know how much importance / interest
>> would there be in such a project for GSoC 2015?
>>
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>
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> your
> hub for all things parallel software development, from weekly thought
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--
Milton Pividori
Blog: www.miltonpividori.com.ar
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Dive into the World of Parallel Programming. The Go Parallel Website,
sponsored by Intel and developed in partnership with Slashdot Media, is your
hub for all things parallel software development, from weekly thought
leadership blogs to news, videos, case studies, tutorials and more. Take a
look and join the conversation now. http://goparallel.sourceforge.net/
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