[Mne_analysis] GSoC Idea, Improving decode module

JR KING jeanremi.king at gmail.com
Mon Mar 14 09:39:53 EDT 2016
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Thanks Asish.

It's good overall. I added some corrections.

Hope that helps,

JR

On 14 March 2016 at 03:38, Asish Panda <asishrocks95 at gmail.com> wrote:

> Hello everyone
>
> Thank you for explaining me the details. Based on that and the original
> idea I have drafted an initial proposal. Please give me your reviews and
> let me know if I am understanding your points correctly. You can check out
> the
> project details section in the wiki page
> <https://github.com/kaichogami/mne-python/wiki/GSoC-Proposal#project-detail>
> .
>
> Thank you
> Asish Panda
>
> On Fri, Mar 11, 2016 at 6:06 AM, Phillip Alday <Phillip.Alday at unisa.edu.au
> > wrote:
>
>> Hi guys,
>>
>> PyMVPA might be a good place to look for inspiration and maybe
>> integration: http://www.pymvpa.org/
>>
>> They have a really nice workflow and API.
>>
>> Best,
>> Phillip
>>
>>
>> > On 11 Mar 2016, at 08:57, JR KING <jeanremi.king at gmail.com> wrote:
>> >
>> > Hi Asish,
>> >
>> > As Denis said, the decoding module is one possible target. Just FYI,
>> there are other possibilities too: e.g. across-subjects stats and viz isn't
>> really well developed/documented.
>> >
>> > Currently the decoding classes have been developed separately, by
>> different authors and with different architectures. IMO, one great goal
>> would thus be to
>> >
>> > 1. (hard) homogenize the existing functions so that they all become
>> strictly compatible with sklearn (i.e, based on BaseEstimator_, using fit,
>> transform, predict and score methods).
>> >
>> > 2. (medium-hard): develop transformer objects that would ultimately
>> allow the users to pipe multiple processing steps: e.g. we typically aim at
>> getting:
>> > make_pipeline(TimeFreq(), InverseTransform(), DataVectorizer(),
>> LogisticRegression())
>> > or
>> > make_pipeline(Filter(10, 30), Covariances(method='shrunk'),
>> Xdawn(n_components=4), TangentSpace(), SVM(kernel='linear'))
>> >
>> > for which all the steps could be typically initialized with inst.info
>> and would take an X and a y to be fitted/predicted/scored.
>> >
>> > 3. (easy) Setup a systematic i/o to store the estimators, the
>> predictions and the scores.
>> >
>> > As a concrete example, to optimize memory and CPU, the GAT currently
>> stores the predictions (y_pred_) in the object, and the scoring approach is
>> performed outside the CV. This storing and scoring isn't following sklearn
>> API. Consequently, one cannot use cross_val_score(GAT). Typically
>> refactoring this kind of feature requires some deep thinking because,
>> unlike sklearn, several decoding module are applied in a "mass
>> multivariate" way: i.e. many multivariate models are fitted on
>> independent/partially common/or even identical data. Optimizing memory and
>> CPU is thus probably the main challenge here.
>> >
>> > I would consequently start by tackling the easy/medium problem first
>> (e.g. i/o in all decoding classes, vizualizing the fitted weights/patterns
>> for each decoding method), and see how we can develop some transformers,
>> such as EpochVectorizer, that would be common across decoding modules to
>> format.
>> >
>> > Hope this helps,
>> >
>> >
>> > JR
>> >
>> >
>> > In summary, this project will involve a series of usability
>> improvements for the decoding module and extend its functionality.
>> > I feel the above statement is quite vague for writing a detailed plan
>> in the proposal. Or perhaps the "improvements" can only be known while the
>> objectives(listed above) are being fulfilled?
>> > Lastly, going a little out of topic, could you now please elaborate on
>> how to set up the cleaner framework of the decoding module, that you
>> mentioned in the last message?
>> >
>> > Thank you
>> > Asish Panda
>> >
>> > On Fri, Feb 26, 2016 at 9:50 PM, Asish Panda <asishrocks95 at gmail.com>
>> wrote:
>> > Hello Jean
>> >
>> > Thank you very much for your response and the issues. I will get my
>> hand dirty right away! :)
>> >
>> > Thank you
>> > Asish Panda
>> >
>> > On Fri, Feb 26, 2016 at 8:37 PM, JR KING <jeanremi.king at gmail.com>
>> wrote:
>> > Hi Asish,
>> >
>> > Thanks for your interest!.
>> >
>> > You can start with one of these easy PR:
>> > https://github.com/mne-tools/mne-python/issues/2874
>> > https://github.com/mne-tools/mne-python/issues/2176
>> > https://github.com/mne-tools/mne-python/issues/2189 (probably needs a
>> bit of discussion)
>> >
>> > Once you're there I can suggest you some more fun things that you could
>> do to set up a cleaner framework for the decoding module.
>> >
>> > All the best,
>> >
>> > Jean-Rémi
>> >
>> > On 26 February 2016 at 09:57, Asish Panda <asishrocks95 at gmail.com>
>> wrote:
>> > Hello everyone,
>> >
>> > I am looking forward to participate in GSoC and I am interested in the
>> idea of improving the decode module. I have installed and set up the
>> development environment and have been trying to get familiar with various
>> modules. However being quite new to MEG, EEG I'm looking for some pointers
>> to start as well as prerequisites to work on decode module.
>> > Lastly I apologize if I have been rude in any manner.
>> >
>> > Thank you
>> > Asish Panda
>> >
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