[Mne_analysis] GSoC Idea, Improving decode module

[Asish Panda]

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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