[Mne_analysis] GSoC Idea, Improving decode module

Asish Panda asishrocks95 at gmail.com
Thu Mar 17 02:25:01 EDT 2016
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Thank you for responding. I am afraid I didn't get any invitation. Could
you kindly re-send it or perhaps share a link here?

Asish Panda

On Thu, Mar 17, 2016 at 2:48 AM, Denis-Alexander Engemann <
denis.engemann at gmail.com> wrote:

> Hi Ashish,
>
> we totally agree with you. With Jean-Rémi we just set up a main project
> and API proposal. See dropbox paper invitation. Let's start a private
> discussion over the next days based on that draft.
>
> Denis
>
>
>
> On Wed, Mar 16, 2016 at 8:31 PM Asish Panda <asishrocks95 at gmail.com>
> wrote:
>
>> Hi,
>>
>> I have been thinking about the priorities of tasks, and I feel that the
>> visualization can done after gsoc. As the main aim is to first make
>> decoding more compatible with scikit-learn, we could also probably shift
>> i/o task for later.
>> Let me know what you guys think. We can discuss this here, gitter, or
>> over hangouts, whichever is more suitable for you guys.
>>
>> Thank you
>>
>> Asish Panda
>>
>> On Tue, Mar 15, 2016 at 11:20 AM, Asish Panda <asishrocks95 at gmail.com>
>> wrote:
>>
>>>
>>>
>>> On Mon, Mar 14, 2016 at 7:21 PM, Denis-Alexander Engemann <
>>> denis.engemann at gmail.com> wrote:
>>>
>>>> Looks also good to me.
>>>>
>>>> At the same time it's ambitions, which is great! We should, however,
>>>> see what are the most important goals such that the rest can be seen as
>>>> nice to have additions but won't determine your overall GSOC success.
>>>> One tiny remark: we should not go use pickling for persistence for
>>>> several reasons. Short: it's not made for long-lived persistence and will
>>>> break. We will rather look into something like saving the estimator
>>>> attributes and their constructor parameters into hd5 files and
>>>> re-instantiate the objects based on this information.
>>>> We might need a hangoug or skype together to decide about the
>>>> priorities of all the elements you listed.
>>>>
>>> I agree. We should have priorities sorted out. When would be a good idea
>>> to have a hangout? I am usually free after 10pm, +5:30GMT.
>>>
>>>>
>>>> --Denis
>>>>
>>>>
>>>>
>>>>
>>>> On Mon, Mar 14, 2016 at 2:40 PM JR KING <jeanremi.king at gmail.com>
>>>> wrote:
>>>>
>>>>> Thanks Asish.
>>>>>
>>>>> It's good overall. I added some corrections.
>>>>>
>>>> Thank you for your help! :)
>>>
>>>>
>>>>> 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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