They have a really nice workflow and API.
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
Hello Jean
Thank you very much for your response and the issues. I will get my hand dirty right away! 
Thank you
Asish Panda
Hi Asish,
Thanks for your interest!.
You can start with one of these easy PR:
BUG: GAT.plot_diagonal with predict_proba and AUC scoring fails b/c calculating chance levels doesn't work · Issue #2874 · mne-tools/mne-python · GitHub
ENH: GAT object should scorer strings as in sklearn.cross_val_score at all. · Issue #2176 · mne-tools/mne-python · GitHub
I/O for time gen class · Issue #2189 · mne-tools/mne-python · GitHub (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-R?mi
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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