# GSOC 2017 Idea Improve the Decoding Module. **URL:** \ **Category:** Mailing List Archive (read-only)\ **Tags:** list-archive\ **Created:** [March 3, 2017, 12:07pm UTC](https://mne.discourse.group/t/gsoc-2017-idea-improve-the-decoding-module/1185 "2017-03-03T12:07:56Z")\ **Posts on this page:** 4\ **Page:** 1
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [March 3, 2017, 12:07pm UTC](https://mne.discourse.group/t/gsoc-2017-idea-improve-the-decoding-module/1185/1 "2017-03-03T12:07:56Z")
Hello everyone, As I introduced my self earlier, I'm a pre-final year student majoring computer science from AMU, India. I have strong coding skills and proficient in Python, Cython and C++. I'm also a small contributor to Scikit Learn and know some of it's internal working. I am looking forward to take part in GSOC 2017, and I'm interested to work on idea- improve the decoding module. I have been looking into the code base and found that it is well structured that help me a lot in becoming comfortable with the code base. I will start contributing to MNE soon, but it will be excellent if anyone can point out some issue just for start. Some pointers will be really helpful to start working on this module. Lastly I apologize if I have been rude in any manner. Thanks Akshay Gupta -------------- next part -------------- An HTML attachment was scrubbed... URL: [http://mail.nmr.mgh.harvard.edu/pipermail/mne\_analysis/attachments/20170303/c5ade851/attachment.html](http://mail.nmr.mgh.harvard.edu/pipermail/mne_analysis/attachments/20170303/c5ade851/attachment.html) ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [March 3, 2017, 6:34pm UTC](https://mne.discourse.group/t/gsoc-2017-idea-improve-the-decoding-module/1185/2 "2017-03-03T18:34:58Z")
hi, thanks for you interest. I'd recommend to watch the project on github, read our tutorials (you'll certainly find typos or more). Maybe this : [https://github.com/mne-tools/mne-python/issues/2828](https://github.com/mne-tools/mne-python/issues/2828) is a good start. This one: [https://github.com/mne-tools/mne-python/issues/3201](https://github.com/mne-tools/mne-python/issues/3201) is also probably doable of you followed the tutorials. Best, Alex ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [March 4, 2017, 11:17am UTC](https://mne.discourse.group/t/gsoc-2017-idea-improve-the-decoding-module/1185/3 "2017-03-04T11:17:08Z")
Thanks for pointing out the issues. I will soon make my hand dirty with them. Regards Akshay Gupta ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [March 13, 2017, 1:47pm UTC](https://mne.discourse.group/t/gsoc-2017-idea-improve-the-decoding-module/1185/4 "2017-03-13T13:47:00Z")
Hello mentors, Let me share my views that I have after following some issues, tutorials, doc's etc. This project will involve clubing of different modules like enhance decoding objects to follow sklearn API, performance improvement in GAT. Follow sklearn API: Make GAT and EMS to use GridSearch and CrossValidation methods of Scikit Learn. In order to optimize memory and CPU time scoring and storing of y\_pred\_ do not follow scikit learn API which disallows GAT and EMS to use those methods. My idea is to design a wrapper aroung GridSearch and CrossValidation methods. So, instead of calling these methods directly from sklearn, they will be called by methods in MNE itself. Function of these wrappers function will be to handle input parameters and output return value of GAT and EMS to make them compatible with GridSearch and Cross Validation. Next coming to GAT optmization: Reference issue #2188 Idea is to initialize each classifier with coeff. found in preceding time thus reducing CPU time. warm\_start feature of classifier in scikit learn can allow us to do so. It has some difficulty like- -\> Classifier is cloned before fit which prevents reusing the same estimator. -\> Not all classifier in sklearn has warm\_start feature. It will be good if we divide this task into sub tasks and tackle each hurdle one by one. If everything goes right I will soon start writing pre-proposal. My request is to correct me if I'm wrong in anything. Suggestion are most welcomed. This is from my own understanding it will be excellent if someone can point out work that are renaming to be done here. Thank you Akshay Gupta