{"action":"created","issue":{"url":"https://api.github.com/repos/alexandrebarachant/pyRiemann/issues/9","labels_url":"https://api.github.com/repos/alexandrebarachant/pyRiemann/issues/9/labels{/name}","comments_url":"https://api.github.com/repos/alexandrebarachant/pyRiemann/issues/9/comments","events_url":"https://api.github.com/repos/alexandrebarachant/pyRiemann/issues/9/events","html_url":"https://github.com/alexandrebarachant/pyRiemann/issues/9","id":88696435,"number":9,"title":"Q: projecting single time point on tangent space?","user":{"login":"kingjr","id":4881164,"avatar_url":"https://avatars.githubusercontent.com/u/4881164?v=3","gravatar_id":"","url":"https://api.github.com/users/kingjr","html_url":"https://github.com/kingjr","followers_url":"https://api.github.com/users/kingjr/followers","following_url":"https://api.github.com/users/kingjr/following{/other_user}","gists_url":"https://api.github.com/users/kingjr/gists{/gist_id}","starred_url":"https://api.github.com/users/kingjr/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/kingjr/subscriptions","organizations_url":"https://api.github.com/users/kingjr/orgs","repos_url":"https://api.github.com/users/kingjr/repos","events_url":"https://api.github.com/users/kingjr/events{/privacy}","received_events_url":"https://api.github.com/users/kingjr/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"milestone":null,"comments":11,"created_at":"2015-06-16T11:18:07Z","updated_at":"2015-06-18T14:32:46Z","closed_at":"2015-06-16T19:56:14Z","body":"Hi, \r\n\r\nI'm trying to play with your code.\r\n\r\nI was wondering whether it would be in principle possible to project a single time point (Nchan x 1) on the tangent space. I was thinking of fitting `TangentSpace` on the complete epochs, but then assess a series of svm on each time point separately in the tangent space. Although I'm not sure this makes sense at all..\r\n\r\nThanks!"},"comment":{"url":"https://api.github.com/repos/alexandrebarachant/pyRiemann/issues/comments/113173815","html_url":"https://github.com/alexandrebarachant/pyRiemann/issues/9#issuecomment-113173815","issue_url":"https://api.github.com/repos/alexandrebarachant/pyRiemann/issues/9","id":113173815,"user":{"login":"alexandrebarachant","id":2145718,"avatar_url":"https://avatars.githubusercontent.com/u/2145718?v=3","gravatar_id":"","url":"https://api.github.com/users/alexandrebarachant","html_url":"https://github.com/alexandrebarachant","followers_url":"https://api.github.com/users/alexandrebarachant/followers","following_url":"https://api.github.com/users/alexandrebarachant/following{/other_user}","gists_url":"https://api.github.com/users/alexandrebarachant/gists{/gist_id}","starred_url":"https://api.github.com/users/alexandrebarachant/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/alexandrebarachant/subscriptions","organizations_url":"https://api.github.com/users/alexandrebarachant/orgs","repos_url":"https://api.github.com/users/alexandrebarachant/repos","events_url":"https://api.github.com/users/alexandrebarachant/events{/privacy}","received_events_url":"https://api.github.com/users/alexandrebarachant/received_events","type":"User","site_admin":false},"created_at":"2015-06-18T14:32:46Z","updated_at":"2015-06-18T14:32:46Z","body":"This is usually the other way around. AUC give higher results, since the accuracy can be influenced by the offset on the prediction probabilities. This especially the case for high dimentional data with a big covariate shift between training and test.\r\n\r\nWhen i switch the scoring to the auc ( `scoring='roc_auc` in `cross_val_score`) i get this \r\n\r\n![figure_1](https://cloud.githubusercontent.com/assets/2145718/8233620/46fda0e2-15a5-11e5-80c3-ef7d7d86e9d3.png)\r\n\r\nHowever, as compared to single time point classification, results are inflated since we use a bigger time window (it contains more information).\r\n\r\nAre you sure `reshape_X` is not messing with the signal ?\r\n\r\nIf you want to speed up the computation, use the parameter `TangentSpace(metric='logeuclid')`, it should not degrades performances too much.\r\n\r\nFinally, your use case is really borderline for the Riemannian Geometry. You are estimating covariance matrices with a really small number of samples, and we have to rely on a heavy regularisation to make it works.  In addition, i did not recommand using RG when you have a number of channel > ~60. For EEG, the noise generally degrades performances if you have too many channel. I'm quite surprised it run with 200 channels. to be honnest, i never tryed this. I guess MEG data are clean enough to make it works"}}