Thanks your method is way better than my rudimentary way; I feel like a newbie, I will definitely do it your way.
Just another thing after running the analysis, how can I get the predictions for each trial?, I'm interested on the actual prediction, not if it was accurate or not On Wed, Sep 23, 2015 at 9:49 PM, Yaroslav Halchenko <[email protected]> wrote: > > On Wed, 23 Sep 2015, Raúl Hernández wrote: > > > Hi, I’m trying to evaluate on trial by trial basis how well a region can > > predict the stimulus being presented to compare it with the participant’s > > judgment of the stimulus. So I’m training the classifier with data from > all the > > trials on all the runs except by the one that I want to predict. > > > > I’m getting really good classifications better than when I was > predicting one > > run using all the others. Supposedly it should be a little better as I’m > > training with a little more data but I’m worried I’m doing something > wrong. > > > > Could anyone let me know if I’m making some sort of mistake? > > > > I know that there should be a more efficient way to do it but I wanted > > something easy, this is my code: > > > > predictions = [] #this is a vector that will contain the predictions of > the > > classifier > > > for i,dsTest in enumerate(ds): #go through all the trials on ds and > separate > > one to test > > > clf = LinearCSVMC() > > > fclf = FeatureSelectionClassifier(clf, fsel) > > > dsTrain = [] > > > dsTrain.append(ds[0:i]) #separates the training data > > > dsTrain.append(ds[i:-1]) > > minor but note that :-1 would select all but last > > $> python -c 'print range(2)[:-1]' > [0] > > you didn't have to do manual splitting but could've simply assigned some > attribute like > > ds.sa['trials'] = np.arange(len(ds)) > > and made use of NFoldPartitioner(attr='trials') and then > CrossValidation... all standard stuff > > back to more optimistic results, as Jo pointed out, to carry out most > trustworthy analysis you should have trained/cross-validated across runs. > Also ds.summary() output last tables could provide you some related > information on trial orders ... which could also contribute to "optimistic" > result (depending on the output of cause.. ;) ) > -- > Yaroslav O. Halchenko > Center for Open Neuroscience http://centerforopenneuroscience.org > Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755 > Phone: +1 (603) 646-9834 Fax: +1 (603) 646-1419 > WWW: http://www.linkedin.com/in/yarik > > _______________________________________________ > Pkg-ExpPsy-PyMVPA mailing list > [email protected] > http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa >
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