Thanks so much Gavin for the useful advise I'll give it a go! cheers ds
On Wed, Jul 16, 2014 at 4:24 AM, Hanson, Gavin Keith <[email protected]> wrote: > I don’t think your rig, as it’s set up, will do what you want. > I attempted to do something similar in my own work, and I think I have a > solution for you. > > First, merge your two tasks together into 1 bold image > fslmerge -t bold_taska.nii.gz bold_taskb.nii.gz bold_taskab.nii.gz > Also double up your attr.txt, so it corresponds to the new double-length > bold image > Now you have a dataset with shape (608, whatever) > ds=fmri_dataset(samples=‘bold_taskab.nii.gz’, targets=attr.targets, > chunks=attr.chunks, mask=‘mask.nii.gz’) > now, do this: > > ds.sa[‘task’]=np.repeat([‘A’,’B’], 304) > > which will label the first half of your data as “A” and the second half > as “B" > > Now zscore, making sure you’re conscious of your task assignment > zscore(ds, chunks_attr=‘task’) > > Now set up the SL > clf=LinearCSVMC() > partitioner=HalfPartitioner(count=2, selection_strategy=‘first’, > attr=‘task’) > cv=CrossValidation(clf, partitioner) > sl=sphere_searchlight(cv, radius=3, postproc=mean_sample(), nproc=16) > res=sl(ds) > > The HalfPartitioner as its set up will split you data into 2 chunks > based on your new ‘task’ attribute. It’ll train on task A and test on task > B, then visa versa. Usually you want the average errors of that, but if > you’re really set on just training on A and testing on B, then omit the > postproc=mean_sample() bit, and you’ll get per-fold error in the res > dataset, and you can find the fold corresponding to what you want. > Anyway, hope that helps. > - Gavin > > ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ > Gavin Hanson, B.S. > Research Assistant > Department of Psychology > University of Kansas > 1415 Jayhawk Blvd., 426 Fraser Hall > Lawrence, KS 66045 > > On Jul 15, 2014, at 6:11 PM, David Soto <[email protected]> wrote: > > Hi, I hope you have enjoyed the worldcup :) > > I am trying a searchlight pipeline for the first time now, it has been > running > for some 6-8 hours and remains on with little RAM and CPU used . To > recapitulate, I am training a SVM on FSL copes from task A > regarding classes X & Y and then testing the model on FSL copes from > task B regarding the same classes. > The shape of training and testing datasets is (304, 902629) > > My searchlight pipeline is the following, would you please let me know if > this is OK? > cheers, > ds > > from mvpa2.suite import * > datapath1='/home/dsoto/Documents/fmri/rawprepro_wmintrosp' > attr = SampleAttributes(os.path.join(datapath1, 'attr.txt')) > ds = fmri_dataset(samples=os.path.join(datapath1, 'bold_taska.nii.gz'), > targets=attr.targets, chunks=attr.chunks) > > ts = fmri_dataset(samples=os.path.join(datapath1, 'bold_taskb.nii.gz'), > targets=attr.targets, chunks=attr.chunks) > > zscore(ds) > zscore(ts) > > clf= LinearCSVMC() > clf.train(ds) > predictions = clf.predict(ts.samples) > #validation= np.mean(predictions== ts.sa.targets) > sl = sphere_searchlight(predictions, radius=3, space='voxel_indices', > postproc=mean_sample()) > sl_map = sl(ds) > > > the ipython gui currently says > > > * [SLC] DBG: Starting off 4 child processes for > nblocks=4 * > > > On Fri, Jul 4, 2014 at 2:44 PM, David Soto <[email protected]> wrote: > >> great thanks! >> >> best of luck in the semifinals! >> >> cheers >> ds >> >> >> On Fri, Jul 4, 2014 at 2:33 PM, Michael Hanke <[email protected]> wrote: >> >>> Hi, >>> >>> On Tue, Jul 01, 2014 at 12:25:40AM +0100, David Soto wrote: >>> > Hi Michael, indeed ..well done for germany today! :). >>> > Thanks for the reply and the suggestion on KNN >>> > I should have been more clear that for each subject I have the >>> > following *block >>> > *sequences >>> > ababbaabbaabbaba in TASK 1 >>> > ababbaabbaabbaba in TASK 2 >>> > >>> > this explains that I have 8 a-betas and 8 b-betas for each task >>> > AND for each subject..so if i concatenate & normalize all the beta data >>> > across subjects I will have 8 x 19 (subjects)= 152 beta images for >>> class a >>> > and the same for class b >>> >>> Ah, I guess you model each task with two regressors (hrf + derivative?). >>> You can also use a basis function set and get even more betas... >>> > >>> > then could I use SVM searchlight trained to discriminate a from b in >>> task1 >>> > betas and tested in the task2 betas? >>> >>> yes, no problem. >>> >>> Cheers, >>> >>> Michael >>> >>> PS: Off to enjoy the quarter finals ... ;-) >>> >>> >>> -- >>> Michael Hanke >>> http://mih.voxindeserto.de >>> >>> _______________________________________________ >>> Pkg-ExpPsy-PyMVPA mailing list >>> [email protected] >>> http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa >>> >> >> >> >> -- >> http://www1.imperial.ac.uk/medicine/people/d.soto/ >> > > > > -- > http://www1.imperial.ac.uk/medicine/people/d.soto/ > _______________________________________________ > Pkg-ExpPsy-PyMVPA mailing list > [email protected] > http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa > > > > _______________________________________________ > Pkg-ExpPsy-PyMVPA mailing list > [email protected] > http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa > -- http://www1.imperial.ac.uk/medicine/people/d.soto/
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