<div dir="ltr"><div class="gmail_default" style="font-family:georgia,serif;font-size:large;color:rgb(0,0,0)">Hi, I hope you have enjoyed the worldcup :)<br><br></div><div class="gmail_default" style="font-family:georgia,serif;font-size:large;color:rgb(0,0,0)">
I am trying a searchlight pipeline for the first time now, it has been running<br>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<br></div><div class="gmail_default" style="font-family:georgia,serif;font-size:large;color:rgb(0,0,0)">
regarding classes X & Y and then testing the model on FSL copes from task B regarding the same classes.</div><div class="gmail_default" style="font-family:georgia,serif;font-size:large;color:rgb(0,0,0)">The shape of training and testing datasets is (304, 902629)<br>
<br>My searchlight pipeline is the following, would you please let me know if this is OK?<br></div><div class="gmail_default" style="font-family:georgia,serif;font-size:large;color:rgb(0,0,0)">cheers,<br>ds<br></div><div class="gmail_default" style="font-family:georgia,serif;font-size:large;color:rgb(0,0,0)">
<br><p style="margin:0px;text-indent:0px">from mvpa2.suite import * </p>
<p style="margin:0px;text-indent:0px">datapath1='/home/dsoto/Documents/fmri/rawprepro_wmintrosp'</p>
<p style="margin:0px;text-indent:0px">attr = SampleAttributes(os.path.join(datapath1, 'attr.txt'))</p><p style="margin:0px;text-indent:0px">ds = fmri_dataset(samples=os.path.join(datapath1, 'bold_taska.nii.gz'),</p>
<p style="margin:0px;text-indent:0px"> targets=attr.targets, chunks=attr.chunks)</p>
<p style="margin:0px;text-indent:0px"> </p>
<p style="margin:0px;text-indent:0px">ts = fmri_dataset(samples=os.path.join(datapath1, 'bold_taskb.nii.gz'),</p>
<p style="margin:0px;text-indent:0px"> targets=attr.targets, chunks=attr.chunks)</p> <br><p style="margin:0px;text-indent:0px">zscore(ds)</p><p style="margin:0px;text-indent:0px">zscore(ts)<br></p>
<p style="margin:0px;text-indent:0px"><br></p>
<p style="margin:0px;text-indent:0px">clf= LinearCSVMC()</p>
<p style="margin:0px;text-indent:0px">clf.train(ds)</p>
<p style="margin:0px;text-indent:0px">predictions = clf.predict(ts.samples)</p>
<p style="margin:0px;text-indent:0px">#validation= np.mean(predictions== ts.sa.targets)</p><p style="margin:0px;text-indent:0px">sl = sphere_searchlight(predictions, radius=3, space='voxel_indices',
</p><p style="margin:0px;text-indent:0px"> postproc=mean_sample())</p><p style="margin:0px;text-indent:0px">sl_map = sl(ds)</p>
<p style="margin:0px;text-indent:0px"><br></p><br></div><div class="gmail_default" style="font-family:georgia,serif;font-size:large;color:rgb(0,0,0)">the ipython gui currently says<br><b><br>[SLC] DBG: Starting off 4 child processes for nblocks=4<br>
</b><br></div></div><div class="gmail_extra"><br><br><div class="gmail_quote">On Fri, Jul 4, 2014 at 2:44 PM, David Soto <span dir="ltr"><<a href="mailto:d.soto.b@gmail.com" target="_blank">d.soto.b@gmail.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div class="gmail_default" style="font-family:georgia,serif;font-size:large;color:rgb(0,0,0)">great thanks!<br>
<br></div><div class="gmail_default" style="font-family:georgia,serif;font-size:large;color:rgb(0,0,0)">
best of luck in the semifinals!<br><br>cheers<br>ds<br></div></div><div class="gmail_extra"><div><div class="h5"><br><br><div class="gmail_quote">On Fri, Jul 4, 2014 at 2:33 PM, Michael Hanke <span dir="ltr"><<a href="mailto:mih@debian.org" target="_blank">mih@debian.org</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Hi,<br>
<div><br>
On Tue, Jul 01, 2014 at 12:25:40AM +0100, David Soto wrote:<br>
> Hi Michael, indeed ..well done for germany today! :).<br>
> Thanks for the reply and the suggestion on KNN<br>
> I should have been more clear that for each subject I have the<br>
</div>> following *block<br>
> *sequences<br>
<div>> ababbaabbaabbaba in TASK 1<br>
> ababbaabbaabbaba in TASK 2<br>
><br>
> this explains that I have 8 a-betas and 8 b-betas for each task<br>
> AND for each subject..so if i concatenate & normalize all the beta data<br>
> across subjects I will have 8 x 19 (subjects)= 152 beta images for class a<br>
> and the same for class b<br>
<br>
</div>Ah, I guess you model each task with two regressors (hrf + derivative?).<br>
You can also use a basis function set and get even more betas...<br>
<div>><br>
> then could I use SVM searchlight trained to discriminate a from b in task1<br>
> betas and tested in the task2 betas?<br>
<br>
</div>yes, no problem.<br>
<br>
Cheers,<br>
<br>
Michael<br>
<br>
PS: Off to enjoy the quarter finals ... ;-)<br>
<span><font color="#888888"><br>
<br>
--<br>
Michael Hanke<br>
<a href="http://mih.voxindeserto.de" target="_blank">http://mih.voxindeserto.de</a><br>
<br>
_______________________________________________<br>
Pkg-ExpPsy-PyMVPA mailing list<br>
<a href="mailto:Pkg-ExpPsy-PyMVPA@lists.alioth.debian.org" target="_blank">Pkg-ExpPsy-PyMVPA@lists.alioth.debian.org</a><br>
<a href="http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa" target="_blank">http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa</a><br>
</font></span></blockquote></div><br><br clear="all"><br></div></div><span class="HOEnZb"><font color="#888888">-- <br><div dir="ltr"><a href="http://www1.imperial.ac.uk/medicine/people/d.soto/" style="color:rgb(17,85,204)" target="_blank">http://www1.imperial.ac.uk/medicine/people/d.soto/</a><br>
</div>
</font></span></div>
</blockquote></div><br><br clear="all"><br>-- <br><div dir="ltr"><a href="http://www1.imperial.ac.uk/medicine/people/d.soto/" style="color:rgb(17,85,204)" target="_blank">http://www1.imperial.ac.uk/medicine/people/d.soto/</a><br>
</div>
</div>