[pymvpa] searchlight analysis

[Pegah Kassraian Fard]

Many thanks for the quick feedback. Mask, e.g. the S1 mask, could be used.
Originally I ran it on already masked data in fact, now I wanted to provide
you with unchanged, original data.

Radius and classifier type: Could be changed, though I believe that SVM are
well suited for fMRI data (inherent regularization etc.). Missed out on
radius, 3 would be more cost-efficient, thx! Though I was/am mostly
concerned with having first a correctly working pipeline, hence as for now
I have not paid too much attention to different variations of
classification.

Cheers, Pegah


On Fri, Sep 8, 2017 at 6:07 PM, Nick Oosterhof <n.n.oosterhof at googlemail.com
> wrote:

> Some minor comments inserted:
>
> > On 8 Sep 2017, at 17:52, Pegah Kassraian Fard <pegahkf at gmail.com> wrote:
> >
> >
> > from glob import glob
> > import os
> > import numpy as np
> >
> > from mvpa2.suite import *
> >
> > %matplotlib inline
> >
> >
> > # enable debug output for searchlight call
> > if __debug__:
> >     debug.active += ["SLC"]
> >
> >
> > # change working directory to 'WB'
> > os.chdir('mypath/WB')
> >
> > # use glob to get the filenames of .nii data into a list
> > nii_fns = glob('beta*.nii')
> >
> > # read data
> >
> > labels = [
> >     1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
> >     2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
> >     7, 7, 7, 7, 7, 7, 7,
> >     1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
> >     2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
> >     7, 7, 7, 7, 7, 7, 7
> > ]
> > grps = np.repeat([0, 1], 37, axis=0) # used for `chuncks`
> >
> > db = mvpa2.datasets.mri.fmri_dataset(
> >     nii_fns, targets=labels, chunks=grps, mask=None, sprefix='vxl',
> tprefix='tpref', add_fa=None
> > )
>
> Is there a reason not to use a mask? At least a brain mask to avoid stuff
> stuff like skull and air?
>
> >
> > # use only the samples of which labels are 1 or 2
> > db12 = db[np.array([label in [1, 2] for label in labels], dtype='bool')]
> >
> > # in-place z-score normalization
> > zscore(db12)
> >
> > # choose classifier
> > clf = LinearNuSVMC()
>
> Have you tried a different classifier, for example Naive Bayes? That one
> is simpler (though usually a bit less sensitive than SVM / LDA in my
> experience)?
>
> >
> > # setup measure to be computed by Searchlight
> > # cross-validated mean transfer using an N-fold dataset splitter
> > cv = CrossValidation(clf, NFoldPartitioner())
> >
> > # define searchlight methods
> > radius_ = 1
>
> That's a tiny radius - why not use something like 3?
>
>
>
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