[pymvpa] between-subject classification

[Anna Manelis]

Dear PYMVPA experts,

I plan to use PYMVPA to classify 2 groups of subjects (patients and
controls). The inputs are betas  that come from the FSL- based analysis
(i.e., cope.nii.gz). I use the SVM classifier in a specific mask on 100
selected features.

Could you please confirm that a short script below is correct:

# copes  are taken from /subject/feet/reg_standard/stats/ folder for each
subject.  For this example, they were copied to sub001, sub002, etc
folders. There is a total of N=10 subjects (in this example).
# I assume that each subject in a group is a chunk. Is that correct?

##################################

from glob import glob
import os
import numpy as np
from mvpa2.suite import*

labels=['hc','hc','hc','hc','hc','pt','pt','pt','pt','pt']
chunks=[1,2,3,4,5,1,2,3,4,5]    # Subject numbering within each group

copes=sorted(glob('/path/to/data/sub0*/cope14.nii.gz'))   # Collect file
names
mask_fname="/path/to/data/my_mask.nii.gz"

db=fmri_dataset(copes, targets=labels, chunks=chunks, sprefix=None,
tprefix=None, mask=mask_fname, add_fa=None)
ds_mni = vstack(db)

# Setup classifier - is in the Hyperalignment example
clf = LinearCSVMC()

# feature selection helpers
nf = 100
fselector = FixedNElementTailSelector(nf, tail='upper',
                                      mode='select', sort=False)
sbfs = SensitivityBasedFeatureSelection(OneWayAnova(), fselector,
                                        enable_ca=['sensitivities'])
# create classifier with automatic feature selection
fsclf = FeatureSelectionClassifier(clf, sbfs)
cv = CrossValidation(fsclf,
                     NFoldPartitioner(),
                     enable_ca=['stats'])

results = cv(ds_mni)
print results

##################################


Thank you,
Anna.
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