Hi,
I am trying to create a "discriminative map" which shows which features
are driving the classifiers performance. The procedure (below) will give
me one map per Nfold. My idea was, to calculate a mean map of those maps
and (may be) threshold it at an arbitrary level.
The alternative I was thinking about, is a "stability analysis", that
picks voxels which have consistent clf weights across cross-validation.
What do you think would be an appropriate way (for linear classifiers)
of visualizing voxels which most strongly affect classifier predictions?
Best regards,
Matthias
from mvpa.suite import *
# ------------------------------------------------------------- #
# load PyMVPA example dataset
attr = SampleAttributes(os.path.join(pymvpa_dataroot, 'attributes.txt'))
dataset = NiftiDataset(samples=os.path.join(pymvpa_dataroot, 'bold.nii.gz'),
labels=attr.labels,
chunks=attr.chunks,
mask=os.path.join(pymvpa_dataroot, 'mask.nii.gz'))
detrend(dataset, perchunk=True, model='linear')
zscore(dataset, perchunk=True, baselinelabels=[0],
targetdtype='float32')
dataset = dataset.selectSamples(
N.array([l in [1, 2] for l in dataset.labels],
dtype='bool'))
# ------------------------------------------------------------- #
clf = FeatureSelectionClassifier(
LinearCSVMC(),
SensitivityBasedFeatureSelection(
OneWayAnova(transformer=N.abs),
FixedNElementTailSelector(50, mode='select',
tail='upper')),
enable_states = ['feature_ids'])
# ------------------------------------------------------------- #
cv = CrossValidatedTransferError(TransferError(clf),
NFoldSplitter(),
enable_states=['confusion'],
harvest_attribs=['transerror.clf.getSensitivityAnalyzer(transformer=N.abs,
force_training=False)()'])
# ------------------------------------------------------------- #
error = cv(dataset)
print 'Error:', error
sensitivities = cv.harvested.values()[0]
dataset.map2Nifti(sensitivities).save('sensitivities.nii.gz')
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