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Classification of SVD-mapped Datasets

Demonstrate the usage of a dataset mapper performing data projection onto singular value components within a cross-validation – for any clasifier.

from mvpa.suite import *

if __debug__:
    debug.active += ["CROSSC"]

#
# 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'))

#
# preprocessing
#

# do chunkswise linear detrending on dataset
detrend(dataset, perchunk=True, model='linear')

# only use 'rest', 'cats' and 'scissors' samples from dataset
dataset = dataset.selectSamples(
                N.array([ l in [0,4,5] for l in dataset.labels],
                dtype='bool'))

# zscore dataset relative to baseline ('rest') mean
zscore(dataset, perchunk=True, baselinelabels=[0], targetdtype='float32')

# remove baseline samples from dataset for final analysis
dataset = dataset.selectSamples(N.array([l != 0 for l in dataset.labels],
                                        dtype='bool'))
print dataset

# Specify the base classifier to be used
# To parametrize the classifier to be used
#   Clf = lambda *args:LinearCSVMC(C=-10, *args)
# Just to assign a particular classifier class
Clf = LinearCSVMC

# define some classifiers: a simple one and several classifiers with
# built-in SVDs
clfs = [('All orig.\nfeatures (%i)' % dataset.nfeatures, Clf()),
        ('All Comps\n(%i)' % (dataset.nsamples \
                 - (dataset.nsamples / len(dataset.uniquechunks)),),
                        MappedClassifier(Clf(), SVDMapper())),
        ('First 5\nComp.', MappedClassifier(Clf(),
                        SVDMapper(selector=range(5)))),
        ('First 30\nComp.', MappedClassifier(Clf(),
                        SVDMapper(selector=range(30)))),
        ('Comp.\n6-30', MappedClassifier(Clf(),
                        SVDMapper(selector=range(5,30))))]


# run and visualize in barplot
results = []
labels = []

for desc, clf in clfs:
    print desc
    cv = CrossValidatedTransferError(
            TransferError(clf),
            NFoldSplitter(),
            enable_states=['results'])
    cv(dataset)

    results.append(cv.results)
    labels.append(desc)

plotBars(results, labels=labels,
         title='Linear C-SVM classification (cats vs. scissors)',
         ylabel='Mean classification error (N-1 cross-validation, 12-fold)',
         distance=0.5)

if cfg.getboolean('examples', 'interactive', True):
    P.show()

Output of the example analysis:

See also

The full source code of this example is included in the PyMVPA source distribution (doc/examples/svdclf.py).