def testCompareToZscore(self):
"""Test by comparing to results of elderly z-score function
"""
for ds in self.dss:
ds1 = deepcopy(ds)
ds2 = deepcopy(ds)
zsm = ZScoreMapper()
zsm.train(ds1)
ds1z = zsm.forward(ds1.samples)
zscore(ds2, perchunk=False)
self.failUnless(N.linalg.norm(ds1z - ds2.samples) < 1e-12)
self.failUnless((ds1.samples == ds.samples).all(),
msg="It seems we modified original dataset!")
ds0 = zsm.reverse(ds1z)
self.failUnless(N.linalg.norm(ds0 - ds.samples) < 1e-12,
msg="Can't reconstruct from z-scores")
def testZScoring(self):
"""Test z-scoring transformation
"""
# dataset: mean=2, std=1
samples = N.array((0, 1, 3, 4, 2, 2, 3, 1, 1, 3, 3, 1, 2, 2, 2, 2)).reshape((16, 1))
data = Dataset(samples=samples, labels=range(16), chunks=[0] * 16)
self.failUnlessEqual(data.samples.mean(), 2.0)
self.failUnlessEqual(data.samples.std(), 1.0)
zscore(data, perchunk=True)
# check z-scoring
check = N.array([-2, -1, 1, 2, 0, 0, 1, -1, -1, 1, 1, -1, 0, 0, 0, 0], dtype="float64").reshape(16, 1)
self.failUnless((data.samples == check).all())
data = Dataset(samples=samples, labels=range(16), chunks=[0] * 16)
zscore(data, perchunk=False)
self.failUnless((data.samples == check).all())
# check z-scoring taking set of labels as a baseline
data = Dataset(samples=samples, labels=[0, 2, 2, 2, 1] + [2] * 11, chunks=[0] * 16)
zscore(data, baselinelabels=[0, 1])
self.failUnless((samples == data.samples + 1.0).all())
# respective labels, i.e. samples with same labels are plotted
# in adjacent columns/rows.
# Note, that the first and largest group corresponds to the
# 'rest' condition in the dataset
P.subplot(122)
plotSamplesDistance(ds, sortbyattr='labels')
P.title('Sample distances (sorted by labels)')
if cfg.getboolean('examples', 'interactive', True):
P.show()
# z-score features individually per chunk
print 'Detrending data'
detrend(ds, perchunk=True, model='regress', polyord=2)
print 'Z-Scoring data'
zscore(ds)
P.subplot(121)
plotSamplesDistance(ds, sortbyattr='chunks')
P.title('Distances: z-scored, detrended (sorted by chunks)')
P.subplot(122)
plotSamplesDistance(ds, sortbyattr='labels')
P.title('Distances: z-scored, detrended (sorted by labels)')
if cfg.getboolean('examples', 'interactive', True):
P.show()
# XXX add some more, maybe show effect of preprocessing
"""
Outputs of the example script. Data prior to preprocessing
