def setUp(self):
# data: 40 sample feature line in 20d space (40x20; samples x features)
self.ndlin = Dataset(samples=N.concatenate(
[N.arange(40) for i in range(20)]).reshape(20,-1).T, labels=1, chunks=1)
# data: 10 sample feature line in 40d space
# (10x40; samples x features)
self.largefeat = Dataset(samples=N.concatenate(
[N.arange(10) for i in range(40)]).reshape(40,-1).T, labels=1, chunks=1)
self.pm = PCAMapper()
class PCAMapperTests(unittest.TestCase):
def setUp(self):
# data: 40 sample feature line in 20d space (40x20; samples x features)
self.ndlin = Dataset(samples=N.concatenate(
[N.arange(40) for i in range(20)]).reshape(20,-1).T, labels=1, chunks=1)
# data: 10 sample feature line in 40d space
# (10x40; samples x features)
self.largefeat = Dataset(samples=N.concatenate(
[N.arange(10) for i in range(40)]).reshape(40,-1).T, labels=1, chunks=1)
self.pm = PCAMapper()
def testSimplePCA(self):
# train PCA
self.pm.train(self.ndlin)
self.failUnlessEqual(self.pm.mix.shape, (20, 20))
# now project data into PCA space
p = self.pm.forward(self.ndlin.samples)
# only first eigenvalue significant
self.failUnless(self.pm.sv[:1] > 1.0)
self.failUnless((self.pm.sv[1:] < 0.0001).all())
# only variance of first component significant
var = p.var(axis=0)
# test that only one component has variance
self.failUnless(var[:1] > 1.0)
self.failUnless((var[1:] < 0.0001).all())
# check that the mapped data can be fully recovered by 'reverse()'
self.failUnless((N.round(self.pm.reverse(p)) == self.ndlin.samples).all())
def testAutoOptimizePCA(self):
# train PCA
self.pm.train(self.largefeat)
# mixing matrix cannot be square
# self.failUnlessEqual(self.pm.mix.shape, (10, 40))
# only first eigenvalue significant
self.failUnless(self.pm.sv[:1] > 10)
self.failUnless((self.pm.sv[1:] < 10).all())
# now project data into PCA space
p = self.pm.forward(self.largefeat.samples)
# only variance of first component significant
var = p.var(axis=0)
# test that only one component has variance
self.failUnless(var[:1] > 1.0)
self.failUnless((var[1:] < 0.0001).all())
# check that the mapped data can be fully recovered by 'reverse()'
rp = self.pm.reverse(p)
self.failUnlessEqual(rp.shape, self.largefeat.samples.shape)
self.failUnless((N.round(rp) == self.largefeat.samples).all())
self.failUnlessEqual(self.pm.getInSize(), 40)
# self.failUnlessEqual(self.pm.getOutSize(), 10)
self.failUnlessEqual(self.pm.getOutSize(), 40)
# copy mapper
pm2 = deepcopy(self.pm)
# now remove all but the first 2 components from the mapper
pm2.selectOut([0,1])
# sanity check
self.failUnlessEqual(pm2.getInSize(), 40)
self.failUnlessEqual(pm2.getOutSize(), 2)
# but orginal mapper must be left intact
self.failUnlessEqual(self.pm.getInSize(), 40)
# self.failUnlessEqual(self.pm.getOutSize(), 10)
self.failUnlessEqual(self.pm.getOutSize(), 40)
# data should still be fully recoverable by 'reverse()'
rp2 = pm2.reverse(p[:,[0,1]])
self.failUnlessEqual(rp2.shape, self.largefeat.samples.shape)
self.failUnless((N.round(rp2) == self.largefeat.samples).all())
