def test_icamapper():
# data: 40 sample feature line in 2d space (40x2; samples x features)
samples = np.vstack([np.arange(40.) for i in range(2)]).T
samples -= samples.mean()
samples += np.random.normal(size=samples.shape, scale=0.1)
ndlin = Dataset(samples)
pm = ICAMapper()
pm.train(ndlin.copy())
assert_equal(pm.proj.shape, (2, 2))
p = pm.forward(ndlin.copy())
assert_equal(p.shape, (40, 2))
# check that the mapped data can be fully recovered by 'reverse()'
assert_array_almost_equal(pm.reverse(p), ndlin)
def test_icamapper():
# data: 40 sample feature line in 2d space (40x2; samples x features)
samples = np.vstack([np.arange(40.) for i in range(2)]).T
samples -= samples.mean()
samples += np.random.normal(size=samples.shape, scale=0.1)
ndlin = Dataset(samples)
pm = ICAMapper()
pm.train(ndlin.copy())
assert_equal(pm.proj.shape, (2, 2))
p = pm.forward(ndlin.copy())
assert_equal(p.shape, (40, 2))
# check that the mapped data can be fully recovered by 'reverse()'
assert_array_almost_equal(pm.reverse(p), ndlin)
def test_icamapper():
# data: 40 sample feature line in 2d space (40x2; samples x features)
samples = np.vstack([np.arange(40.) for i in range(2)]).T
samples -= samples.mean()
samples += np.random.normal(size=samples.shape, scale=0.1)
ndlin = Dataset(samples)
pm = ICAMapper()
try:
pm.train(ndlin.copy())
assert_equal(pm.proj.shape, (2, 2))
p = pm.forward(ndlin.copy())
assert_equal(p.shape, (40, 2))
# check that the mapped data can be fully recovered by 'reverse()'
assert_array_almost_equal(pm.reverse(p), ndlin)
except mdp.NodeException:
# do not puke if the ICA did not converge at all -- that is not our
# fault but MDP's
pass
def test_pcamapper():
# data: 40 sample feature line in 20d space (40x20; samples x features)
ndlin = Dataset(np.concatenate([np.arange(40)
for i in range(20)]).reshape(20,-1).T)
pm = PCAMapper()
# train PCA
assert_raises(mdp.NodeException, pm.train, ndlin)
ndlin.samples = ndlin.samples.astype('float')
ndlin_noise = ndlin.copy()
ndlin_noise.samples += np.random.random(size=ndlin.samples.shape)
# we have no variance for more than one PCA component, hence just one
# actual non-zero eigenvalue
assert_raises(mdp.NodeException, pm.train, ndlin)
pm.train(ndlin_noise)
assert_equal(pm.proj.shape, (20, 20))
# now project data into PCA space
p = pm.forward(ndlin.samples)
assert_equal(p.shape, (40, 20))
# check that the mapped data can be fully recovered by 'reverse()'
assert_array_almost_equal(pm.reverse(p), ndlin)
def test_pcamapper():
# data: 40 sample feature line in 20d space (40x20; samples x features)
ndlin = Dataset(
np.concatenate([np.arange(40) for i in range(20)]).reshape(20, -1).T)
pm = PCAMapper()
# train PCA
assert_raises(mdp.NodeException, pm.train, ndlin)
ndlin.samples = ndlin.samples.astype('float')
ndlin_noise = ndlin.copy()
ndlin_noise.samples += np.random.random(size=ndlin.samples.shape)
# we have no variance for more than one PCA component, hence just one
# actual non-zero eigenvalue
assert_raises(mdp.NodeException, pm.train, ndlin)
pm.train(ndlin_noise)
assert_equal(pm.proj.shape, (20, 20))
# now project data into PCA space
p = pm.forward(ndlin.samples)
assert_equal(p.shape, (40, 20))
# check that the mapped data can be fully recovered by 'reverse()'
assert_array_almost_equal(pm.reverse(p), ndlin)
def test_from_wizard():
samples = np.arange(12).reshape((4, 3)).view(myarray)
labels = range(4)
chunks = [1, 1, 2, 2]
ds = Dataset(samples, sa={'targets': labels, 'chunks': chunks})
ds.init_origids('both')
first = ds.sa.origids
# now do again and check that they get regenerated
ds.init_origids('both')
assert_false(first is ds.sa.origids)
assert_array_equal(first, ds.sa.origids)
ok_(is_datasetlike(ds))
ok_(not is_datasetlike(labels))
# array subclass survives
ok_(isinstance(ds.samples, myarray))
## XXX stuff that needs thought:
# ds.sa (empty) has this in the public namespace:
# add, get, getvalue, has_key, is_set, items, listing, name, names
# owner, remove, reset, setvalue, which_set
# maybe we need some form of leightweightCollection?
assert_array_equal(ds.samples, samples)
assert_array_equal(ds.sa.targets, labels)
assert_array_equal(ds.sa.chunks, chunks)
# same should work for shortcuts
assert_array_equal(ds.targets, labels)
assert_array_equal(ds.chunks, chunks)
ok_(sorted(ds.sa.keys()) == ['chunks', 'origids', 'targets'])
ok_(sorted(ds.fa.keys()) == ['origids'])
# add some more
ds.a['random'] = 'blurb'
# check stripping attributes from a copy
cds = ds.copy() # full copy
ok_(sorted(cds.sa.keys()) == ['chunks', 'origids', 'targets'])
ok_(sorted(cds.fa.keys()) == ['origids'])
ok_(sorted(cds.a.keys()) == ['random'])
cds = ds.copy(sa=[], fa=[], a=[]) # plain copy
ok_(cds.sa.keys() == [])
ok_(cds.fa.keys() == [])
ok_(cds.a.keys() == [])
cds = ds.copy(sa=['targets'], fa=None, a=['random']) # partial copy
ok_(cds.sa.keys() == ['targets'])
ok_(cds.fa.keys() == ['origids'])
ok_(cds.a.keys() == ['random'])
# there is not necessarily a mapper present
ok_(not ds.a.has_key('mapper'))
# has to complain about misshaped samples attributes
assert_raises(ValueError, Dataset.from_wizard, samples, labels + labels)
# check that we actually have attributes of the expected type
ok_(isinstance(ds.sa['targets'], ArrayCollectable))
# the dataset will take care of not adding stupid stuff
assert_raises(ValueError, ds.sa.__setitem__, 'stupid', np.arange(3))
assert_raises(ValueError, ds.fa.__setitem__, 'stupid', np.arange(4))
# or change proper attributes to stupid shapes
try:
ds.sa.targets = np.arange(3)
except ValueError:
pass
else:
ok_(False, msg="Assigning value with improper shape to attribute "
"did not raise exception.")
