def test_subset():
data = np.array(
[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
[16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31],
[32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47],
[48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]])
# float array doesn't work
sm = FeatureSliceMapper(np.ones(16))
assert_raises(IndexError, sm.forward, data)
# full mask
sm = FeatureSliceMapper(slice(None))
# should not change single samples
assert_array_equal(sm.forward(data[0:1].copy()), data[0:1])
# or multi-samples
assert_array_equal(sm.forward(data.copy()), data)
sm.train(data)
# same on reverse
assert_array_equal(sm.reverse(data[0:1].copy()), data[0:1])
# or multi-samples
assert_array_equal(sm.reverse(data.copy()), data)
# identical mappers
sm_none = FeatureSliceMapper(slice(None))
sm_int = FeatureSliceMapper(np.arange(16))
sm_bool = FeatureSliceMapper(np.ones(16, dtype='bool'))
sms = [sm_none, sm_int, sm_bool]
# test subsets
sids = [3, 4, 5, 6]
bsubset = np.zeros(16, dtype='bool')
bsubset[sids] = True
subsets = [sids, slice(3, 7), bsubset, [3, 3, 4, 4, 6, 6, 6, 5]]
# all test subset result in equivalent masks, hence should do the same to
# the mapper and result in identical behavior
for st in sms:
for i, sub in enumerate(subsets):
# shallow copy
orig = copy(st)
subsm = FeatureSliceMapper(sub)
# should do copy-on-write for all important stuff!!
assert_true(orig.is_mergable(subsm))
orig += subsm
# test if selection did its job
if i == 3:
# special case of multiplying features
assert_array_equal(orig.forward1(data[0].copy()), subsets[i])
else:
assert_array_equal(orig.forward1(data[0].copy()), sids)
## all of the above shouldn't change the original mapper
#assert_array_equal(sm.get_mask(), np.arange(16))
# check for some bug catcher
# no 3D input
#assert_raises(IndexError, sm.forward, np.ones((3,2,1)))
# no input of wrong length
if __debug__:
# checked only in __debug__
assert_raises(ValueError, sm.forward, np.ones(4))
def test_subset():
data = np.array(
[[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
[16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31],
[32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47],
[48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]])
# float array doesn't work
sm = FeatureSliceMapper(np.ones(16))
assert_raises(IndexError, sm.forward, data)
# full mask
sm = FeatureSliceMapper(slice(None))
# should not change single samples
assert_array_equal(sm.forward(data[0:1].copy()), data[0:1])
# or multi-samples
assert_array_equal(sm.forward(data.copy()), data)
sm.train(data)
# same on reverse
assert_array_equal(sm.reverse(data[0:1].copy()), data[0:1])
# or multi-samples
assert_array_equal(sm.reverse(data.copy()), data)
# identical mappers
sm_none = FeatureSliceMapper(slice(None))
sm_int = FeatureSliceMapper(np.arange(16))
sm_bool = FeatureSliceMapper(np.ones(16, dtype='bool'))
sms = [sm_none, sm_int, sm_bool]
# test subsets
sids = [3,4,5,6]
bsubset = np.zeros(16, dtype='bool')
bsubset[sids] = True
subsets = [sids, slice(3,7), bsubset, [3,3,4,4,6,6,6,5]]
# all test subset result in equivalent masks, hence should do the same to
# the mapper and result in identical behavior
for st in sms:
for i, sub in enumerate(subsets):
# shallow copy
orig = copy(st)
subsm = FeatureSliceMapper(sub)
# should do copy-on-write for all important stuff!!
assert_true(orig.is_mergable(subsm))
orig += subsm
# test if selection did its job
if i == 3:
# special case of multiplying features
assert_array_equal(orig.forward1(data[0].copy()), subsets[i])
else:
assert_array_equal(orig.forward1(data[0].copy()), sids)
## all of the above shouldn't change the original mapper
#assert_array_equal(sm.get_mask(), np.arange(16))
# check for some bug catcher
# no 3D input
#assert_raises(IndexError, sm.forward, np.ones((3,2,1)))
# no input of wrong length
if __debug__:
# checked only in __debug__
assert_raises(ValueError, sm.forward, np.ones(4))
def test_eep_bin():
eb = EEPBin(os.path.join(pymvpa_dataroot, 'eep.bin'))
assert_equal(eb.nchannels, 32)
assert_equal(eb.nsamples, 2)
assert_equal(eb.ntimepoints, 4)
assert_true(eb.t0 - eb.dt < 0.00000001)
assert_equal(len(eb.channels), 32)
assert_equal(eb.data.shape, (2, 32, 4))
def test_eep_bin():
eb = EEPBin(os.path.join(pymvpa_dataroot, 'eep.bin'))
assert_equal(eb.nchannels, 32)
assert_equal(eb.nsamples, 2)
assert_equal(eb.ntimepoints, 4)
assert_true(eb.t0 - eb.dt < 0.00000001)
assert_equal(len(eb.channels), 32)
assert_equal(eb.data.shape, (2, 32, 4))
def test_chainmapper():
# the chain needs at lest one mapper
assert_raises(ValueError, ChainMapper, [])
# a typical first mapper is to flatten
cm = ChainMapper([FlattenMapper()])
# few container checks
assert_equal(len(cm), 1)
assert_true(isinstance(cm[0], FlattenMapper))
# now training
# come up with data
samples_shape = (2, 2, 4)
data_shape = (4,) + samples_shape
data = np.arange(np.prod(data_shape)).reshape(data_shape)
pristinedata = data.copy()
target = [
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
[16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31],
[32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47],
[48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63],
]
target = np.array(target)
# if it is not trained it knows nothing
cm.train(data)
# a new mapper should appear when doing feature selection
cm.append(FeatureSliceMapper(range(1, 16)))
assert_equal(cm.forward1(data[0]).shape, (15,))
assert_equal(len(cm), 2)
# multiple slicing
cm.append(FeatureSliceMapper([9, 14]))
assert_equal(cm.forward1(data[0]).shape, (2,))
assert_equal(len(cm), 3)
# check reproduction
cm_clone = eval(repr(cm))
assert_equal(repr(cm_clone), repr(cm))
# what happens if we retrain the whole beast an same data as before
cm.train(data)
assert_equal(cm.forward1(data[0]).shape, (2,))
assert_equal(len(cm), 3)
# let's map something
mdata = cm.forward(data)
assert_array_equal(mdata, target[:, [10, 15]])
# and back
rdata = cm.reverse(mdata)
# original shape
assert_equal(rdata.shape, data.shape)
# content as far it could be restored
assert_array_equal(rdata[rdata > 0], data[rdata > 0])
assert_equal(np.sum(rdata > 0), 8)
def test_attrpermute():
ds = give_data()
ds.sa['ids'] = range(len(ds))
pristine_data = ds.samples.copy()
permutation = AttributePermutator(['targets', 'ids'], assure=True)
pds = permutation(ds)
# should not touch the data
assert_array_equal(pristine_data, pds.samples)
# even keep the very same array
assert_true(pds.samples.base is ds.samples)
# there is no way that it can be the same attribute
assert_false(np.all(pds.sa.ids == ds.sa.ids))
# ids should reflect permutation setup
assert_array_equal(pds.sa.targets, ds.sa.targets[pds.sa.ids])
# other attribute should remain intact
assert_array_equal(pds.sa.chunks, ds.sa.chunks)
# now chunk-wise permutation
permutation = AttributePermutator('ids', limit='chunks')
pds = permutation(ds)
# first ten should remain first ten
assert_false(np.any(pds.sa.ids[:10] > 9))
# same thing, but only permute single chunk
permutation = AttributePermutator('ids', limit={'chunks': 3})
pds = permutation(ds)
# one chunk should change
assert_false(np.any(pds.sa.ids[30:40] > 39))
assert_false(np.any(pds.sa.ids[30:40] < 30))
# the rest not
assert_array_equal(pds.sa.ids[:30], range(30))
# or a list of chunks
permutation = AttributePermutator('ids', limit={'chunks': [3,4]})
pds = permutation(ds)
# two chunks should change
assert_false(np.any(pds.sa.ids[30:50] > 49))
assert_false(np.any(pds.sa.ids[30:50] < 30))
# the rest not
assert_array_equal(pds.sa.ids[:30], range(30))
# and now try generating more permutations
nruns = 2
permutation = AttributePermutator(['targets', 'ids'], assure=True, count=nruns)
pds = list(permutation.generate(ds))
assert_equal(len(pds), nruns)
for p in pds:
assert_false(np.all(p.sa.ids == ds.sa.ids))
# permute feature attrs
ds.fa['ids'] = range(ds.shape[1])
permutation = AttributePermutator('fa.ids', assure=True)
pds = permutation(ds)
assert_false(np.all(pds.fa.ids == ds.fa.ids))
def test_chainmapper():
# the chain needs at lest one mapper
assert_raises(ValueError, ChainMapper, [])
# a typical first mapper is to flatten
cm = ChainMapper([FlattenMapper()])
# few container checks
assert_equal(len(cm), 1)
assert_true(isinstance(cm[0], FlattenMapper))
# now training
# come up with data
samples_shape = (2, 2, 4)
data_shape = (4, ) + samples_shape
data = np.arange(np.prod(data_shape)).reshape(data_shape)
pristinedata = data.copy()
target = [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
[16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31],
[32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47],
[48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]]
target = np.array(target)
# if it is not trained it knows nothing
cm.train(data)
# a new mapper should appear when doing feature selection
cm.append(FeatureSliceMapper(range(1, 16)))
assert_equal(cm.forward1(data[0]).shape, (15, ))
assert_equal(len(cm), 2)
# multiple slicing
cm.append(FeatureSliceMapper([9, 14]))
assert_equal(cm.forward1(data[0]).shape, (2, ))
assert_equal(len(cm), 3)
# check reproduction
cm_clone = eval(repr(cm))
assert_equal(repr(cm_clone), repr(cm))
# what happens if we retrain the whole beast an same data as before
cm.train(data)
assert_equal(cm.forward1(data[0]).shape, (2, ))
assert_equal(len(cm), 3)
# let's map something
mdata = cm.forward(data)
assert_array_equal(mdata, target[:, [10, 15]])
# and back
rdata = cm.reverse(mdata)
# original shape
assert_equal(rdata.shape, data.shape)
# content as far it could be restored
assert_array_equal(rdata[rdata > 0], data[rdata > 0])
assert_equal(np.sum(rdata > 0), 8)
def test_splitter():
ds = give_data()
# split with defaults
spl1 = Splitter('chunks')
assert_raises(NotImplementedError, spl1, ds)
splits = list(spl1.generate(ds))
assert_equal(len(splits), len(ds.sa['chunks'].unique))
for split in splits:
# it should have perform basic slicing!
assert_true(split.samples.base is ds.samples)
assert_equal(len(split.sa['chunks'].unique), 1)
assert_true('lastsplit' in split.a)
assert_true(splits[-1].a.lastsplit)
# now again, more customized
spl2 = Splitter('targets', attr_values = [0,1,1,2,3,3,3], count=4,
noslicing=True)
splits = list(spl2.generate(ds))
assert_equal(len(splits), 4)
for split in splits:
# it should NOT have perform basic slicing!
assert_false(split.samples.base is ds.samples)
assert_equal(len(split.sa['targets'].unique), 1)
assert_equal(len(split.sa['chunks'].unique), 10)
assert_true(splits[-1].a.lastsplit)
# two should be identical
assert_array_equal(splits[1].samples, splits[2].samples)
# now go wild and split by feature attribute
ds.fa['roi'] = np.repeat([0,1], 5)
# splitter should auto-detect that this is a feature attribute
spl3 = Splitter('roi')
splits = list(spl3.generate(ds))
assert_equal(len(splits), 2)
for split in splits:
assert_true(split.samples.base is ds.samples)
assert_equal(len(split.fa['roi'].unique), 1)
assert_equal(split.shape, (100, 5))
# and finally test chained splitters
cspl = ChainNode([spl2, spl3, spl1])
splits = list(cspl.generate(ds))
# 4 target splits and 2 roi splits each and 10 chunks each
assert_equal(len(splits), 80)
def test_collections():
sa = SampleAttributesCollection()
assert_equal(len(sa), 0)
assert_raises(ValueError, sa.__setitem__, 'test', 0)
l = range(5)
sa['test'] = l
# auto-wrapped
assert_true(isinstance(sa['test'], ArrayCollectable))
assert_equal(len(sa), 1)
# names which are already present in dict interface
assert_raises(ValueError, sa.__setitem__, 'values', range(5))
sa_c = copy.deepcopy(sa)
assert_equal(len(sa), len(sa_c))
assert_array_equal(sa.test, sa_c.test)
def test_collections():
sa = SampleAttributesCollection()
assert_equal(len(sa), 0)
assert_raises(ValueError, sa.__setitem__, 'test', 0)
l = range(5)
sa['test'] = l
# auto-wrapped
assert_true(isinstance(sa['test'], ArrayCollectable))
assert_equal(len(sa), 1)
# names which are already present in dict interface
assert_raises(ValueError, sa.__setitem__, 'values', range(5))
sa_c = copy.deepcopy(sa)
assert_equal(len(sa), len(sa_c))
assert_array_equal(sa.test, sa_c.test)
def test_glmnet_r():
# not the perfect dataset with which to test, but
# it will do for now.
#data = datasets['dumb2']
# for some reason the R code fails with the dumb data
data = datasets['chirp_linear']
clf = GLMNET_R()
clf.train(data)
# prediction has to be almost perfect
# test with a correlation
pre = clf.predict(data.samples)
corerr = corr_error(pre, data.targets)
if cfg.getboolean('tests', 'labile', default='yes'):
assert_true(corerr < .2)
def test_glmnet_r():
# not the perfect dataset with which to test, but
# it will do for now.
#data = datasets['dumb2']
# for some reason the R code fails with the dumb data
data = datasets['chirp_linear']
clf = GLMNET_R()
clf.train(data)
# prediction has to be almost perfect
# test with a correlation
pre = clf.predict(data.samples)
corerr = CorrErrorFx()(pre, data.targets)
if cfg.getboolean('tests', 'labile', default='yes'):
assert_true(corerr < .2)
def test_balancer():
ds = give_data()
# only mark the selection in an attribute
bal = Balancer()
res = bal(ds)
# we get a new dataset, with shared samples
assert_false(ds is res)
assert_true(ds.samples is res.samples.base)
# should kick out 2 samples in each chunk of 10
assert_almost_equal(np.mean(res.sa.balanced_set), 0.8)
# same as above, but actually apply the selection
bal = Balancer(apply_selection=True, count=5)
# just run it once
res = bal(ds)
# we get a new dataset, with shared samples
assert_false(ds is res)
# should kick out 2 samples in each chunk of 10
assert_equal(len(res), int(0.8 * len(ds)))
# now use it as a generator
dses = list(bal.generate(ds))
assert_equal(len(dses), 5)
# with limit
bal = Balancer(limit={'chunks': 3}, apply_selection=True)
res = bal(ds)
assert_equal(res.sa['chunks'].unique, (3,))
assert_equal(get_nelements_per_value(res.sa.targets).values(),
[2] * 4)
# fixed amount
bal = Balancer(amount=1, limit={'chunks': 3}, apply_selection=True)
res = bal(ds)
assert_equal(get_nelements_per_value(res.sa.targets).values(),
[1] * 4)
# fraction
bal = Balancer(amount=0.499, limit=None, apply_selection=True)
res = bal(ds)
assert_array_equal(
np.round(np.array(get_nelements_per_value(ds.sa.targets).values()) * 0.5),
np.array(get_nelements_per_value(res.sa.targets).values()))
# check on feature attribute
ds.fa['one'] = np.tile([1,2], 5)
ds.fa['chk'] = np.repeat([1,2], 5)
bal = Balancer(attr='one', amount=2, limit='chk', apply_selection=True)
res = bal(ds)
assert_equal(get_nelements_per_value(res.fa.one).values(),
[4] * 2)
def test_array_collectable():
c = ArrayCollectable()
# empty by default
assert_equal(c.name, None)
assert_equal(c.value, None)
# late assignment
c.name = 'somename'
assert_raises(ValueError, c._set, 12345)
assert_equal(c.value, None)
c.value = np.arange(5)
assert_equal(c.name, 'somename')
assert_array_equal(c.value, np.arange(5))
# immediate content
data = np.random.random(size=(3,10))
c = ArrayCollectable(data.copy(), 'myname',
"This is a test", length=3)
assert_equal(c.name, 'myname')
assert_array_equal(c.value, data)
assert_equal(c.__doc__, "This is a test")
assert_equal(str(c), 'myname')
# repr
from numpy import array
e = eval(repr(c))
assert_equal(e.name, 'myname')
assert_array_almost_equal(e.value, data)
assert_equal(e.__doc__, "This is a test")
# cannot assign array of wrong length
assert_raises(ValueError, c._set, np.arange(5))
assert_equal(len(c), 3)
# shallow copy DOES create a view of value array
c.value = np.arange(3)
d = copy.copy(c)
assert_true(d.value.base is c.value)
# names starting with _ are not allowed
assert_raises(ValueError, c._set_name, "_underscore")
def test_array_collectable():
c = ArrayCollectable()
# empty by default
assert_equal(c.name, None)
assert_equal(c.value, None)
# late assignment
c.name = 'somename'
assert_raises(ValueError, c._set, 12345)
assert_equal(c.value, None)
c.value = np.arange(5)
assert_equal(c.name, 'somename')
assert_array_equal(c.value, np.arange(5))
# immediate content
data = np.random.random(size=(3, 10))
c = ArrayCollectable(data.copy(), 'myname', "This is a test", length=3)
assert_equal(c.name, 'myname')
assert_array_equal(c.value, data)
assert_equal(c.__doc__, "This is a test")
assert_equal(str(c), 'myname')
# repr
from numpy import array
e = eval(repr(c))
assert_equal(e.name, 'myname')
assert_array_almost_equal(e.value, data)
assert_equal(e.__doc__, "This is a test")
# cannot assign array of wrong length
assert_raises(ValueError, c._set, np.arange(5))
assert_equal(len(c), 3)
# shallow copy DOES create a view of value array
c.value = np.arange(3)
d = copy.copy(c)
assert_true(d.value.base is c.value)
# names starting with _ are not allowed
assert_raises(ValueError, c._set_name, "_underscore")
def test_slicing(self):
hs = HalfPartitioner()
spl = Splitter(attr='partitions')
splits = list(hs.generate(self.data))
for s in splits:
# partitioned dataset shared the data
assert_true(s.samples.base is self.data.samples)
splits = [ list(spl.generate(p)) for p in hs.generate(self.data) ]
for s in splits:
# we get slicing all the time
assert_true(s[0].samples.base.base is self.data.samples)
assert_true(s[1].samples.base.base is self.data.samples)
spl = Splitter(attr='partitions', noslicing=True)
splits = [ list(spl.generate(p)) for p in hs.generate(self.data) ]
for s in splits:
# we no slicing at all
assert_false(s[0].samples.base is self.data.samples)
assert_false(s[1].samples.base is self.data.samples)
nfs = NFoldPartitioner()
spl = Splitter(attr='partitions')
splits = [ list(spl.generate(p)) for p in nfs.generate(self.data) ]
for i, s in enumerate(splits):
# training only first and last split
if i == 0 or i == len(splits) - 1:
assert_true(s[0].samples.base.base is self.data.samples)
else:
assert_true(s[0].samples.base is None)
# we get slicing all the time
assert_true(s[1].samples.base.base is self.data.samples)
step_ds = Dataset(np.random.randn(20,2),
sa={'chunks': np.tile([0,1], 10)})
oes = OddEvenPartitioner()
spl = Splitter(attr='partitions')
splits = list(oes.generate(step_ds))
for s in splits:
# partitioned dataset shared the data
assert_true(s.samples.base is step_ds.samples)
splits = [ list(spl.generate(p)) for p in oes.generate(step_ds) ]
assert_equal(len(splits), 2)
for s in splits:
# we get slicing all the time
assert_true(s[0].samples.base.base is step_ds.samples)
assert_true(s[1].samples.base.base is step_ds.samples)
def test_slicing(self):
spl = HalfSplitter()
splits = [ (train, test) for (train, test) in spl(self.data) ]
for s in splits:
# we get slicing all the time
assert_true(s[0].samples.base is self.data.samples)
assert_true(s[1].samples.base is self.data.samples)
spl = HalfSplitter(noslicing=True)
splits = [ (train, test) for (train, test) in spl(self.data) ]
for s in splits:
# we no slicing at all
assert_false(s[0].samples.base is self.data.samples)
assert_false(s[1].samples.base is self.data.samples)
spl = NFoldSplitter()
splits = [ (train, test) for (train, test) in spl(self.data) ]
for i, s in enumerate(splits):
# training only first and last split
if i == 0 or i == len(splits) - 1:
assert_true(s[0].samples.base is self.data.samples)
else:
assert_false(s[0].samples.base is self.data.samples)
# we get slicing all the time
assert_true(s[1].samples.base is self.data.samples)
step_ds = Dataset(np.random.randn(20,2),
sa={'chunks': np.tile([0,1], 10)})
spl = OddEvenSplitter()
splits = [ (train, test) for (train, test) in spl(step_ds) ]
assert_equal(len(splits), 2)
for s in splits:
# we get slicing all the time
assert_true(s[0].samples.base is step_ds.samples)
assert_true(s[1].samples.base is step_ds.samples)
def test_simpleboxcar():
data = np.atleast_2d(np.arange(10)).T
sp = np.arange(10)
# check if stupid thing don't work
assert_raises(ValueError, BoxcarMapper, sp, 0)
# now do an identity transformation
bcm = BoxcarMapper(sp, 1)
trans = bcm.forward(data)
# ,0 is a feature below, so we get explicit 2D out of 1D
assert_array_equal(trans[:, 0], data)
# now check for illegal boxes
if __debug__:
# condition is checked only in __debug__
assert_raises(ValueError, BoxcarMapper(sp, 2).train, data)
# now something that should work
nbox = 9
boxlength = 2
sp = np.arange(nbox)
bcm = BoxcarMapper(sp, boxlength)
trans = bcm(data)
# check that is properly upcasts the dimensionality
assert_equal(trans.shape, (nbox, boxlength) + data.shape[1:])
# check actual values, squeezing the last dim for simplicity
assert_array_equal(trans.squeeze(),
np.vstack((np.arange(9), np.arange(9) + 1)).T)
# now test for proper data shape
data = np.ones((10, 3, 4, 2))
sp = [2, 4, 3, 5]
trans = BoxcarMapper(sp, 4)(data)
assert_equal(trans.shape, (4, 4, 3, 4, 2))
# test reverse
data = np.arange(240).reshape(10, 3, 4, 2)
sp = [2, 4, 3, 5]
boxlength = 2
m = BoxcarMapper(sp, boxlength)
m.train(data)
mp = m.forward(data)
assert_equal(mp.shape, (4, 2, 3, 4, 2))
# try full reconstruct
mr = m.reverse(mp)
# shape has to match
assert_equal(mr.shape, (len(sp) * boxlength, ) + data.shape[1:])
# only known samples are part of the results
assert_true((mr >= 24).all())
assert_true((mr < 168).all())
# check proper reconstruction of non-conflicting sample
assert_array_equal(mr[0].ravel(), np.arange(48, 72))
# check proper reconstruction of samples being part of multiple
# mapped samples
assert_array_equal(mr[1].ravel(), np.arange(72, 96))
# test reverse of a single sample
singlesample = np.arange(48).reshape(2, 3, 4, 2)
assert_array_equal(singlesample, m.reverse1(singlesample))
# should not work for shape mismatch, but it does work and is useful when
# reverse mapping sample attributes
#assert_raises(ValueError, m.reverse, singlesample[0])
# check broadcasting of 'raw' samples into proper boxcars on forward()
bc = m.forward1(np.arange(24).reshape(3, 4, 2))
assert_array_equal(bc, np.array(2 * [np.arange(24).reshape(3, 4, 2)]))
def test_chainmapper():
# the chain needs at lest one mapper
assert_raises(ValueError, ChainMapper, [])
# a typical first mapper is to flatten
cm = ChainMapper([FlattenMapper()])
# few container checks
assert_equal(len(cm), 1)
assert_true(isinstance(cm[0], FlattenMapper))
# now training
# come up with data
samples_shape = (2, 2, 4)
data_shape = (4,) + samples_shape
data = np.arange(np.prod(data_shape)).reshape(data_shape)
pristinedata = data.copy()
target = [
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
[16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31],
[32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47],
[48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63],
]
target = np.array(target)
# if it is not trained it knows nothing
cm.train(data)
# a new mapper should appear when doing feature selection
cm.append(StaticFeatureSelection(range(1, 16)))
assert_equal(cm.forward1(data[0]).shape, (15,))
assert_equal(len(cm), 2)
# multiple slicing
cm.append(StaticFeatureSelection([9, 14]))
assert_equal(cm.forward1(data[0]).shape, (2,))
assert_equal(len(cm), 3)
# check reproduction
if __debug__:
# debug mode needs special test as it enhances the repr output
# with module info and id() appendix for objects
import mvpa
cm_clone = eval(repr(cm))
assert_equal("#".join(repr(cm_clone).split("#")[:-1]), "#".join(repr(cm).split("#")[:-1]))
else:
cm_clone = eval(repr(cm))
assert_equal(repr(cm_clone), repr(cm))
# what happens if we retrain the whole beast an same data as before
cm.train(data)
assert_equal(cm.forward1(data[0]).shape, (2,))
assert_equal(len(cm), 3)
# let's map something
mdata = cm.forward(data)
assert_array_equal(mdata, target[:, [10, 15]])
# and back
rdata = cm.reverse(mdata)
# original shape
assert_equal(rdata.shape, data.shape)
# content as far it could be restored
assert_array_equal(rdata[rdata > 0], data[rdata > 0])
assert_equal(np.sum(rdata > 0), 8)
# Lets construct a dataset with mapper assigned and see
# if sub-selecting a feature adjusts trailing StaticFeatureSelection
# appropriately
ds_subsel = Dataset.from_wizard(data, mapper=cm)[:, 1]
tail_sfs = ds_subsel.a.mapper[-1]
assert_equal(repr(tail_sfs), "StaticFeatureSelection(slicearg=array([14]))")
def test_simpleboxcar():
data = np.atleast_2d(np.arange(10)).T
sp = np.arange(10)
# check if stupid thing don't work
assert_raises(ValueError, BoxcarMapper, sp, 0)
# now do an identity transformation
bcm = BoxcarMapper(sp, 1)
trans = bcm.forward(data)
# ,0 is a feature below, so we get explicit 2D out of 1D
assert_array_equal(trans[:,0], data)
# now check for illegal boxes
if __debug__:
# condition is checked only in __debug__
assert_raises(ValueError, BoxcarMapper(sp, 2).train, data)
# now something that should work
nbox = 9
boxlength = 2
sp = np.arange(nbox)
bcm = BoxcarMapper(sp, boxlength)
trans = bcm(data)
# check that is properly upcasts the dimensionality
assert_equal(trans.shape, (nbox, boxlength) + data.shape[1:])
# check actual values, squeezing the last dim for simplicity
assert_array_equal(trans.squeeze(), np.vstack((np.arange(9), np.arange(9)+1)).T)
# now test for proper data shape
data = np.ones((10,3,4,2))
sp = [ 2, 4, 3, 5 ]
trans = BoxcarMapper(sp, 4)(data)
assert_equal(trans.shape, (4,4,3,4,2))
# test reverse
data = np.arange(240).reshape(10, 3, 4, 2)
sp = [ 2, 4, 3, 5 ]
boxlength = 2
m = BoxcarMapper(sp, boxlength)
m.train(data)
mp = m.forward(data)
assert_equal(mp.shape, (4, 2, 3, 4, 2))
# try full reconstruct
mr = m.reverse(mp)
# shape has to match
assert_equal(mr.shape, (len(sp) * boxlength,) + data.shape[1:])
# only known samples are part of the results
assert_true((mr >= 24).all())
assert_true((mr < 168).all())
# check proper reconstruction of non-conflicting sample
assert_array_equal(mr[0].ravel(), np.arange(48, 72))
# check proper reconstruction of samples being part of multiple
# mapped samples
assert_array_equal(mr[1].ravel(), np.arange(72, 96))
# test reverse of a single sample
singlesample = np.arange(48).reshape(2, 3, 4, 2)
assert_array_equal(singlesample, m.reverse1(singlesample))
# should not work for shape mismatch, but it does work and is useful when
# reverse mapping sample attributes
#assert_raises(ValueError, m.reverse, singlesample[0])
# check broadcasting of 'raw' samples into proper boxcars on forward()
bc = m.forward1(np.arange(24).reshape(3, 4, 2))
assert_array_equal(bc, np.array(2 * [np.arange(24).reshape(3, 4, 2)]))
def test_slicing(self):
spl = HalfSplitter()
splits = [(train, test) for (train, test) in spl(self.data)]
for s in splits:
# we get slicing all the time
assert_true(s[0].samples.base is self.data.samples)
assert_true(s[1].samples.base is self.data.samples)
spl = HalfSplitter(noslicing=True)
splits = [(train, test) for (train, test) in spl(self.data)]
for s in splits:
# we no slicing at all
assert_false(s[0].samples.base is self.data.samples)
assert_false(s[1].samples.base is self.data.samples)
spl = NFoldSplitter()
splits = [(train, test) for (train, test) in spl(self.data)]
for i, s in enumerate(splits):
# training only first and last split
if i == 0 or i == len(splits) - 1:
assert_true(s[0].samples.base is self.data.samples)
else:
assert_false(s[0].samples.base is self.data.samples)
# we get slicing all the time
assert_true(s[1].samples.base is self.data.samples)
step_ds = Dataset(np.random.randn(20, 2),
sa={'chunks': np.tile([0, 1], 10)})
spl = OddEvenSplitter()
splits = [(train, test) for (train, test) in spl(step_ds)]
assert_equal(len(splits), 2)
for s in splits:
# we get slicing all the time
assert_true(s[0].samples.base is step_ds.samples)
assert_true(s[1].samples.base is step_ds.samples)
def test_flatten():
samples_shape = (2, 2, 4)
data_shape = (4, ) + samples_shape
data = np.arange(np.prod(data_shape)).reshape(data_shape).view(myarray)
pristinedata = data.copy()
target = [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
[16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31],
[32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47],
[48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]]
target = np.array(target).view(myarray)
index_target = np.array([[0, 0, 0], [0, 0, 1], [0, 0, 2], [0, 0, 3],
[0, 1, 0], [0, 1, 1], [0, 1, 2], [0, 1, 3],
[1, 0, 0], [1, 0, 1], [1, 0, 2], [1, 0, 3],
[1, 1, 0], [1, 1, 1], [1, 1, 2], [1, 1, 3]])
# array subclass survives
ok_(isinstance(data, myarray))
# actually, there should be no difference between a plain FlattenMapper and
# a chain that only has a FlattenMapper as the one element
for fm in [
FlattenMapper(inspace='voxel'),
ChainMapper([
FlattenMapper(inspace='voxel'),
FeatureSliceMapper(slice(None))
])
]:
# not working if untrained
assert_raises(RuntimeError, fm.forward1,
np.arange(np.sum(samples_shape) + 1))
fm.train(data)
ok_(isinstance(fm.forward(data), myarray))
ok_(isinstance(fm.forward1(data[2]), myarray))
assert_array_equal(fm.forward(data), target)
assert_array_equal(fm.forward1(data[2]), target[2])
assert_raises(ValueError, fm.forward, np.arange(4))
# all of that leaves that data unmodified
assert_array_equal(data, pristinedata)
# reverse mapping
ok_(isinstance(fm.reverse(target), myarray))
ok_(isinstance(fm.reverse1(target[0]), myarray))
ok_(isinstance(fm.reverse(target[1:2]), myarray))
assert_array_equal(fm.reverse(target), data)
assert_array_equal(fm.reverse1(target[0]), data[0])
assert_array_equal(fm.reverse(target[1:2]), data[1:2])
assert_raises(ValueError, fm.reverse, np.arange(14))
# check one dimensional data, treated as scalar samples
oned = np.arange(5)
fm.train(Dataset(oned))
# needs 2D
assert_raises(ValueError, fm.forward, oned)
# doesn't match mapper, since Dataset turns `oned` into (5,1)
assert_raises(ValueError, fm.forward, oned)
assert_equal(Dataset(oned).nfeatures, 1)
# try dataset mode, with some feature attribute
fattr = np.arange(np.prod(samples_shape)).reshape(samples_shape)
ds = Dataset(data, fa={'awesome': fattr.copy()})
assert_equal(ds.samples.shape, data_shape)
fm.train(ds)
dsflat = fm.forward(ds)
ok_(isinstance(dsflat, Dataset))
ok_(isinstance(dsflat.samples, myarray))
assert_array_equal(dsflat.samples, target)
assert_array_equal(dsflat.fa.awesome,
np.arange(np.prod(samples_shape)))
assert_true(isinstance(dsflat.fa['awesome'], ArrayCollectable))
# test index creation
assert_array_equal(index_target, dsflat.fa.voxel)
# and back
revds = fm.reverse(dsflat)
ok_(isinstance(revds, Dataset))
ok_(isinstance(revds.samples, myarray))
assert_array_equal(revds.samples, data)
assert_array_equal(revds.fa.awesome, fattr)
assert_true(isinstance(revds.fa['awesome'], ArrayCollectable))
assert_false('voxel' in revds.fa)
def test_flatten():
samples_shape = (2, 2, 4)
data_shape = (4,) + samples_shape
data = np.arange(np.prod(data_shape)).reshape(data_shape).view(myarray)
pristinedata = data.copy()
target = [[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
[16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31],
[32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47],
[48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]]
target = np.array(target).view(myarray)
index_target = np.array([[0, 0, 0], [0, 0, 1], [0, 0, 2], [0, 0, 3],
[0, 1, 0], [0, 1, 1], [0, 1, 2], [0, 1, 3],
[1, 0, 0], [1, 0, 1], [1, 0, 2], [1, 0, 3],
[1, 1, 0], [1, 1, 1], [1, 1, 2], [1, 1, 3]])
# array subclass survives
ok_(isinstance(data, myarray))
# actually, there should be no difference between a plain FlattenMapper and
# a chain that only has a FlattenMapper as the one element
for fm in [FlattenMapper(space='voxel'),
ChainMapper([FlattenMapper(space='voxel'),
StaticFeatureSelection(slice(None))])]:
# not working if untrained
assert_raises(RuntimeError,
fm.forward1,
np.arange(np.sum(samples_shape) + 1))
fm.train(data)
ok_(isinstance(fm.forward(data), myarray))
ok_(isinstance(fm.forward1(data[2]), myarray))
assert_array_equal(fm.forward(data), target)
assert_array_equal(fm.forward1(data[2]), target[2])
assert_raises(ValueError, fm.forward, np.arange(4))
# all of that leaves that data unmodified
assert_array_equal(data, pristinedata)
# reverse mapping
ok_(isinstance(fm.reverse(target), myarray))
ok_(isinstance(fm.reverse1(target[0]), myarray))
ok_(isinstance(fm.reverse(target[1:2]), myarray))
assert_array_equal(fm.reverse(target), data)
assert_array_equal(fm.reverse1(target[0]), data[0])
assert_array_equal(fm.reverse(target[1:2]), data[1:2])
assert_raises(ValueError, fm.reverse, np.arange(14))
# check one dimensional data, treated as scalar samples
oned = np.arange(5)
fm.train(Dataset(oned))
# needs 2D
assert_raises(ValueError, fm.forward, oned)
# doesn't match mapper, since Dataset turns `oned` into (5,1)
assert_raises(ValueError, fm.forward, oned)
assert_equal(Dataset(oned).nfeatures, 1)
# try dataset mode, with some feature attribute
fattr = np.arange(np.prod(samples_shape)).reshape(samples_shape)
ds = Dataset(data, fa={'awesome': fattr.copy()})
assert_equal(ds.samples.shape, data_shape)
fm.train(ds)
dsflat = fm.forward(ds)
ok_(isinstance(dsflat, Dataset))
ok_(isinstance(dsflat.samples, myarray))
assert_array_equal(dsflat.samples, target)
assert_array_equal(dsflat.fa.awesome, np.arange(np.prod(samples_shape)))
assert_true(isinstance(dsflat.fa['awesome'], ArrayCollectable))
# test index creation
assert_array_equal(index_target, dsflat.fa.voxel)
# and back
revds = fm.reverse(dsflat)
ok_(isinstance(revds, Dataset))
ok_(isinstance(revds.samples, myarray))
assert_array_equal(revds.samples, data)
assert_array_equal(revds.fa.awesome, fattr)
assert_true(isinstance(revds.fa['awesome'], ArrayCollectable))
assert_false('voxel' in revds.fa)