def test_repr():
# this time give mask only by its target length
sm = StaticFeatureSelection(slice(None), space='myspace')
# check reproduction
sm_clone = eval(repr(sm))
assert_equal(repr(sm_clone), repr(sm))
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_sampleslicemapper():
# this does nothing but Dataset.__getitem__ which is tested elsewhere -- but
# at least we run it
ds = datasets['uni2small']
ssm = SampleSliceMapper(slice(3, 8, 2))
sds = ssm(ds)
assert_equal(len(sds), 3)
def test_repr():
# this time give mask only by its target length
sm = FeatureSliceMapper(slice(None), inspace="myspace")
# check reproduction
sm_clone = eval(repr(sm))
assert_equal(repr(sm_clone), repr(sm))
def test_repeater():
reps = 4
r = Repeater(reps, space='OMG')
dsl = [ds for ds in r.generate(Dataset([0,1]))]
assert_equal(len(dsl), reps)
for i, ds in enumerate(dsl):
assert_equal(ds.a.OMG, i)
def test_forward_dense_array_mapper():
mask = np.ones((3, 2), dtype="bool")
map_ = mask_mapper(mask)
# test shape reports
assert_equal(map_.forward1(mask).shape, (6,))
# test 1sample mapping
assert_array_equal(map_.forward1(np.arange(6).reshape(3, 2)), [0, 1, 2, 3, 4, 5])
# test 4sample mapping
foursample = map_.forward(np.arange(24).reshape(4, 3, 2))
assert_array_equal(
foursample, [[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11], [12, 13, 14, 15, 16, 17], [18, 19, 20, 21, 22, 23]]
)
# check incomplete masks
mask[1, 1] = 0
map_ = mask_mapper(mask)
assert_equal(map_.forward1(mask).shape, (5,))
assert_array_equal(map_.forward1(np.arange(6).reshape(3, 2)), [0, 1, 2, 4, 5])
# check that it doesn't accept wrong dataspace
assert_raises(ValueError, map_.forward, np.arange(4).reshape(2, 2))
# check fail if neither mask nor shape
assert_raises(ValueError, mask_mapper)
# check that a full mask is automatically created when providing shape
m = mask_mapper(shape=(2, 3, 4))
mp = m.forward1(np.arange(24).reshape(2, 3, 4))
assert_array_equal(mp, np.arange(24))
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_simple_n_minus_one_cv(self):
data = get_mv_pattern(3)
data.init_origids('samples')
self.failUnless( data.nsamples == 120 )
self.failUnless( data.nfeatures == 2 )
self.failUnless(
(data.sa.targets == \
[0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0] * 6).all())
self.failUnless(
(data.sa.chunks == \
[k for k in range(1, 7) for i in range(20)]).all())
assert_equal(len(np.unique(data.sa.origids)), data.nsamples)
transerror = TransferError(sample_clf_nl)
cv = CrossValidatedTransferError(
transerror,
NFoldSplitter(cvtype=1),
enable_ca=['confusion', 'training_confusion',
'samples_error'])
results = cv(data)
self.failUnless((results.samples < 0.2).all() and (results.samples >= 0.0).all())
# TODO: test accessibility of {training_,}confusion{,s} of
# CrossValidatedTransferError
self.failUnless(isinstance(cv.ca.samples_error, dict))
self.failUnless(len(cv.ca.samples_error) == data.nsamples)
# one value for each origid
assert_array_equal(sorted(cv.ca.samples_error.keys()),
sorted(data.sa.origids))
for k, v in cv.ca.samples_error.iteritems():
self.failUnless(len(v) == 1)
def test_simple_n_minus_one_cv(self):
data = get_mv_pattern(3)
data.init_origids('samples')
self.failUnless(data.nsamples == 120)
self.failUnless(data.nfeatures == 2)
self.failUnless(
(data.sa.targets == \
[0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0] * 6).all())
self.failUnless(
(data.sa.chunks == \
[k for k in range(1, 7) for i in range(20)]).all())
assert_equal(len(np.unique(data.sa.origids)), data.nsamples)
transerror = TransferError(sample_clf_nl)
cv = CrossValidatedTransferError(
transerror,
NFoldSplitter(cvtype=1),
enable_ca=['confusion', 'training_confusion', 'samples_error'])
results = cv(data)
self.failUnless((results.samples < 0.2).all()
and (results.samples >= 0.0).all())
# TODO: test accessibility of {training_,}confusion{,s} of
# CrossValidatedTransferError
self.failUnless(isinstance(cv.ca.samples_error, dict))
self.failUnless(len(cv.ca.samples_error) == data.nsamples)
# one value for each origid
assert_array_equal(sorted(cv.ca.samples_error.keys()),
sorted(data.sa.origids))
for k, v in cv.ca.samples_error.iteritems():
self.failUnless(len(v) == 1)
def test_repr():
# this time give mask only by its target length
sm = FeatureSliceMapper(slice(None), inspace='myspace')
# check reproduction
sm_clone = eval(repr(sm))
assert_equal(repr(sm_clone), repr(sm))
def test_strip_boundary():
ds = datasets['hollow']
ds.sa['btest'] = np.repeat([0,1], 20)
sn = StripBoundariesSamples('btest', 1, 2)
sds = sn(ds)
assert_equal(len(sds), len(ds) - 3)
for i in [19, 20, 21]:
assert_false(i in sds.samples.sid)
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_sphere():
# test sphere initialization
s = ne.Sphere(1)
center0 = (0, 0, 0)
center1 = (1, 1, 1)
assert_equal(len(s(center0)), 7)
target = array([array([-1, 0, 0]),
array([ 0, -1, 0]),
array([ 0, 0, -1]),
array([0, 0, 0]),
array([0, 0, 1]),
array([0, 1, 0]),
array([1, 0, 0])])
# test of internals -- no recomputation of increments should be done
prev_increments = s._increments
assert_array_equal(s(center0), target)
ok_(prev_increments is s._increments)
# query lower dimensionality
_ = s((0, 0))
ok_(not prev_increments is s._increments)
# test Sphere call
target = [array([0, 1, 1]),
array([1, 0, 1]),
array([1, 1, 0]),
array([1, 1, 1]),
array([1, 1, 2]),
array([1, 2, 1]),
array([2, 1, 1])]
res = s(center1)
assert_array_equal(array(res), target)
# They all should be tuples
ok_(np.all([isinstance(x, tuple) for x in res]))
# test for larger diameter
s = ne.Sphere(4)
assert_equal(len(s(center1)), 257)
# test extent keyword
#s = ne.Sphere(4,extent=(1,1,1))
#assert_array_equal(array(s((0,0,0))), array([[0,0,0]]))
# test Errors during initialisation and call
#assert_raises(ValueError, ne.Sphere, 2)
#assert_raises(ValueError, ne.Sphere, 1.0)
# no longer extent available
assert_raises(TypeError, ne.Sphere, 1, extent=(1))
assert_raises(TypeError, ne.Sphere, 1, extent=(1.0, 1.0, 1.0))
s = ne.Sphere(1)
#assert_raises(ValueError, s, (1))
if __debug__:
# No float coordinates allowed for now...
# XXX might like to change that ;)
#
assert_raises(ValueError, s, (1.0, 1.0, 1.0))
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_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_glmnet_r_sensitivities():
data = datasets['chirp_linear']
clf = GLMNET_R()
clf.train(data)
# now ask for the sensitivities WITHOUT having to pass the dataset
# again
sens = clf.get_sensitivity_analyzer(force_train=False)(None)
assert_equal(sens.shape, (1, data.nfeatures))
def test_glmnet_r_sensitivities():
data = datasets['chirp_linear']
clf = GLMNET_R()
clf.train(data)
# now ask for the sensitivities WITHOUT having to pass the dataset
# again
sens = clf.get_sensitivity_analyzer(force_training=False)()
assert_equal(sens.shape, (1, data.nfeatures))
def test_glmnet_c_sensitivities():
data = normal_feature_dataset(perlabel=10, nlabels=2, nfeatures=4)
# use GLMNET on binary problem
clf = GLMNET_C()
clf.train(data)
# now ask for the sensitivities WITHOUT having to pass the dataset
# again
sens = clf.get_sensitivity_analyzer(force_training=False)()
#failUnless(sens.shape == (data.nfeatures,))
assert_equal(sens.shape, (len(data.UT), data.nfeatures))
def test_glmnet_c_sensitivities():
data = normal_feature_dataset(perlabel=10, nlabels=2, nfeatures=4)
# use GLMNET on binary problem
clf = GLMNET_C()
clf.train(data)
# now ask for the sensitivities WITHOUT having to pass the dataset
# again
sens = clf.get_sensitivity_analyzer(force_train=False)(None)
#failUnless(sens.shape == (data.nfeatures,))
assert_equal(sens.shape, (len(data.UT), data.nfeatures))
def test_harvesting(self):
# get a dataset with a very high SNR
data = get_mv_pattern(10)
# do crossval with default errorfx and 'mean' combiner
transerror = TransferError(clfswh['linear'][0])
cv = CrossValidatedTransferError(
transerror,
NFoldSplitter(cvtype=1),
harvest_attribs=['transerror.clf.ca.training_time'])
result = cv(data)
ok_(cv.ca.harvested.has_key('transerror.clf.ca.training_time'))
assert_equal(len(cv.ca.harvested['transerror.clf.ca.training_time']),
len(data.UC))
def test_harvesting(self):
# get a dataset with a very high SNR
data = get_mv_pattern(10)
# do crossval with default errorfx and 'mean' combiner
transerror = TransferError(clfswh['linear'][0])
cv = CrossValidatedTransferError(
transerror,
NFoldSplitter(cvtype=1),
harvest_attribs=['transerror.clf.ca.training_time'])
result = cv(data)
ok_(cv.ca.harvested.has_key('transerror.clf.ca.training_time'))
assert_equal(len(cv.ca.harvested['transerror.clf.ca.training_time']),
len(data.UC))
def test_attrmap_repr():
assert_equal(repr(AttributeMap()), "AttributeMap()")
assert_equal(repr(AttributeMap(dict(a=2, b=1))),
"AttributeMap({'a': 2, 'b': 1})")
assert_equal(repr(AttributeMap(dict(a=2, b=1), mapnumeric=True)),
"AttributeMap({'a': 2, 'b': 1}, mapnumeric=True)")
assert_equal(repr(AttributeMap(dict(a=2, b=1), mapnumeric=True, collisions_resolution='tuple')),
"AttributeMap({'a': 2, 'b': 1}, mapnumeric=True, collisions_resolution='tuple')")
def test_sifter():
# somewhat duplicating the doctest
ds = Dataset(samples=np.arange(8).reshape((4,2)),
sa={'chunks': [ 0 , 1 , 2 , 3 ],
'targets': ['c', 'c', 'p', 'p']})
par = ChainNode([NFoldPartitioner(cvtype=2, attr='chunks'),
Sifter([('partitions', 2),
('targets', ['c', 'p'])])
])
dss = list(par.generate(ds))
assert_equal(len(dss), 4)
for ds_ in dss:
testing = ds[ds_.sa.partitions == 2]
assert_array_equal(np.unique(testing.sa.targets), ['c', 'p'])
# and we still have both targets present in training
training = ds[ds_.sa.partitions == 1]
assert_array_equal(np.unique(training.sa.targets), ['c', 'p'])
def test_featuregroup_mapper():
ds = Dataset(np.arange(24).reshape(3,8))
ds.fa['roi'] = [0, 1] * 4
# just to check
ds.sa['chunks'] = np.arange(3)
# correct results
csamples = [[3, 4], [11, 12], [19, 20]]
croi = [0, 1]
cchunks = np.arange(3)
m = mean_group_feature(['roi'])
mds = m.forward(ds)
assert_equal(mds.shape, (3, 2))
assert_array_equal(mds.samples, csamples)
assert_array_equal(mds.fa.roi, np.unique([0, 1] * 4))
# FAs should simply remain the same
assert_array_equal(mds.sa.chunks, np.arange(3))
def test_discarded_boundaries(self):
ds = datasets['hollow']
# four runs
ds.sa['chunks'] = np.repeat(np.arange(4), 10)
# do odd even splitting for lots of boundaries in few splits
part = ChainNode([OddEvenPartitioner(),
StripBoundariesSamples('chunks', 1, 2)])
parts = [d.samples.sid for d in part.generate(ds)]
# both dataset should have the same samples, because the boundaries are
# identical and the same sample should be stripped
assert_array_equal(parts[0], parts[1])
# we strip 3 samples per boundary
assert_equal(len(parts[0]), len(ds) - (3 * 3))
for i in [9, 10, 11, 19, 20, 21, 29, 30, 31]:
assert_false(i in parts[0])
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_simple_n_minus_one_cv(self):
data = get_mv_pattern(3)
data.init_origids('samples')
self.failUnless( data.nsamples == 120 )
self.failUnless( data.nfeatures == 2 )
self.failUnless(
(data.sa.targets == \
[0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0] * 6).all())
self.failUnless(
(data.sa.chunks == \
[k for k in range(1, 7) for i in range(20)]).all())
assert_equal(len(np.unique(data.sa.origids)), data.nsamples)
cv = CrossValidation(sample_clf_nl, NFoldPartitioner(),
enable_ca=['stats', 'training_stats'])
# 'samples_error'])
results = cv(data)
self.failUnless((results.samples < 0.2).all() and (results.samples >= 0.0).all())
def test_distances():
a = np.array([3,8])
b = np.array([6,4])
# test distances or yarik recalls unit testing ;)
assert_equal(cartesian_distance(a, b), 5.0)
assert_equal(manhatten_distance(a, b), 7)
assert_equal(absmin_distance(a, b), 4)
def test_distances():
a = np.array([3, 8])
b = np.array([6, 4])
# test distances or yarik recalls unit testing ;)
assert_equal(cartesian_distance(a, b), 5.0)
assert_equal(manhatten_distance(a, b), 7)
assert_equal(absmin_distance(a, b), 4)
def test_samplesgroup_mapper():
data = np.arange(24).reshape(8,3)
labels = [0, 1] * 4
chunks = np.repeat(np.array((0,1)),4)
# correct results
csamples = [[3, 4, 5], [6, 7, 8], [15, 16, 17], [18, 19, 20]]
clabels = [0, 1, 0, 1]
cchunks = [0, 0, 1, 1]
ds = dataset_wizard(samples=data, targets=labels, chunks=chunks)
# add some feature attribute -- just to check
ds.fa['checker'] = np.arange(3)
ds.init_origids('samples')
m = mean_group_sample(['targets', 'chunks'])
mds = m.forward(ds)
assert_array_equal(mds.samples, csamples)
# FAs should simply remain the same
assert_array_equal(mds.fa.checker, np.arange(3))
# now without grouping
m = mean_sample()
# forwarding just the samples should yield the same result
assert_array_equal(m.forward(ds.samples),
m.forward(ds).samples)
# directly apply to dataset
# using untrained mapper
m = mean_group_sample(['targets', 'chunks'])
mapped = ds.get_mapped(m)
assert_equal(mapped.nsamples, 4)
assert_equal(mapped.nfeatures, 3)
assert_array_equal(mapped.samples, csamples)
assert_array_equal(mapped.targets, clabels)
assert_array_equal(mapped.chunks, cchunks)
# make sure origids get regenerated
assert_array_equal([s.count('+') for s in mapped.sa.origids], [1] * 4)
def test_partitionmapper():
ds = give_data()
oep = OddEvenPartitioner()
parts = list(oep.generate(ds))
assert_equal(len(parts), 2)
for i, p in enumerate(parts):
assert_array_equal(p.sa['partitions'].unique, [1, 2])
assert_equal(p.a.partitions_set, i)
assert_equal(len(p), len(ds))
def test_forward_dense_array_mapper():
mask = np.ones((3,2), dtype='bool')
map_ = mask_mapper(mask)
# test shape reports
assert_equal(map_.forward1(mask).shape, (6,))
# test 1sample mapping
assert_array_equal(map_.forward1(np.arange(6).reshape(3,2)),
[0,1,2,3,4,5])
# test 4sample mapping
foursample = map_.forward(np.arange(24).reshape(4,3,2))
assert_array_equal(foursample,
[[0,1,2,3,4,5],
[6,7,8,9,10,11],
[12,13,14,15,16,17],
[18,19,20,21,22,23]])
# check incomplete masks
mask[1,1] = 0
map_ = mask_mapper(mask)
assert_equal(map_.forward1(mask).shape, (5,))
assert_array_equal(map_.forward1(np.arange(6).reshape(3,2)),
[0,1,2,4,5])
# check that it doesn't accept wrong dataspace
assert_raises(ValueError, map_.forward, np.arange(4).reshape(2,2))
# check fail if neither mask nor shape
assert_raises(ValueError, mask_mapper)
# check that a full mask is automatically created when providing shape
m = mask_mapper(shape=(2, 3, 4))
mp = m.forward1(np.arange(24).reshape(2, 3, 4))
assert_array_equal(mp, np.arange(24))
def test_eep_load():
eb = EEPBin(os.path.join(pymvpa_dataroot, 'eep.bin'))
ds = [ eep_dataset(source, targets=[1, 2]) for source in
(eb, os.path.join(pymvpa_dataroot, 'eep.bin')) ]
for d in ds:
assert_equal(d.nsamples, 2)
assert_equal(d.nfeatures, 128)
assert_equal(np.unique(d.fa.channels[4*23:4*23+4]), 'Pz')
assert_array_almost_equal([np.arange(-0.002, 0.005, 0.002)] * 32,
d.a.mapper.reverse1(d.fa.timepoints))
def test_eep_load():
eb = EEPBin(os.path.join(pymvpa_dataroot, 'eep.bin'))
ds = [
eep_dataset(source, targets=[1, 2])
for source in (eb, os.path.join(pymvpa_dataroot, 'eep.bin'))
]
for d in ds:
assert_equal(d.nsamples, 2)
assert_equal(d.nfeatures, 128)
assert_equal(np.unique(d.fa.channels[4 * 23:4 * 23 + 4]), 'Pz')
assert_array_almost_equal([np.arange(-0.002, 0.005, 0.002)] * 32,
d.a.mapper.reverse1(d.fa.timepoints))
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_sphere():
# test sphere initialization
s = ne.Sphere(1)
center0 = (0, 0, 0)
center1 = (1, 1, 1)
assert_equal(len(s(center0)), 7)
target = array([
array([-1, 0, 0]),
array([0, -1, 0]),
array([0, 0, -1]),
array([0, 0, 0]),
array([0, 0, 1]),
array([0, 1, 0]),
array([1, 0, 0])
])
# test of internals -- no recomputation of increments should be done
prev_increments = s._increments
assert_array_equal(s(center0), target)
ok_(prev_increments is s._increments)
# query lower dimensionality
_ = s((0, 0))
ok_(not prev_increments is s._increments)
# test Sphere call
target = [
array([0, 1, 1]),
array([1, 0, 1]),
array([1, 1, 0]),
array([1, 1, 1]),
array([1, 1, 2]),
array([1, 2, 1]),
array([2, 1, 1])
]
res = s(center1)
assert_array_equal(array(res), target)
# They all should be tuples
ok_(np.all([isinstance(x, tuple) for x in res]))
# test for larger diameter
s = ne.Sphere(4)
assert_equal(len(s(center1)), 257)
# test extent keyword
#s = ne.Sphere(4,extent=(1,1,1))
#assert_array_equal(array(s((0,0,0))), array([[0,0,0]]))
# test Errors during initialisation and call
#assert_raises(ValueError, ne.Sphere, 2)
#assert_raises(ValueError, ne.Sphere, 1.0)
# no longer extent available
assert_raises(TypeError, ne.Sphere, 1, extent=(1))
assert_raises(TypeError, ne.Sphere, 1, extent=(1.0, 1.0, 1.0))
s = ne.Sphere(1)
#assert_raises(ValueError, s, (1))
if __debug__:
# No float coordinates allowed for now...
# XXX might like to change that ;)
#
assert_raises(ValueError, s, (1.0, 1.0, 1.0))
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_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_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_reverse_dense_array_mapper():
mask = np.ones((3,2), dtype='bool')
mask[1,1] = 0
map_ = mask_mapper(mask)
rmapped = map_.reverse1(np.arange(1,6))
assert_equal(rmapped.shape, (3,2))
assert_equal(rmapped[1,1], 0)
assert_equal(rmapped[2,1], 5)
# check that it doesn't accept wrong dataspace
assert_raises(ValueError, map_, np.arange(6))
rmapped2 = map_.reverse(np.arange(1,11).reshape(2,5))
assert_equal(rmapped2.shape, (2,3,2))
assert_equal(rmapped2[0,1,1], 0 )
assert_equal(rmapped2[1,1,1], 0 )
assert_equal(rmapped2[0,2,1], 5 )
assert_equal(rmapped2[1,2,1], 10 )
def test_datasetmapping():
# 6 samples, 4 features
data = np.arange(24).reshape(6, 4)
ds = Dataset(data,
sa={
'timepoints': np.arange(6),
'multidim': data.copy()
},
fa={'fid': np.arange(4)})
# with overlapping and non-overlapping boxcars
startpoints = [0, 1, 4]
boxlength = 2
bm = BoxcarMapper(startpoints, boxlength, inspace='boxy')
# train is critical
bm.train(ds)
mds = bm.forward(ds)
assert_equal(len(mds), len(startpoints))
assert_equal(mds.nfeatures, boxlength)
# all samples attributes remain, but the can rotated/compressed into
# multidimensional attributes
assert_equal(sorted(mds.sa.keys()),
['boxy_onsetidx'] + sorted(ds.sa.keys()))
assert_equal(mds.sa.multidim.shape,
(len(startpoints), boxlength, ds.nfeatures))
assert_equal(mds.sa.timepoints.shape, (len(startpoints), boxlength))
assert_array_equal(mds.sa.timepoints.flatten(),
np.array([(s, s + 1) for s in startpoints]).flatten())
assert_array_equal(mds.sa.boxy_onsetidx, startpoints)
# feature attributes also get rotated and broadcasted
assert_array_equal(mds.fa.fid, [ds.fa.fid, ds.fa.fid])
# and finally there is a new one
assert_array_equal(mds.fa.boxy_offsetidx,
np.repeat(np.arange(boxlength), 4).reshape(2, -1))
# now see how it works on reverse()
rds = bm.reverse(mds)
# we got at least something of all original attributes back
assert_equal(sorted(rds.sa.keys()), sorted(ds.sa.keys()))
assert_equal(sorted(rds.fa.keys()), sorted(ds.fa.keys()))
# it is not possible to reconstruct the full samples array
# some samples even might show up multiple times (when there are overlapping
# boxcars
assert_array_equal(
rds.samples,
np.array([[0, 1, 2, 3], [4, 5, 6, 7], [4, 5, 6, 7], [8, 9, 10, 11],
[16, 17, 18, 19], [20, 21, 22, 23]]))
assert_array_equal(rds.sa.timepoints, [0, 1, 1, 2, 4, 5])
assert_array_equal(rds.sa.multidim, ds.sa.multidim[rds.sa.timepoints])
# but feature attributes should be fully recovered
assert_array_equal(rds.fa.fid, ds.fa.fid)
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_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_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_attrmap():
map_default = {'eins': 0, 'zwei': 2, 'sieben': 1}
map_custom = {'eins': 11, 'zwei': 22, 'sieben': 33}
literal = ['eins', 'zwei', 'sieben', 'eins', 'sieben', 'eins']
literal_nonmatching = ['uno', 'dos', 'tres']
num_default = [0, 2, 1, 0, 1, 0]
num_custom = [11, 22, 33, 11, 33, 11]
# no custom mapping given
am = AttributeMap()
assert_false(am)
ok_(len(am) == 0)
assert_array_equal(am.to_numeric(literal), num_default)
assert_array_equal(am.to_literal(num_default), literal)
ok_(am)
ok_(len(am) == 3)
#
# Tests for recursive mapping + preserving datatype
class myarray(np.ndarray):
pass
assert_raises(KeyError, am.to_literal, [(1, 2), 2, 0])
literal_fancy = [(1, 2), 2, [0], np.array([0, 1]).view(myarray)]
literal_fancy_tuple = tuple(literal_fancy)
literal_fancy_array = np.array(literal_fancy, dtype=object)
for l in (literal_fancy, literal_fancy_tuple,
literal_fancy_array):
res = am.to_literal(l, recurse=True)
assert_equal(res[0], ('sieben', 'zwei'))
assert_equal(res[1], 'zwei')
assert_equal(res[2], ['eins'])
assert_array_equal(res[3], ['eins', 'sieben'])
# types of result and subsequences should be preserved
ok_(isinstance(res, l.__class__))
ok_(isinstance(res[0], tuple))
ok_(isinstance(res[1], str))
ok_(isinstance(res[2], list))
ok_(isinstance(res[3], myarray))
# yet another example
a = np.empty(1, dtype=object)
a[0] = (0, 1)
res = am.to_literal(a, recurse=True)
ok_(isinstance(res[0], tuple))
#
# with custom mapping
am = AttributeMap(map=map_custom)
assert_array_equal(am.to_numeric(literal), num_custom)
assert_array_equal(am.to_literal(num_custom), literal)
# if not numeric nothing is mapped
assert_array_equal(am.to_numeric(num_custom), num_custom)
# even if the map doesn't fit
assert_array_equal(am.to_numeric(num_default), num_default)
# need to_numeric first
am = AttributeMap()
assert_raises(RuntimeError, am.to_literal, [1,2,3])
# stupid args
assert_raises(ValueError, AttributeMap, map=num_custom)
# map mismatch
am = AttributeMap(map=map_custom)
if __debug__:
# checked only in __debug__
assert_raises(KeyError, am.to_numeric, literal_nonmatching)
# needs reset and should work afterwards
am.clear()
assert_array_equal(am.to_numeric(literal_nonmatching), [2, 0, 1])
# and now reverse
am = AttributeMap(map=map_custom)
assert_raises(KeyError, am.to_literal, num_default)
# dict-like interface
am = AttributeMap()
ok_([(k, v) for k, v in am.iteritems()] == [])
def test_reverse_dense_array_mapper():
mask = np.ones((3, 2), dtype="bool")
mask[1, 1] = 0
map_ = mask_mapper(mask)
rmapped = map_.reverse1(np.arange(1, 6))
assert_equal(rmapped.shape, (3, 2))
assert_equal(rmapped[1, 1], 0)
assert_equal(rmapped[2, 1], 5)
# check that it doesn't accept wrong dataspace
assert_raises(ValueError, map_.forward, np.arange(6))
rmapped2 = map_.reverse(np.arange(1, 11).reshape(2, 5))
assert_equal(rmapped2.shape, (2, 3, 2))
assert_equal(rmapped2[0, 1, 1], 0)
assert_equal(rmapped2[1, 1, 1], 0)
assert_equal(rmapped2[0, 2, 1], 5)
assert_equal(rmapped2[1, 2, 1], 10)
def test_zscore():
"""Test z-scoring transformation
"""
# dataset: mean=2, std=1
samples = np.array((0, 1, 3, 4, 2, 2, 3, 1, 1, 3, 3, 1, 2, 2, 2, 2)).\
reshape((16, 1))
data = dataset_wizard(samples.copy(), targets=range(16), chunks=[0] * 16)
assert_equal(data.samples.mean(), 2.0)
assert_equal(data.samples.std(), 1.0)
data_samples = data.samples.copy()
zscore(data, chunks_attr='chunks')
# copy should stay intact
assert_equal(data_samples.mean(), 2.0)
assert_equal(data_samples.std(), 1.0)
# we should be able to operate on ndarrays
# But we can't change type inplace for an array, can't we?
assert_raises(TypeError, zscore, data_samples, chunks_attr=None)
# so lets do manually
data_samples = data_samples.astype(float)
zscore(data_samples, chunks_attr=None)
assert_array_equal(data.samples, data_samples)
print data_samples
# check z-scoring
check = np.array([-2, -1, 1, 2, 0, 0, 1, -1, -1, 1, 1, -1, 0, 0, 0, 0],
dtype='float64').reshape(16, 1)
assert_array_equal(data.samples, check)
data = dataset_wizard(samples.copy(), targets=range(16), chunks=[0] * 16)
zscore(data, chunks_attr=None)
assert_array_equal(data.samples, check)
# check z-scoring taking set of labels as a baseline
data = dataset_wizard(samples.copy(),
targets=[0, 2, 2, 2, 1] + [2] * 11,
chunks=[0] * 16)
zscore(data, param_est=('targets', [0, 1]))
assert_array_equal(samples, data.samples + 1.0)
# check that zscore modifies in-place; only guaranteed if no upcasting is
# necessary
samples = samples.astype('float')
data = dataset_wizard(samples,
targets=[0, 2, 2, 2, 1] + [2] * 11,
chunks=[0] * 16)
zscore(data, param_est=('targets', [0, 1]))
assert_array_equal(samples, data.samples)
# these might be duplicating code above -- but twice is better than nothing
# dataset: mean=2, std=1
raw = np.array((0, 1, 3, 4, 2, 2, 3, 1, 1, 3, 3, 1, 2, 2, 2, 2))
# dataset: mean=12, std=1
raw2 = np.array((0, 1, 3, 4, 2, 2, 3, 1, 1, 3, 3, 1, 2, 2, 2, 2)) + 10
# zscore target
check = [-2, -1, 1, 2, 0, 0, 1, -1, -1, 1, 1, -1, 0, 0, 0, 0]
ds = dataset_wizard(raw.copy(), targets=range(16), chunks=[0] * 16)
pristine = dataset_wizard(raw.copy(), targets=range(16), chunks=[0] * 16)
zm = ZScoreMapper()
# should do global zscore by default
zm.train(ds) # train
assert_array_almost_equal(zm.forward(ds), np.transpose([check]))
# should not modify the source
assert_array_equal(pristine, ds)
# if we tell it a different mean it should obey the order
zm = ZScoreMapper(params=(3,1))
zm.train(ds)
assert_array_almost_equal(zm.forward(ds), np.transpose([check]) - 1 )
assert_array_equal(pristine, ds)
# let's look at chunk-wise z-scoring
ds = dataset_wizard(np.hstack((raw.copy(), raw2.copy())),
targets=range(32),
chunks=[0] * 16 + [1] * 16)
# by default chunk-wise
zm = ZScoreMapper()
zm.train(ds) # train
assert_array_almost_equal(zm.forward(ds), np.transpose([check + check]))
# we should be able to do that same manually
zm = ZScoreMapper(params={0: (2,1), 1: (12,1)})
zm.train(ds) # train
assert_array_almost_equal(zm.forward(ds), np.transpose([check + check]))
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_basic_collectable():
c = Collectable()
# empty by default
assert_equal(c.name, None)
assert_equal(c.value, None)
assert_equal(c.__doc__, None)
# late assignment
c.name = 'somename'
c.value = 12345
assert_equal(c.name, 'somename')
assert_equal(c.value, 12345)
# immediate content
c = Collectable('value', 'myname', "This is a test")
assert_equal(c.name, 'myname')
assert_equal(c.value, 'value')
assert_equal(c.__doc__, "This is a test")
assert_equal(str(c), 'myname')
# repr
e = eval(repr(c))
assert_equal(e.name, 'myname')
assert_equal(e.value, 'value')
assert_equal(e.__doc__, "This is a test")
# shallow copy does not create a view of value array
c.value = np.arange(5)
d = copy.copy(c)
assert_false(d.value.base is c.value)
# names starting with _ are not allowed
assert_raises(ValueError, c._set_name, "_underscore")