def test_zcore_repr():
# Just basic test if everything is sane... no proper comparison
for m in (ZScoreMapper(chunks_attr=None),
ZScoreMapper(params=(3, 1)),
ZScoreMapper()):
mr = eval(repr(m))
ok_(isinstance(mr, ZScoreMapper))
def test_zcore_repr():
# Just basic test if everything is sane... no proper comparison
for m in (ZScoreMapper(chunks_attr=None),
ZScoreMapper(params=(3, 1)),
ZScoreMapper()):
mr = eval(repr(m))
ok_(isinstance(mr, ZScoreMapper))
def test_none_splitter(self):
nos = NoneSplitter()
splits = [(train, test) for (train, test) in nos(self.data)]
self.failUnless(len(splits) == 1)
self.failUnless(splits[0][0] == None)
self.failUnless(splits[0][1].nsamples == 100)
nos = NoneSplitter(mode='first')
splits = [(train, test) for (train, test) in nos(self.data)]
self.failUnless(len(splits) == 1)
self.failUnless(splits[0][1] == None)
self.failUnless(splits[0][0].nsamples == 100)
# test sampling tools
# specified value
nos = NoneSplitter(nrunspersplit=3, npertarget=10)
splits = [(train, test) for (train, test) in nos(self.data)]
self.failUnless(len(splits) == 3)
for split in splits:
self.failUnless(split[0] == None)
self.failUnless(split[1].nsamples == 40)
ok_(split[1].get_nsamples_per_attr('targets').values() ==
[10, 10, 10, 10])
# auto-determined
nos = NoneSplitter(nrunspersplit=3, npertarget='equal')
splits = [(train, test) for (train, test) in nos(self.data)]
self.failUnless(len(splits) == 3)
for split in splits:
self.failUnless(split[0] == None)
self.failUnless(split[1].nsamples == 100)
ok_(split[1].get_nsamples_per_attr('targets').values() ==
[25, 25, 25, 25])
def test_aggregation(self):
data = dataset_wizard(np.arange( 20 ).reshape((4, 5)), targets=1, chunks=1)
ag_data = aggregate_features(data, np.mean)
ok_(ag_data.nsamples == 4)
ok_(ag_data.nfeatures == 1)
assert_array_equal(ag_data.samples[:, 0], [2, 7, 12, 17])
def test_aggregation(self):
data = dataset_wizard(np.arange(20).reshape((4, 5)),
targets=1,
chunks=1)
ag_data = aggregate_features(data, np.mean)
ok_(ag_data.nsamples == 4)
ok_(ag_data.nfeatures == 1)
assert_array_equal(ag_data.samples[:, 0], [2, 7, 12, 17])
def test_attrmap_conflicts():
am_n = AttributeMap({'a':1, 'b':2, 'c':1})
am_t = AttributeMap({'a':1, 'b':2, 'c':1}, collisions_resolution='tuple')
am_l = AttributeMap({'a':1, 'b':2, 'c':1}, collisions_resolution='lucky')
q_f = ['a', 'b', 'a', 'c']
# should have no effect on forward mapping
ok_(np.all(am_n.to_numeric(q_f) == am_t.to_numeric(q_f)))
ok_(np.all(am_t.to_numeric(q_f) == am_l.to_numeric(q_f)))
assert_raises(ValueError, am_n.to_literal, [2])
r_t = am_t.to_literal([2, 1])
r_l = am_l.to_literal([2, 1])
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_subset_filler():
sm = StaticFeatureSelection(np.arange(3))
sm_f0 = StaticFeatureSelection(np.arange(3), filler=0)
sm_fm1 = StaticFeatureSelection(np.arange(3), filler=-1)
sm_fnan = StaticFeatureSelection(np.arange(3), filler=np.nan)
data = np.arange(12).astype(float).reshape((2, -1))
sm.train(data)
data_forwarded = sm.forward(data)
for m in (sm, sm_f0, sm_fm1, sm_fnan):
m.train(data)
assert_array_equal(data_forwarded, m.forward(data))
data_back_fm1 = sm_fm1.reverse(data_forwarded)
ok_(np.all(data_back_fm1[:, 3:] == -1))
data_back_fnan = sm_fnan.reverse(data_forwarded)
ok_(np.all(np.isnan(data_back_fnan[:, 3:])))
def test_subset_filler():
sm = FeatureSliceMapper(np.arange(3))
sm_f0 = FeatureSliceMapper(np.arange(3), filler=0)
sm_fm1 = FeatureSliceMapper(np.arange(3), filler=-1)
sm_fnan = FeatureSliceMapper(np.arange(3), filler=np.nan)
data = np.arange(12).astype(float).reshape((2, -1))
sm.train(data)
data_forwarded = sm.forward(data)
for m in (sm, sm_f0, sm_fm1, sm_fnan):
m.train(data)
assert_array_equal(data_forwarded, m.forward(data))
data_back_fm1 = sm_fm1.reverse(data_forwarded)
ok_(np.all(data_back_fm1[:, 3:] == -1))
data_back_fnan = sm_fnan.reverse(data_forwarded)
ok_(np.all(np.isnan(data_back_fnan[:, 3:])))
def test_cached_query_engine():
"""Test cached query engine
"""
sphere = ne.Sphere(1)
# dataset with just one "space"
ds = datasets['3dlarge']
qe0 = ne.IndexQueryEngine(myspace=sphere)
qec = ne.CachedQueryEngine(qe0)
# and ground truth one
qe = ne.IndexQueryEngine(myspace=sphere)
results_ind = []
results_kw = []
def cmp_res(res1, res2):
comp = [x == y for x, y in zip(res1, res2)]
ok_(np.all(comp))
for iq, q in enumerate((qe, qec)):
q.train(ds)
# sequential train on the same should be ok in both cases
q.train(ds)
res_ind = [q[fid] for fid in xrange(ds.nfeatures)]
res_kw = [q(myspace=x) for x in ds.fa.myspace]
# test if results match
cmp_res(res_ind, res_kw)
results_ind.append(res_ind)
results_kw.append(res_kw)
# now check if results of cached were the same as of regular run
cmp_res(results_ind[0], results_ind[1])
# Now do sanity checks
assert_raises(ValueError, qec.train, ds[:, :-1])
assert_raises(ValueError, qec.train, ds.copy())
ds2 = ds.copy()
qec.untrain()
qec.train(ds2)
# should be the same results on the copy
cmp_res(results_ind[0], [qec[fid] for fid in xrange(ds.nfeatures)])
cmp_res(results_kw[0], [qec(myspace=x) for x in ds.fa.myspace])
ok_(qec.train(ds2) is None)
def test_cached_query_engine():
"""Test cached query engine
"""
sphere = ne.Sphere(1)
# dataset with just one "space"
ds = datasets['3dlarge']
qe0 = ne.IndexQueryEngine(myspace=sphere)
qec = ne.CachedQueryEngine(qe0)
# and ground truth one
qe = ne.IndexQueryEngine(myspace=sphere)
results_ind = []
results_kw = []
def cmp_res(res1, res2):
comp = [x == y for x, y in zip(res1, res2)]
ok_(np.all(comp))
for iq, q in enumerate((qe, qec)):
q.train(ds)
# sequential train on the same should be ok in both cases
q.train(ds)
res_ind = [q[fid] for fid in xrange(ds.nfeatures)]
res_kw = [q(myspace=x) for x in ds.fa.myspace]
# test if results match
cmp_res(res_ind, res_kw)
results_ind.append(res_ind)
results_kw.append(res_kw)
# now check if results of cached were the same as of regular run
cmp_res(results_ind[0], results_ind[1])
# Now do sanity checks
assert_raises(ValueError, qec.train, ds[:, :-1])
assert_raises(ValueError, qec.train, ds.copy())
ds2 = ds.copy()
qec.untrain()
qec.train(ds2)
# should be the same results on the copy
cmp_res(results_ind[0], [qec[fid] for fid in xrange(ds.nfeatures)])
cmp_res(results_kw[0], [qec(myspace=x) for x in ds.fa.myspace])
ok_(qec.train(ds2) is None)
def test_none_splitter(self):
nos = NoneSplitter()
splits = [ (train, test) for (train, test) in nos(self.data) ]
self.failUnless(len(splits) == 1)
self.failUnless(splits[0][0] == None)
self.failUnless(splits[0][1].nsamples == 100)
nos = NoneSplitter(mode='first')
splits = [ (train, test) for (train, test) in nos(self.data) ]
self.failUnless(len(splits) == 1)
self.failUnless(splits[0][1] == None)
self.failUnless(splits[0][0].nsamples == 100)
# test sampling tools
# specified value
nos = NoneSplitter(nrunspersplit=3,
npertarget=10)
splits = [ (train, test) for (train, test) in nos(self.data) ]
self.failUnless(len(splits) == 3)
for split in splits:
self.failUnless(split[0] == None)
self.failUnless(split[1].nsamples == 40)
ok_(split[1].get_nsamples_per_attr('targets').values() ==
[10,10,10,10])
# auto-determined
nos = NoneSplitter(nrunspersplit=3,
npertarget='equal')
splits = [ (train, test) for (train, test) in nos(self.data) ]
self.failUnless(len(splits) == 3)
for split in splits:
self.failUnless(split[0] == None)
self.failUnless(split[1].nsamples == 100)
ok_(split[1].get_nsamples_per_attr('targets').values() ==
[25,25,25,25])
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_samples_attributes(self):
sa = SampleAttributes(os.path.join(pymvpa_dataroot,
'attributes_literal.txt'),
literallabels=True)
ok_(sa.nrows == 1452, msg='There should be 1452 samples')
# convert to event list, with some custom attr
ev = find_events(**sa)
ok_(len(ev) == 17 * (max(sa.chunks) + 1),
msg='Not all events got detected.')
ok_(ev[0]['targets'] == ev[-1]['targets'] == 'rest',
msg='First and last event are rest condition.')
ok_(ev[-1]['onset'] + ev[-1]['duration'] == sa.nrows,
msg='Something is wrong with the timiing of the events')
def test_samples_attributes(self):
sa = SampleAttributes(os.path.join(pymvpa_dataroot,
'attributes_literal.txt'),
literallabels=True)
ok_(sa.nrows == 1452, msg='There should be 1452 samples')
# convert to event list, with some custom attr
ev = find_events(**sa)
ok_(len(ev) == 17 * (max(sa.chunks) + 1),
msg='Not all events got detected.')
ok_(ev[0]['targets'] == ev[-1]['targets'] == 'rest',
msg='First and last event are rest condition.')
ok_(ev[-1]['onset'] + ev[-1]['duration'] == sa.nrows,
msg='Something is wrong with the timiing of the events')
def test_sphere_distance_func():
# Test some other distance
se = ne.Sphere(3)
sm = ne.Sphere(3, distance_func=manhatten_distance)
rese = se((10, 5))
resm = sm((10, 5))
for res in rese, resm:
# basic test for duplicates (I think we forgotten to test for them)
ok_(len(res) == len(set(res)))
# in manhatten distance we should all be no further than 3 "steps" away
ok_(np.all([np.sum(np.abs(np.array(x) - (10, 5))) <= 3 for x in resm]))
# in euclidean we are taking shortcuts ;)
ok_(np.any([np.sum(np.abs(np.array(x) - (10, 5))) > 3 for x in rese]))
def test_sphere_distance_func():
# Test some other distance
se = ne.Sphere(3)
sm = ne.Sphere(3, distance_func=manhatten_distance)
rese = se((10, 5))
resm = sm((10, 5))
for res in rese, resm:
# basic test for duplicates (I think we forgotten to test for them)
ok_(len(res) == len(set(res)))
# in manhatten distance we should all be no further than 3 "steps" away
ok_(np.all([np.sum(np.abs(np.array(x) - (10, 5))) <= 3 for x in resm]))
# in euclidean we are taking shortcuts ;)
ok_(np.any([np.sum(np.abs(np.array(x) - (10, 5))) > 3 for x in rese]))
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_sphere_scaled():
s1 = ne.Sphere(3)
s = ne.Sphere(3, element_sizes=(1, 1))
# Should give exactly the same results since element_sizes are 1s
for p in ((0, 0), (-23, 1)):
assert_array_equal(s1(p), s(p))
ok_(len(s(p)) == len(set(s(p))))
# Raise exception if query dimensionality does not match element_sizes
assert_raises(ValueError, s, (1, ))
s = ne.Sphere(3, element_sizes=(1.5, 2))
assert_array_equal(s((0, 0)), [(-2, 0), (-1, -1), (-1, 0), (-1, 1),
(0, -1), (0, 0), (0, 1), (1, -1), (1, 0),
(1, 1), (2, 0)])
s = ne.Sphere(1.5, element_sizes=(1.5, 1.5, 1.5))
res = s((0, 0, 0))
ok_(np.all([np.sqrt(np.sum(np.array(x)**2)) <= 1.5 for x in res]))
ok_(len(res) == 7)
# all neighbors so no more than 1 voxel away -- just a cube, for
# some "sphere" effect radius had to be 3.0 ;)
td = np.sqrt(3 * 1.5**2)
s = ne.Sphere(td, element_sizes=(1.5, 1.5, 1.5))
res = s((0, 0, 0))
ok_(np.all([np.sqrt(np.sum(np.array(x)**2)) <= td for x in res]))
ok_(np.all([np.sum(np.abs(x) > 1) == 0 for x in res]))
ok_(len(res) == 27)
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 cmp_res(res1, res2):
comp = [x == y for x, y in zip(res1, res2)]
ok_(np.all(comp))
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 cmp_res(res1, res2):
comp = [x == y for x, y in zip(res1, res2)]
ok_(np.all(comp))
def test_sphere_scaled():
s1 = ne.Sphere(3)
s = ne.Sphere(3, element_sizes=(1, 1))
# Should give exactly the same results since element_sizes are 1s
for p in ((0, 0), (-23, 1)):
assert_array_equal(s1(p), s(p))
ok_(len(s(p)) == len(set(s(p))))
# Raise exception if query dimensionality does not match element_sizes
assert_raises(ValueError, s, (1,))
s = ne.Sphere(3, element_sizes=(1.5, 2))
assert_array_equal(s((0, 0)),
[(-2, 0), (-1, -1), (-1, 0), (-1, 1),
(0, -1), (0, 0), (0, 1),
(1, -1), (1, 0), (1, 1), (2, 0)])
s = ne.Sphere(1.5, element_sizes=(1.5, 1.5, 1.5))
res = s((0, 0, 0))
ok_(np.all([np.sqrt(np.sum(np.array(x)**2)) <= 1.5 for x in res]))
ok_(len(res) == 7)
# all neighbors so no more than 1 voxel away -- just a cube, for
# some "sphere" effect radius had to be 3.0 ;)
td = np.sqrt(3*1.5**2)
s = ne.Sphere(td, element_sizes=(1.5, 1.5, 1.5))
res = s((0, 0, 0))
ok_(np.all([np.sqrt(np.sum(np.array(x)**2)) <= td for x in res]))
ok_(np.all([np.sum(np.abs(x) > 1) == 0 for x in res]))
ok_(len(res) == 27)
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)