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_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_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_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")