def test_mapper_vs_zscore():
"""Test by comparing to results of elderly z-score function
"""
# data: 40 sample feature line in 20d space (40x20; samples x features)
dss = [
dataset_wizard(np.concatenate(
[np.arange(40) for i in range(20)]).reshape(20,-1).T,
targets=1, chunks=1),
] + datasets.values()
for ds in dss:
ds1 = deepcopy(ds)
ds2 = deepcopy(ds)
zsm = ZScoreMapper(chunks_attr=None)
assert_raises(RuntimeError, zsm.forward, ds1.samples)
zsm.train(ds1)
ds1z = zsm.forward(ds1.samples)
zscore(ds2, chunks_attr=None)
assert_array_almost_equal(ds1z, ds2.samples)
assert_array_equal(ds1.samples, ds.samples)
def test_mapper_vs_zscore():
"""Test by comparing to results of elderly z-score function
"""
# data: 40 sample feature line in 20d space (40x20; samples x features)
dss = [
dataset_wizard(np.concatenate(
[np.arange(40) for i in range(20)]).reshape(20,-1).T,
targets=1, chunks=1),
] + datasets.values()
for ds in dss:
ds1 = deepcopy(ds)
ds2 = deepcopy(ds)
zsm = ZScoreMapper(chunks_attr=None)
assert_raises(RuntimeError, zsm.forward, ds1.samples)
zsm.train(ds1)
ds1z = zsm.forward(ds1.samples)
zscore(ds2, chunks_attr=None)
assert_array_almost_equal(ds1z, ds2.samples)
assert_array_equal(ds1.samples, ds.samples)
def test_dataset_summary():
for ds in datasets.values() + [Dataset(np.array([None], dtype=object))]:
s = ds.summary()
ok_(s.startswith(str(ds)[1:-1])) # we strip surrounding '<...>'
# TODO: actual test of what was returned; to do that properly
# RF the summary() so it is a dictionary
summaries = []
if 'targets' in ds.sa:
summaries += ['Sequence statistics']
if 'chunks' in ds.sa:
summaries += ['Summary for targets', 'Summary for chunks']
# By default we should get all kinds of summaries
if not 'Number of unique targets >' in s:
for summary in summaries:
ok_(summary in s)
# If we give "wrong" targets_attr we should see none of summaries
s2 = ds.summary(targets_attr='bogus')
for summary in summaries:
ok_(not summary in s2)
