def test_split_clf_on_chainpartitioner(self):
# pretty much a smoke test for #156
ds = datasets['uni2small']
part = ChainNode([
NFoldPartitioner(cvtype=1),
Balancer(attr='targets',
count=2,
limit='partitions',
apply_selection=True)
])
partitions = list(part.generate(ds))
sclf = SplitClassifier(sample_clf_lin,
part,
enable_ca=['stats', 'splits'])
sclf.train(ds)
pred = sclf.predict(ds)
assert_equal(len(pred), len(ds)) # rudimentary check
assert_equal(len(sclf.ca.splits), len(partitions))
assert_equal(len(sclf.clfs), len(partitions))
# now let's do sensitivity analyzer just in case
sclf.untrain()
sensana = sclf.get_sensitivity_analyzer()
sens = sensana(ds)
# basic check that sensitivities varied across splits
from mvpa2.mappers.fx import FxMapper
sens_stds = FxMapper('samples', np.std, uattrs=['targets'])(sens)
assert_true(np.any(sens_stds != 0))
def test_split_clf_on_chainpartitioner(self):
# pretty much a smoke test for #156
ds = datasets['uni2small']
part = ChainNode([NFoldPartitioner(cvtype=1),
Balancer(attr='targets', count=2,
limit='partitions', apply_selection=True)])
partitions = list(part.generate(ds))
sclf = SplitClassifier(sample_clf_lin, part, enable_ca=['stats', 'splits'])
sclf.train(ds)
pred = sclf.predict(ds)
assert_equal(len(pred), len(ds)) # rudimentary check
assert_equal(len(sclf.ca.splits), len(partitions))
assert_equal(len(sclf.clfs), len(partitions))
# now let's do sensitivity analyzer just in case
sclf.untrain()
sensana = sclf.get_sensitivity_analyzer()
sens = sensana(ds)
# basic check that sensitivities varied across splits
from mvpa2.mappers.fx import FxMapper
sens_stds = FxMapper('samples', np.std, uattrs=['targets'])(sens)
assert_true(np.any(sens_stds != 0))
