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_classifier(self):
ds = self.data_bin_1
clf = SplitClassifier(
clf=SameSignClassifier(),
enable_ca=['stats', 'training_stats', 'feature_ids'])
clf.train(ds) # train the beast
error = clf.ca.stats.error
tr_error = clf.ca.training_stats.error
clf2 = clf.clone()
cv = CrossValidation(clf2,
NFoldPartitioner(),
postproc=mean_sample(),
enable_ca=['stats', 'training_stats'])
cverror = cv(ds)
cverror = cverror.samples.squeeze()
tr_cverror = cv.ca.training_stats.error
self.assertEqual(
error,
cverror,
msg="We should get the same error using split classifier as"
" using CrossValidation. Got %s and %s" % (error, cverror))
self.assertEqual(
tr_error,
tr_cverror,
msg="We should get the same training error using split classifier as"
" using CrossValidation. Got %s and %s" % (tr_error, tr_cverror))
self.assertEqual(clf.ca.stats.percent_correct,
100,
msg="Dummy clf should train perfectly")
# CV and SplitClassifier should get the same confusion matrices
assert_array_equal(clf.ca.stats.matrix, cv.ca.stats.matrix)
self.assertEqual(len(clf.ca.stats.sets),
len(ds.UC),
msg="Should have 1 confusion per each split")
self.assertEqual(
len(clf.clfs),
len(ds.UC),
msg="Should have number of classifiers equal # of epochs")
self.assertEqual(clf.predict(ds.samples),
list(ds.targets),
msg="Should classify correctly")
# feature_ids must be list of lists, and since it is not
# feature-selecting classifier used - we expect all features
# to be utilized
# NOT ANYMORE -- for BoostedClassifier we have now union of all
# used features across slave classifiers. That makes
# semantics clear. If you need to get deeper -- use upcoming
# harvesting facility ;-)
# self.assertEqual(len(clf.feature_ids), len(ds.uniquechunks))
# self.assertTrue(np.array([len(ids)==ds.nfeatures
# for ids in clf.feature_ids]).all())
# Just check if we get it at all ;-)
summary = clf.summary()
def test_split_classifier(self):
ds = self.data_bin_1
clf = SplitClassifier(clf=SameSignClassifier(),
enable_ca=['stats', 'training_stats',
'feature_ids'])
clf.train(ds) # train the beast
error = clf.ca.stats.error
tr_error = clf.ca.training_stats.error
clf2 = clf.clone()
cv = CrossValidation(clf2, NFoldPartitioner(), postproc=mean_sample(),
enable_ca=['stats', 'training_stats'])
cverror = cv(ds)
cverror = cverror.samples.squeeze()
tr_cverror = cv.ca.training_stats.error
self.assertEqual(error, cverror,
msg="We should get the same error using split classifier as"
" using CrossValidation. Got %s and %s"
% (error, cverror))
self.assertEqual(tr_error, tr_cverror,
msg="We should get the same training error using split classifier as"
" using CrossValidation. Got %s and %s"
% (tr_error, tr_cverror))
self.assertEqual(clf.ca.stats.percent_correct,
100,
msg="Dummy clf should train perfectly")
# CV and SplitClassifier should get the same confusion matrices
assert_array_equal(clf.ca.stats.matrix,
cv.ca.stats.matrix)
self.assertEqual(len(clf.ca.stats.sets),
len(ds.UC),
msg="Should have 1 confusion per each split")
self.assertEqual(len(clf.clfs), len(ds.UC),
msg="Should have number of classifiers equal # of epochs")
self.assertEqual(clf.predict(ds.samples), list(ds.targets),
msg="Should classify correctly")
# feature_ids must be list of lists, and since it is not
# feature-selecting classifier used - we expect all features
# to be utilized
# NOT ANYMORE -- for BoostedClassifier we have now union of all
# used features across slave classifiers. That makes
# semantics clear. If you need to get deeper -- use upcoming
# harvesting facility ;-)
# self.assertEqual(len(clf.feature_ids), len(ds.uniquechunks))
# self.assertTrue(np.array([len(ids)==ds.nfeatures
# for ids in clf.feature_ids]).all())
# Just check if we get it at all ;-)
summary = clf.summary()
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_regressions(self, regr):
"""Simple tests on regressions
"""
if not externals.exists('scipy'):
raise SkipTest
else:
from mvpa2.misc.errorfx import corr_error
ds = datasets['chirp_linear']
# we want numeric labels to maintain the previous behavior, especially
# since we deal with regressions here
ds.sa.targets = AttributeMap().to_numeric(ds.targets)
cve = CrossValidation(regr, NFoldPartitioner(), postproc=mean_sample(),
errorfx=corr_error, enable_ca=['training_stats', 'stats'])
# check the default
#self.assertTrue(cve.transerror.errorfx is corr_error)
corr = np.asscalar(cve(ds).samples)
# Our CorrErrorFx should never return NaN
self.assertTrue(not np.isnan(corr))
self.assertTrue(corr == cve.ca.stats.stats['CCe'])
splitregr = SplitClassifier(
regr, partitioner=OddEvenPartitioner(),
enable_ca=['training_stats', 'stats'])
splitregr.train(ds)
split_corr = splitregr.ca.stats.stats['CCe']
split_corr_tr = splitregr.ca.training_stats.stats['CCe']
for confusion, error in (
(cve.ca.stats, corr),
(splitregr.ca.stats, split_corr),
(splitregr.ca.training_stats, split_corr_tr),
):
#TODO: test confusion statistics
# Part of it for now -- CCe
for conf in confusion.summaries:
stats = conf.stats
if cfg.getboolean('tests', 'labile', default='yes'):
self.assertTrue(stats['CCe'] < 0.5)
self.assertEqual(stats['CCe'], stats['Summary CCe'])
s0 = confusion.as_string(short=True)
s1 = confusion.as_string(short=False)
for s in [s0, s1]:
self.assertTrue(len(s) > 10,
msg="We should get some string representation "
"of regression summary. Got %s" % s)
if cfg.getboolean('tests', 'labile', default='yes'):
self.assertTrue(error < 0.2,
msg="Regressions should perform well on a simple "
"dataset. Got correlation error of %s " % error)
# Test access to summary statistics
# YOH: lets start making testing more reliable.
# p-value for such accident to have is verrrry tiny,
# so if regression works -- it better has at least 0.5 ;)
# otherwise fix it! ;)
# YOH: not now -- issues with libsvr in SG and linear kernel
if cfg.getboolean('tests', 'labile', default='yes'):
self.assertTrue(confusion.stats['CCe'] < 0.5)
# just to check if it works fine
split_predictions = splitregr.predict(ds.samples)
def test_regressions(self, regr):
"""Simple tests on regressions
"""
if not externals.exists('scipy'):
raise SkipTest
else:
from mvpa2.misc.errorfx import corr_error
ds = datasets['chirp_linear']
# we want numeric labels to maintain the previous behavior, especially
# since we deal with regressions here
ds.sa.targets = AttributeMap().to_numeric(ds.targets)
cve = CrossValidation(regr, NFoldPartitioner(), postproc=mean_sample(),
errorfx=corr_error, enable_ca=['training_stats', 'stats'])
# check the default
#self.assertTrue(cve.transerror.errorfx is corr_error)
corr = np.asscalar(cve(ds).samples)
# Our CorrErrorFx should never return NaN
self.assertTrue(not np.isnan(corr))
self.assertTrue(corr == cve.ca.stats.stats['CCe'])
splitregr = SplitClassifier(
regr, partitioner=OddEvenPartitioner(),
enable_ca=['training_stats', 'stats'])
splitregr.train(ds)
split_corr = splitregr.ca.stats.stats['CCe']
split_corr_tr = splitregr.ca.training_stats.stats['CCe']
for confusion, error in (
(cve.ca.stats, corr),
(splitregr.ca.stats, split_corr),
(splitregr.ca.training_stats, split_corr_tr),
):
#TODO: test confusion statistics
# Part of it for now -- CCe
for conf in confusion.summaries:
stats = conf.stats
if cfg.getboolean('tests', 'labile', default='yes'):
self.assertTrue(stats['CCe'] < 0.5)
self.assertEqual(stats['CCe'], stats['Summary CCe'])
s0 = confusion.as_string(short=True)
s1 = confusion.as_string(short=False)
for s in [s0, s1]:
self.assertTrue(len(s) > 10,
msg="We should get some string representation "
"of regression summary. Got %s" % s)
if cfg.getboolean('tests', 'labile', default='yes'):
self.assertTrue(error < 0.2,
msg="Regressions should perform well on a simple "
"dataset. Got correlation error of %s " % error)
# Test access to summary statistics
# YOH: lets start making testing more reliable.
# p-value for such accident to have is verrrry tiny,
# so if regression works -- it better has at least 0.5 ;)
# otherwise fix it! ;)
# YOH: not now -- issues with libsvr in SG and linear kernel
if cfg.getboolean('tests', 'labile', default='yes'):
self.assertTrue(confusion.stats['CCe'] < 0.5)
# just to check if it works fine
split_predictions = splitregr.predict(ds.samples)