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()
