def first_test(self):
d = POSEvalDict()
d.add('NOUN', 'NOUN')
d.add('NOUN', 'VERB')
self.assertEqual(d.recall(), 50.)
self.assertEqual(d.accuracy(), 50.)
self.assertEqual(d.precision(), 50.)
def nfold_xaml():
xaml_paths = glob("/Users/rgeorgi/Documents/code/dissertation/data/annotation/filtered/*.xml")
lang_test = {}
lang_train = {}
lang_all = {}
tagger = StanfordPOSTagger(tagger_model)
for xaml_path in xaml_paths:
lang = os.path.basename(xaml_path)[:3]
xc = xc_load(xaml_path)
train, dev, test = split_instances(xc, train=0.5, test=0.5, dev=0.0)
lang_train[lang] = train
lang_all[lang] = train+test
lang_test[lang] = test
# Now, build our classifiers...
all_other = POSEvalDict()
all_all = POSEvalDict()
all_odin = POSEvalDict()
all_proj = POSEvalDict()
for lang in lang_all.keys():
other_lang_instances = []
all_lang_instances = lang_train[lang]
for other_lang in lang_all.keys():
if other_lang != lang:
other_lang_instances.extend(lang_all[other_lang])
all_lang_instances.extend(lang_all[other_lang])
other_lang_classifier = extract_from_instances(other_lang_instances, 'test.class', 'test.feats', '/dev/null')
all_lang_classifier = extract_from_instances(all_lang_instances, 'all.class', 'all.feats', '/dev/null')
test_instances = lang_test[lang]
print(lang)
prj_other_eval, cls_other_eval = evaluate_classifier_on_instances(test_instances, other_lang_classifier, tagger)
prj_all_eval, cls_all_eval = evaluate_classifier_on_instances(test_instances, all_lang_classifier, tagger)
prj_odin_eval, cls_odin_eval = evaluate_classifier_on_instances(test_instances, MalletMaxent('/Users/rgeorgi/Documents/code/dissertation/gc.classifier'), tagger)
all_other += cls_other_eval
all_all += cls_all_eval
all_odin += cls_odin_eval
all_proj += prj_all_eval
print('ALL')
print('{:.2f},{:.2f},{:.2f},{:.2f},{:.2f}'.format(all_proj.precision(), all_proj.unaligned(), all_other.accuracy(), all_all.accuracy(), all_odin.accuracy()))
print(all_proj.error_matrix(csv=True))
def second_test(self):
d = POSEvalDict()
d.add('NOUN', 'NOUN')
d.add('NOUN', 'VERB')
d.add('NOUN', 'VERB')
d.add('VERB', 'NOUN')
d.add('VERB', 'VERB')
self.assertAlmostEqual(d.recall(), 40)
self.assertAlmostEqual(d.precision(), 40)
self.assertAlmostEqual(d.accuracy(), 40)
self.assertAlmostEqual(d.tag_recall('NOUN'), 33.3, places=1)
self.assertAlmostEqual(d.tag_precision('NOUN'), 50.0)
self.assertAlmostEqual(d.tag_recall('VERB'), 50, places=1)
self.assertAlmostEqual(d.tag_precision('VERB'), 33.3, places=1)
