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)