def evaluate_on_trial_taxo():
relations_fpath = join(RES_DIR,"relations.csv") # assuming features "hyper_in_hypo_i" and "hypo2hyper_substract"
taxo_fpath = relations_fpath + "-taxo.csv"
print "Relations:", relations_fpath
print "Unpruned taxonomy:", taxo_fpath
taxo_features = TaxonomyFeatures(TaxonomyResources(), relations_fpath=relations_fpath, lang="en")
taxo_predict = TaxonomyPredictor(taxo_features)
taxo_predict.predict_by_global_threshold(threshold=0, field="hypo2hyper_substract", or_correct_predict=False)
taxo_predict.predict_by_global_threshold(threshold=0, field="hyper_in_hypo_i", or_correct_predict=True)
taxo_predict.save(taxo_fpath)
taxo_predict.evaluate(field="correct_predict")
for max_knn in [1, 2, 3, 5]:
taxo_knn_fpath = relations_fpath + "-taxo-knn" + unicode(max_knn) + ".csv"
taxo_predict.predict_by_local_threshold(threshold=0, max_knn=max_knn, field="hypo2hyper_substract", or_correct_predict=False)
taxo_predict.predict_by_global_threshold(threshold=0, field="hyper_in_hypo_i", or_correct_predict=True)
taxo_predict.save(taxo_knn_fpath)
taxo_predict.evaluate(field="correct_predict")
def evaluate_on_trial_taxo():
relations_fpath = join(RES_DIR,"relations.csv") # assuming features "hyper_in_hypo_i" and "hypo2hyper_substract"
taxo_fpath = relations_fpath + "-taxo.csv"
print "Relations:", relations_fpath
print "Unpruned taxonomy:", taxo_fpath
taxo_features = TaxonomyFeatures(TaxonomyResources(), relations_fpath=relations_fpath, lang="en")
taxo_predict = TaxonomyPredictor(taxo_features)
taxo_predict.predict_by_global_threshold(threshold=0, field="hypo2hyper_substract", or_correct_predict=False)
taxo_predict.predict_by_global_threshold(threshold=0, field="hyper_in_hypo_i", or_correct_predict=True)
taxo_predict.save(taxo_fpath)
taxo_predict.evaluate(field="correct_predict")
for max_knn in [1, 2, 3, 5]:
taxo_knn_fpath = relations_fpath + "-taxo-knn" + unicode(max_knn) + ".csv"
taxo_predict.predict_by_local_threshold(threshold=0, max_knn=max_knn, field="hypo2hyper_substract", or_correct_predict=False)
taxo_predict.predict_by_global_threshold(threshold=0, field="hyper_in_hypo_i", or_correct_predict=True)
taxo_predict.save(taxo_knn_fpath)
taxo_predict.evaluate(field="correct_predict")
def extract_semeval_taxo(input_voc_pattern, language, mode, classifiers_pattern):
taxo_res_common, taxo_res_domain = load_res(language, mode)
for voc_fpath in sorted(glob(input_voc_pattern)):
for space in [False]: #, True]:
s = "-space" if space else ""
relations_fpath = voc_fpath + s + "-relations.csv"
taxo_fpath = relations_fpath + "-taxo.csv"
print "\n", voc_fpath, "\n", "="*50
print "Relations:", relations_fpath
print "Unpruned taxonomy:", taxo_fpath
taxo_res_domain_voc = get_taxo_res_domain_voc(taxo_res_domain, voc_fpath)
taxo_res_voc = combine_taxo_res(taxo_res_common, taxo_res_domain_voc)
taxo_features = TaxonomyFeatures(taxo_res_voc, voc_fpath, lang=language)
if mode == "simple":
taxo_features.fill_direct_isas()
taxo_features.fill_substrings(must_have_space=space)
taxo_features.hypo2hyper_ratio()
taxo_predict = TaxonomyPredictor(taxo_features)
taxo_predict.predict_by_global_threshold(threshold=0, field="hypo2hyper_substract", or_correct_predict=False)
taxo_predict.predict_by_global_threshold(threshold=0, field="hyper_in_hypo_i", or_correct_predict=True)
taxo_predict.save(taxo_fpath)
for max_knn in [1, 2, 3, 5]:
taxo_knn_fpath = relations_fpath + "-taxo-knn" + unicode(max_knn) + ".csv"
taxo_predict.predict_by_local_threshold(threshold=0, max_knn=max_knn, field="hypo2hyper_substract", or_correct_predict=False)
taxo_predict.predict_by_global_threshold(threshold=0, field="hyper_in_hypo_i", or_correct_predict=True)
taxo_predict.save(taxo_knn_fpath)
elif mode == "super":
taxo_features.fill_super_features()
for classifier_dir in glob(classifiers_pattern):
try:
print "Predicting with:", classifier_dir
taxo_predict = TaxonomyPredictor(taxo_features)
method = taxo_predict.predict_by_classifier(classifier_dir)
taxo_predict.save(taxo_fpath + "-" + method + ".csv")
taxo_predict.save(taxo_fpath + "-" + method + "-conf.csv", conf=True)
except:
print format_exc()
def extract_semeval_taxo(input_voc_pattern, language, mode, classifiers_pattern, test_en):
#Laedt alle Datensaetze(auch alle Domaenen, aus vocabularies)
taxo_res_common, taxo_res_domain = load_res(language, mode, test_en)
for voc_fpath in sorted(glob(input_voc_pattern)):
for space in [False, True]:
s = "-space" if space else ""
relations_fpath = voc_fpath + s + "-relations.csv"
taxo_fpath = relations_fpath + "-taxo.csv"
print "\n", voc_fpath, "\n", "="*50
print "Relations:", relations_fpath
print "Unpruned taxonomy:", taxo_fpath
#Laedt domain-datenset und kombiniert sie mit dem allgemeinen Datenset
taxo_res_domain_voc = get_taxo_res_domain_voc(taxo_res_domain, voc_fpath)
taxo_res_voc = combine_taxo_res(taxo_res_common, taxo_res_domain_voc)
taxo_features = TaxonomyFeatures(taxo_res_voc, voc_fpath, lang=language)
if mode == "simple":
taxo_features.fill_direct_isas()
taxo_features.fill_substrings(must_have_space=space)
taxo_features.hypo2hyper_ratio()
taxo_predict = TaxonomyPredictor(taxo_features)
taxo_predict.predict_by_global_threshold(threshold=0, field="hypo2hyper_substract", or_correct_predict=False)
taxo_predict.predict_by_global_threshold(threshold=0, field="hyper_in_hypo_i", or_correct_predict=True)
taxo_predict.save(taxo_fpath)
for max_knn in [1, 2, 3, 5]:
#hypo2hyper fuer pattern
#hyperinhypoi feur substring
taxo_knn_fpath = relations_fpath + "-taxo-knn" + unicode(max_knn) + ".csv"
taxo_predict.predict_by_local_threshold(threshold=0, max_knn=max_knn, field="hypo2hyper_substract", or_correct_predict=False)
taxo_predict.predict_by_global_threshold(threshold=0, field="hyper_in_hypo_i", or_correct_predict=True)
taxo_predict.save(taxo_knn_fpath)
elif mode == "super":
taxo_features.fill_super_features()
for classifier_dir in glob(classifiers_pattern):
try:
print "Predicting with:", classifier_dir
taxo_predict = TaxonomyPredictor(taxo_features)
method = taxo_predict.predict_by_classifier(classifier_dir)
taxo_predict.save(taxo_fpath + "-" + method + ".csv")
taxo_predict.save(taxo_fpath + "-" + method + "-conf.csv", conf=True)
except:
print format_exc()
