def evaluate(self,
word_idxs_list_valid,
labels_list_valid,
test_texts,
terms_true_list,
task,
opinions_ture_list=None):
aspect_true_cnt, aspect_sys_cnt, aspect_hit_cnt = 0, 0, 0
opinion_true_cnt, opinion_sys_cnt, opinion_hit_cnt = 0, 0, 0
error_sents, error_terms = list(), list()
correct_sent_idxs = list()
for sent_idx, (word_idxs, labels, text, terms_true) in enumerate(
zip(word_idxs_list_valid, labels_list_valid, test_texts,
terms_true_list)):
labels_pred, sequence_lengths = self.predict_batch([word_idxs],
task)
labels_pred = labels_pred[0]
aspect_terms_sys = self.get_terms_from_label_list(
labels_pred, text, 1, 2)
new_hit_cnt = utils.count_hit(terms_true, aspect_terms_sys)
aspect_true_cnt += len(terms_true)
aspect_sys_cnt += len(aspect_terms_sys)
aspect_hit_cnt += new_hit_cnt
if new_hit_cnt == aspect_true_cnt:
correct_sent_idxs.append(sent_idx)
if opinions_ture_list is None:
continue
opinion_terms_sys = self.get_terms_from_label_list(
labels_pred, text, 3, 4)
opinion_terms_true = opinions_ture_list[sent_idx]
new_hit_cnt = utils.count_hit(opinion_terms_true,
opinion_terms_sys)
opinion_hit_cnt += new_hit_cnt
opinion_true_cnt += len(opinion_terms_true)
opinion_sys_cnt += len(opinion_terms_sys)
# save_json_objs(error_sents, 'd:/data/aspect/semeval14/error-sents.txt')
# with open('d:/data/aspect/semeval14/error-sents.txt', 'w', encoding='utf-8') as fout:
# for sent, terms in zip(error_sents, error_terms):
# terms_true = [t['term'].lower() for t in sent['terms']] if 'terms' in sent else list()
# fout.write('{}\n{}\n\n'.format(sent['text'], terms))
# with open('d:/data/aspect/semeval14/lstmcrf-correct.txt', 'w', encoding='utf-8') as fout:
# fout.write('\n'.join([str(i) for i in correct_sent_idxs]))
aspect_p, aspect_r, aspect_f1 = utils.prf1(aspect_true_cnt,
aspect_sys_cnt,
aspect_hit_cnt)
if opinions_ture_list is None:
return aspect_p, aspect_r, aspect_f1, 0, 0, 0
opinion_p, opinion_r, opinion_f1 = utils.prf1(opinion_true_cnt,
opinion_sys_cnt,
opinion_hit_cnt)
return aspect_p, aspect_r, aspect_f1, opinion_p, opinion_r, opinion_f1
def evaluate(self,
word_embed_seqs,
test_texts,
terms_true_list,
opinions_ture_list=None,
dst_aspects_file=None,
dst_opinions_file=None):
aspect_true_cnt, aspect_sys_cnt, aspect_hit_cnt = 0, 0, 0
opinion_true_cnt, opinion_sys_cnt, opinion_hit_cnt = 0, 0, 0
error_sents, error_terms = list(), list()
correct_sent_idxs = list()
aspect_terms_sys_list, opinion_terms_sys_list = list(), list()
for sent_idx, (word_embed_seq, text, terms_true) in enumerate(
zip(word_embed_seqs, test_texts, terms_true_list)):
labels_pred, sequence_lengths = self.predict_batch(
[word_embed_seq])
labels_pred = labels_pred[0]
aspect_terms_sys = self.get_terms_from_label_list(
labels_pred, text, 1, 2)
new_hit_cnt = utils.count_hit(terms_true, aspect_terms_sys)
aspect_true_cnt += len(terms_true)
aspect_sys_cnt += len(aspect_terms_sys)
aspect_hit_cnt += new_hit_cnt
if new_hit_cnt == aspect_true_cnt:
correct_sent_idxs.append(sent_idx)
if opinions_ture_list is None:
continue
opinion_terms_sys = self.get_terms_from_label_list(
labels_pred, text, 3, 4)
opinion_terms_true = opinions_ture_list[sent_idx]
new_hit_cnt = utils.count_hit(opinion_terms_true,
opinion_terms_sys)
opinion_hit_cnt += new_hit_cnt
opinion_true_cnt += len(opinion_terms_true)
opinion_sys_cnt += len(opinion_terms_sys)
if dst_aspects_file is not None:
utils.write_terms_list(aspect_terms_sys_list, dst_aspects_file)
if dst_opinions_file is not None:
utils.write_terms_list(opinion_terms_sys_list, dst_opinions_file)
aspect_p, aspect_r, aspect_f1 = utils.prf1(aspect_true_cnt,
aspect_sys_cnt,
aspect_hit_cnt)
if opinions_ture_list is None:
return aspect_p, aspect_r, aspect_f1, 0, 0, 0
opinion_p, opinion_r, opinion_f1 = utils.prf1(opinion_true_cnt,
opinion_sys_cnt,
opinion_hit_cnt)
return aspect_p, aspect_r, aspect_f1, opinion_p, opinion_r, opinion_f1
def evaluate(self, texts, word_idxs_list_valid, word_span_seqs, tok_texts, terms_true_list, task,
opinions_ture_list=None, dst_aspects_result_file=None, dst_opinion_result_file=None,
save_result=False):
aspect_true_cnt, aspect_sys_cnt, aspect_hit_cnt = 0, 0, 0
opinion_true_cnt, opinion_sys_cnt, opinion_hit_cnt = 0, 0, 0
correct_sent_idxs = list()
aspect_terms_sys_list, opinion_terms_sys_list = list(), list()
for sent_idx, (word_idxs, tok_text, terms_true) in enumerate(zip(
word_idxs_list_valid, tok_texts, terms_true_list)):
labels_pred, sequence_lengths = self.predict_batch([word_idxs], task)
labels_pred = labels_pred[0]
if word_span_seqs is None:
aspect_terms_sys = utils.get_terms_from_label_list(labels_pred, tok_text, 1, 2)
else:
aspect_terms_sys = utils.recover_terms(texts[sent_idx], word_span_seqs[sent_idx], labels_pred, 1, 2)
aspect_terms_sys = [t.lower() for t in aspect_terms_sys]
aspect_terms_sys_list.append(aspect_terms_sys)
new_hit_cnt = utils.count_hit(terms_true, aspect_terms_sys)
aspect_true_cnt += len(terms_true)
aspect_sys_cnt += len(aspect_terms_sys)
aspect_hit_cnt += new_hit_cnt
if new_hit_cnt == aspect_true_cnt:
correct_sent_idxs.append(sent_idx)
if opinions_ture_list is None:
continue
opinion_terms_sys = utils.get_terms_from_label_list(labels_pred, tok_text, 3, 4)
opinion_terms_sys_list.append(opinion_terms_sys)
opinion_terms_true = opinions_ture_list[sent_idx]
new_hit_cnt = utils.count_hit(opinion_terms_true, opinion_terms_sys)
opinion_hit_cnt += new_hit_cnt
opinion_true_cnt += len(opinion_terms_true)
opinion_sys_cnt += len(opinion_terms_sys)
aspect_p, aspect_r, aspect_f1 = utils.prf1(aspect_true_cnt, aspect_sys_cnt, aspect_hit_cnt)
if opinions_ture_list is None:
return aspect_p, aspect_r, aspect_f1, 0, 0, 0
opinion_p, opinion_r, opinion_f1 = utils.prf1(opinion_true_cnt, opinion_sys_cnt, opinion_hit_cnt)
if dst_aspects_result_file is not None and save_result:
datautils.write_terms_list(aspect_terms_sys_list, dst_aspects_result_file)
logging.info('write aspects to {}'.format(dst_aspects_result_file))
if dst_opinion_result_file is not None and save_result:
datautils.write_terms_list(opinion_terms_sys_list, dst_opinion_result_file)
logging.info('write opinions to {}'.format(dst_opinion_result_file))
return aspect_p, aspect_r, aspect_f1, opinion_p, opinion_r, opinion_f1
def check_unseen_terms():
train_sents_file = 'd:/data/aspect/semeval14/laptops/laptops_train_sents.json'
train_aspect_terms, train_opinion_terms = __get_all_terms(train_sents_file)
sents_file = 'd:/data/aspect/semeval14/laptops/laptops_test_sents.json'
lstmcrf_aspects_file = 'd:/data/aspect/semeval14/lstmcrf-aspects.txt'
lstmcrf_opinions_file = 'd:/data/aspect/semeval14/lstmcrf-opinions.txt'
nrdj_aspects_file = 'd:/data/aspect/semeval14/nrdj-opinions-malt.txt'
nrdj_opinions_file = 'd:/data/aspect/semeval14/nrdj-opinions-malt.txt'
rule_aspects_file = 'd:/data/aspect/semeval14/laptops/laptops-test-aspect-rule-result.txt'
sents = utils.load_json_objs(sents_file)
lc_aspects_list = utils.load_json_objs(lstmcrf_aspects_file)
nrdj_aspects_list = utils.load_json_objs(nrdj_aspects_file)
rule_aspects_list = utils.load_json_objs(rule_aspects_file)
terms_true_list, terms_nrdj_list = list(), list()
n_true, n_nrdj, n_hit = 0, 0, 0
n_lc, n_lc_hit = 0, 0
for sent, lc_aspects, nrdj_aspects, rule_aspects in zip(
sents, lc_aspects_list, nrdj_aspects_list, rule_aspects_list):
# terms = [t['term'].lower() for t in sent.get('terms', list())]
terms = [t.lower() for t in sent.get('opinions', list())]
# terms = [t for t in terms if t in train_aspect_terms]
# print(terms, nrdj_aspects)
terms_true_list.append(terms)
terms_nrdj_list.append(nrdj_aspects)
n_true += len(terms)
n_nrdj += len(nrdj_aspects)
n_hit += utils.count_hit(terms, nrdj_aspects)
for t in terms:
if t not in nrdj_aspects:
print(t)
print(sent['text'])
n_lc += len(lc_aspects)
n_lc_hit += utils.count_hit(terms, lc_aspects)
# lc_correct = __is_correct(lc_aspects, terms)
# nrdj_correct = __is_correct(nrdj_aspects, terms)
# rule_correct = __is_correct(rule_aspects, terms)
# if not lc_correct and not rule_correct and nrdj_correct:
# print(sent['text'])
# print(terms)
# print(lc_aspects)
# print(rule_aspects)
# print(nrdj_aspects)
# print()
print(n_true, n_nrdj)
p, r, f1 = utils.prf1(n_true, n_nrdj, n_hit)
print(p, r, f1)
p, r, f1 = utils.prf1(n_true, n_lc, n_lc_hit)
print(p, r, f1)
def __evaluate(terms_sys_list, terms_true_list, dep_tags_list, pos_tags_list, sent_texts):
correct_sent_idxs = list()
hit_cnt, true_cnt, sys_cnt = 0, 0, 0
for sent_idx, (terms_sys, terms_true, dep_tags, pos_tags) in enumerate(
zip(terms_sys_list, terms_true_list, dep_tags_list, pos_tags_list)):
true_cnt += len(terms_true)
sys_cnt += len(terms_sys)
# new_hit_cnt = __count_hit(terms_true, aspect_terms)
new_hit_cnt = utils.count_hit(terms_true, terms_sys)
if new_hit_cnt == len(terms_true) and new_hit_cnt == len(terms_sys):
correct_sent_idxs.append(sent_idx)
hit_cnt += new_hit_cnt
# if len(terms_true) and new_hit_cnt < len(terms_true):
# print(terms_true)
# print(terms_sys)
# print(sent_texts[sent_idx])
# print(pos_tags)
# print(dep_tags)
# print()
# __save_never_hit_terms(sents, terms_sys_list, 'd:/data/aspect/semeval14/tmp.txt')
print('hit={}, true={}, sys={}'.format(hit_cnt, true_cnt, sys_cnt))
p = hit_cnt / (sys_cnt + 1e-8)
r = hit_cnt / (true_cnt + 1e-8)
print(p, r, 2 * p * r / (p + r + 1e-8))
return correct_sent_idxs
def evaluate_ao_extraction(true_labels_list,
pred_labels_list,
test_texts,
aspects_true_list,
opinions_ture_list=None,
error_file=None):
aspect_true_cnt, aspect_sys_cnt, aspect_hit_cnt = 0, 0, 0
opinion_true_cnt, opinion_sys_cnt, opinion_hit_cnt = 0, 0, 0
error_sents, error_terms_true, error_terms_sys = list(), list(), list()
correct_sent_idxs = list()
if aspects_true_list is not None:
aspect_label_beg, aspect_label_in, opinion_label_beg, opinion_label_in = 1, 2, 3, 4
else:
aspect_label_beg, aspect_label_in, opinion_label_beg, opinion_label_in = 3, 4, 1, 2
for sent_idx, (true_labels, pred_labels, text) in enumerate(
zip(true_labels_list, pred_labels_list, test_texts)):
if aspects_true_list is not None:
aspects_true = aspects_true_list[sent_idx]
aspect_terms_sys = get_terms_from_label_list(
pred_labels, text, aspect_label_beg, aspect_label_in)
new_hit_cnt = count_hit(aspects_true, aspect_terms_sys)
aspect_true_cnt += len(aspects_true)
aspect_sys_cnt += len(aspect_terms_sys)
aspect_hit_cnt += new_hit_cnt
if new_hit_cnt == aspect_true_cnt:
correct_sent_idxs.append(sent_idx)
if opinions_ture_list is None:
continue
opinion_terms_sys = get_terms_from_label_list(pred_labels, text,
opinion_label_beg,
opinion_label_in)
opinion_terms_true = opinions_ture_list[sent_idx]
new_hit_cnt = count_hit(opinion_terms_true, opinion_terms_sys)
opinion_hit_cnt += new_hit_cnt
opinion_true_cnt += len(opinion_terms_true)
opinion_sys_cnt += len(opinion_terms_sys)
if new_hit_cnt < len(opinion_terms_true):
error_sents.append(text)
error_terms_true.append(opinion_terms_true)
error_terms_sys.append(opinion_terms_sys)
# save_json_objs(error_sents, 'd:/data/aspect/semeval14/error-sents.txt')
if error_file is not None:
with open(error_file, 'w', encoding='utf-8') as fout:
for sent, terms_true, terms_sys in zip(error_sents,
error_terms_true,
error_terms_sys):
fout.write('{}\n{}\n{}\n\n'.format(sent, terms_true,
terms_sys))
logging.info('error written to {}'.format(error_file))
# with open('d:/data/aspect/semeval14/lstmcrf-correct.txt', 'w', encoding='utf-8') as fout:
# fout.write('\n'.join([str(i) for i in correct_sent_idxs]))
aspect_p, aspect_r, aspect_f1, opinion_p, opinion_r, opinion_f1 = 0, 0, 0, 0, 0, 0
if aspects_true_list is not None:
aspect_p, aspect_r, aspect_f1 = prf1(aspect_true_cnt, aspect_sys_cnt,
aspect_hit_cnt)
if opinions_ture_list is not None:
opinion_p, opinion_r, opinion_f1 = prf1(opinion_true_cnt,
opinion_sys_cnt,
opinion_hit_cnt)
return aspect_p, aspect_r, aspect_f1, opinion_p, opinion_r, opinion_f1
def evaluate(self,
texts,
word_idxs_list_valid,
word_span_seqs,
tok_texts,
terms_true_list,
task,
opinions_ture_list=None,
dst_aspects_result_file=None,
dst_opinion_result_file=None,
save_result=False):
aspect_true_cnt, aspect_sys_cnt, aspect_hit_cnt = 0, 0, 0
opinion_true_cnt, opinion_sys_cnt, opinion_hit_cnt = 0, 0, 0
error_sents, error_terms = list(), list()
correct_sent_idxs = list()
aspect_terms_sys_list, opinion_terms_sys_list = list(), list()
for sent_idx, (word_idxs, tok_text, terms_true) in enumerate(
zip(word_idxs_list_valid, tok_texts, terms_true_list)):
labels_pred, sequence_lengths = self.predict_batch([word_idxs],
task)
labels_pred = labels_pred[0]
if word_span_seqs is None:
aspect_terms_sys = self.get_terms_from_label_list(
labels_pred, tok_text, 1, 2)
else:
aspect_terms_sys = utils.recover_terms(
texts[sent_idx], word_span_seqs[sent_idx], labels_pred, 1,
2)
aspect_terms_sys = [t.lower() for t in aspect_terms_sys]
aspect_terms_sys_list.append(aspect_terms_sys)
# print(terms_true, aspect_terms_sys)
new_hit_cnt = utils.count_hit(terms_true, aspect_terms_sys)
aspect_true_cnt += len(terms_true)
aspect_sys_cnt += len(aspect_terms_sys)
aspect_hit_cnt += new_hit_cnt
if new_hit_cnt == aspect_true_cnt:
correct_sent_idxs.append(sent_idx)
if opinions_ture_list is None:
continue
opinion_terms_sys = self.get_terms_from_label_list(
labels_pred, tok_text, 3, 4)
opinion_terms_sys_list.append(opinion_terms_sys)
opinion_terms_true = opinions_ture_list[sent_idx]
new_hit_cnt = utils.count_hit(opinion_terms_true,
opinion_terms_sys)
opinion_hit_cnt += new_hit_cnt
opinion_true_cnt += len(opinion_terms_true)
opinion_sys_cnt += len(opinion_terms_sys)
# save_json_objs(error_sents, 'd:/data/aspect/semeval14/error-sents.txt')
# with open('d:/data/aspect/semeval14/error-sents.txt', 'w', encoding='utf-8') as fout:
# for sent, terms in zip(error_sents, error_terms):
# terms_true = [t['term'].lower() for t in sent['terms']] if 'terms' in sent else list()
# fout.write('{}\n{}\n\n'.format(sent['text'], terms))
# with open('d:/data/aspect/semeval14/lstmcrf-correct.txt', 'w', encoding='utf-8') as fout:
# fout.write('\n'.join([str(i) for i in correct_sent_idxs]))
if dst_aspects_result_file is not None and save_result:
utils.write_terms_list(aspect_terms_sys_list,
dst_aspects_result_file)
logging.info('write aspects to {}'.format(dst_aspects_result_file))
if dst_opinion_result_file is not None and save_result:
utils.write_terms_list(opinion_terms_sys_list,
dst_opinion_result_file)
logging.info(
'write opinions to {}'.format(dst_opinion_result_file))
aspect_p, aspect_r, aspect_f1 = utils.prf1(aspect_true_cnt,
aspect_sys_cnt,
aspect_hit_cnt)
if opinions_ture_list is None:
return aspect_p, aspect_r, aspect_f1, 0, 0, 0
opinion_p, opinion_r, opinion_f1 = utils.prf1(opinion_true_cnt,
opinion_sys_cnt,
opinion_hit_cnt)
return aspect_p, aspect_r, aspect_f1, opinion_p, opinion_r, opinion_f1
