def test_entities_at_the_end():
words = "Shyam lives in New York".split()
gold = "B-PER O O B-LOC I-LOC".split()
pred = "B-PER O O B-LOC O".split()
print("Input gold. This should be perfect.")
counts = evaluate(map(lambda p: " ".join(p), zip(words, gold, gold)))
overall, by_type = metrics(counts)
report(counts)
assert overall.fscore == 1.0
print("This should be 50% F1")
counts = evaluate(map(lambda p: " ".join(p), zip(words, gold, pred)))
overall, by_type = metrics(counts)
report(counts)
assert overall.fscore == 0.5
assert by_type["PER"].fscore == 1.0
assert by_type["LOC"].fscore == 0.0
print("This should be 50% F1")
counts = evaluate(map(lambda p: " ".join(p), zip(words, pred, gold)))
overall, by_type = metrics(counts)
report(counts)
assert overall.fscore == 0.5
assert by_type["PER"].fscore == 1.0
assert by_type["LOC"].fscore == 0.0
def test(data):
if not os.path.exists(RESULT_PATH):
os.makedirs(RESULT_PATH)
with tf.Graph().as_default() as g:
x = tf.placeholder(tf.int32, [None, None])
y_ = tf.placeholder(tf.int64, [None, None])
word_emb = tf.Variable(datautil._word_emb,
dtype=tf.float32,
name='word_emb')
x_emb = tf.nn.embedding_lookup(word_emb, x)
y = ner_forward.forward(x_emb, is_train=False, regularizer=None)
predict = tf.argmax(y, -1)
saver = tf.train.Saver()
x_batch = []
for i in range(len(data)):
pad_lst = [0] * (MAX_SEQ_LEN - len(data[i][0]))
x_pad = data[i][0] + pad_lst
x_batch.append(x_pad)
while True:
with tf.Session() as sess:
# This will return a dict like variable (with key: value)
# ckpt.model_checkpoint_path will get the string value of "model_checkpoint_path"
# tf.train.latest_checkpoint can do the same thing
ckpt = tf.train.get_checkpoint_state(
ner_backward.MODEL_SAVE_PATH)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
# Parse the string to extract the number of the ckpt filename
global_step = ckpt.model_checkpoint_path.split(
'/')[-1].split('-')[-1]
predict_id = sess.run(predict, feed_dict={x: x_batch})
filename = os.path.join(RESULT_PATH, 'ner.result')
fw = open(filename, 'w')
for i in range(len(data)):
fw.write('{} {} {}\n'.format("<S>", "O", "O"))
for j in range(len(data[i][0])):
word = data[i][2][j]
predict_str = datautil.id2label(predict_id[i][j])
label_str = datautil.id2label(data[i][1][j])
fw.write('{} {} {}\n'.format(
word, label_str, predict_str))
fw.write('{} {} {}\n\n'.format("<E>", "O", "O"))
fw.close()
print("After %s training step(s), test result is:" %
(global_step))
conlleval.evaluate(filename)
time.sleep(TEST_INTERVAL_SECS)
def eval(self, sess, dataset):
IOB_output = []
for batch in dataset:
input_sents, input_words, input_tags, input_lengths, input_words_lens = batch
feed_dict = {
self.input_sents: input_sents,
self.input_words: input_words,
self.input_tags: input_tags,
self.input_lengths: input_lengths,
self.input_words_lens: input_words_lens,
self.dropout_keep_prob: 1.0
}
predicted_seqs = sess.run(self.predicted_seqs, feed_dict=feed_dict)
batch_size = predicted_seqs.shape[0]
for i in range(batch_size):
seq_length = input_lengths[i]
to_tag = FLAGS.id_to_tags_map
for j in range(seq_length):
line = ['-'] * 2 + [to_tag[input_tags[i, j]]
] + [to_tag[predicted_seqs[i, j]]]
IOB_output.append(' '.join(line))
IOB_output.append('\n')
print(len(IOB_output))
print(IOB_output[:10])
return conlleval.evaluate(IOB_output)
def conll_summary(tokens, gold, pred, config):
"""Return string summarizing performance using CoNLL criteria."""
index_to_label = {v: k for k, v in config.label_to_index.items()}
acc = accuracy(gold, pred)
gold = map(lambda i: index_to_label[i], as_dense(gold))
pred = map(lambda i: index_to_label[i], as_dense(pred))
# Format as space-separated (token, gold, pred) strings for CoNLL eval.
if len(tokens) != len(gold) or len(gold) != len(pred):
raise ValueError('counts do not match')
formatted = [' '.join(t) for t in zip(tokens, gold, pred)]
o, by_type = conlleval.metrics(conlleval.evaluate(formatted))
nlen = max(len(name) for name in by_type.keys())
summaries = [
'%.2f%% acc %.2f%% f (%.1fp %.1fr %dtp %dfp %dfn)' %
(100. * acc, 100. * o.fscore, 100. * o.prec, 100. * o.rec, o.tp, o.fp,
o.fn)
]
config.results_log[config.model_name_log][
config.dataset_name_log] = o.fscore
for name, r in sorted(by_type.items()):
summaries.append('%*s %.2f%% f (%.1fp %.1fr %dtp %dfp %dfn)' %
(nlen, name, 100. * r.fscore, 100. * r.prec,
100. * r.rec, r.tp, r.fp, r.fn))
return '\n'.join(summaries)
def validate_one_batch(self, test_batch, task_name, log_writer, epoch):
S, Q = test_batch
Q_tag_ids = Q['tag_ids']
S_tag_ids = S['tag_ids']
Q_seq_len_list = Q['lens']
Q_seq_len_list_plus2 = [x + 2 for x in Q_seq_len_list]
Q_tag_ids_padded = pad_tag_ids(Q_tag_ids)
S_tag_ids_padded = pad_tag_ids(S_tag_ids)
Q['tag_ids'] = Q_tag_ids_padded
S['tag_ids'] = S_tag_ids_padded
logits = self([S, Q])
loss = self.crf_loss(logits, Q_tag_ids_padded, Q_seq_len_list_plus2)
pred_tags, pred_best_score = crf.crf_decode(
potentials=logits,
transition_params=self.trans_p,
sequence_length=Q_seq_len_list_plus2)
pred_tags_masked = seq_masking(pred_tags, Q_seq_len_list_plus2)
p_tags_char, _ = get_id2tag_V2(pred_tags_masked,
Q_seq_len_list_plus2,
taskname=task_name)
t_tags_char, _ = get_id2tag_V2(Q_tag_ids_padded,
Q_seq_len_list_plus2,
taskname=task_name)
(P, R, F1), _ = evaluate(t_tags_char, p_tags_char, verbose=True)
write_to_log(loss, P, R, F1, t_tags_char, log_writer, epoch)
return (loss, pred_tags_masked, Q_tag_ids_padded, P, R, F1)
def evaluate(model, x_test, y_test, labels, MAX_SEQUENCE_LENGTH):
total = []
for lang in get_lang_list(): # x_test.keys():
y_pred = model.predict(x_test[lang])
pred_tags_all = []
true_tags_all = []
for i, seq in enumerate(y_pred):
for j in range(MAX_SEQUENCE_LENGTH):
indx = np.argmax(y_test[lang][i][j])
true_label = labels[indx]
if "[PAD]" in true_label or "[CLS]" in true_label in true_label:
continue
true_tags_all.append(true_label)
indx = np.argmax(seq[j])
pred_label = labels[indx]
pred_tags_all.append(pred_label)
prec, rec, f1 = conlleval.evaluate(true_tags_all,
pred_tags_all,
verbose=False)
print("Lang {} scores {} {} {}".format(lang, prec, rec, f1))
total.append(f1)
print("All f-scores {}".format(total))
print("Overall average f-score mean {} and variance {}".format(
np.mean(total), np.var(total)))
def eval(self, sess, dataset):
iterator = dataset.make_one_shot_iterator()
next_batch_op = iterator.get_next()
IOB_output = []
while True:
try:
next_batch = sess.run(next_batch_op)
input_x, input_y, input_lengths = next_batch
except:
break
feed_dict = {
self.input_sents: input_x,
self.input_lengths: input_lengths,
self.dropout_keep_prob: 1.0
}
predicted_seqs = sess.run(self.predicted_seqs, feed_dict=feed_dict)
batch_size = predicted_seqs.shape[0]
for i in range(batch_size):
seq_length = input_lengths[i]
to_tag = FLAGS.id_to_tag_map
for j in range(seq_length):
line = ['-'] * 2 + [to_tag[input_y[i, j]]
] + [to_tag[predicted_seqs[i, j]]]
IOB_output.append(' '.join(line))
IOB_output.append('\n')
print(len(IOB_output))
print(IOB_output[:10])
return conlleval.evaluate(IOB_output)
def chunking_eval(self, dataloader):
self.coarse_tagger.eval()
binary_preds, binary_golds = [], []
pbar = tqdm(enumerate(dataloader), total=len(dataloader))
for i, (X, lengths, y_0, y_bin, y_final, y_dm) in pbar:
binary_golds.extend(y_0)
X, lengths = X.cuda(), lengths.cuda()
preds = self.coarse_tagger.chunking(X, y_0, True, lengths)
binary_preds.extend(preds)
binary_preds = np.concatenate(binary_preds, axis=0)
binary_preds = list(binary_preds)
binary_golds = np.concatenate(binary_golds, axis=0)
binary_golds = list(binary_golds)
_bin_pred = []
_bin_gold = []
temp = {"B": "B-A", "I": "I-A", "O": "O"}
for bin_pred, bin_gold in zip(binary_preds, binary_golds):
bin_slot_pred = y0_set[bin_pred]
bin_slot_gold = y0_set[bin_gold]
_bin_gold.append(temp[bin_slot_gold])
_bin_pred.append(temp[bin_slot_pred])
(pre, rec, f1), d = conlleval.evaluate(_bin_gold, _bin_pred, logger)
return f1
def evaluate(results, idx_token, idx_label, writer=None):
"""Evaluate prediction results.
:param results: A List of which each item is a tuple
(predictions, gold labels, sequence lengths, tokens) of a batch.
:param idx_token: Index to token dictionary.
:param idx_label: Index to label dictionary.
:param writer: An object (file object) with a write() function. Extra output.
:return: F-score, precision, and recall.
"""
# b: batch, s: sequence
outputs = []
for preds_b, golds_b, len_b, tokens_b in results:
for preds_s, golds_s, len_s, tokens_s in zip(preds_b, golds_b, len_b,
tokens_b):
l = int(len_s.item())
preds_s = preds_s.data.tolist()[:l]
golds_s = golds_s.data.tolist()[:l]
tokens_s = tokens_s.data.tolist()[:l]
for p, g, t in zip(preds_s, golds_s, tokens_s):
token = idx_token.get(t, C.UNK_INDEX)
outputs.append('{} {} {}'.format(token, idx_label.get(g, 0),
idx_label.get(p, 0)))
outputs.append('')
counts = conlleval.evaluate(outputs)
overall, by_type = conlleval.metrics(counts)
conlleval.report(counts)
if writer:
conlleval.report(counts, out=writer)
writer.flush()
return overall.fscore, overall.prec, overall.rec
def evaluate(model, dataset, word_vocab, label_vocab):
model.eval()
losses = []
scores = []
true_tags = []
pred_tags = []
sents = []
for i, (sent, tags) in enumerate(dataset):
with torch.no_grad():
sent, tags = sent.to(device), tags.to(device)
sent = sent.unsqueeze(0)
tags = tags.unsqueeze(0)
losses.append(model.loss(sent, tags).cpu().detach().item())
score, pred_tag_seq = model(sent)
scores.append(score.cpu().detach().numpy())
true_tags.append([label_vocab.itos[i] for i in tags.tolist()[0]])
pred_tags.append([label_vocab.itos[i] for i in pred_tag_seq[0]])
sents.append([word_vocab.itos[i] for i in sent[0]])
print('Avg evaluation loss:', np.mean(losses))
acc, rec, f1 = conlleval.evaluate(
[tag for tags in true_tags for tag in tags],
[tag for tags in pred_tags for tag in tags],
verbose=True)
# print('\n5 random evaluation samples:')
# for idx in np.random.randint(0, len(sents), size=5):
# print('SENT:', ' '.join(sents[idx]))
# print('TRUE:', ' '.join(true_tags[idx]))
# print('PRED:', ' '.join(pred_tags[idx]))
# print('-'*20)
return sents, true_tags, pred_tags, f1
def get_results(self, name):
p = (float(self.main_correct_count) / float(self.main_predicted_count)) if (self.main_predicted_count > 0) else 0.0
r = (float(self.main_correct_count) / float(self.main_total_count)) if (self.main_total_count > 0) else 0.0
f = (2.0 * p * r / (p + r)) if (p+r > 0.0) else 0.0
f05 = ((1.0 + 0.5*0.5) * p * r / ((0.5*0.5 * p) + r)) if (p+r > 0.0) else 0.0
results = collections.OrderedDict()
results[name + "_cost_avg"] = self.cost_sum / float(self.token_count)
results[name + "_cost_sum"] = self.cost_sum
results[name + "_main_predicted_count"] = self.main_predicted_count
results[name + "_main_total_count"] = self.main_total_count
results[name + "_main_correct_count"] = self.main_correct_count
results[name + "_p"] = p
results[name + "_r"] = r
results[name + "_f"] = f
results[name + "_f05"] = f05
results[name + "_accuracy"] = self.correct_sum / float(self.token_count)
results[name + "_token_count"] = self.token_count
results[name + "_time"] = float(time.time()) - float(self.start_time)
results[name + "_correct_sum"] = self.correct_sum
if self.label2id is not None and self.conll_eval == True:
conll_counts = conlleval.evaluate(self.conll_format)
conll_metrics_overall, conll_metrics_by_type = conlleval.metrics(conll_counts)
results[name + "_conll_accuracy"] = float(conll_counts.correct_tags) / float(conll_counts.token_counter)
results[name + "_conll_p"] = conll_metrics_overall.prec
results[name + "_conll_r"] = conll_metrics_overall.rec
results[name + "_conll_f"] = conll_metrics_overall.fscore
# for i, m in sorted(conll_metrics_by_type.items()):
# results[name + "_conll_p_" + str(i)] = m.prec
# results[name + "_conll_r_" + str(i)] = m.rec
# results[name + "_conll_f_" + str(i)] = m.fscore #str(m.fscore) + " " + str(conll_counts.t_found_guessed[i])
return results, self.conll_format
def test_format():
words = "Shyam lives in New York .".split()
gold = "B-PER O O B-LOC I-LOC O".split()
pred = "B-PER O O B-LOC O O".split()
print("Testing inputting the wrong format. This should get an exception")
try:
evaluate([1, 2, 3])
except Exception as e:
print(e)
pred = "B-PER O O B-LOC I-MISC O".split()
print("This should be 50% F1")
counts = evaluate(map(lambda p: " ".join(p), zip(words, gold, pred)))
overall, by_type = metrics(counts)
report(counts)
assert overall.fscore == 0.4
def conlleval_evaluate(documents):
"""Return conlleval evaluation results for Documents as counts."""
# conlleval.py has a file-based API, so use StringIO
conll_string = StringIO()
write_conll(documents, out=conll_string)
conll_string.seek(0)
return evaluate(conll_string)
def eval(self, dataset_name, log_output=None):
dataset = self.datasets.get(dataset_name, None)
if dataset is None:
return
results = []
logger.info('Evaluating {} ({})'.format(self.name, dataset_name))
set_loss = 0
for tokens, labels, chars, seq_lens, char_lens in dataset.get_dataset(
volatile=True, gpu=self.gpu):
preds, loss = self.model.predict(tokens, labels, seq_lens, chars,
char_lens)
set_loss += float(loss.data[0])
for pred, gold, seq_len, ts in zip(preds, labels, seq_lens,
tokens):
l = int(seq_len.data[0])
pred = pred.data.tolist()[:l]
gold = gold.data.tolist()[:l]
ts = ts.data.tolist()[:l]
for p, g, t in zip(pred, gold, ts):
t = self.idx_token.get(t, 'UNK')
results.append('{} {} {}'.format(t, self.idx_label[g],
self.idx_label[p]))
results.append('')
counts = evaluate(results)
overall, by_type = metrics(counts)
report(counts)
logger.info('Loss: {:.5f}'.format(set_loss))
return SCORES(fscore=overall.fscore,
precision=overall.prec,
recall=overall.rec,
loss=set_loss)
def main(argv):
argparser = argument_parser()
args = argparser.parse_args(argv[1:])
seq_len = args.max_seq_length # abbreviation
pretrained_model, tokenizer = load_pretrained(args)
train_words, train_tags = read_conll(args.train_data)
test_words, test_tags = read_conll(args.test_data)
train_data = process_sentences(train_words, train_tags, tokenizer, seq_len)
test_data = process_sentences(test_words, test_tags, tokenizer, seq_len)
label_list = get_labels(train_data.labels)
tag_map = {l: i for i, l in enumerate(label_list)}
inv_tag_map = {v: k for k, v in tag_map.items()}
init_prob, trans_prob = viterbi_probabilities(train_data.labels, tag_map)
train_x = encode(train_data.combined_tokens, tokenizer, seq_len)
test_x = encode(test_data.combined_tokens, tokenizer, seq_len)
train_y, train_weights = label_encode(train_data.combined_labels, tag_map,
seq_len)
test_y, test_weights = label_encode(test_data.combined_labels, tag_map,
seq_len)
ner_model = create_ner_model(pretrained_model, len(tag_map))
optimizer = create_optimizer(len(train_x[0]), args)
ner_model.compile(optimizer,
loss='sparse_categorical_crossentropy',
sample_weight_mode='temporal',
metrics=['sparse_categorical_accuracy'])
ner_model.fit(train_x,
train_y,
sample_weight=train_weights,
epochs=args.num_train_epochs,
batch_size=args.batch_size)
if args.ner_model_dir is not None:
label_list = [v for k, v in sorted(list(inv_tag_map.items()))]
save_ner_model(ner_model, tokenizer, label_list, args)
save_viterbi_probabilities(init_prob, trans_prob, inv_tag_map, args)
probs = ner_model.predict(test_x, batch_size=args.batch_size)
preds = np.argmax(probs, axis=-1)
pred_tags = []
for i, pred in enumerate(preds):
pred_tags.append(
[inv_tag_map[t] for t in pred[1:len(test_data.tokens[i]) + 1]])
lines = write_result(args.output_file, test_data.words, test_data.lengths,
test_data.tokens, test_data.labels, pred_tags)
c = conlleval.evaluate(lines)
conlleval.report(c)
return 0
def check_path(self, path, files, zip_data):
logging.info("path: {}".format(path))
logging.info("files: {}".format(files))
testfile_path = ''
testfile_key = ''
path_key = ''
for filename in files:
logging.info("testing filename: {}".format(filename))
if path is None or path == '':
logging.info("filename={}".format(filename))
testfile_path = os.path.abspath(
os.path.join(self.ref_dir, filename))
testfile_key = filename
path_key = filename
else:
logging.info("path={}".format(path))
testfile_path = os.path.abspath(
os.path.join(self.ref_dir, path, filename))
testfile_key = os.path.join(path, filename)
path_key = path
logging.info("path_key={}".format(path_key))
# set up score value for matching output correctly
score = self.default_score
if path_key in self.path_score:
score = self.path_score[path_key]
tally = 0.0
self.perf[path_key] = 0.0
logging.info("Checking {}".format(testfile_key))
if testfile_key in zip_data:
with open(testfile_path, 'rt') as ref:
ref_data = list(
filter(
lambda k: k,
[str(x).strip() for x in ref.read().splitlines()]))
output_data = list(
filter(lambda k: k, [
str(x, 'utf-8').strip()
for x in zip_data[testfile_key].splitlines()
]))
output_data = output_data[:len(ref_data)]
if len(ref_data) == len(output_data):
logging.info("ref, output {}".format(
list(zip(ref_data, output_data))))
(prec, recall,
tally) = conlleval.evaluate(ref_data, output_data)
logging.info("score {}: {}".format(
testfile_key, tally))
else:
logging.info(
"length mismatch between output and reference")
tally = 0.
self.perf[path_key] = tally
def evaluate(args, data, model, id2label, all_ori_tokens):
model.eval()
sampler = SequentialSampler(data)
dataloader = DataLoader(data,
sampler=sampler,
batch_size=args.train_batch_size)
logger.info("***** Running eval *****")
# logger.info(f" Num examples = {len(data)}")
# logger.info(f" Batch size = {args.eval_batch_size}")
pred_labels = []
ori_labels = []
for b_i, (input_ids, input_mask, segment_ids, label_ids, bbox, bbox_pos_id,
bbox_num) in enumerate(tqdm(dataloader, desc="Evaluating")):
input_ids = input_ids.to(args.device)
input_mask = input_mask.to(args.device)
segment_ids = segment_ids.to(args.device)
label_ids = label_ids.to(args.device)
bbox = bbox.to(args.device)
bbox_pos_id = bbox_pos_id.to(args.device)
bbox_num = bbox_num.to(args.device)
with torch.no_grad():
logits = model.predict(input_ids, segment_ids, input_mask, bbox,
bbox_pos_id, bbox_num)
# logits = torch.argmax(F.log_softmax(logits, dim=2), dim=2)
# logits = logits.detach().cpu().numpy()
for l in logits: # logits-> List[List[int]]
pred_labels.append([id2label[idx] for idx in l])
for l in label_ids: # tensor
ori_labels.append([id2label[idx.item()] for idx in l])
eval_list = []
for ori_tokens, oril, prel in zip(all_ori_tokens, ori_labels, pred_labels):
for ot, ol, pl in zip(ori_tokens, oril, prel):
if ot in ["[CLS]", "[SEP]"]:
continue
if len(f"{ot} {ol} {pl}\n".split(" ")) != 3:
continue
eval_list.append(f"{ot} {ol} {pl}\n")
eval_list.append("\n")
# eval the model
counts = conlleval.evaluate(eval_list)
conlleval.report(counts)
# namedtuple('Metrics', 'tp fp fn prec rec fscore')
overall, by_type = conlleval.metrics(counts)
return overall, by_type
def evaluating_batch(model, datas):
save = False
adv = 0
true_tags_all=[]
pred_tags_all=[]
macro=[]
for data in datas:
true_tags = []
pred_tags = []
sentence_in=Variable(torch.LongTensor(data['words']))
chars2_mask=Variable(torch.LongTensor(data['chars']))
caps=Variable(torch.LongTensor(data['caps']))
targets = torch.LongTensor(data['tags'])
chars2_length = data['char_length']
word_length=data['word_length']
ground_truth_id = data['tags'][0]
if use_gpu:
sentence_in = sentence_in.cuda()
targets = targets.cuda()
chars2_mask = chars2_mask.cuda()
caps = caps.cuda()
val, out = model(sentence= sentence_in, caps = caps,chars = chars2_mask,
chars2_length= chars2_length,word_length=word_length)
predicted_id = out
for (true_id, pred_id) in zip(ground_truth_id, predicted_id[0]):
true_tags.append(id_to_tag[true_id])
pred_tags.append(id_to_tag[pred_id])
true_tags_all.extend(true_tags)
pred_tags_all.extend(pred_tags)
df = pd.DataFrame({'true':true_tags,'pred':pred_tags})
if sum(df['true']!=df['pred'])>0:
adv = adv+1
df=df[df['true']!='O'] #only tags
if len(df)!=0:
macro.append(sum(df['true']==df['pred'])/len(df))
df_tags = pd.DataFrame({'true':true_tags_all,'pred':pred_tags_all})
df_tags = df_tags[df_tags['true']!='O']
print('Micro acc_tag:', sum(df_tags['true']==df_tags['pred'])/len(df_tags))
print('Macro acc_tag:', np.mean(macro))
prec, rec, new_F = evaluate(true_tags_all, pred_tags_all, verbose=False)
print('F 1:',new_F)
print('Hit:', adv/len(datas))
def evaluate(args, task_id, data, model, id2label, all_ori_words, file_name=None):
model.eval()
sampler = SequentialSampler(data)
dataloader = DataLoader(data, sampler=sampler, batch_size=args.train_batch_size)
task_id = torch.tensor(task_id, dtype=torch.long).to(args.device)
logger.info("***** Running eval *****")
logger.info(f" Num examples = {len(data)}")
pred_labels = []
ori_labels = []
for b_i, batch in enumerate(tqdm(dataloader, desc="Evaluating")):
batch = tuple(t.to(args.device) for t in batch)
if args.need_charcnn:
input_word_ids, input_mask, label_ids, label_mask, char_ids = batch
else:
input_word_ids, input_mask, label_ids, label_mask = batch
char_ids = None
with torch.no_grad():
logits = model.predict(task_id, input_word_ids, char_ids, input_mask)
# print(len(all_ori_words), [len(x) for x in all_ori_words])
# print(len(logits), [len(x) for x in logits])
# print(len(label_ids), [len(x) for x in label_ids])
# print(len(input_mask), [sum(x) for x in input_mask])
# print(len(label_mask), [sum(x) for x in label_mask])
for predL, goldL, maskL in zip(logits, label_ids, label_mask):
for p, g, mask in zip(predL, goldL, maskL):
if mask.item() == 1:
pred_labels.append(id2label[p])
ori_labels.append(id2label[g.item()])
pred_labels.append(None)
ori_labels.append(None)
ori_words = []
for sent in all_ori_words:
ori_words.extend(sent+[None])
eval_list = []
# print(len(pred_labels), len(ori_labels), len(ori_words))
for plabel, olabel, word in zip(pred_labels, ori_labels, ori_words):
if plabel is not None:
eval_list.append(f"{word} {olabel} {plabel}\n")
else:
eval_list.append("\n")
if file_name is not None:
with open(file_name, "w", encoding="utf-8") as f:
for line in eval_list:
f.write(line)
# eval the model
counts = conlleval.evaluate(eval_list)
conlleval.report(counts)
def evaluate_f1(labels_pred_list, label_true_list):
id2tag = {
test_data_loader.tag2label[key]: key
for key in test_data_loader.tag2label
}
res_pred = []
for label_ in labels_pred_list:
res_pred.extend([id2tag[l] for l in label_])
res_true = []
for label_ in label_true_list:
res_true.extend([id2tag[l] for l in label_])
prec, rec, f1 = evaluate(res_pred, res_true, verbose=True)
return prec, rec, f1
def calculate_labeling_scores(results, report=True):
outputs = []
for p_b, g_b, t_b, l_b in results:
for p_s, g_s, t_s, l_s in zip(p_b, g_b, t_b, l_b):
p_s = p_s[:l_s]
for p, g, t in zip(p_s, g_s, t_s):
outputs.append('{} {} {}'.format(t, g, p))
outputs.append('')
counts = conlleval.evaluate(outputs)
overall, by_type = conlleval.metrics(counts)
if report:
conlleval.report(counts)
return (overall.fscore * 100.0, overall.prec * 100.0, overall.rec * 100.0)
def eval2(model):
y_true = []
y_pred = []
for sentence, tags in zip(test_sentences, test_tags):
tags_pred = model.decode([sentence])
for word, true_tag, pred_tag in zip(sentence, tags, tags_pred[0]):
y_true.append(true_tag)
y_pred.append(pred_tag)
precision, recall, f1_score = evaluate(y_true, y_pred, verbose=False)
return "Precision: %.2f%%\tRecall: %.2f%%\tF1_score: %.2f%%".format(
precision, recall, f1_score)
def inner_train_one_step(self, batches, epochNum, task_name, log_writer,
log_dir):
'''
:param self:
:param batches: one batch data: [[sentence],[sentence],....]
sentence=[[chars],[charids],[tags],[tag_ids]]
:param inner_epochNum:
:return:
'''
# tf.summary.trace_on(graph=True,profiler=True) # 开启Trace（可选）
batch_Nums = len(batches)
losses, P_ts, R_ts, F1_ts = [], [], [], []
# =====run model=======
with tqdm(total=batch_Nums) as bar:
for batch_num in range(batch_Nums):
batch = batches[batch_num]
seq_ids_padded, tag_ids_padded, seq_len_list = get_train_data_from_batch(
batch)
with tf.GradientTape() as tape:
# print(batch[0]) # 调试用
logits = self(seq_ids_padded)
loss = self.crf_loss(logits, tag_ids_padded, seq_len_list)
pred_tags, pred_best_score = crf.crf_decode(
potentials=logits,
transition_params=self.trans_p,
sequence_length=seq_len_list)
grads = tape.gradient(loss, self.trainable_variables)
self.optimizer.apply_gradients(
zip(grads, self.trainable_variables))
# optimizer.minimize(loss, [myModel_bilstm.trainable_variables])
pred_tags_masked = seq_masking(pred_tags, seq_len_list)
p_tags_char, p_tagsid_flatten = get_id2tag(pred_tags_masked,
taskname=task_name)
t_tags_char, t_tagsid_flatten = get_id2tag(tag_ids_padded,
taskname=task_name)
(P_t, R_t, F1_t), _ = evaluate(t_tags_char,
p_tags_char,
verbose=False)
losses.append(loss)
P_ts.append(P_t)
R_ts.append(R_t)
F1_ts.append(F1_t)
print('train_loss:{}, train_P:{}'.format(loss, P_t))
bar.update(1)
with log_writer.as_default():
tf.summary.scalar("loss", np.mean(losses), step=epochNum)
tf.summary.scalar("P", np.mean(P_ts), step=epochNum)
tf.summary.scalar("R", np.mean(R_ts), step=epochNum)
tf.summary.scalar("F1", np.mean(F1_ts), step=epochNum)
def compare(gold_toks, gold_tags, pred_toks, pred_tags):
if len(gold_toks) != len(pred_toks):
raise ValueError('sentence count mismatch: {} in gold, {} in pred'.\
format(len(gold_toks), len(pred_toks)))
lines = []
for g_toks, g_tags, p_toks, p_tags in zip(gold_toks, gold_tags, pred_toks,
pred_tags):
if g_toks != p_toks:
raise ValueError('text mismatch: gold "{}", pred "{}"'.\
format(g_toks, p_toks))
for (g_tok, g_tag, p_tag) in zip(g_toks, g_tags, p_tags):
lines.append('{}\t{}\t{}'.format(g_tok, g_tag, p_tag))
return conlleval.report(conlleval.evaluate(lines))
def calculate_metrics(dataset):
all_true_tag_ids, all_predicted_tag_ids = [], []
for x, y in dataset:
output = ner_model.predict(x)
predictions = np.argmax(output, axis=-1)
predictions = np.reshape(predictions, [-1])
true_tag_ids = np.reshape(y, [-1])
mask = (true_tag_ids > 0) & (predictions > 0)
true_tag_ids = true_tag_ids[mask]
predicted_tag_ids = predictions[mask]
all_true_tag_ids.append(true_tag_ids)
all_predicted_tag_ids.append(predicted_tag_ids)
all_true_tag_ids = np.concatenate(all_true_tag_ids)
all_predicted_tag_ids = np.concatenate(all_predicted_tag_ids)
predicted_tags = [mapping[tag] for tag in all_predicted_tag_ids]
real_tags = [mapping[tag] for tag in all_true_tag_ids]
evaluate(real_tags, predicted_tags)
def get_output_file(all_logit, all_label, decode, out):
decode.pop(len(decode) - 1)
assert len(all_logit) == len(all_label)
evalseq = []
for i in range(len(all_logit)):
evalseq.append("{} {} {}".format(
i,
decode[int(all_label[i])]
if int(all_label[i]) in decode.keys() else "O",
decode[int(all_logit[i])]
if int(all_logit[i]) in decode.keys() else "O",
))
count = conlleval.evaluate(evalseq)
conlleval.report(count, out)
def conll_eval_counts(ypred, ytruth, labels):
ytruth_max = ytruth.argmax(axis=2)
ypred_max = ypred.argmax(axis=2)
conf_matrix = None
eval_counts = ceval.EvalCounts()
label_keys = ordered_label_keys(labels)
for i in range(len(ypred_max)):
true_seq = [labels[x] for x in ytruth_max[i].tolist()]
pred_seq = [labels[x] for x in ypred_max[i].tolist()]
c = ceval.evaluate(['%s %s' % x for x in zip(true_seq, pred_seq)])
eval_counts.add(c)
cm = confusion_matrix(true_seq, pred_seq, label_keys)
conf_matrix = cm if conf_matrix is None else conf_matrix + cm
return eval_counts, conf_matrix
def inner_train_one_step(self, batches, inner_iters, inner_epochNum,
outer_epochNum, task_name, log_writer):
'''
:param self:
:param batches: one batch data: [[sentence],[sentence],....]
sentence=[[chars],[charids],[tags],[tag_ids]]
:param inner_epochNum:
:return:
'''
batches_len = len(batches)
# =====run model=======
for batch_num in range(batches_len):
batch = batches[batch_num]
seq_ids_padded, tag_ids_padded, seq_len_list = get_train_data_from_batch(
batch)
with tf.GradientTape() as tape:
logits = self(seq_ids_padded)
loss = self.crf_loss(logits, tag_ids_padded, seq_len_list)
pred_tags, pred_best_score = crf.crf_decode(
potentials=logits,
transition_params=self.trans_p,
sequence_length=seq_len_list)
grads = tape.gradient(loss, self.trainable_variables)
self.optimizer.apply_gradients(zip(grads,
self.trainable_variables))
# optimizer.minimize(loss, [myModel_bilstm.trainable_variables])
pred_tags_masked = seq_masking(pred_tags, seq_len_list)
p_tags_char, p_tagsid_flatten = get_id2tag(pred_tags_masked,
taskname=task_name)
t_tags_char, t_tagsid_flatten = get_id2tag(tag_ids_padded,
taskname=task_name)
(P_t, R_t, F1_t), _ = evaluate(t_tags_char, p_tags_char, verbose=False)
with log_writer.as_default():
step = batch_num + 1 + inner_epochNum * batches_len
tf.summary.scalar("loss",
loss,
step=inner_epochNum +
outer_epochNum * inner_iters)
tf.summary.scalar("P", P_t, step=inner_epochNum)
tf.summary.scalar("R", R_t, step=inner_epochNum)
tf.summary.scalar("F", F1_t, step=inner_epochNum)
return (loss, P_t)
def inner_train_one_step(self, batches, inner_epochNum, ckpt_manager, log_writer=None):
'''
:param self:
:param batches: one batch data: [[sentence],[sentence],....]
sentence=[[chars],[charids],[tags],[tag_ids]]
:param inner_epochNum:
:return:
'''
batch_size = len(batches)
print('========================batchsiez', batch_size)
# =====run model=======
with tqdm(total=batch_size) as bar:
for batch_num in range(batch_size):
batch = batches[batch_num]
seq_ids_padded, tag_ids_padded, seq_len_list = get_train_data_from_batch(batch)
with tf.GradientTape() as tape:
logits = self(seq_ids_padded)
loss = self.crf_loss(logits, tag_ids_padded, seq_len_list)
pred_tags, pred_best_score = crf.crf_decode(potentials=logits, transition_params=self.trans_p,
sequence_length=seq_len_list)
grads = tape.gradient(loss, self.trainable_variables)
self.optimizer.apply_gradients(zip(grads, self.trainable_variables))
# optimizer.minimize(loss, [myModel_bilstm.trainable_variables])
bar.update(1)
pred_tags_masked = seq_masking(pred_tags, seq_len_list)
p_tags_char, p_tagsid_flatten = get_id2tag(pred_tags_masked)
t_tags_char, t_tagsid_flatten = get_id2tag(tag_ids_padded)
try:
(P_t, R_t, F1_t),_ = evaluate(t_tags_char, p_tags_char, verbose=True)
except Exception as e:
print(e)
with log_writer.as_default():
step = batch_num + 1 + inner_epochNum * batch_size
tf.summary.scalar("loss", loss, step=inner_epochNum)
tf.summary.scalar("P", P_t, step=inner_epochNum)
tf.summary.scalar("R", R_t, step=inner_epochNum)
tf.summary.scalar("F", F1_t, step=inner_epochNum)
ckpt_manager.save(checkpoint_number=inner_epochNum)
def evaluate(results, idx_token, idx_label, writer=None):
"""Evaluate prediction results.
:param results: A List of which each item is a tuple
(predictions, gold labels, sequence lengths, tokens) of a batch.
:param idx_token: Index to token dictionary.
:param idx_label: Index to label dictionary.
:param writer: An object (file object) with a write() function. Extra output.
:return: F-score, precision, and recall.
"""
# b: batch, s: sequence
outputs = []
# preds: predictions
# golds: answers?
# len: length of something
# tokens: original words?
for preds_b, golds_b, len_b, tokens_b in results:
for preds_s, golds_s, len_s, tokens_s in zip(preds_b, golds_b, len_b, tokens_b):
l = int(len_s.item())
preds_s = preds_s.data.tolist()[:l]
golds_s = golds_s.data.tolist()[:l]
tokens_s = tokens_s.data.tolist()[:l]
for p, g, t in zip(preds_s, golds_s, tokens_s):
token = idx_token.get(t, C.UNK)
# if token == '': # debug
# token = '<$UNK$>'
# print(idx_token) # debug
# print("p: ", p, ", g: ", g, ", t: ", t, ", corresponding token:", token, "|") # DEBUG
outputs.append('{} {} {}'.format(
token, idx_label.get(g, 0), idx_label.get(p, 0)))
outputs.append('')
# print("OUTPUTS: ", outputs) # DEBUG # seems like outputs is right but counts is wrong
# Why is english-covered-test not like the other, uncovered datasets? is this causing an issue?
counts = conlleval.evaluate(outputs)
# print("counts: ", counts) # DEBUG
overall, by_type = conlleval.metrics(counts)
conlleval.report(counts)
if writer:
conlleval.report(counts, out=writer)
writer.flush()
return overall.fscore, overall.prec, overall.rec
def conll_summary(tokens, gold, pred, config):
"""Return string summarizing performance using CoNLL criteria."""
index_to_label = { v: k for k, v in config.label_to_index.items() }
acc = accuracy(gold, pred)
gold = map(lambda i: index_to_label[i], as_dense(gold))
pred = map(lambda i: index_to_label[i], as_dense(pred))
# Format as space-separated (token, gold, pred) strings for CoNLL eval.
if len(tokens) != len(gold) or len(gold) != len(pred):
raise ValueError('counts do not match')
formatted = [' '.join(t) for t in zip(tokens, gold, pred)]
o, by_type = conlleval.metrics(conlleval.evaluate(formatted))
nlen = max(len(name) for name in by_type.keys())
summaries = ['%.2f%% acc %.2f%% f (%.1fp %.1fr %dtp %dfp %dfn)' % (
100.*acc, 100.*o.fscore, 100.*o.prec, 100.*o.rec, o.tp, o.fp, o.fn
)]
for name, r in sorted(by_type.items()):
summaries.append('%*s %.2f%% f (%.1fp %.1fr %dtp %dfp %dfn)' % (
nlen, name, 100.*r.fscore, 100.*r.prec, 100.*r.rec, r.tp, r.fp, r.fn
))
return '\n'.join(summaries)