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import json
from collections import defaultdict
import logging
logger = logging.getLogger(__name__)
class ReaderForRelationDecoding():
"""Define text data reader and preprocess data for entity relation
joint decoding on ACE dataset.
"""
def __init__(self, file_path, is_test=False, max_len=dict()):
"""This function defines file path and some settings
Arguments:
file_path {str} -- file path
Keyword Arguments:
is_test {bool} -- indicate training or testing (default: {False})
max_len {dict} -- max length for some namespace (default: {dict()})
"""
self.file_path = file_path
self.is_test = is_test
self.max_len = dict(max_len)
self.seq_lens = defaultdict(list)
def __iter__(self):
"""Generator function
"""
with open(self.file_path, 'r') as fin:
for line in fin:
line = json.loads(line)
sentence = {}
state, results = self.get_tokens(line)
self.seq_lens['tokens'].append(len(results['tokens']))
if not state or ('tokens' in self.max_len and len(results['tokens']) > self.max_len['tokens']
and not self.is_test):
if not self.is_test:
continue
sentence.update(results)
state, results = self.get_wordpiece_tokens(line)
self.seq_lens['wordpiece_tokens'].append(len(results['wordpiece_tokens']))
if not state or ('wordpiece_tokens' in self.max_len
and len(results['wordpiece_tokens']) > self.max_len['wordpiece_tokens']):
if not self.is_test:
continue
sentence.update(results)
# if len(sentence['tokens']) != len(sentence['wordpiece_tokens_index']):
# logger.error(
# "sentence id: {} wordpiece_tokens_index length is not equal to tokens.".format(line['sentId']))
# # logger.error(sentence['tokens'], sentence['wordpiece_tokens_index'])
# # logger.error("lengths: {}, {}, {}".format(len(sentence['tokens']), len(sentence['wordpiece_tokens_index']),
# # len(line['labelIds'])))
# continue
if len(sentence['wordpiece_tokens']) != len(sentence['wordpiece_segment_ids']):
logger.error(
"sentence id: {} wordpiece_tokens length is not equal to wordpiece_segment_ids.".
format(line['sentId']))
continue
state, results = self.get_label(line, len(sentence['tokens']))
for key, result in results.items():
self.seq_lens[key].append(len(result))
if key in self.max_len and len(result) > self.max_len[key]:
state = False
if not state:
continue
sentence.update(results)
yield sentence
def get_tokens(self, line):
"""This function splits text into tokens
Arguments:
line {dict} -- text
Returns:
bool -- execute state
dict -- results: tokens
"""
results = {}
if 'sentText' not in line:
logger.error("sentence id: {} doesn't contain 'sentText'.".format(line['sentId']))
return False, results
results['text'] = line['sentText']
if 'tokens' in line:
results['tokens'] = line['tokens']
else:
results['tokens'] = line['sentText'].strip().split(' ')
return True, results
def get_wordpiece_tokens(self, line):
"""This function splits wordpiece text into wordpiece tokens
Arguments:
line {dict} -- text
Returns:
bool -- execute state
dict -- results: tokens
"""
results = {}
if 'wordpieceSentText' not in line or 'wordpieceTokensIndex' not in line or 'wordpieceSegmentIds' not in line:
logger.error(
"sentence id: {} doesn't contain 'wordpieceSentText' or 'wordpieceTokensIndex' or 'wordpieceSegmentIds'."
.format(line['sentId']))
return False, results
wordpiece_tokens = line['wordpieceSentText'].strip().split(' ')
results['wordpiece_tokens'] = wordpiece_tokens
results['wordpiece_tokens_index'] = [span[0] for span in line['wordpieceTokensIndex']]
results['wordpiece_segment_ids'] = list(line['wordpieceSegmentIds'])
return True, results
def get_label(self, line, sentence_length):
"""This function constructs mapping relation from span to entity label
and span pair to relation label, and joint entity relation label matrix.
Arguments:
line {dict} -- text
sentence_length {int} -- sentence length
Returns:
bool -- execute state
dict -- ent2rel: entity span mapping to entity label,
span2rel: two entity span mapping to relation label,
joint_label_matrix: joint entity relation label matrix
"""
results = {}
if 'entityMentions' not in line:
logger.error("sentence id: {} doesn't contain 'entityMentions'.".format(line['sentId']))
return False, results
entity_pos = [0] * sentence_length
idx2ent = {}
for entity in line['entityMentions']:
st, ed = entity['span_ids'][0], entity['span_ids'][-1]
idx2ent[entity['emId']] = ((st, ed), entity['text'])
if st > ed or st < 0 or st > sentence_length or ed < 0 or ed > sentence_length:
logger.error("sentence id: {} offset error. st: {}, ed: {}".format(line['sentId'], st, ed))
return False, results
j = 0
for i in range(st, ed):
if entity_pos[i] != 0:
logger.error("sentence id: {} entity span overlap.".format(line['sentId']))
return False, results
entity_pos[i] = 1
j += 1
if 'relationMentions' not in line:
logger.error("sentence id: {} doesn't contain 'relationMentions'.".format(line['sentId']))
return False, results
for relation in line['relationMentions']:
if relation['arg1']['emId'] not in idx2ent or relation['arg2']['emId'] not in idx2ent:
logger.error("sentence id: {} entity not exists .".format(line['sentId']))
continue
if 'labelIds' not in line:
logger.error("sentence id: {} doesn't contain 'labelIds'.".format(line['sentId']))
return False, results
if 'relationIds' not in line:
logger.error("sentence id: {} doesn't contain 'relationIds'.".format(line['sentId']))
return False, results
if 'argumentIds' not in line:
logger.error("sentence id: {} doesn't contain 'argumentIds'.".format(line['sentId']))
return False, results
results["label_ids"] = line["labelIds"]
results["relation_ids"] = line["relationIds"]
results["argument_ids"] = line["argumentIds"]
return True, results
def get_seq_lens(self):
return self.seq_lens
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