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import os
import json
import jsonlines
import re
import random
from collections import defaultdict
import argparse
import transformers
from transformers import BartTokenizer
import torch
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from .data import IEDataset, my_collate
MAX_LENGTH = 424
MAX_TGT_LENGTH = 72
DOC_STRIDE = 256
print("data_module4.py")
class RAMSDataModule(pl.LightningDataModule):
def __init__(self, args):
super().__init__()
self.hparams = args
self.tokenizer = BartTokenizer.from_pretrained('facebook/bart-large')
self.tokenizer.add_tokens([' <arg>', ' <tgr>'])
def get_event_type(self, ex):
evt_type = []
for evt in ex['evt_triggers']:
for t in evt[2]:
evt_type.append(t[0])
return evt_type
def create_gold_gen(self, ex, ontology_dict, mark_trigger=True):
# 设置三个总列表、存放输入模板、输出模板
INPUT = []
OUTPUT = []
CONTEXT = []
evt_type = self.get_event_type(ex)[0]
context_words = [w for sent in ex['sentences'] for w in sent]
input_template = ontology_dict[evt_type.replace('n/a', 'unspecified')]['template']
i = len(input_template)
input_list = []
for x in range(i):
str = re.sub(r'<arg\d>', '<arg>', input_template[x])
input_list.append(str)
# 其中input_list种存放的是 原始数据中<arg1> 全部替换为 <arg> 之后的模板 下一步应该进行分词
temp = []
for x in range(i):
space_tokenized_template = input_list[x].split(' ')
temp.append(space_tokenized_template)
space_tokenized_template = []
# 其中temp中存放的都是分词后的模板 下一步对temp中的所有元素进行tokenize
tokenized_input_template = []
for x in range(len(temp)):
for w in temp[x]:
tokenized_input_template.extend(self.tokenizer.tokenize(w, add_prefix_space=True))
INPUT.append(tokenized_input_template)
tokenized_input_template = []
template = ontology_dict[evt_type.replace('n/a', 'unspecified')]['template']
for lidx, triple in enumerate(ex['gold_evt_links']):
# 触发词 论元 论元
# 例子： "gold_evt_links":
# [[[40, 40], [33, 33], "evt089arg01victim"],
# [[40, 40], [28, 28], "evt089arg02place"]]
trigger_span, argument_span, arg_name = triple
# 第几个论元
arg_num = ontology_dict[evt_type.replace('n/a', 'unspecified')][arg_name]
# 具体论元内容 短语
arg_text = ' '.join(context_words[argument_span[0]:argument_span[1] + 1])
# 通过正则表达式的方式将模板中的每个<arg> 替换为具体的论元内容
for index in range(len(template)):
if arg_num in template[index]:
break
else:
continue
template[index] = re.sub('<{}>'.format(arg_num), arg_text, template[index])
trigger = ex['evt_triggers'][0]
if mark_trigger:
trigger_span_start = trigger[0]
trigger_span_end = trigger[1] + 2 # one for inclusion, one for extra start marker
# 触发词之前的单词
prefix = self.tokenizer.tokenize(' '.join(context_words[:trigger[0]]), add_prefix_space=True)
# 触发词短语
tgt = self.tokenizer.tokenize(' '.join(context_words[trigger[0]: trigger[1] + 1]),
add_prefix_space=True)
# 触发词之后的单词
suffix = self.tokenizer.tokenize(' '.join(context_words[trigger[1] + 1:]), add_prefix_space=True)
context = prefix + [' <tgr>', ] + tgt + [' <tgr>', ] + suffix
else:
context = self.tokenizer.tokenize(' '.join(context_words), add_prefix_space=True)
# 将context放入CONTEXT中
for w in range(i):
CONTEXT.append(context)
output_template = []
# 此时的template中已经全部替换为论元短语 这部是将<arg1> 替换为<arg>
for i in range(len(template)):
output_template.append(re.sub(r'<arg\d>', '<arg>', template[i]))
spaceout_tokenized_template = []
for i in range(len(output_template)):
spaceout_tokenized_template.append(output_template[i].split(' '))
tokenized_out_template = []
for i in range(len(spaceout_tokenized_template)):
for w in spaceout_tokenized_template[i]:
tokenized_out_template.extend(self.tokenizer.tokenize(w, add_prefix_space=True))
OUTPUT.append(tokenized_out_template)
tokenized_out_template = []
return INPUT, OUTPUT, CONTEXT
def load_ontology(self):
ontology_dict = {}
with open('aida_ontology_fj-5.csv', 'r') as f:
for lidx, line in enumerate(f):
if lidx == 0: # header
continue
fields = line.strip().split(',')
if len(fields) < 2:
break
evt_type = fields[0]
if evt_type in ontology_dict.keys():
args = fields[2:]
ontology_dict[evt_type]['template'].append(fields[1])
for i, arg in enumerate(args):
if arg != '':
ontology_dict[evt_type]['arg{}'.format(i + 1)] = arg
ontology_dict[evt_type][arg] = 'arg{}'.format(i + 1)
else:
ontology_dict[evt_type] = {}
args = fields[2:]
ontology_dict[evt_type]['template'] = []
ontology_dict[evt_type]['template'].append(fields[1])
for i, arg in enumerate(args):
if arg != '':
ontology_dict[evt_type]['arg{}'.format(i + 1)] = arg
ontology_dict[evt_type][arg] = 'arg{}'.format(i + 1)
return ontology_dict
def prepare_data(self):
if not os.path.exists('span_4_preprocessed_data1'):
os.makedirs('span_4_preprocessed_data1')
ontology_dict = self.load_ontology()
# print(ontology_dict['contact.prevarication.broadcast'])
for split, f in [('train', self.hparams.train_file), ('val', self.hparams.val_file),
('test', self.hparams.test_file)]:
with open(f, 'r') as reader, open('span_4_preprocessed_data1/{}.jsonl'.format(split), 'w') as writer:
for lidx, line in enumerate(reader):
ex = json.loads(line.strip())
input_template, output_template, context = self.create_gold_gen(ex, ontology_dict,
self.hparams.mark_trigger)
ontology_dict = self.load_ontology()
length = len(input_template)
# print(input_template)
# print(output_template)
# print(context)
for i in range(length):
input_tokens = self.tokenizer.encode_plus(input_template[i], context[i],
add_special_tokens=True,
add_prefix_space=True,
max_length=MAX_LENGTH,
truncation='only_second',
padding='max_length')
# target_tokens
tgt_tokens = self.tokenizer.encode_plus(output_template[i],
add_special_tokens=True,
add_prefix_space=True,
max_length=MAX_TGT_LENGTH,
truncation=True,
padding='max_length')
# input_ids 单词在词典中的编码
# tgt_tokens 指定对哪些词进行self_attention操作
processed_ex = {
# 'idx': lidx,
'doc_key': ex['doc_key'],
'input_token_ids': input_tokens['input_ids'],
'input_attn_mask': input_tokens['attention_mask'],
'tgt_token_ids': tgt_tokens['input_ids'],
'tgt_attn_mask': tgt_tokens['attention_mask'],
}
#print(processed_ex)
writer.write(json.dumps(processed_ex) + "\n")
def train_dataloader(self):
dataset = IEDataset('span_4_preprocessed_data1/train.jsonl')
dataloader = DataLoader(dataset,
pin_memory=True, num_workers=2,
collate_fn=my_collate,
batch_size=self.hparams.train_batch_size,
shuffle=True)
return dataloader
def val_dataloader(self):
dataset = IEDataset('span_4_preprocessed_data1/val.jsonl')
dataloader = DataLoader(dataset, pin_memory=True, num_workers=2,
collate_fn=my_collate,
batch_size=self.hparams.eval_batch_size, shuffle=False)
return dataloader
def test_dataloader(self):
dataset = IEDataset('span_4_preprocessed_data1/test.jsonl')
dataloader = DataLoader(dataset, pin_memory=True, num_workers=2,
collate_fn=my_collate,
batch_size=self.hparams.eval_batch_size, shuffle=False)
return dataloader
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--train-file', type=str, default='data/RAMS_1.0/data/train.jsonlines')
parser.add_argument('--val-file', type=str, default='data/RAMS_1.0/data/dev.jsonlines')
parser.add_argument('--test-file', type=str, default='data/RAMS_1.0/data/test.jsonlines')
parser.add_argument('--train_batch_size', type=int, default=2)
parser.add_argument('--eval_batch_size', type=int, default=4)
parser.add_argument('--mark-trigger', action='store_true', default=True)
args = parser.parse_args()
dm = RAMSDataModule(args=args)
dm.prepare_data()
# training dataloader
dataloader = dm.train_dataloader()
for idx, batch in enumerate(dataloader):
print(batch)
break
# val dataloader
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