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import os
import json
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_module.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):
'''assumes that each line only contains 1 event.
Input: <s> Template with special <arg> placeholders </s> </s> Passage </s>
Output: <s> Template with arguments and <arg> when no argument is found.
'''
evt_type = self.get_event_type(ex)[0]
context_words = [w for sent in ex['sentences'] for w in sent ]
template = ontology_dict[evt_type.replace('n/a','unspecified')]['template']
input_template = re.sub(r'<arg\d>', '<arg>', template)
space_tokenized_input_template = input_template.split(' ')
tokenized_input_template = []
for w in space_tokenized_input_template:
tokenized_input_template.extend(self.tokenizer.tokenize(w, add_prefix_space=True))
for triple in ex['gold_evt_links']:
trigger_span, argument_span, arg_name = triple
arg_num = ontology_dict[evt_type.replace('n/a','unspecified')][arg_name]
arg_text = ' '.join(context_words[argument_span[0]:argument_span[1]+1])
template = re.sub('<{}>'.format(arg_num),arg_text , template)
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
prefix = self.tokenizer.tokenize(' '.join(context_words[:trigger[0]]), add_prefix_space=True)
tgt = self.tokenizer.tokenize(' '.join(context_words[trigger[0]: trigger[1]+1]), add_prefix_space=True)
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)
output_template = re.sub(r'<arg\d>','<arg>', template )
space_tokenized_template = output_template.split(' ')
tokenized_template = []
for w in space_tokenized_template:
tokenized_template.extend(self.tokenizer.tokenize(w, add_prefix_space=True))
return tokenized_input_template, tokenized_template, context
def load_ontology(self):
# read ontology
ontology_dict ={}
with open('aida_ontology_cleaned.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]
args = fields[2:]
ontology_dict[evt_type] = {
'template': 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)
x = 1
while(x > 0):
#print(ontology_dict)
x = x - 1
return ontology_dict
def prepare_data(self):
if not os.path.exists('span_preprocessed_data'):
os.makedirs('span_preprocessed_data')
ontology_dict = self.load_ontology()
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_preprocessed_data/{}.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)
input_tokens = self.tokenizer.encode_plus(input_template, context,
add_special_tokens=True,
add_prefix_space=True,
max_length=MAX_LENGTH,
truncation='only_second',
padding='max_length')
tgt_tokens = self.tokenizer.encode_plus(output_template,
add_special_tokens=True,
add_prefix_space=True,
max_length=MAX_TGT_LENGTH,
truncation=True,
padding='max_length')
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'],
}
writer.write(json.dumps(processed_ex) + '\n')
def train_dataloader(self):
dataset = IEDataset('span_preprocessed_data/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_preprocessed_data/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_preprocessed_data/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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