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dataloader.py
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dataloader.py
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# -*- coding: utf-8 -*-
import os
import json
import random
from collections import OrderedDict, Collection
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
class BaseDataset(Dataset):
def __init__(self,
dataset_path,
max_length,
tokenize,
N, K, Q, O, use_BIO=True):
self.raw_data = json.load(open(dataset_path, "r"))
self.classes = self.raw_data.keys()
self.max_length = max_length - 2
self.tokenize = tokenize
self.N = N
self.K = K
self.Q = Q
self.O = O
self.use_BIO = use_BIO
def __len__(self):
return 99999999
def __getitem__(self, index):
target_classes = random.sample(self.classes, self.N)
label2id, id2label = self.build_dict(target_classes)
support_set = {'tokens': [], 'trigger_label': [], 'B-mask': [], 'I-mask': [], 'att-mask': [], 'text-mask': []}
query_set = {'tokens': [], 'trigger_label': [], 'B-mask': [], 'I-mask': [], 'att-mask': [], 'text-mask': []}
for i, class_name in enumerate(target_classes):
indices = np.random.choice(
list(range(len(self.raw_data[class_name]))),
self.K + self.Q, False)
count = 0
for j in indices:
if count < self.K:
instance = self.preprocess(self.raw_data[class_name][j], [class_name])
token_ids, label_ids, B_mask, I_mask, att_mask, text_mask = self.tokenize(instance, label2id)
support_set['tokens'].append(token_ids)
support_set['trigger_label'].append(label_ids)
support_set['B-mask'].append(B_mask)
support_set['I-mask'].append(I_mask)
support_set['att-mask'].append(att_mask)
support_set['text-mask'].append(text_mask)
else:
instance = self.preprocess(self.raw_data[class_name][j], target_classes)
token_ids, label_ids, B_mask, I_mask, att_mask, text_mask = self.tokenize(instance, label2id)
query_set['tokens'].append(token_ids)
query_set['trigger_label'].append(label_ids)
query_set['B-mask'].append(B_mask)
query_set['I-mask'].append(I_mask)
query_set['att-mask'].append(att_mask)
query_set['text-mask'].append(text_mask)
count += 1
for k, v in support_set.items():
support_set[k] = torch.stack(v)
for k, v in query_set.items():
query_set[k] = torch.stack(v)
return support_set, query_set, id2label
def preprocess(self, instance, event_type_list):
raise NotImplementedError
def build_dict(self, event_type_list):
label2id = OrderedDict()
id2label = OrderedDict()
label2id['O'] = 0
id2label[0] = 'O'
label2id['PAD'] = -100
id2label[-100] = 'PAD'
for i, event_type in enumerate(event_type_list):
if self.use_BIO:
label2id['B-' + event_type] = 2*i + 1
label2id['I-' + event_type] = 2*i + 2
id2label[2*i + 1] = 'B-' + event_type
id2label[2*i + 2] = 'I-' + event_type
else:
label2id['I-' + event_type] = i+1
id2label[i+1] = 'I-' + event_type
return label2id, id2label
class FewEventDataset(BaseDataset):
def __getitem__(self, index):
target_classes = random.sample(self.classes, self.N)
label2id, id2label = self.build_dict(target_classes)
support_set = {'tokens': [], 'trigger_label': [], 'B-mask': [], 'I-mask': [], "att-mask": [], 'text-mask': []}
query_set = {'tokens': [], 'trigger_label': [], 'B-mask': [], 'I-mask': [], "att-mask": [], 'text-mask': []}
for i, class_name in enumerate(target_classes):
indices = np.random.choice(
list(range(len(self.raw_data[class_name]))),
self.K + self.Q, False)
count = 0
for j in indices:
if count < self.K:
instance = self.preprocess(self.raw_data[class_name][j], class_name, [class_name])
token_ids, label_ids, B_mask, I_mask, att_mask, text_mask = self.tokenize(instance, label2id)
support_set['tokens'].append(token_ids)
support_set['trigger_label'].append(label_ids)
support_set['B-mask'].append(B_mask)
support_set['I-mask'].append(I_mask)
support_set['att-mask'].append(att_mask)
support_set['text-mask'].append(text_mask)
else:
instance = self.preprocess(self.raw_data[class_name][j], class_name, target_classes)
token_ids, label_ids, B_mask, I_mask, att_mask, text_mask = self.tokenize(instance, label2id)
query_set['tokens'].append(token_ids)
query_set['trigger_label'].append(label_ids)
query_set['B-mask'].append(B_mask)
query_set['I-mask'].append(I_mask)
query_set['att-mask'].append(att_mask)
query_set['text-mask'].append(text_mask)
count += 1
for k, v in support_set.items():
support_set[k] = torch.stack(v)
for k, v in query_set.items():
query_set[k] = torch.stack(v)
return support_set, query_set, id2label
def preprocess(self, instance, event_type, event_type_list):
result = {'tokens': [], 'trigger_label': [], 'B-mask':[], 'I-mask': []}
sentence = instance['tokens']
result['tokens'] = sentence
trigger_label = ['O'] * len(sentence)
B_mask = [0] * len(sentence)
I_mask = [0] * len(sentence)
trigger_length = len(instance['trigger'])
trigger_start_pos = instance['position'][0]
trigger_end_pos = trigger_start_pos + trigger_length
for i in range(trigger_start_pos, trigger_end_pos):
if self.use_BIO:
if i == trigger_start_pos:
trigger_label[i] = f"B-{event_type}"
B_mask[i] = 1
else:
trigger_label[i] = f"I-{event_type}"
I_mask[i] = 1
else:
trigger_label[i] = f"I-{event_type}"
I_mask[i] = 1
result['trigger_label'] = trigger_label
result['B-mask'] = B_mask
result['I-mask'] = I_mask
return result
def collate_fn(data):
batch_support = {'tokens': [], 'trigger_label': [], 'B-mask':[], 'I-mask': [], 'att-mask': [], 'text-mask': []}
batch_query = {'tokens': [], 'trigger_label': [], 'B-mask':[], 'I-mask': [], 'att-mask': [], 'text-mask': []}
batch_id2label = []
support_sets, query_sets, id2labels = zip(*data)
for i in range(len(support_sets)):
for k in support_sets[i]:
batch_support[k].append(support_sets[i][k])
for k in query_sets[i]:
batch_query[k].append(query_sets[i][k])
batch_id2label.append(id2labels[i])
for k in batch_support:
batch_support[k] = torch.cat(batch_support[k], 0)
for k in batch_query:
batch_query[k] = torch.cat(batch_query[k], 0)
return batch_support, batch_query, batch_id2label
def split_json_data(json_data: dict):
train_data, dev_data, test_data = {}, {}, {}
event_types = list(json_data.keys())
random.shuffle(event_types)
train_types = event_types[:80]
dev_types = event_types[80: 90]
test_types = event_types[90: 100]
for k in train_types:
train_data[k] = json_data[k]
for k in dev_types:
dev_data[k] = json_data[k]
for k in test_types:
test_data[k] = json_data[k]
return train_data, dev_data, test_data
def get_loader(dataset_name,
mode,
max_length,
tokenize,
N, K, Q, O,
batch_size,
use_BIO=False,
num_workers=4,
collate_fn=collate_fn):
root_data_dir = "/home/xxx/code/PA-CRF/data"
if mode == "TRAIN":
data_file = "meta_train_dataset.json"
elif mode == "DEV":
data_file = "meta_dev_dataset.json"
elif mode == "TEST":
data_file = "meta_test_dataset.json"
else:
raise ValueError("Error mode!")
dataset_path = os.path.join(root_data_dir, "FewEvent", data_file)
dataset = FewEventDataset(dataset_path,
max_length,
tokenize,
N, K, Q, O, use_BIO)
dataloader = DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=False,
pin_memory=True,
num_workers=num_workers,
collate_fn=collate_fn)
return iter(dataloader)