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data_utils.py
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data_utils.py
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# -*- coding: utf-8 -*-
# ------------------
# @Author: BinLiang
# @Mail: bin.liang@stu.hit.edu.cn
# ------------------
import os
import pickle
import numpy as np
import torch
from torch.utils.data import Dataset
# from transformers import BertTokenizer
from pytorch_pretrained_bert import BertTokenizer
#from gensim import corpora,models
from sklearn.metrics.pairwise import cosine_similarity
def build_tokenizer(fnames, max_seq_len, dat_fname):
if os.path.exists(dat_fname):
print('loading tokenizer:', dat_fname)
tokenizer = pickle.load(open(dat_fname, 'rb'))
else:
text = ''
for fname in fnames:
fin = open(fname, 'r', encoding='utf-8', newline='\n', errors='ignore')
lines = fin.readlines()
fin.close()
for i in range(0, len(lines), 4):#3
text_raw = lines[i].strip().lower()
target = lines[i+1].lower().strip()
text += text_raw + target + " "
tokenizer = Tokenizer(max_seq_len)
tokenizer.fit_on_text(text)
pickle.dump(tokenizer, open(dat_fname, 'wb'))
return tokenizer
def _load_word_vec(path, word2idx=None, embed_dim=300):
fin = open(path, 'r', encoding='utf-8', newline='\n', errors='ignore')
word_vec = {}
for line in fin:
tokens = line.rstrip().split()
word, vec = ' '.join(tokens[:-embed_dim]), tokens[-embed_dim:]
if word in word2idx.keys():
word_vec[word] = np.asarray(vec, dtype='float32')
return word_vec
def build_embedding_matrix(word2idx, embed_dim, dat_fname):
if os.path.exists(dat_fname):
print('loading embedding_matrix:', dat_fname)
embedding_matrix = pickle.load(open(dat_fname, 'rb'))
else:
print('loading word vectors...')
embedding_matrix = np.zeros((len(word2idx) + 2, embed_dim)) # idx 0 and len(word2idx)+1 are all-zeros
fname = './glove.twitter.27B/glove.twitter.27B.' + str(embed_dim) + 'd.txt' \
if embed_dim != 300 else './glove.42B.300d.txt'
word_vec = _load_word_vec(fname, word2idx=word2idx, embed_dim=embed_dim)
print('building embedding_matrix:', dat_fname)
for word, i in word2idx.items():
vec = word_vec.get(word)
if vec is not None:
# words not found in embedding index will be all-zeros.
embedding_matrix[i] = vec
pickle.dump(embedding_matrix, open(dat_fname, 'wb'))
return embedding_matrix
def pad_and_truncate(sequence, maxlen, dtype='int64', padding='post', truncating='post', value=0):
x = (np.ones(maxlen) * value).astype(dtype)
if truncating == 'pre':
trunc = sequence[-maxlen:]
else:
trunc = sequence[:maxlen]
trunc = np.asarray(trunc, dtype=dtype)
if padding == 'post':
x[:len(trunc)] = trunc
else:
x[-len(trunc):] = trunc
return x
class Tokenizer(object):
def __init__(self, max_seq_len, lower=True):
self.lower = lower
self.max_seq_len = max_seq_len
self.word2idx = {}
self.idx2word = {}
self.idx = 1
def fit_on_text(self, text):
if self.lower:
text = text.lower()
words = text.split()
for word in words:
if word not in self.word2idx:
self.word2idx[word] = self.idx
self.idx2word[self.idx] = word
self.idx += 1
def text_to_sequence(self, text, reverse=False, padding='post', truncating='post'):
if self.lower:
text = text.lower()
words = text.split()
unknownidx = len(self.word2idx)+1
sequence = [self.word2idx[w] if w in self.word2idx else unknownidx for w in words]
if len(sequence) == 0:
sequence = [0]
if reverse:
sequence = sequence[::-1]
return pad_and_truncate(sequence, self.max_seq_len, padding=padding, truncating=truncating)
class Tokenizer4Bert:
def __init__(self, max_seq_len, pretrained_bert_name):
self.tokenizer = BertTokenizer.from_pretrained(pretrained_bert_name)
self.max_seq_len = max_seq_len
def text_to_sequence(self, text, reverse=False, padding='post', truncating='post'):
sequence = self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize(text))
if len(sequence) == 0:
sequence = [0]
if reverse:
sequence = sequence[::-1]
return pad_and_truncate(sequence, self.max_seq_len, padding=padding, truncating=truncating)
class ZSSDDataset(Dataset):
def __init__(self, fname, tokenizer):
fin = open(fname, 'r', encoding='utf-8', newline='\n', errors='ignore')
lines = fin.readlines()
fin.close()
all_data = []
for i in range(0, len(lines), 4):
text = lines[i].lower().strip()
target = lines[i+1].lower().strip()
polarity = lines[i+2].strip()
cross_label = lines[i+3].strip()
text_indices = tokenizer.text_to_sequence(text)
target_indices = tokenizer.text_to_sequence(target)
target_len = np.sum(target_indices != 0)
polarity = int(polarity)
cross_label = int(cross_label)
text_len = np.sum(text_indices != 0)
concat_bert_indices = tokenizer.text_to_sequence('[CLS] ' + text + ' [SEP] ' + target + " [SEP]")
concat_segments_indices = [0] * (text_len + 2) + [1] * (target_len + 1)
concat_segments_indices = pad_and_truncate(concat_segments_indices, tokenizer.max_seq_len)
text_bert_indices = tokenizer.text_to_sequence("[CLS] " + text + " [SEP]")
target_bert_indices = tokenizer.text_to_sequence("[CLS] " + target + " [SEP]")
data = {
'concat_bert_indices': concat_bert_indices,
'concat_segments_indices': concat_segments_indices,
'text_bert_indices': text_bert_indices,
'target_bert_indices': target_bert_indices,
'text_indices': text_indices,
'target_indices': target_indices,
'polarity': polarity,
'cross_label': cross_label,
'text': text,
'target': target
}
all_data.append(data)
self.data = all_data
def __getitem__(self, index):
return self.data[index]
def __len__(self):
return len(self.data)