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data_utils.py
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data_utils.py
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# -*- coding: utf-8 -*-
import os
import pickle
import spacy
import numpy as np
def load_word_vec(path, word2idx=None):
fin = open(path, 'r', encoding='utf-8', newline='\n', errors='ignore')
word_vec = {}
for line in fin:
tokens = line.rstrip().split()
if word2idx is None or tokens[0] in word2idx.keys():
try:
word_vec[tokens[0]] = np.asarray(tokens[1:], dtype='float32')
except:
print('WARNING: corrupted word vector of {} when being loaded from GloVe.'.format(tokens[0]))
return word_vec
def build_embedding_matrix(word2idx, embed_dim, type):
embedding_matrix_file_name = '{0}_{1}_embedding_matrix.pkl'.format(str(embed_dim), type)
if os.path.exists(embedding_matrix_file_name):
print('loading embedding_matrix:', embedding_matrix_file_name)
embedding_matrix = pickle.load(open(embedding_matrix_file_name, 'rb'))
else:
print('loading word vectors ...')
embedding_matrix = np.zeros((len(word2idx), embed_dim))
embedding_matrix[1, :] = np.random.uniform(-1/np.sqrt(embed_dim), 1/np.sqrt(embed_dim), (1, embed_dim))
fname = '../glove.42B.300d.txt'
word_vec = load_word_vec(fname, word2idx=word2idx)
print('building embedding_matrix:', embedding_matrix_file_name)
for word, i in word2idx.items():
vec = word_vec.get(word)
if vec is not None:
embedding_matrix[i] = vec
pickle.dump(embedding_matrix, open(embedding_matrix_file_name, 'wb'))
return embedding_matrix
class Tokenizer(object):
def __init__(self, word2idx=None):
if word2idx is None:
self.word2idx = {}
self.idx2word = {}
self.idx = 0
self.word2idx['<pad>'] = self.idx
self.idx2word[self.idx] = '<pad>'
self.idx += 1
self.word2idx['<unk>'] = self.idx
self.idx2word[self.idx] = '<unk>'
self.idx += 1
else:
self.word2idx = word2idx
self.idx2word = {v:k for k,v in word2idx.items()}
def fit_on_text(self, text):
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):
text = text.lower()
words = text.split()
unknownidx = 1
sequence = [self.word2idx[w] if w in self.word2idx else unknownidx for w in words]
if len(sequence) == 0:
sequence = [0]
return sequence
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 ABSADataset(object):
def __init__(self, data):
self.data = data
def __getitem__(self, index):
return self.data[index]
def __len__(self):
return len(self.data)
class ABSADatesetReader:
@staticmethod
def __read_text__(fnames):
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), 3):
text_raw = lines[i].lower().strip()
try:
entity, attribute = lines[i + 1].lower().strip().split()
except:
entity = lines[i + 1].lower().strip()
attribute = ''
text += text_raw + " " + entity + " " + attribute + " "
return text
@staticmethod
def __read_data__(fname, tokenizer):
fin = open(fname, 'r', encoding='utf-8', newline='\n', errors='ignore')
lines = fin.readlines()
fin.close()
fin = open(fname + '.graph_entity', 'rb')
idx2gragh = pickle.load(fin)
fin.close()
fin = open(fname + '.graph_attribute', 'rb')
idx2gragh_a = pickle.load(fin)
fin.close()
all_data = []
graph_id = 0
for i in range(0, len(lines), 3):
text = lines[i].lower().strip()
polarity = lines[i+2].strip()
text_indices = tokenizer.text_to_sequence(text)
try:
polarity = int(polarity)+1
except:
print(text)
dependency_graph = idx2gragh[graph_id]
aspect_graph = idx2gragh_a[graph_id]
data = {
'text_indices': text_indices,
'polarity': polarity,
'dependency_graph': dependency_graph,
'aspect_graph': aspect_graph,
}
all_data.append(data)
graph_id += 1
return all_data
def __init__(self, dataset='15_rest', knowledge_base='conceptnet',embed_dim=300):
print("preparing {0} dataset ...".format(dataset))
fname = {
'14': {
'train': './dataset/{}/14_train.raw.tokenized'.format(knowledge_base),
'test': './dataset/{}/14_test.raw.tokenized'.format(knowledge_base)
},
'15_rest': {
'train': './dataset/{}/15_rest_train.raw.tokenized'.format(knowledge_base),
'test': './dataset/{}/15_rest_test.raw.tokenized'.format(knowledge_base)
},
'16_rest': {
'train': './dataset/{}/16_rest_train.raw.tokenized'.format(knowledge_base),
'test': './dataset/{}/16_rest_test.raw.tokenized'.format(knowledge_base)
},
'15_lap': {
'train': './dataset/{}/15_lap_train.raw.tokenized'.format(knowledge_base),
'test': './dataset/{}/15_lap_test.raw.tokenized'.format(knowledge_base)
},
'16_lap': {
'train': './dataset/{}/16_lap_train.raw.tokenized'.format(knowledge_base),
'test': './dataset/{}/16_lap_test.raw.tokenized'.format(knowledge_base)
},
}
text = ABSADatesetReader.__read_text__([fname[dataset]['train'], fname[dataset]['test']])
if os.path.exists(dataset+'_word2idx.pkl'):
print("loading {0} tokenizer...".format(dataset))
with open(dataset+'_word2idx.pkl', 'rb') as f:
word2idx = pickle.load(f)
tokenizer = Tokenizer(word2idx=word2idx)
else:
tokenizer = Tokenizer()
tokenizer.fit_on_text(text)
with open(dataset+'_word2idx.pkl', 'wb') as f:
pickle.dump(tokenizer.word2idx, f)
self.embedding_matrix = build_embedding_matrix(tokenizer.word2idx, embed_dim, dataset)
self.train_data = ABSADataset(ABSADatesetReader.__read_data__(fname[dataset]['train'], tokenizer))
self.test_data = ABSADataset(ABSADatesetReader.__read_data__(fname[dataset]['test'], tokenizer))