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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 numpy as np
from tag_utils import to2bio
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(data_dir, word2idx, embed_dim, type):
embedding_matrix_file_name = '{0}_{1}_embedding_matrix.pkl'.format(str(embed_dim), type)
if os.path.exists(os.path.join(data_dir, embedding_matrix_file_name)):
print('>>> loading embedding matrix:', embedding_matrix_file_name)
embedding_matrix = pickle.load(open(os.path.join(data_dir, embedding_matrix_file_name), 'rb'))
else:
print('>>> loading word vectors ...')
# words not found in embedding index will be randomly initialized.
embedding_matrix = np.random.uniform(-1/np.sqrt(embed_dim), 1/np.sqrt(embed_dim), (len(word2idx), embed_dim))
# <pad>
embedding_matrix[0, :] = np.zeros((1, embed_dim))
fname = 'data/glove.840B.300d.txt'
word_vec = load_word_vec(fname, word2idx=word2idx, embed_dim=embed_dim)
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(os.path.join(data_dir, 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
def build_tokenizer(data_dir):
if os.path.exists(os.path.join(data_dir, 'word2idx.pkl')):
print('>>> loading {0} tokenizer...'.format(data_dir))
with open(os.path.join(data_dir, 'word2idx.pkl'), 'rb') as f:
word2idx = pickle.load(f)
tokenizer = Tokenizer(word2idx=word2idx)
else:
filenames = [os.path.join(data_dir, '%s.txt' % set_type) for set_type in ['train', 'dev', 'test']]
all_text = ''
for filename in filenames:
with open(filename, 'r', encoding='utf-8') as fp:
for line in fp:
text = line.strip().split('####')[0]
all_text += (text + ' ')
tokenizer = Tokenizer()
tokenizer.fit_on_text(all_text)
print('>>> saving {0} tokenizer...'.format(data_dir))
with open(os.path.join(data_dir, 'word2idx.pkl'), 'wb') as f:
pickle.dump(tokenizer.word2idx, f)
return tokenizer
class ABSADataReader(object):
def __init__(self, data_dir):
self.tag_map, self.reverse_tag_map = self._get_tag_map()
self.polarity_map = {'N':0, 'NEU':1, 'NEG':2, 'POS':3} # NO_RELATION is 0
self.reverse_polarity_map = {v:k for k,v in self.polarity_map.items()}
self.data_dir = data_dir
def get_train(self, tokenizer):
return self._create_dataset('train', tokenizer)
def get_dev(self, tokenizer):
return self._create_dataset('dev', tokenizer)
def get_test(self, tokenizer):
return self._create_dataset('test', tokenizer)
@staticmethod
def _get_tag_map():
tag_list = ['O', 'B', 'I']
tag_map = {tag:i for i, tag in enumerate(tag_list)}
reverse_tag_map = {i:tag for i, tag in enumerate(tag_list)}
return tag_map, reverse_tag_map
def _create_dataset(self, set_type, tokenizer):
all_data = []
filename = os.path.join(self.data_dir, '%s.asote.v2.pair' % set_type)
print('filename: %s' % filename)
fp = open(filename, 'r', encoding='utf-8')
lines = fp.readlines()
fp.close()
for i in range(0, len(lines), 2):
text = lines[i].strip()
pairs = lines[i+1].strip().split(';')
text_indices = tokenizer.text_to_sequence(text)
seq_len = len(text_indices)
ap_tags = ['O'] * seq_len
op_tags = ['O'] * seq_len
ap_op_tags = ['O'] * seq_len
triplet_indices = np.zeros((seq_len, seq_len), dtype=np.int64)
ap_spans = []
op_spans = []
triplets = []
for pair in pairs:
if not pair:
continue
pair = eval(pair)
ap_beg, ap_end = pair[0]
op_beg, op_end = pair[1]
polarity_str = pair[2]
ap_tags[ap_beg:ap_end+1] = ['T'] * (ap_end-ap_beg+1)
op_tags[op_beg:op_end+1] = ['T'] * (op_end-op_beg+1)
ap_op_tags[ap_beg:ap_end+1] = ['T-AP'] * (ap_end-ap_beg+1)
ap_op_tags[op_beg:op_end+1] = ['T-OP'] * (op_end-op_beg+1)
polarity = self.polarity_map[polarity_str]
triplet_indices[ap_end, op_end] = polarity
if (ap_beg, ap_end) not in ap_spans:
ap_spans.append((ap_beg, ap_end))
if (op_beg, op_end) not in op_spans:
op_spans.append((op_beg, op_end))
triplets.append((ap_beg, ap_end, op_beg, op_end, polarity))
# convert from ot to bio
ap_tags = to2bio(ap_tags)
op_tags = to2bio(op_tags)
ap_op_tags = to2bio(ap_op_tags)
ap_indices = [self.tag_map[tag] for tag in ap_tags]
op_indices = [self.tag_map[tag] for tag in op_tags]
data = {
'text_indices': text_indices,
'ap_indices': ap_indices,
'op_indices': op_indices,
'triplet_indices': triplet_indices,
'ap_spans': ap_spans,
'op_spans': op_spans,
'triplets': triplets,
}
all_data.append(data)
return all_data
class ABSADataReaderV2(ABSADataReader):
def __init__(self, data_dir):
super(ABSADataReaderV2, self).__init__(data_dir)
def _create_dataset(self, set_type, tokenizer):
all_data = []
filename = os.path.join(self.data_dir, '%s_triplets.txt' % set_type)
fp = open(filename, 'r', encoding='utf-8')
lines = fp.readlines()
fp.close()
for i in range(len(lines)):
text, pairs = lines[i].strip().split('####')
text_indices = tokenizer.text_to_sequence(text)
seq_len = len(text_indices)
ap_tags = ['O'] * seq_len
op_tags = ['O'] * seq_len
ap_op_tags = ['O'] * seq_len
triplet_indices = np.zeros((seq_len, seq_len), dtype=np.int64)
ap_spans = []
op_spans = []
triplets = []
for pair in eval(pairs):
ap_beg, ap_end = pair[0][0], pair[0][-1]
op_beg, op_end = pair[1][0], pair[1][-1]
polarity_str = pair[2]
ap_tags[ap_beg:ap_end+1] = ['T'] * (ap_end-ap_beg+1)
op_tags[op_beg:op_end+1] = ['T'] * (op_end-op_beg+1)
ap_op_tags[ap_beg:ap_end+1] = ['T-AP'] * (ap_end-ap_beg+1)
ap_op_tags[op_beg:op_end+1] = ['T-OP'] * (op_end-op_beg+1)
polarity = self.polarity_map[polarity_str]
triplet_indices[ap_end, op_end] = polarity
if (ap_beg, ap_end) not in ap_spans:
ap_spans.append((ap_beg, ap_end))
if (op_beg, op_end) not in op_spans:
op_spans.append((op_beg, op_end))
triplets.append((ap_beg, ap_end, op_beg, op_end, polarity))
# convert from ot to bio
ap_tags = to2bio(ap_tags)
op_tags = to2bio(op_tags)
ap_op_tags = to2bio(ap_op_tags)
ap_indices = [self.tag_map[tag] for tag in ap_tags]
op_indices = [self.tag_map[tag] for tag in op_tags]
data = {
'text_indices': text_indices,
'ap_indices': ap_indices,
'op_indices': op_indices,
'triplet_indices': triplet_indices,
'ap_spans': ap_spans,
'op_spans': op_spans,
'triplets': triplets,
}
all_data.append(data)
return all_data