TextCNN-For-Relation-Classification/data_helpers.py at master · DengChan/TextCNN-For-Relation-Classification · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
import numpy as np
import re
import utils
from configure import FLAGS


def word_tokenizer(sentence):
    return sentence.split()


def clean_str(text):
    text = text.lower()
    # Clean the text
    text = re.sub(r"[^A-Za-z0-9^,!.\/'+-=]", " ", text)
    text = re.sub(r"what's", "what is ", text)
    text = re.sub(r"that's", "that is ", text)
    text = re.sub(r"there's", "there is ", text)
    text = re.sub(r"it's", "it is ", text)
    text = re.sub(r"\'s", " ", text)
    text = re.sub(r"\'ve", " have ", text)
    text = re.sub(r"can't", "can not ", text)
    text = re.sub(r"n't", " not ", text)
    text = re.sub(r"i'm", "i am ", text)
    text = re.sub(r"\'re", " are ", text)
    text = re.sub(r"\'d", " would ", text)
    text = re.sub(r"\'ll", " will ", text)
    text = re.sub(r",", " ", text)
    text = re.sub(r"\.", " ", text)
    text = re.sub(r"!", " ! ", text)
    text = re.sub(r"\/", " ", text)
    text = re.sub(r"\^", " ^ ", text)
    text = re.sub(r"\+", " + ", text)
    text = re.sub(r"\-", " - ", text)
    text = re.sub(r"\=", " = ", text)
    text = re.sub(r"'", " ", text)
    text = re.sub(r"(\d+)(k)", r"\g<1>000", text)
    text = re.sub(r":", " : ", text)
    text = re.sub(r" e g ", " eg ", text)
    text = re.sub(r" b g ", " bg ", text)
    text = re.sub(r" u s ", " american ", text)
    text = re.sub(r"\0s", "0", text)
    text = re.sub(r" 9 11 ", "911", text)
    text = re.sub(r"e - mail", "email", text)
    text = re.sub(r"j k", "jk", text)
    text = re.sub(r"\s{2,}", " ", text)

    return text.strip()


def to_one_hot(lst):
    offset = np.array(lst)
    return np.eye(np.max(offset)+1)[offset]


def get_relative_pos(data, max_sentence_length):
    pos1 = []
    pos2 = []
    for idx in range(len(data)):
        tokens = word_tokenizer(data[idx][1])
        e1 = data[idx][2]
        e2 = data[idx][3]
        p1 = ""
        p2 = ""
        # 获得相对实体位置距离的向量
        # 实体所在位置的值为最大索引，前面的值递减 后面的值递增
        for word_idx in range(len(tokens)):
            p1 += str((max_sentence_length-1)+word_idx-e1) + " "
            p2 += str((max_sentence_length-1)+word_idx-e2) + " "
        pos1.append(p1)
        pos2.append(p2)
    return pos1, pos2


def load_data(path):
    print("Loading data...")
    data = []
    lines = [line.strip() for line in open(path)]
    max_sentence_length = 0
    # 遍历每组数据

    # 3        "The <e1>author</e1> of a keygen uses a <e2>disassembler</e2> to look at the raw assembly code."
    # Instrument - Agency(e2, e1)
    # Comment:
    num_data = 0
    for idx in range(0, len(lines), 4):
        num_data = num_data+1
        id = lines[idx].split('\t')[0]
        relation = lines[idx + 1]

        sentence = lines[idx].split('\t')[1]
        sentence = sentence.replace("<e1>", " _e11_ ")
        sentence = sentence.replace("</e1>", " _e12_ ")
        sentence = sentence.replace("<e2>", " _e21_ ")
        sentence = sentence.replace("</e2>", " _e22_ ")
        sentence = clean_str(sentence)

        tokens = word_tokenizer(sentence)
        if max_sentence_length < len(tokens):
            max_sentence_length = len(tokens)
        # 实体1索引
        e1 = tokens.index('e12')-1
        # 实体2索引
        e2 = tokens.index('e22')-1
        data.append([id, sentence, e1, e2, relation])
    print(path)
    print("Max length = {0} , number of data is {1}".format(max_sentence_length, num_data))

    # 得到text
    text = [item[1] for item in data]

    # 得到关系的标签并转为One-hot
    labels = [utils.class2label[item[4]] for item in data]
    # to one-hot
    labels = to_one_hot(labels)
    labels = labels.astype(np.uint8)

    # 获得位置向量
    pos1, pos2 = get_relative_pos(data, FLAGS.max_sentence_length)
    print("Done with loading data.")
    return text, labels, pos1, pos2


def batch_iter(data, batch_size, num_epochs, shuffle=True):
    data = np.array(data)
    data_size = len(data)
    batch_per_epoch = int((data_size-1)/batch_size)+1
    for epoch in range(num_epochs):
        if shuffle:
            shuffle_indices = np.random.permutation(np.arange(data_size))
            data = data[shuffle_indices]
        for batch in range(batch_per_epoch):
            start_index = batch*batch_size
            end_index = min((batch+1)*batch_size, data_size)
            yield [data[start_index:end_index], batch_per_epoch]


if __name__ == '__main__':
    train_file = 'SemEval2010_task8_all_data/SemEval2010_task8_training/TRAIN_FILE.TXT'
    test_file = 'SemEval2010_task8_all_data/SemEval2010_task8_testing_keys/TEST_FILE_FULL.TXT'
    text, labels, pos1, pos2 = load_data(train_file)
    print(labels.shape[1])
    print(len(text[0].split()))