-
Notifications
You must be signed in to change notification settings - Fork 827
/
process_data.py
146 lines (138 loc) · 5.04 KB
/
process_data.py
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
143
144
145
146
import numpy as np
import cPickle
from collections import defaultdict
import sys, re
import pandas as pd
def build_data_cv(data_folder, cv=10, clean_string=True):
"""
Loads data and split into 10 folds.
"""
revs = []
pos_file = data_folder[0]
neg_file = data_folder[1]
vocab = defaultdict(float)
with open(pos_file, "rb") as f:
for line in f:
rev = []
rev.append(line.strip())
if clean_string:
orig_rev = clean_str(" ".join(rev))
else:
orig_rev = " ".join(rev).lower()
words = set(orig_rev.split())
for word in words:
vocab[word] += 1
datum = {"y":1,
"text": orig_rev,
"num_words": len(orig_rev.split()),
"split": np.random.randint(0,cv)}
revs.append(datum)
with open(neg_file, "rb") as f:
for line in f:
rev = []
rev.append(line.strip())
if clean_string:
orig_rev = clean_str(" ".join(rev))
else:
orig_rev = " ".join(rev).lower()
words = set(orig_rev.split())
for word in words:
vocab[word] += 1
datum = {"y":0,
"text": orig_rev,
"num_words": len(orig_rev.split()),
"split": np.random.randint(0,cv)}
revs.append(datum)
return revs, vocab
def get_W(word_vecs, k=300):
"""
Get word matrix. W[i] is the vector for word indexed by i
"""
vocab_size = len(word_vecs)
word_idx_map = dict()
W = np.zeros(shape=(vocab_size+1, k), dtype='float32')
W[0] = np.zeros(k, dtype='float32')
i = 1
for word in word_vecs:
W[i] = word_vecs[word]
word_idx_map[word] = i
i += 1
return W, word_idx_map
def load_bin_vec(fname, vocab):
"""
Loads 300x1 word vecs from Google (Mikolov) word2vec
"""
word_vecs = {}
with open(fname, "rb") as f:
header = f.readline()
vocab_size, layer1_size = map(int, header.split())
binary_len = np.dtype('float32').itemsize * layer1_size
for line in xrange(vocab_size):
word = []
while True:
ch = f.read(1)
if ch == ' ':
word = ''.join(word)
break
if ch != '\n':
word.append(ch)
if word in vocab:
word_vecs[word] = np.fromstring(f.read(binary_len), dtype='float32')
else:
f.read(binary_len)
return word_vecs
def add_unknown_words(word_vecs, vocab, min_df=1, k=300):
"""
For words that occur in at least min_df documents, create a separate word vector.
0.25 is chosen so the unknown vectors have (approximately) same variance as pre-trained ones
"""
for word in vocab:
if word not in word_vecs and vocab[word] >= min_df:
word_vecs[word] = np.random.uniform(-0.25,0.25,k)
def clean_str(string, TREC=False):
"""
Tokenization/string cleaning for all datasets except for SST.
Every dataset is lower cased except for TREC
"""
string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string)
string = re.sub(r"\'s", " \'s", string)
string = re.sub(r"\'ve", " \'ve", string)
string = re.sub(r"n\'t", " n\'t", string)
string = re.sub(r"\'re", " \'re", string)
string = re.sub(r"\'d", " \'d", string)
string = re.sub(r"\'ll", " \'ll", string)
string = re.sub(r",", " , ", string)
string = re.sub(r"!", " ! ", string)
string = re.sub(r"\(", " \( ", string)
string = re.sub(r"\)", " \) ", string)
string = re.sub(r"\?", " \? ", string)
string = re.sub(r"\s{2,}", " ", string)
return string.strip() if TREC else string.strip().lower()
def clean_str_sst(string):
"""
Tokenization/string cleaning for the SST dataset
"""
string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string)
string = re.sub(r"\s{2,}", " ", string)
return string.strip().lower()
if __name__=="__main__":
w2v_file = sys.argv[1]
data_folder = ["rt-polarity.pos","rt-polarity.neg"]
print "loading data...",
revs, vocab = build_data_cv(data_folder, cv=10, clean_string=True)
max_l = np.max(pd.DataFrame(revs)["num_words"])
print "data loaded!"
print "number of sentences: " + str(len(revs))
print "vocab size: " + str(len(vocab))
print "max sentence length: " + str(max_l)
print "loading word2vec vectors...",
w2v = load_bin_vec(w2v_file, vocab)
print "word2vec loaded!"
print "num words already in word2vec: " + str(len(w2v))
add_unknown_words(w2v, vocab)
W, word_idx_map = get_W(w2v)
rand_vecs = {}
add_unknown_words(rand_vecs, vocab)
W2, _ = get_W(rand_vecs)
cPickle.dump([revs, W, W2, word_idx_map, vocab], open("mr.p", "wb"))
print "dataset created!"