forked from hugochan/KATE
-
Notifications
You must be signed in to change notification settings - Fork 0
/
legacy_pred.py
166 lines (139 loc) · 6.01 KB
/
legacy_pred.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
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
'''
Created on Nov, 2016
@author: hugo
'''
from __future__ import absolute_import
import argparse
import math
import numpy as np
from autoencoder.core.ae import AutoEncoder, load_model
# from autoencoder.core.deepae import DeepAutoEncoder
from autoencoder.preprocessing.preprocessing import load_corpus, doc2vec
from autoencoder.utils.op_utils import vecnorm, revdict, unitmatrix #, corrupted_matrix
from autoencoder.utils.io_utils import dump_json, write_file
# def get_topics(ae, vocab, topn=10):
# topics = []
# topic_codes = np.identity(ae.dim)
# dists = ae.decoder.predict(topic_codes)
# dists /= np.sum(dists, axis=1).reshape(ae.dim, 1)
# for idx in range(ae.dim):
# token_idx = np.argsort(dists[idx])[::-1][:topn]
# topic = zip([vocab[x] for x in token_idx], dists[idx][token_idx])
# topics.append(topic)
# return topics
def calc_pairwise_cosine(ae):
weights = ae.encoder.get_weights()[0]
weights = unitmatrix(weights, axis=0) # normalize
n = weights.shape[1]
score = []
for i in range(n):
for j in range(i + 1, n):
score.append(np.arccos(weights[:, i].dot(weights[:, j])))
return np.mean(score), np.std(score)
def calc_pairwise_dev(ae):
# the average squared deviation from 0 (90 degree)
weights = ae.encoder.get_weights()[0]
weights = unitmatrix(weights, axis=0) # normalize
n = weights.shape[1]
score = 0.
for i in range(n):
for j in range(i + 1, n):
score += (weights[:, i].dot(weights[:, j]))**2
return np.sqrt(2. * score / n / (n - 1))
def get_similar_words(ae, query_id, vocab, topn=10):
weights = ae.encoder.get_weights()[0]
weights = unitmatrix(weights) # normalize
query = weights[query_id]
score = query.dot(weights.T)
vidx = score.argsort()[::-1][:topn]
return [vocab[idx] for idx in vidx]
def translate_words(ae, query, vocab, revocab, topn=10):
weights = ae.encoder.get_weights()[0]
weights = unitmatrix(weights) # normalize
query_vec = weights[vocab[query[0]]] - weights[vocab[query[1]]] + weights[vocab[query[2]]]
score = query_vec.dot(weights.T)
vidx = score.argsort()[::-1][:topn]
return [revocab[idx] for idx in vidx]
def get_topics(ae, vocab, topn=10):
topics = []
weights = ae.encoder.get_weights()[0]
for idx in range(ae.dim):
token_idx = np.argsort(weights[:, idx])[::-1][:topn]
topics.append([vocab[x] for x in token_idx])
return topics
def get_topics_strength(ae, vocab, topn=10):
topics = []
weights = ae.encoder.get_weights()[0]
for idx in range(ae.dim):
token_idx = np.argsort(weights[:, idx])[::-1][:topn]
topics.append([(vocab[x], weights[x, idx]) for x in token_idx])
return topics
def print_topics(topics):
for i in range(len(topics)):
str_topic = ' + '.join(['%s * %s' % (prob, token) for token, prob in topics[i]])
print 'topic %s:' % i
print str_topic
print
def test(args):
corpus = load_corpus(args.input)
vocab, docs = corpus['vocab'], corpus['docs']
n_vocab = len(vocab)
doc_keys = docs.keys()
X_docs = []
for k in doc_keys:
X_docs.append(vecnorm(doc2vec(docs[k], n_vocab), 'logmax1', 0))
del docs[k]
X_docs = np.r_[X_docs]
model = AutoEncoder
# model = DeepAutoEncoder
ae = load_model(model, args.load_arch, args.load_weights)
doc_codes = ae.encoder.predict(X_docs)
dump_json(dict(zip(doc_keys, doc_codes.tolist())), args.output)
print 'Saved doc codes file to %s' % args.output
if args.save_topics:
topics_strength = get_topics_strength(ae, revdict(vocab), topn=10)
save_topics_strength(topics_strength, args.save_topics)
# topics = get_topics(ae, revdict(vocab), topn=10)
# write_file(topics, args.save_topics)
print 'Saved topics file to %s' % args.save_topics
if args.sample_words:
revocab = revdict(vocab)
queries = ['weapon', 'christian', 'compani', 'israel', 'law', 'hockey', 'comput', 'space']
words = []
for each in queries:
words.append(get_similar_words(ae, vocab[each], revocab, topn=11))
write_file(words, args.sample_words)
print 'Saved sample words file to %s' % args.sample_words
if args.translate_words:
revocab = revdict(vocab)
queries = [['father', 'man', 'woman'], ['mother', 'woman', 'man']]
for each in queries:
print each
print translate_words(ae, each, vocab, revocab, topn=10)
if args.calc_distinct:
# mean, std = calc_pairwise_cosine(ae)
# print 'Average pairwise angle (pi): %s (%s)' % (mean / math.pi, std / math.pi)
sd = calc_pairwise_dev(ae)
print 'Average squared deviation from 0 (90 degree): %s' % sd
def save_topics_strength(topics_prob, out_file):
try:
with open(out_file, 'w') as datafile:
for topic in topics_prob:
datafile.write(' + '.join(["%s * %s" % each for each in topic]) + '\n')
datafile.write('\n')
except Exception as e:
raise e
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', type=str, required=True, help='path to the input corpus file')
parser.add_argument('-o', '--output', type=str, required=True, help='path to the output doc codes file')
parser.add_argument('-st', '--save_topics', type=str, help='path to the output topics file')
parser.add_argument('-sw', '--sample_words', type=str, help='path to the output sample words file')
parser.add_argument('-tw', '--translate_words', action='store_true', help='translate words flag')
parser.add_argument('-cd', '--calc_distinct', action='store_true', help='calc average pairwise angle')
parser.add_argument('-la', '--load_arch', type=str, required=True, help='path to the trained arch file')
parser.add_argument('-lw', '--load_weights', type=str, required=True, help='path to the trained weights file')
args = parser.parse_args()
test(args)
if __name__ == '__main__':
main()