-
Notifications
You must be signed in to change notification settings - Fork 0
/
utils.py
672 lines (577 loc) · 19.6 KB
/
utils.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
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
import numpy as np
from load_word2vec import *
import time,datetime
import re
import nltk
from blocking.block import *
def hello():
print('hello test import!')
# 单独加载journal,因为训练集中journal的样例太少了
def load_all_journals(fp):
# fp = open('v3/all_journal_1614_.txt', 'r')
# lines = fp.readlines()
journals_l = []
for line in open(fp, 'r'):
journals_l.append(line.lower())
journals = [journal.split() for journal in journals_l]
return journals
def load_all_v3titles(fp):
# fp = open('v3/titles4v3.txt', 'r')
# lines = fp.readlines()
titles_l = []
for line in open(fp, 'r'):
titles_l.append(line.lower())
titles = [title.split() for title in titles_l]
return titles
def load_all_v3authors(fp):
# fp = open('v3/authors4v3.txt', 'r')
# fp = open('dataset_workshop/linked_authors_no_punctuation.txt', 'r') # linked author train CNN
# lines = fp.readlines()
authors_l = []
for line in open(fp, 'r'):
authors_l.append(line.lower())
authors = [author.split() for author in authors_l]
return authors
# read samples from training dataset,return list of all titles, authors and journals without duplicates sample.
def readData2(ftrain):
titles_set = set()
authors_set = set()
# journals_set = set()
title_length = 0
journal_length = 0
fp = open(ftrain, 'r')
samples = fp.readlines()
for sample in samples:
# print(sample)
temp = sample.strip().split('#$')
if len(temp) == 3:
title = temp[0]
authors = temp[1]
journal = temp[2]
# title, authors, journal = sample.strip().split('#$')
if len(title.split(' ')) > title_length:
title_length = len(title.split(' '))
if len(journal.split(' ')) > journal_length:
journal_length = len(journal.split(' '))
# build titles set
titles_set.add(title.strip('.'))
# build author set
author = authors.split(',')
for a in author:
authors_set.add(a)
# build journal set
# journals_set.add(journal.strip('.'))
titles_list = [t.split() for t in titles_set]
authors_list = [s.split() for s in authors_set if s != '']
# journals_list = [j.split() for j in journals_set]
return max(title_length, journal_length), titles_list, authors_list #, journals_list
def makePaddedList(maxl, sent_contents, pad_symbol='<p>'):
# maxl = max([len(sent) for sent in sent_contents])
# print("padding maxl:", maxl)
T = []
for sent in sent_contents:
t = []
lenth = len(sent)
for i in range(lenth):
t.append(sent[i])
for i in range(lenth, maxl):
t.append(pad_symbol)
T.append(t)
return T
def makePaddedList_index(maxl, sent_contents, pad_symbol):
T = []
for sent in sent_contents:
t = []
lenth = len(sent)
for i in range(lenth):
t.append(sent[i])
for i in range(lenth, maxl):
t.append(pad_symbol)
T.append(t)
return T
# longer-trim, shorter-padding
def makePaddedList2(maxl, sent_contents, pad_symbol):
T = []
for sent in sent_contents:
t = []
lenth = len(sent)
if lenth < maxl:
for i in range(lenth):
t.append(sent[i])
for i in range(lenth, maxl):
t.append(pad_symbol)
else:
for i in range(maxl):
t.append(sent[i])
T.append(t)
return T
def makeWordList(sent_list):
"""
:param sent_list:
:return:返回一个字典,{'word1':index1,'word2':index2,....},index从1开始
"""
wf = {}
for sent in sent_list: # 构造字典wf,键是单个word,值是出现的次数
for w in sent:
if w in wf:
wf[w] += 1
else:
wf[w] = 0
wl = {}
i = 0
wl['unkown'] = 0
for w, f in wf.items(): # 构造字典wl,键是单个word, 值是下标,从1开始
# print(w, ' ', f)
i += 1
wl[w] = i
return wl
def makePosFeatures(sent_contents):
"""
:param sent_contents:
:return:sent_contents中每个sentence都构建一个list,存放sentence中每个word的标注信息.
"""
pos_tag_list = []
for sent in sent_contents:
# print(sent)
pos_tag = nltk.pos_tag(sent)
# print(pos_tag)
pos_tag = list(zip(*pos_tag))[1] # 拆开pos_tag
# print(pos_tag)
pos_tag_list.append(pos_tag)
return pos_tag_list
def mapWordToId(sent_contents, word_dict):
"""
:param sent_contents:
:param word_dict:
:return:把所有的sentence变成矩阵(None, embedding_dimensionality),横坐标是每个word在word_dict中对应的index,纵坐标是每个sentence
"""
T = []
for sent in sent_contents:
t = []
for w in sent:
t.append(word_dict[w])
T.append(t)
return T
def mapLabelToId(sent_labels, label_dict):
return [label_dict[label] for label in sent_labels]
def batch_iter(data, batch_size, num_epochs, shuffle=True):
"""
含有yield说明不是一个普通函数,是一个Generator.
函数效果:对data,一共分成num_epochs个阶段(epoch),在每个epoch内,如果shuffle=True,就将data重新洗牌,
批量生成(yield)一批一批的重洗过的data,每批大小是batch_size,一共生成int(len(data)/batch_size)+1批。
Generate a batch iterator for a dataset.
:param data:
:param batch_size:每批data的size
:param num_epochs:阶段数目
:param shuffle:洗牌
:return:
"""
data = np.array(data)
data_size = len(data)
num_batch_per_epoch = int(len(data)/batch_size) + 1 # 每段的batch数目
for epoch in range(num_epochs):
if shuffle:
# np.random.permutation(),得到一个重新排列的序列(Array)
# np.arrange(),得到一个均匀间隔的array.
shuffle_indices = np.random.permutation(np.arange(data_size))
shuffle_data = data[shuffle_indices] # 重新洗牌的data
else:
shuffle_data = data
for batch_num in range(num_batch_per_epoch):
start_index = batch_num * batch_size
end_index = min((batch_num + 1) * batch_size, data_size)
yield shuffle_data[start_index:end_index] # all elements index between start_index and end_inde
# convert samples data [[w1,w2,,,],[],[],[]]to matrix [[i1,i2],[],[],[]]according the vocabulary,
# matrix's indexes are the location of the word in the vocabulary.
def sample2index_matrix(samples, vocab, max_length):
outer_index_list = []
for sample in samples:
outer_index_list.append(index_sample(sample, vocab, max_length))
return outer_index_list
def index_sample(sample, vocab, max_length):
current_index = 0
inner_index_list = []
for word in sample:
index = np.where(vocab == word)
if len(index[0]) != 0:
i = index[0][0]
else:
i = 1 # vocab 中没有的就设定为<p>
inner_index_list.append(i)
if word == '<p>':
for c in range(current_index, max_length-1):
inner_index_list.append(1)
break
current_index += 1
return inner_index_list
def sample2index_matrix2(samples, vocab):
outer_index_list = []
for sample in samples:
outer_index_list.append(index_sample2(sample, vocab))
return outer_index_list
def index_sample2(sample, vocab):
inner_index_list = []
for word in sample:
index = np.where(vocab == word)
if len(index[0]) != 0:
i = index[0][0]
else:
i = 1 # vocab 中没有的就设定为<p>
inner_index_list.append(i)
if word == '<p>':
break
return inner_index_list
def load_journal4test(journal_path):
o = open(journal_path, 'r')
lines = o.readlines()
journals4test = []
j_labels = []
for journal in lines:
# print(journal)
journals4test.append(journal.strip())
j_labels.append(2)
return journals4test, j_labels
def load_author4test(author_path):
o = open(author_path, 'r')
lines = o.readlines()
authors4test = []
a_labels = []
for author in lines:
# print(author)
authors4test.append(author.strip())
a_labels.append(1)
return authors4test, a_labels
def load_title4test(title_path):
o = open(title_path, 'r')
lines = o.readlines()
titles4test = []
t_labels = []
for title in lines:
# print(title)
titles4test.append(title.strip())
t_labels.append(0)
return titles4test, t_labels
# save experiment result,only save samples which was categorized wrongly.
def save_experiment_result(result_path, x_raw, y_test, predictions, Accuracy):
write = open(result_path, 'w+')
size = len(x_raw)
l = ''
p = ''
write.write('Classification Accuracy: ' + str(Accuracy)+'\n')
for i in range(size):
label = str(y_test[i])
prediction = str(predictions[i])
if label != prediction:
if label == '0':
l = 'T'
elif label == '1':
l = 'A'
elif label == '2':
l = 'J'
if prediction == '0':
p = 'T'
elif prediction == '1':
p = 'A'
elif prediction == '2':
p = 'J'
print(x_raw[i]+' '+l+' '+p)
write.write(x_raw[i]+'\t'+l+'\t'+p+'\n')
write.close()
def load_data_not_word2vec():
titles = []
authors = []
journal = []
j_fp = open('v3/all_journal_1614_.txt', 'r')
j_lines = j_fp.readlines()
for line in j_lines:
journal.append(line.strip())
j_fp.close()
t_fp = open('v3/titles4v3.txt', 'r')
t_lines = t_fp.readlines()
for line in t_lines:
titles.append(line.strip())
t_fp.close()
a_fp = open('v3/authors4v3.txt', 'r')
a_lines = a_fp.readlines()
for line in a_lines:
authors.append(line.strip())
a_fp.close()
return titles, authors, journal
# 调整预测结果,因为每条记录都只有一个title和一个journal,所以在softmax的概率中,当模型的预测相同时,
# 概率相对更大的被调整成相应label.
# revise the predictions according the strategy.
def revise_predictions(predictions, loss):
t_index = np.where(predictions == 0)
# print(t_index)
max_temp = -10000
max_index = 0
if len(t_index[0]) > 1:
# print('more title,error!')
for i in t_index[0]:
if max_temp < loss[i][0]:
max_temp = loss[i][0]
max_index = i
for i in t_index[0]:
if i != max_index:
predictions[i] = 2
# print('title max index:', max_index)
# print(predictions)
j_index = np.where(predictions == 2)
# print(j_index)
if len(j_index[0]) > 1:
# print('more journal,error!')
for i in j_index[0]:
# print(i)
if max_temp < loss[i][2]:
max_temp = loss[i][2]
max_index = i
for i in j_index[0]:
if i != max_index:
predictions[i] = 1
# print('title max index:', max_index)
# print("after revise:", predictions)
return predictions
# block = np.arrange(len(blocks))
def all_revise_predictions(predictions, loss, block):
result = []
copy_predictions = predictions.copy()
# print(copy_predictions)
# print(loss)
# print(block)
for p in predictions:
max_temp = -10000
max_index = 0
# print(predictions)
# print('p:', str(p))
index = np.where(copy_predictions == p)
# print(index[0])
if len(index[0]) > 1:
for i in index[0]:
if max_temp < loss[i][p]:
max_temp = loss[i][p]
max_index = i
# print(max_temp)
# print('max_index:', str(max_index))
# print('rest label:')
# print(make_rest_label(copy_predictions, block))
for i in index[0]:
if i != max_index:
copy_predictions[i] = make_rest_label(copy_predictions, block)[0]
# else:
# continue
# print(copy_predictions)
if (np.sort(copy_predictions) == block).all():
result = [str(p) for p in copy_predictions]
return result
# block = np.arrange(len(blocks))
def greddy_predictions(loss, block):
predictions = loss.argmax(1)
score = loss.max(1)
# print('prediction:', predictions)
# print('score:', score)
# print('sum score:', np.sum(score))
if len(loss[0]) < len(LABEL_DICT):
return predictions, score
result = []
copy_predictions = predictions.copy()
# print(copy_predictions)
# print(loss)
# print(block)
l = 0
for ind, p in enumerate(predictions):
max_temp = -10000
max_index = 0
# print(predictions)
# print('p:', str(p))
index = np.where(copy_predictions == p)
# print(index[0])
if len(index[0]) > 1:
max_index_index = 0
for i in index[0]:
if max_temp < loss[i][p]:
max_temp = loss[i][p]
max_index_index = i
# print('max_temp', max_temp)
# print('index:', ind)
# score[ind] = max_temp
# print('max_index:', str(max_index))
# print('rest label:')
# print(make_rest_label(copy_predictions, block))
for i in index[0]:
if i != max_index_index:
# print(i)
rest_label_index = make_rest_label(copy_predictions, block)[0]
copy_predictions[i] = rest_label_index
# print(loss[rest_label_index][p])
# max_index = predictions[max_index_index]
# else:
# max_index = predictions[index[0][0]]
# print('max_index:', max_index)
# print(loss[l][max_index])
# score[l] = loss[l][max_index]
# l += 1
# print(copy_predictions)
# print('max_index:', max_index)
# print(loss[rest_label_index][p])
if (np.sort(copy_predictions) == block).all():
result = [str(p) for p in copy_predictions]
# print(predictions)
# print(copy_predictions)
revise_score = revise_score_list(predictions, copy_predictions, loss)
# print(score)
# print(revise_score)
return result, revise_score
def revise_score_list(predictions, copy_predictions, loss):
score = []
for i in range(len(predictions)):
if predictions[i] != copy_predictions[i]:
score.append(loss[i][copy_predictions[i]])
else:
score.append(loss[i][predictions[i]])
return np.array(score)
def make_rest_label(predictions, block):
rest_label = []
for b in block:
if b not in predictions:
rest_label.append(b)
return rest_label
# judge if string is more numeric or text
def n_or_t(block):
reobj = re.compile('\d')
results = reobj.findall(block)
num_ratio = len(results)/len(block)
# "page 1-9",这应该是number占比最小的情况了
if num_ratio >= 0.25:
token = 'n'
else:
token = 't'
return token
def read_test_data(file_path):
samples = []
labels = []
read = open(file_path, 'r')
lines = read.readlines()
# print(len(lines))
for line in lines:
if re.match(r'\[', line):
label = [x for x in eval(line)]
labels.append(label)
else:
samples.append(line)
return samples, labels
# match numeric block and return the corresponding label:year-3,page-5,volume-4,
# if match failed return 6
def match_regex(block):
label = 6
year_regex1 = r'.*([1-2][0-9]{3})' # '2014
year_regex2 = r'.*(\([1-2][0-9]{3}\))' # '(2014)'
year_regex = "|".join([year_regex2, year_regex1])
year_match_result = re.match(year_regex, block)
page_regex1 = r'.*([0-9]+\-[0-9]+$)'
page_regex2 = r'.*([0-9]+.\-.[0-9]+$)'
page_regex3 = r'.*(pages.[0-9]+.\-.[0-9]+$)'
page_regex4 = r'.*(pp.[0-9]+.\-.[0-9]+$)'
page_regex = "|".join([page_regex1, page_regex2, page_regex3, page_regex4])
page_match_result = re.match(page_regex, block)
volume_regex = r'.[0-9]+\([0-9]+\)'
volume_match_result = re.match(volume_regex, block)
if year_match_result:
label = 3
elif page_match_result:
label = 5
elif volume_match_result:
label = 4
return label
def label_numeric(x_numeric):
numeric_predictions = []
for block in x_numeric:
numeric_predictions.append(match_regex(block))
return numeric_predictions
def fixed_length_list(num):
lis = []
for i in range(num):
lis.append([])
return lis
def same_elem_count(l1, l2):
return len([i for i in l1 if i in l2])
# merge tow predictions
def merge_predictions(t_predictions, t_index, n_predictions, n_index):
max_length = len(t_predictions) + len(n_predictions)
predictions = fixed_length_list(max_length)
c = 0
for t_i in t_index:
predictions[t_i] = t_predictions[c]
c += 1
d = 0
for n_i in n_index:
predictions[n_i] = n_predictions[d]
d += 1
return predictions
# 准备y矩阵,生成one-hot矩阵
def build_y_train_publication(titles_contents, authors_contents, journals_contents):
print("Building label dict:")
titles_length = len(titles_contents)
authors_length = len(authors_contents)
journals_length = len(journals_contents)
t_list = ['T' for i in range(titles_length)]
a_list = ['A' for a in range(authors_length)]
j_list = ['J' for j in range(journals_length)]
label_list = t_list + a_list + j_list
label_dict = {'T': 0, 'A': 1, 'J': 2}
label_dict_size = len(label_dict)
print("Preparing y_train:")
y_t = mapLabelToId(label_list, label_dict)
y_train = np.zeros((len(y_t), label_dict_size))
for i in range(len(y_t)):
y_train[i][y_t[i]] = 1
print("Preparing y_train over!")
return y_train, label_dict_size
if __name__ == '__main__':
print('he.he.'.strip('.'))
# j = load_all_journals()
# print(j)
# print('main')
# samples, labels = read_test_data('data/temp_ada.txt')
# for i in range(len(samples)):
# print('x:', samples[i])
# print('y:', labels[i])
# print(len(samples))
# print(len(predictions))
# print(samples[10])
# print(labels[10])
# for i in labels[10]:
# print(i)
# x = [0, 0, 1, 1, 1, 2, 2, 5, 3]
# y = [0, 1, 1, 1, 1, 1, 2, 5, 3]
#
# a = float(same_elem_count(x, y))
# print(a)
# try:
# x_raw = samples[10].strip().split(',')
# print(x_raw)
# x_numeric = []
# x_text = []
# numeric_index = []
# text_index = []
# for x in x_raw:
# token = n_or_t(x)
# if token == 't':
# x_text.append(x)
# text_index.append(x_raw.index(x))
# if token == 'n':
# x_numeric.append(x)
# numeric_index.append(x_raw.index(x))
#
# # x_text send into CNN model , got loss and predictions
# text_predictions = [0, 1, 1, 1, 1, 1, 2]
# # text_predictions = revise_predictions(predictions, loss)
#
# num_predictions = label_numeric(x_numeric)
# print(num_predictions)
#
#
#
# # input_x = [x.split() for x in x_raw]
# # print(input_x)
# except Exception as e:
# print("Exception:%s" % e)