forked from shuyo/ldig
-
Notifications
You must be signed in to change notification settings - Fork 0
/
ldig.py
executable file
·519 lines (425 loc) · 20.3 KB
/
ldig.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
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# ldig : Language Detector with Infinite-Gram
# This code is available under the MIT License.
# (c)2011 Nakatani Shuyo / Cybozu Labs Inc.
import os, sys, re, codecs, json
import optparse
import numpy
import html.entities as htmlentitydefs
import subprocess
import da
class ldig(object):
def __init__(self, model_dir):
self.features = os.path.join(model_dir, 'features')
self.labels = os.path.join(model_dir, 'labels.json')
self.param = os.path.join(model_dir, 'parameters.npy')
self.doublearray = os.path.join(model_dir, 'doublearray.npz')
def load_da(self):
trie = da.DoubleArray()
trie.load(self.doublearray)
return trie
def load_features(self):
features = []
with open(self.features, 'r') as f:
pre_feature = ""
for n, s in enumerate(f):
m = re.match(r'(.+)\t([0-9]+)', s)
if not m:
sys.exit("irregular feature : '%s' at %d" % (s, n + 1))
if pre_feature >= m.group(1):
sys.exit("unordered feature : '%s' at %d" % (s, n + 1))
pre_feature = m.group(1)
features.append(m.groups())
return features
def load_labels(self):
with open(self.labels, 'r') as f:
return json.load(f)
def init(self, temp_path, corpus_list, lbff, ngram_bound):
"""
Extract features from corpus and generate TRIE(DoubleArray) data
- load corpus
- generate temporary file for maxsubst
- generate double array and save it
- parameter: lbff = lower bound of feature frequency
"""
labels = []
with open(temp_path, 'w') as f:
for file in corpus_list:
with open(file, 'r') as g:
for i, s in enumerate(g):
label, text, org_text = normalize_text(s)
if label is None or label == "":
sys.stderr.write("no label data at %d in %s \n" % (i+1, file))
continue
if label not in labels:
labels.append(label)
f.write(text)
f.write("\n")
labels.sort()
print ("labels: %d" % len(labels))
with open(self.labels, 'w') as f:
f.write(json.dumps(labels))
print ("generating max-substrings...")
temp_features = self.features + ".temp"
maxsubst = options.maxsubst
if os.name == 'nt': maxsubst += ".exe"
subprocess.call([maxsubst, temp_path, temp_features])
# count features
M = 0
features = []
r1 = re.compile(u'.\u0001.')
r2 = re.compile(u'[A-Za-z\u00a1-\u00a3\u00bf-\u024f\u1e00-\u1eff\u0370-\u03ff\u0400-\u04ff]') # added greek (\u0370-\u03ff) and cyrillic (\u0400–\u04ff) alphabets
with open(temp_features, 'r') as f:
for line in f:
i = line.index('\t')
st = line[0:i]
c = int(line[i+1:-1])
if c >= lbff and len(st) <= ngram_bound and (not r1.search(st)) and r2.search(st) and (st[0] != u'\u0001' or st[-1] != u'\u0001'):
M += 1
features.append((st, line))
print ("# of features = %d" % M)
features.sort()
with open(self.features, 'w') as f:
for s in features:
f.write(s[1])
generate_doublearray(self.doublearray, [s[0] for s in features])
numpy.save(self.param, numpy.zeros((M, len(labels))))
def shrink(self):
features = self.load_features()
param = numpy.load(self.param)
list = (numpy.abs(param).sum(1) > 0.0000001)
new_param = param[list]
print ("# of features : %d => %d" % (param.shape[0], new_param.shape[0]))
numpy.save(self.param, new_param)
new_features = []
with open(self.features, 'w') as f:
for i, x in enumerate(list):
if x:
f.write("%s\t%s\n" % features[i])
new_features.append(features[i][0])
generate_doublearray(self.doublearray, new_features)
def debug(self, args):
features = self.load_features()
trie = self.load_da()
labels = self.load_labels()
param = numpy.load(self.param)
for st in args:
label, text, org_text = normalize_text(st)
events = trie.extract_features(u"\u0001" + text + u"\u0001")
print ("orig: '%s'" % st)
print ("norm: '%s'" % text)
sum = numpy.zeros(len(labels))
print ("id\tfeat\tfreq\t%s" % "\t".join(labels))
for id in sorted(events, key=lambda id:features[id][0]):
phi = param[id,]
sum += phi * events[id]
print ("%d\t%s\t%d\t%s" % (id,features[id][0], events[id], "\t".join(["%0.2f" % x for x in phi])))
exp_w = numpy.exp(sum - sum.max())
prob = exp_w / exp_w.sum()
print ("\t\t\t%s" % "\t".join(["%0.2f" % x for x in sum]))
print ("\t\t\t%s" % "\t".join(["%0.1f%%" % (x * 100) for x in prob]))
def learn(self, options, args):
trie = self.load_da()
param = numpy.load(self.param)
labels = self.load_labels()
import time
print ("loading corpus... " + time.strftime("%H:%M:%S", time.localtime()))
corpus, idlist = load_corpus(args, labels)
print ("inference... " + time.strftime("%H:%M:%S", time.localtime()))
inference(param, labels, corpus, idlist, trie, options)
print ("finish... " + time.strftime("%H:%M:%S", time.localtime()))
numpy.save(self.param, param)
def detect(self, options, args):
trie = self.load_da()
param = numpy.load(self.param)
labels = self.load_labels()
log_likely = likelihood(param, labels, trie, args, options)
# from http://www.programming-magic.com/20080820002254/
reference_regex = re.compile(u'&(#x?[0-9a-f]+|[a-z]+);', re.IGNORECASE)
num16_regex = re.compile(u'#x\d+', re.IGNORECASE)
num10_regex = re.compile(u'#\d+', re.IGNORECASE)
def htmlentity2unicode(text):
result = u''
i = 0
while True:
match = reference_regex.search(text, i)
if match is None:
result += text[i:]
break
result += text[i:match.start()]
i = match.end()
name = match.group(1)
if name in htmlentitydefs.name2codepoint.keys():
result += unichr(htmlentitydefs.name2codepoint[name])
elif num16_regex.match(name):
result += unichr(int(u'0'+name[1:], 16))
elif num10_regex.match(name):
result += unichr(int(name[1:]))
return result
def normalize_twitter(text):
"""normalization for twitter"""
text = re.sub(r'(@|#|https?:\/\/)[^ ]+', '', text)
text = re.sub(r'(^| )[:;x]-?[\(\)dop]($| )', ' ', text) # facemark
text = re.sub(r'(^| )(rt[ :]+)*', ' ', text)
text = re.sub(r'([hj])+([aieo])+(\1+\2+){1,}', r'\1\2\1\2', text, re.IGNORECASE) # laugh
text = re.sub(r' +(via|live on) *$', '', text)
return text
re_ignore_i = re.compile(r'[^Iİ]')
re_turkish_alphabet = re.compile(u'[\u011e\u011f\u0130\u0131]')
vietnamese_norm = {
u'\u0041\u0300':u'\u00C0', u'\u0045\u0300':u'\u00C8', u'\u0049\u0300':u'\u00CC', u'\u004F\u0300':u'\u00D2',
u'\u0055\u0300':u'\u00D9', u'\u0059\u0300':u'\u1EF2', u'\u0061\u0300':u'\u00E0', u'\u0065\u0300':u'\u00E8',
u'\u0069\u0300':u'\u00EC', u'\u006F\u0300':u'\u00F2', u'\u0075\u0300':u'\u00F9', u'\u0079\u0300':u'\u1EF3',
u'\u00C2\u0300':u'\u1EA6', u'\u00CA\u0300':u'\u1EC0', u'\u00D4\u0300':u'\u1ED2', u'\u00E2\u0300':u'\u1EA7',
u'\u00EA\u0300':u'\u1EC1', u'\u00F4\u0300':u'\u1ED3', u'\u0102\u0300':u'\u1EB0', u'\u0103\u0300':u'\u1EB1',
u'\u01A0\u0300':u'\u1EDC', u'\u01A1\u0300':u'\u1EDD', u'\u01AF\u0300':u'\u1EEA', u'\u01B0\u0300':u'\u1EEB',
u'\u0041\u0301':u'\u00C1', u'\u0045\u0301':u'\u00C9', u'\u0049\u0301':u'\u00CD', u'\u004F\u0301':u'\u00D3',
u'\u0055\u0301':u'\u00DA', u'\u0059\u0301':u'\u00DD', u'\u0061\u0301':u'\u00E1', u'\u0065\u0301':u'\u00E9',
u'\u0069\u0301':u'\u00ED', u'\u006F\u0301':u'\u00F3', u'\u0075\u0301':u'\u00FA', u'\u0079\u0301':u'\u00FD',
u'\u00C2\u0301':u'\u1EA4', u'\u00CA\u0301':u'\u1EBE', u'\u00D4\u0301':u'\u1ED0', u'\u00E2\u0301':u'\u1EA5',
u'\u00EA\u0301':u'\u1EBF', u'\u00F4\u0301':u'\u1ED1', u'\u0102\u0301':u'\u1EAE', u'\u0103\u0301':u'\u1EAF',
u'\u01A0\u0301':u'\u1EDA', u'\u01A1\u0301':u'\u1EDB', u'\u01AF\u0301':u'\u1EE8', u'\u01B0\u0301':u'\u1EE9',
u'\u0041\u0303':u'\u00C3', u'\u0045\u0303':u'\u1EBC', u'\u0049\u0303':u'\u0128', u'\u004F\u0303':u'\u00D5',
u'\u0055\u0303':u'\u0168', u'\u0059\u0303':u'\u1EF8', u'\u0061\u0303':u'\u00E3', u'\u0065\u0303':u'\u1EBD',
u'\u0069\u0303':u'\u0129', u'\u006F\u0303':u'\u00F5', u'\u0075\u0303':u'\u0169', u'\u0079\u0303':u'\u1EF9',
u'\u00C2\u0303':u'\u1EAA', u'\u00CA\u0303':u'\u1EC4', u'\u00D4\u0303':u'\u1ED6', u'\u00E2\u0303':u'\u1EAB',
u'\u00EA\u0303':u'\u1EC5', u'\u00F4\u0303':u'\u1ED7', u'\u0102\u0303':u'\u1EB4', u'\u0103\u0303':u'\u1EB5',
u'\u01A0\u0303':u'\u1EE0', u'\u01A1\u0303':u'\u1EE1', u'\u01AF\u0303':u'\u1EEE', u'\u01B0\u0303':u'\u1EEF',
u'\u0041\u0309':u'\u1EA2', u'\u0045\u0309':u'\u1EBA', u'\u0049\u0309':u'\u1EC8', u'\u004F\u0309':u'\u1ECE',
u'\u0055\u0309':u'\u1EE6', u'\u0059\u0309':u'\u1EF6', u'\u0061\u0309':u'\u1EA3', u'\u0065\u0309':u'\u1EBB',
u'\u0069\u0309':u'\u1EC9', u'\u006F\u0309':u'\u1ECF', u'\u0075\u0309':u'\u1EE7', u'\u0079\u0309':u'\u1EF7',
u'\u00C2\u0309':u'\u1EA8', u'\u00CA\u0309':u'\u1EC2', u'\u00D4\u0309':u'\u1ED4', u'\u00E2\u0309':u'\u1EA9',
u'\u00EA\u0309':u'\u1EC3', u'\u00F4\u0309':u'\u1ED5', u'\u0102\u0309':u'\u1EB2', u'\u0103\u0309':u'\u1EB3',
u'\u01A0\u0309':u'\u1EDE', u'\u01A1\u0309':u'\u1EDF', u'\u01AF\u0309':u'\u1EEC', u'\u01B0\u0309':u'\u1EED',
u'\u0041\u0323':u'\u1EA0', u'\u0045\u0323':u'\u1EB8', u'\u0049\u0323':u'\u1ECA', u'\u004F\u0323':u'\u1ECC',
u'\u0055\u0323':u'\u1EE4', u'\u0059\u0323':u'\u1EF4', u'\u0061\u0323':u'\u1EA1', u'\u0065\u0323':u'\u1EB9',
u'\u0069\u0323':u'\u1ECB', u'\u006F\u0323':u'\u1ECD', u'\u0075\u0323':u'\u1EE5', u'\u0079\u0323':u'\u1EF5',
u'\u00C2\u0323':u'\u1EAC', u'\u00CA\u0323':u'\u1EC6', u'\u00D4\u0323':u'\u1ED8', u'\u00E2\u0323':u'\u1EAD',
u'\u00EA\u0323':u'\u1EC7', u'\u00F4\u0323':u'\u1ED9', u'\u0102\u0323':u'\u1EB6', u'\u0103\u0323':u'\u1EB7',
u'\u01A0\u0323':u'\u1EE2', u'\u01A1\u0323':u'\u1EE3', u'\u01AF\u0323':u'\u1EF0', u'\u01B0\u0323':u'\u1EF1',
}
re_vietnamese = re.compile(u'[AEIOUYaeiouy\u00C2\u00CA\u00D4\u00E2\u00EA\u00F4\u0102\u0103\u01A0\u01A1\u01AF\u01B0][\u0300\u0301\u0303\u0309\u0323]')
re_latin_cont = re.compile(u'([a-z\u00e0-\u024f])\\1{2,}')
re_symbol_cont = re.compile(u'([^a-z\u00e0-\u024f])\\1{1,}')
def normalize_text(org):
m = re.match(r'([-A-Za-z]+)\t(.+)', org)
if m:
label, org = m.groups()
else:
label = ""
m = re.search(r'\t([^\t]+)$', org)
if m:
s = m.group(0)
else:
s = org
s = htmlentity2unicode(s)
s = re.sub(u'[\u2010-\u2015]', '-', s)
s = re.sub(u'[0-9]+', '0', s)
s = re.sub(u'[^\u0020-\u007e\u00a1-\u024f\u0300-\u036f\u0370-\u03FF\u0400-\u04ff\u1e00-\u1eff]+', ' ', s) # added greek (\u0370-\u03ff) and cyrillic (\u0400–\u04ff) alphabets
s = re.sub(u' +', ' ', s)
# vietnamese normalization
s = re_vietnamese.sub(lambda x:vietnamese_norm[x.group(0)], s)
# lower case with Turkish
s = re_ignore_i.sub(lambda x:x.group(0).lower(), s)
s = s.replace(u'İ', u'\u0069') # This is to manage this question : https://bugs.python.org/issue17252
#if re_turkish_alphabet.search(s):
#s = s.replace(u'I', u'\u0131')
#s = s.lower()
# Romanian normalization
s = s.replace(u'\u0219', u'\u015f').replace(u'\u021b', u'\u0163')
s = normalize_twitter(s)
s = re_latin_cont.sub(r'\1\1', s)
s = re_symbol_cont.sub(r'\1', s)
return label, s.strip(), org
# load courpus
def load_corpus(filelist, labels):
idlist = dict((x, []) for x in labels)
corpus = []
for filename in filelist:
f = open(filename, 'r')
for i, s in enumerate(f):
label, text, org_text = normalize_text(s)
if label not in labels:
sys.exit("unknown label '%s' at %d in %s " % (label, i+1, filename))
idlist[label].append(len(corpus))
corpus.append((label, text, org_text))
f.close()
return corpus, idlist
def shuffle(idlist):
n = max(len(idlist[lang]) for lang in idlist)
list = []
for lang in idlist:
text_ids = idlist[lang]
n_text = len(text_ids)
list += text_ids * (n // n_text)
numpy.random.shuffle(text_ids)
list += text_ids[:n % n_text]
numpy.random.shuffle(list)
return list
# prediction probability
# this code comes from https://github.com/Nift/ldig/commit/75fc547dc1c3412d216fba83d759b38b414e528a in order to port it to Python 3
def predict(param, events):
names = ['id', 'data']
formats = ['f8', 'f8']
dtype = dict(names=names, formats=formats)
res1 = list(events.keys())
arr = numpy.asarray(res1) # numpy.fromiter(res1.iteritems(), dtype=dtype, count=len(res1))
array = arr.astype(int)
res2 = list(events.values())
valuesA = numpy.asarray(res2) # numpy.fromiter(res2.iteritems(), dtype=dtype, count=len(res2))
sum_w = numpy.dot(param[array,].T, valuesA)
exp_w = numpy.exp(sum_w - sum_w.max())
return exp_w / exp_w.sum()
# inference and learning
def inference(param, labels, corpus, idlist, trie, options):
K = len(labels)
M = param.shape[0]
list = shuffle(idlist)
N = len(list)
WHOLE_REG_INT = (N / options.n_whole_reg) + 1
# learning rate
eta = options.eta
if options.reg_const:
penalties = numpy.zeros_like(param)
alpha = pow(0.9, -1.0 / N)
uk = 0
corrects = numpy.zeros(K, dtype=int)
counts = numpy.zeros(K, dtype=int)
for m, target in enumerate(list):
label, text, org_text = corpus[target]
events = trie.extract_features(u"\u0001" + text + u"\u0001")
label_k = labels.index(label)
y = predict(param, events)
predict_k = y.argmax()
counts[label_k] += 1
if label_k == predict_k:
corrects[label_k] += 1
# learning
if options.reg_const:
eta *= alpha
uk += options.reg_const * eta / N
y[label_k] -= 1
y *= eta
if options.reg_const:
indexes = events
if (N - m) % WHOLE_REG_INT == 1:
print ("full regularization: %d / %d" % (m, N))
indexes = range(M)
for id in indexes:
prm = param[id]
pnl = penalties[id]
if id in events: prm -= y * events[id]
for j in range(K):
w = prm[j]
if w > 0:
w1 = w - uk - pnl[j]
if w1 > 0:
prm[j] = w1
pnl[j] += w1 - w
else:
prm[j] = 0
pnl[j] -= w
elif w < 0:
w1 = w + uk - pnl[j]
if w1 < 0:
prm[j] = w1
pnl[j] += w1 - w
else:
prm[j] = 0
pnl[j] -= w
else:
for id, freq in events.items():
param[id,] -= y * freq
for lbl, crct, cnt in zip(labels, corrects, counts):
if cnt > 0:
print ("> %s = %d / %d = %.2f" % (lbl, crct, cnt, 100.0 * crct / cnt))
print ("> total = %d / %d = %.2f" % (corrects.sum(), N, 100.0 * corrects.sum() / N))
list = (numpy.abs(param).sum(1) > 0.0000001)
print ("> # of relevant features = %d / %d" % (list.sum(), M))
def likelihood(param, labels, trie, filelist, options):
K = len(labels)
corrects = numpy.zeros(K, dtype=int)
counts = numpy.zeros(K, dtype=int)
label_map = dict((x, i) for i, x in enumerate(labels))
n_available_data = 0
log_likely = 0.0
for filename in filelist:
f = open(filename, 'r')
for i, s in enumerate(f):
label, text, org_text = normalize_text(s)
if label not in label_map:
sys.stderr.write("WARNING : unknown label '%s' at %d in %s (ignore the later same labels)\n" % (label, i+1, filename))
label_map[label] = -1
label_k = label_map[label]
events = trie.extract_features(u"\u0001" + text + u"\u0001")
y = predict(param, events)
predict_k = y.argmax()
if label_k >= 0:
if y[label_k]==0: # lkevers: added in order to avoid warning because of log(0) below
y[label_k]=0.0000000001
log_likely -= numpy.log(y[label_k])
n_available_data += 1
counts[label_k] += 1
if label_k == predict_k and y[predict_k] >= 0.6:
corrects[predict_k] += 1
predict_lang = labels[predict_k]
if y[predict_k] < 0.6: predict_lang = ""
print ("%s\t%s\t%s" % (label, predict_lang, org_text))
f.close()
if n_available_data > 0:
log_likely /= n_available_data
for lbl, crct, cnt in zip(labels, corrects, counts):
if cnt > 0:
print ("> %s = %d / %d = %.2f" % (lbl, crct, cnt, 100.0 * crct / cnt))
print ("> total = %d / %d = %.2f" % (corrects.sum(), n_available_data, 100.0 * corrects.sum() / n_available_data))
print ("> average negative log likelihood = %.3f" % log_likely)
return log_likely
def generate_doublearray(file, features):
trie = da.DoubleArray()
trie.initialize(features)
trie.save(file)
if __name__ == '__main__':
#sys.stdout = codecs.getwriter('utf-8')(sys.stdout)
parser = optparse.OptionParser()
parser.add_option("-m", dest="model", help="model directory")
parser.add_option("--init", dest="init", help="initialize model", action="store_true")
parser.add_option("--learning", dest="learning", help="learn model", action="store_true")
parser.add_option("--shrink", dest="shrink", help="remove irrevant features", action="store_true")
parser.add_option("--debug", dest="debug", help="detect command line text for debug", action="store_true")
# for initialization
parser.add_option("--ff", dest="bound_feature_freq", help="threshold of feature frequency (for initialization)", type="int", default=8)
parser.add_option("-n", dest="ngram_bound", help="n-gram upper bound (for initialization)", type="int", default=99999)
parser.add_option("-x", dest="maxsubst", help="max substring extractor", default="./maxsubst")
# for learning
parser.add_option("-e", "--eta", dest="eta", help="learning rate", type="float", default=0.1)
parser.add_option("-r", "--regularity", dest="reg_const", help="regularization constant", type="float")
parser.add_option("--wr", dest="n_whole_reg", help="number of whole regularizations", type="int", default=2)
(options, args) = parser.parse_args()
if not options.model: parser.error("need model directory (-m)")
detector = ldig(options.model)
if options.init:
if not os.path.exists(options.model):
os.mkdir(options.model)
if len(args) == 0:
parser.error("need corpus")
else:
if not os.path.exists(detector.features):
parser.error("features file doesn't exist")
if not os.path.exists(detector.labels):
parser.error("labels file doesn't exist")
if not os.path.exists(detector.param):
parser.error("parameters file doesn't exist")
if options.init:
temp_path = os.path.join(options.model, 'temp')
detector.init(temp_path, args, options.bound_feature_freq, options.ngram_bound)
elif options.debug:
detector.debug(args)
elif options.shrink:
detector.shrink()
elif options.learning:
detector.learn(options, args)
else:
detector.detect(options, args)
#import cProfile
#cProfile.runctx('detector.detect(options, args)', globals(), locals(), 'ldig.profile')