1
|
|
|
"""Package containing all the main inline structures |
2
|
|
|
|
3
|
|
|
.. Authors: |
4
|
|
|
Philippe Dessauw |
5
|
|
|
[email protected] |
6
|
|
|
|
7
|
|
|
.. Sponsor: |
8
|
|
|
Alden Dima |
9
|
|
|
[email protected] |
10
|
|
|
Information Systems Group |
11
|
|
|
Software and Systems Division |
12
|
|
|
Information Technology Laboratory |
13
|
|
|
National Institute of Standards and Technology |
14
|
|
|
http://www.nist.gov/itl/ssd/is |
15
|
|
|
""" |
16
|
|
|
from __future__ import division |
17
|
|
|
from math import floor |
18
|
|
|
from numpy.lib.function_base import median |
19
|
|
|
from collections import Counter |
20
|
|
|
import inspect |
21
|
|
|
from os.path import exists |
22
|
|
|
import operator |
23
|
|
|
from nltk.util import ngrams as nltk_ngrams |
24
|
|
|
from denoiser.models.inline.hashing import ocr_key_list_to_str, ocr_key_hash, anagram_hash |
25
|
|
|
from apputils.pickling import load, save |
26
|
|
|
import re |
27
|
|
|
from operator import add |
28
|
|
|
|
29
|
|
|
|
30
|
|
|
def truncate_map(occurence_map): |
31
|
|
|
"""Truncate an occurence map by removing uncommon iteration |
32
|
|
|
|
33
|
|
|
Parameters: |
34
|
|
|
occurence_map (dict): Dictionary containing word as key and occurence as value |
35
|
|
|
|
36
|
|
|
Returns: |
37
|
|
|
dict: Truncated map |
38
|
|
|
""" |
39
|
|
|
# Get occurences distribution |
40
|
|
|
distribution = Counter(occurence_map.values()) |
41
|
|
|
dist_median = median(distribution.values()) |
42
|
|
|
|
43
|
|
|
# Compute upper bound |
44
|
|
|
limit = 0.99 |
45
|
|
|
dist_upper_median = sorted([v for v in distribution.values() if v > dist_median]) |
46
|
|
|
dist_upper_bound = int(floor(len(dist_upper_median) * limit)) |
47
|
|
|
|
48
|
|
|
# Compute new distribution |
49
|
|
|
min_dist_value = dist_upper_median[dist_upper_bound - 1] |
50
|
|
|
distribution = {k: v for k, v in distribution.items() if v <= min_dist_value} |
51
|
|
|
|
52
|
|
|
# Return new occurence map |
53
|
|
|
return {k: v for k, v in occurence_map.items() if v in distribution.keys()} |
54
|
|
|
|
55
|
|
|
|
56
|
|
|
class InlineStructure(object): |
57
|
|
|
"""Abstract inline structure |
58
|
|
|
""" |
59
|
|
|
|
60
|
|
|
def __init__(self, filename): |
61
|
|
|
self.filename = filename |
62
|
|
|
|
63
|
|
|
if exists(self.filename): |
64
|
|
|
self.load() |
65
|
|
|
|
66
|
|
|
def append_data(self, **kwargs): |
67
|
|
|
"""Append data to the structure |
68
|
|
|
|
69
|
|
|
Args: |
70
|
|
|
**kwargs: Arbitrary keyword arguments |
71
|
|
|
|
72
|
|
|
Raise: |
73
|
|
|
NotImplementedError: Not yet implemented |
74
|
|
|
""" |
75
|
|
|
raise NotImplementedError("Function "+inspect.stack()[0][3]+" has not been implemented") |
76
|
|
|
|
77
|
|
|
def load(self): |
78
|
|
|
"""Load the structure from the file if it exists |
79
|
|
|
""" |
80
|
|
|
if not exists(self.filename): |
81
|
|
|
return |
82
|
|
|
|
83
|
|
|
def save(self): |
84
|
|
|
"""Save the structure to the file |
85
|
|
|
|
86
|
|
|
Raise: |
87
|
|
|
NotImplementedError: Not yet implemented |
88
|
|
|
""" |
89
|
|
|
raise NotImplementedError("Function "+inspect.stack()[0][3]+" has not been implemented") |
90
|
|
|
|
91
|
|
|
|
92
|
|
|
class NGramsStructure(InlineStructure): |
93
|
|
|
"""Abstract n-gram structure |
94
|
|
|
""" |
95
|
|
|
|
96
|
|
|
def __init__(self, filename): |
97
|
|
|
self.ngrams = Counter() |
98
|
|
|
self.ngrams_pruned = Counter() |
99
|
|
|
|
100
|
|
|
super(NGramsStructure, self).__init__(filename) |
101
|
|
|
|
102
|
|
|
def append_data(self, **kwargs): |
103
|
|
|
raise NotImplementedError("Function "+inspect.stack()[0][3]+" has not been implemented") |
104
|
|
|
|
105
|
|
|
def prune(self, rate): |
106
|
|
|
"""Prune ngrams list given the rate of data to keep |
107
|
|
|
|
108
|
|
|
Args: |
109
|
|
|
rate (float): Limit rate of data to keep |
110
|
|
|
""" |
111
|
|
|
if rate >= 1: |
112
|
|
|
self.ngrams_pruned = self.ngrams |
113
|
|
|
return |
114
|
|
|
|
115
|
|
|
pruned_target = {} |
116
|
|
|
|
117
|
|
|
truncated_target = truncate_map(self.ngrams) |
118
|
|
|
sorted_target = sorted(truncated_target.iteritems(), key=operator.itemgetter(1), reverse=True) |
119
|
|
|
|
120
|
|
|
total = len(sorted_target) |
121
|
|
|
registered = 0 |
122
|
|
|
current_occ = 0 |
123
|
|
|
for (data, occurence) in sorted_target: |
124
|
|
|
if registered / total >= rate and occurence != current_occ: |
125
|
|
|
break |
126
|
|
|
|
127
|
|
|
current_occ = occurence |
128
|
|
|
pruned_target[data] = occurence |
129
|
|
|
registered += 1 |
130
|
|
|
|
131
|
|
|
self.ngrams_pruned = Counter(pruned_target) |
132
|
|
|
|
133
|
|
|
def load(self): |
134
|
|
|
super(NGramsStructure, self).load() |
135
|
|
|
|
136
|
|
|
def save(self): |
137
|
|
|
super(NGramsStructure, self).save() |
138
|
|
|
|
139
|
|
|
|
140
|
|
|
class Dictionary(InlineStructure): |
141
|
|
|
"""Dictionary |
142
|
|
|
""" |
143
|
|
|
|
144
|
|
|
def __init__(self, filename): |
145
|
|
|
self.dictionary = list() |
146
|
|
|
|
147
|
|
|
super(Dictionary, self).__init__(filename) |
148
|
|
|
|
149
|
|
|
def append_data(self, unigrams): |
150
|
|
|
word_list = [] |
151
|
|
|
|
152
|
|
|
aspell_dict = "models/aspell.en.dict" |
153
|
|
|
with open(aspell_dict, "r") as f: |
154
|
|
|
for line in f: |
155
|
|
|
word_list.append(line.strip("\r\n")) |
156
|
|
|
|
157
|
|
|
plc_set = set(unigrams) |
158
|
|
|
word_set = set(word_list) |
159
|
|
|
|
160
|
|
|
self.dictionary = list(plc_set.intersection(word_set)) |
161
|
|
|
self.save() |
162
|
|
|
|
163
|
|
|
def load(self): |
164
|
|
|
super(Dictionary, self).load() |
165
|
|
|
|
166
|
|
|
self.dictionary = load(self.filename) |
167
|
|
|
|
168
|
|
|
def save(self): |
169
|
|
|
save(self.dictionary, self.filename) |
170
|
|
|
|
171
|
|
|
|
172
|
|
|
class Unigrams(NGramsStructure): |
173
|
|
|
"""Unigrams list |
174
|
|
|
""" |
175
|
|
|
|
176
|
|
|
def __init__(self, filename): |
177
|
|
|
self.raw_unigrams = Counter() # Unigrams not submitted to case modification |
178
|
|
|
|
179
|
|
|
super(Unigrams, self).__init__(filename) |
180
|
|
|
|
181
|
|
|
def append_data(self, text_data): |
182
|
|
|
unigrams = [token[1] for paragraph in text_data.text for line in paragraph for token in line.tokens |
183
|
|
|
if line.grade != 0 and not token[1] is None and len(token[1]) > 1] |
184
|
|
|
|
185
|
|
|
unigrams_counter = Counter(unigrams) |
186
|
|
|
self.raw_unigrams += unigrams_counter |
187
|
|
|
|
188
|
|
|
self.save() |
189
|
|
|
return unigrams |
190
|
|
|
|
191
|
|
|
def generate_low_case(self, altcase_map): |
192
|
|
|
"""Generate lower case unigrams |
193
|
|
|
|
194
|
|
|
Args: |
195
|
|
|
altcase_map (dict): List of alternative case word for a given lowercase word |
196
|
|
|
""" |
197
|
|
|
low_unigrams = {key: 0 for key in altcase_map.keys()} |
198
|
|
|
|
199
|
|
|
for unigram, alt_case_list in altcase_map.items(): |
200
|
|
|
low_unigrams[unigram] = sum([self.raw_unigrams[alt_case] for alt_case in alt_case_list]) |
201
|
|
|
|
202
|
|
|
self.ngrams = Counter(low_unigrams) |
203
|
|
|
self.save() |
204
|
|
|
|
205
|
|
|
def load(self): |
206
|
|
|
super(Unigrams, self).load() |
207
|
|
|
|
208
|
|
|
data = load(self.filename) |
209
|
|
|
|
210
|
|
|
self.raw_unigrams = data["raw_unigrams"] |
211
|
|
|
self.ngrams = data["unigrams"] |
212
|
|
|
self.ngrams_pruned = data["unigrams_pruned"] |
213
|
|
|
|
214
|
|
|
def save(self): |
215
|
|
|
data = { |
216
|
|
|
"raw_unigrams": self.raw_unigrams, |
217
|
|
|
"unigrams": self.ngrams, |
218
|
|
|
"unigrams_pruned": self.ngrams_pruned |
219
|
|
|
} |
220
|
|
|
|
221
|
|
|
save(data, self.filename) |
222
|
|
|
|
223
|
|
|
|
224
|
|
|
class Bigrams(NGramsStructure): |
225
|
|
|
"""Bigrams list |
226
|
|
|
""" |
227
|
|
|
|
228
|
|
|
def __init__(self, filename): |
229
|
|
|
super(Bigrams, self).__init__(filename) |
230
|
|
|
|
231
|
|
|
def append_data(self, unigrams): |
232
|
|
|
bigrams = [bigram[0].lower()+" "+bigram[1].lower() for bigram in nltk_ngrams(unigrams, 2) |
233
|
|
|
if len(bigram[0]) > 1 and len(bigram[1]) > 1] |
234
|
|
|
|
235
|
|
|
self.ngrams += Counter(bigrams) |
236
|
|
|
self.prune(0.35) |
237
|
|
|
|
238
|
|
|
self.save() |
239
|
|
|
|
240
|
|
|
def load(self): |
241
|
|
|
super(Bigrams, self).load() |
242
|
|
|
|
243
|
|
|
data = load(self.filename) |
244
|
|
|
|
245
|
|
|
self.ngrams = data["bigrams"] |
246
|
|
|
self.ngrams_pruned = data["bigrams_pruned"] |
247
|
|
|
|
248
|
|
|
def save(self): |
249
|
|
|
data = { |
250
|
|
|
"bigrams": self.ngrams, |
251
|
|
|
"bigrams_pruned": self.ngrams_pruned |
252
|
|
|
} |
253
|
|
|
|
254
|
|
|
save(data, self.filename) |
255
|
|
|
|
256
|
|
|
|
257
|
|
|
class AltCaseMap(InlineStructure): |
258
|
|
|
"""Alternative case map |
259
|
|
|
""" |
260
|
|
|
|
261
|
|
|
def __init__(self, filename): |
262
|
|
|
self.altcase_map = {} |
263
|
|
|
self.altcase_pruned_map = {} |
264
|
|
|
|
265
|
|
|
super(AltCaseMap, self).__init__(filename) |
266
|
|
|
|
267
|
|
|
def append_data(self, unigrams): |
268
|
|
|
_altcase_map = {unigram.lower(): set() for unigram in unigrams.keys()} |
269
|
|
|
|
270
|
|
|
for unigram in unigrams.keys(): |
271
|
|
|
_altcase_map[unigram.lower()].add(unigram) |
272
|
|
|
|
273
|
|
|
self.altcase_map = {key: set(value) for key, value in _altcase_map.items()} |
274
|
|
|
self.save() |
275
|
|
|
|
276
|
|
|
def prune(self, unigrams_pruned): |
277
|
|
|
"""Prume the map given selected unigrams |
278
|
|
|
|
279
|
|
|
Args: |
280
|
|
|
unigrams_pruned (dict): List of unigrams to keep in the final list |
281
|
|
|
""" |
282
|
|
|
self.altcase_pruned_map = {unigram: self.altcase_map[unigram] for unigram in unigrams_pruned.keys()} |
283
|
|
|
self.save() |
284
|
|
|
|
285
|
|
|
def load(self): |
286
|
|
|
super(AltCaseMap, self).load() |
287
|
|
|
|
288
|
|
|
data = load(self.filename) |
289
|
|
|
|
290
|
|
|
self.altcase_map = data["altcase"] |
291
|
|
|
self.altcase_pruned_map = data["altcase_pruned"] |
292
|
|
|
|
293
|
|
|
def save(self): |
294
|
|
|
data = { |
295
|
|
|
"altcase": self.altcase_map, |
296
|
|
|
"altcase_pruned": self.altcase_pruned_map |
297
|
|
|
} |
298
|
|
|
|
299
|
|
|
save(data, self.filename) |
300
|
|
|
|
301
|
|
|
|
302
|
|
|
class OcrKeyMap(InlineStructure): |
303
|
|
|
"""OCR Key map |
304
|
|
|
""" |
305
|
|
|
|
306
|
|
|
def __init__(self, filename): |
307
|
|
|
self.ocrkey_map = {} |
308
|
|
|
|
309
|
|
|
super(OcrKeyMap, self).__init__(filename) |
310
|
|
|
|
311
|
|
|
def append_data(self, unigrams): |
312
|
|
|
word_list = [] |
313
|
|
|
|
314
|
|
|
aspell_dict = "models/aspell.en.dict" |
315
|
|
|
with open(aspell_dict, "r") as f: |
316
|
|
|
for line in f: |
317
|
|
|
word_list.append(line.strip("\r\n")) |
318
|
|
|
|
319
|
|
|
word_set = set(word_list) |
320
|
|
|
unigram_set = set(unigrams.keys()) |
321
|
|
|
|
322
|
|
|
ocr_key_map = {ocr_key_list_to_str(ocr_key_hash(word)): set() for word in unigram_set.intersection(word_set)} |
323
|
|
|
|
324
|
|
|
# Every word contained in the mixed case map and the dictionary |
325
|
|
|
for word in unigram_set.intersection(word_set): |
326
|
|
|
h_list = ocr_key_hash(word) |
327
|
|
|
h_str = ocr_key_list_to_str(h_list) |
328
|
|
|
|
329
|
|
|
ocr_key_map[h_str].add(word) # Add the word to the tab |
330
|
|
|
|
331
|
|
|
combine_struct = {key: set() for key in self.ocrkey_map.keys() + ocr_key_map.keys()} |
332
|
|
|
|
333
|
|
|
for key, value in self.ocrkey_map.items() + ocr_key_map.items(): |
334
|
|
|
combine_struct[key] = combine_struct[key].union(value) |
335
|
|
|
|
336
|
|
|
self.ocrkey_map = combine_struct |
337
|
|
|
self.save() |
338
|
|
|
|
339
|
|
|
def load(self): |
340
|
|
|
super(OcrKeyMap, self).load() |
341
|
|
|
|
342
|
|
|
self.ocrkey_map = load(self.filename) |
343
|
|
|
|
344
|
|
|
def save(self): |
345
|
|
|
save(self.ocrkey_map, self.filename) |
346
|
|
|
|
347
|
|
|
|
348
|
|
|
class AnagramMap(InlineStructure): |
349
|
|
|
"""Anagram map |
350
|
|
|
""" |
351
|
|
|
|
352
|
|
|
def __init__(self, filename): |
353
|
|
|
self.anagram_hashmap = {} |
354
|
|
|
self.anagram_alphabet = {} |
355
|
|
|
|
356
|
|
|
super(AnagramMap, self).__init__(filename) |
357
|
|
|
|
358
|
|
|
def append_data(self, bigrams, unigrams): |
359
|
|
|
anaghash_map = {anagram_hash(word): set() for word in bigrams.keys() + unigrams.keys()} |
360
|
|
|
|
361
|
|
|
for word in bigrams.keys() + unigrams.keys(): |
362
|
|
|
anaghash_map[anagram_hash(word)].add(word) |
363
|
|
|
|
364
|
|
|
self.anagram_hashmap = anaghash_map |
365
|
|
|
|
366
|
|
|
clean_word = re.compile(r"^[a-zA-Z '-]+$") |
367
|
|
|
alphabet = set() |
368
|
|
|
|
369
|
|
|
for word in unigrams: |
370
|
|
|
word = " "+word+" " |
371
|
|
|
chars = [char for char in word] # Getting letters from the word |
372
|
|
|
chars += map(add, chars[:-1], chars[1:]) # Adding bigrams to the list |
373
|
|
|
|
374
|
|
|
alphabet = alphabet.union([anagram_hash(char) for char in set(chars) |
375
|
|
|
if not clean_word.match(char) is None]) |
376
|
|
|
|
377
|
|
|
alphabet.add(0) |
378
|
|
|
|
379
|
|
|
self.anagram_alphabet = alphabet |
380
|
|
|
self.save() |
381
|
|
|
|
382
|
|
|
def load(self): |
383
|
|
|
super(AnagramMap, self).load() |
384
|
|
|
|
385
|
|
|
data = load(self.filename) |
386
|
|
|
|
387
|
|
|
self.anagram_hashmap = data["hashmap"] |
388
|
|
|
self.anagram_alphabet = data["alphabet"] |
389
|
|
|
|
390
|
|
|
def save(self): |
391
|
|
|
data = { |
392
|
|
|
"hashmap": self.anagram_hashmap, |
393
|
|
|
"alphabet": self.anagram_alphabet |
394
|
|
|
} |
395
|
|
|
|
396
|
|
|
save(data, self.filename) |
397
|
|
|
|