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Files changed (41) hide show
  1. Bash_handler.sh +118 -0
  2. Dockerfile +19 -0
  3. app.py +16 -0
  4. data/RC/rel2id.json +1 -0
  5. data/RC/test.txt +1037 -0
  6. data/RC/train.txt +1037 -0
  7. data/RC/train_f.txt +0 -0
  8. data/train/test.txt +0 -0
  9. data/train/train.txt +0 -0
  10. fast/07fc1ce0-86ac-11e8-91d7-a1ac35ac08c4-0.json +0 -0
  11. fast/3cb4fa20-89cb-11e8-a485-d149999fe64b-0.json +2961 -0
  12. fast/RC/rel2id.json +1 -0
  13. fast/RC/train.txt +1037 -0
  14. models/CCC/best-model.pt +3 -0
  15. models/RC/SemEval/best_model.pt +3 -0
  16. models/RC/SemEval/rel2id.json +1 -0
  17. models/RC/new/best_model.pt +3 -0
  18. models/RC/new/rel2id.json +1 -0
  19. requirements.txt +5 -0
  20. setup.py +16 -0
  21. src/graph/GUI.py +246 -0
  22. src/graph/__pycache__/GUI.cpython-311.pyc +0 -0
  23. src/graph/__pycache__/GUI.cpython-39.pyc +0 -0
  24. src/graph/out +1 -0
  25. src/graph/out.json +1 -0
  26. src/graph/out_file.json +1 -0
  27. src/scripts/Error_handling.txt +15 -0
  28. src/scripts/Json_formats.py +166 -0
  29. src/scripts/Tagged_document.py +37 -0
  30. src/scripts/Test.py +20 -0
  31. src/scripts/Train_model.py +57 -0
  32. src/scripts/__pycache__/functionsner.cpython-311.pyc +0 -0
  33. src/scripts/__pycache__/functionsner.cpython-39.pyc +0 -0
  34. src/scripts/__pycache__/functionsrc.cpython-311.pyc +0 -0
  35. src/scripts/__pycache__/functionsrc.cpython-39.pyc +0 -0
  36. src/scripts/__pycache__/upsampling.cpython-311.pyc +0 -0
  37. src/scripts/__pycache__/upsampling.cpython-39.pyc +0 -0
  38. src/scripts/functionsner.py +467 -0
  39. src/scripts/functionsrc.py +718 -0
  40. src/scripts/model_rc.py +466 -0
  41. src/scripts/upsampling.py +517 -0
Bash_handler.sh ADDED
@@ -0,0 +1,118 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ # NER software handler
3
+
4
+ if [ $# -gt 0 ]
5
+ then
6
+ MODE="$1"
7
+ STANDARD="False"
8
+ FAST="False"
9
+ CUDA="False"
10
+ UFLAG="False"
11
+ if [ ${MODE} == 'TRAIN' ]
12
+ then
13
+ shift # past argument
14
+ if [ $# -gt 1 ]
15
+ then
16
+ while [[ $# -gt 1 ]]; do
17
+ case $1 in
18
+ -f|--fast)
19
+ FAST="$2"
20
+ shift # past argument
21
+ shift # past value
22
+ ;;
23
+
24
+ -m|--model)
25
+ MODEL="$2"
26
+ shift # past argument
27
+ shift # past value
28
+ ;;
29
+
30
+ -s|--standard)
31
+ STANDARD="$2"
32
+ shift # past argument
33
+ shift # past value
34
+ ;;
35
+
36
+ -id|--inputdir)
37
+ INPUTDIR="$2"
38
+ shift # past argument
39
+ shift # past value
40
+ ;;
41
+
42
+ -u|--upsampleflag)
43
+ UFLAG="$2"
44
+ shift # past argument
45
+ shift # past value
46
+ ;;
47
+
48
+ -cu|--cuda)
49
+ CUDA="$2"
50
+ shift # past argument
51
+ shift # past value
52
+ ;;
53
+
54
+ esac
55
+ done
56
+ python src/scripts/Train_model.py -f ${FAST} -m ${MODEL} -s ${STANDARD} -id "${INPUTDIR}" -u "${UFLAG}" -cu "${CUDA}"
57
+ else
58
+ echo Not arguments the script requires at least input directory
59
+ fi
60
+
61
+
62
+ elif [ $1 == 'USE' ]
63
+ then
64
+ shift # past argument
65
+ if [ $# -gt 1 ]
66
+ then
67
+ while [[ $# -gt 1 ]]; do
68
+ case $1 in
69
+ -m|--model)
70
+ MODEL="$2"
71
+ shift # past argument
72
+ shift # past value
73
+ ;;
74
+
75
+ -id|--inputdir)
76
+ INPUTDIR="$2"
77
+ shift # past argument
78
+ shift # past value
79
+ ;;
80
+
81
+ -od|--outputdir)
82
+ OUTPUTDIR="$2"
83
+ shift # past argument
84
+ shift # past value
85
+ ;;
86
+
87
+ -cu|--cuda)
88
+ CUDA="$2"
89
+ shift # past argument
90
+ shift # past value
91
+ ;;
92
+
93
+ esac
94
+
95
+ done
96
+ if [ -n "${OUTPUTDIR}" ] && [ -n "${CUDA}" ]; then
97
+ python src/scripts/Tagged_document.py -m ${MODEL} -id "${INPUTDIR}" -od "${OUTPUTDIR}" -cu "${CUDA}"
98
+
99
+ elif [[ -n "${OUTPUTDIR}" ]]; then
100
+ python src/scripts/Tagged_document.py -m ${MODEL} -id "${INPUTDIR}" -od "${OUTPUTDIR}"
101
+
102
+ elif [[ -n "${CUDA}" ]]; then
103
+ python src/scripts/Tagged_document.py -m ${MODEL} -id "${INPUTDIR}" -cu "${CUDA}"
104
+
105
+ else
106
+ python src/scripts/Tagged_document.py -m ${MODEL} -id "${INPUTDIR}"
107
+ fi
108
+
109
+
110
+ else
111
+ echo Not arguments the script requires at least model and input file
112
+ fi
113
+
114
+ else
115
+ echo invalid option, USE for use a model, TRAIN for train a new model
116
+ fi
117
+
118
+ fi
Dockerfile ADDED
@@ -0,0 +1,19 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ FROM ubuntu:18.04
2
+ RUN apt-get update
3
+ RUN apt-get upgrade -y
4
+ RUN apt install -y software-properties-common
5
+ RUN apt-get install --reinstall ca-certificates
6
+ RUN add-apt-repository ppa:deadsnakes/ppa
7
+ RUN apt-get install -y python3.9
8
+ RUN apt install -y python3.9-distutils
9
+ RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 1
10
+ RUN update-alternatives --install /usr/bin/python python /usr/bin/python3.9 1
11
+ RUN apt-get install -y python3-pip
12
+ RUN pip3 install --upgrade setuptools
13
+ RUN pip3 install --upgrade pip
14
+ RUN pip3 install --upgrade distlib
15
+ WORKDIR /workspace
16
+ ADD . /workspace/
17
+ ENV HOME=/workspace
18
+ RUN pip install -r requirements.txt
19
+ CMD ["python", "execute_GUI.py"]
app.py ADDED
@@ -0,0 +1,16 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # -*- coding: utf-8 -*-
2
+ """
3
+ Created on Tue Dec 13 17:15:20 2022
4
+
5
+ @author: gita
6
+ """
7
+ import os
8
+ import sys
9
+ default_path = os.path.dirname(os.path.abspath(__file__))
10
+ os.chdir(default_path)
11
+ sys.path.insert(0, default_path+'/src/graph')
12
+
13
+ from src.graph.GUI import execute_GUI
14
+
15
+ if __name__ == '__main__':
16
+ execute_GUI()
data/RC/rel2id.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"Product-Producer": 0, "Cause-Effect": 1, "Content-Container": 2, "Component-Whole": 3, "Other": 4, "Entity-Destination": 5, "Instrument-Agency": 6, "Entity-Origin": 7, "Message-Topic": 8, "Member-Collection": 9}
data/RC/test.txt ADDED
@@ -0,0 +1,1037 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
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2
+ system O -
3
+ as O -
4
+ described O -
5
+ above O -
6
+ has O -
7
+ its O -
8
+ greatest O -
9
+ application O -
10
+ in O -
11
+ an O -
12
+ arrayed O -
13
+ configuration Whole -
14
+ of O -
15
+ antenna O -
16
+ elements Component -
17
+ . O -
18
+
19
+ The O Other
20
+ child Orelation1 -
21
+ was O -
22
+ carefully O -
23
+ wrapped O -
24
+ and O -
25
+ bound O -
26
+ into O -
27
+ the O -
28
+ cradle Orelation2 -
29
+ by O -
30
+ means O -
31
+ of O -
32
+ a O -
33
+ cord O -
34
+ . O -
35
+
36
+ The O Instrument-Agency
37
+ author Agency -
38
+ of O -
39
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40
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41
+ uses O -
42
+ a O -
43
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44
+ to O -
45
+ look O -
46
+ at O -
47
+ the O -
48
+ raw O -
49
+ assembly O -
50
+ code O -
51
+ . O -
52
+
53
+ A O Other
54
+ misty O -
55
+ ridge Orelation1 -
56
+ uprises O -
57
+ from O -
58
+ the O -
59
+ surge Orelation2 -
60
+ . O -
61
+
62
+ The O Member-Collection
63
+ student Member -
64
+ association Collection -
65
+ is O -
66
+ the O -
67
+ voice O -
68
+ of O -
69
+ the O -
70
+ undergraduate O -
71
+ student O -
72
+ population O -
73
+ of O -
74
+ the O -
75
+ State O -
76
+ University O -
77
+ of O -
78
+ New O -
79
+ York O -
80
+ at O -
81
+ Buffalo O -
82
+ . O -
83
+
84
+ This O Other
85
+ is O -
86
+ the O -
87
+ sprawling O -
88
+ complex Orelation1 -
89
+ that O -
90
+ is O -
91
+ Peru O -
92
+ 's O -
93
+ largest O -
94
+ producer Orelation2 -
95
+ of O -
96
+ silver O -
97
+ . O -
98
+
99
+ The O Cause-Effect
100
+ current O -
101
+ view O -
102
+ is O -
103
+ that O -
104
+ the O -
105
+ chronic O -
106
+ inflammation Effect -
107
+ in O -
108
+ the O -
109
+ distal O -
110
+ part O -
111
+ of O -
112
+ the O -
113
+ stomach O -
114
+ caused O -
115
+ by O -
116
+ Helicobacter O -
117
+ pylori O -
118
+ infection Cause -
119
+ results O -
120
+ in O -
121
+ an O -
122
+ increased O -
123
+ acid O -
124
+ production O -
125
+ from O -
126
+ the O -
127
+ non-infected O -
128
+ upper O -
129
+ corpus O -
130
+ region O -
131
+ of O -
132
+ the O -
133
+ stomach O -
134
+ . O -
135
+
136
+ People Entity -
137
+ have O -
138
+ been O -
139
+ moving O -
140
+ back O -
141
+ into O -
142
+ downtown Destination -
143
+ . O -
144
+
145
+ The O Content-Container
146
+ lawsonite Content -
147
+ was O -
148
+ contained O -
149
+ in O -
150
+ a O -
151
+ platinum Container -
152
+ crucible Container -
153
+ and O -
154
+ the O -
155
+ counter-weight O -
156
+ was O -
157
+ a O -
158
+ plastic O -
159
+ crucible O -
160
+ with O -
161
+ metal O -
162
+ pieces O -
163
+ . O -
164
+
165
+ The O Entity-Destination
166
+ solute O -
167
+ was O -
168
+ placed O -
169
+ inside O -
170
+ a O -
171
+ beaker O -
172
+ and O -
173
+ 5 O -
174
+ mL O -
175
+ of O -
176
+ the O -
177
+ solvent Entity -
178
+ was O -
179
+ pipetted O -
180
+ into O -
181
+ a O -
182
+ 25 O -
183
+ mL O -
184
+ glass O -
185
+ flask Destination -
186
+ for O -
187
+ each O -
188
+ trial O -
189
+ . O -
190
+
191
+ The O Member-Collection
192
+ fifty O -
193
+ essays Member -
194
+ collected O -
195
+ in O -
196
+ this O -
197
+ volume Collection -
198
+ testify O -
199
+ to O -
200
+ most O -
201
+ of O -
202
+ the O -
203
+ prominent O -
204
+ themes O -
205
+ from O -
206
+ Professor O -
207
+ Quispel O -
208
+ 's O -
209
+ scholarly O -
210
+ career O -
211
+ . O -
212
+
213
+ Their O Other
214
+ composer Orelation1 -
215
+ has O -
216
+ sunk O -
217
+ into O -
218
+ oblivion Orelation2 -
219
+ . O -
220
+
221
+ The O Message-Topic
222
+ Pulitzer O -
223
+ Committee O -
224
+ issues O -
225
+ an O -
226
+ official O -
227
+ citation Message -
228
+ explaining O -
229
+ the O -
230
+ reasons Topic -
231
+ for O -
232
+ the O -
233
+ award O -
234
+ . O -
235
+
236
+ The O Cause-Effect
237
+ burst Effect -
238
+ has O -
239
+ been O -
240
+ caused O -
241
+ by O -
242
+ water O -
243
+ hammer O -
244
+ pressure Cause -
245
+ . O -
246
+
247
+ Even O Instrument-Agency
248
+ commercial O -
249
+ networks Agency -
250
+ have O -
251
+ moved O -
252
+ into O -
253
+ high-definition Instrument -
254
+ broadcast Instrument -
255
+ . O -
256
+
257
+ It O Message-Topic
258
+ was O -
259
+ a O -
260
+ friendly O -
261
+ call Message -
262
+ to O -
263
+ remind O -
264
+ them O -
265
+ about O -
266
+ the O -
267
+ bill Topic -
268
+ and O -
269
+ make O -
270
+ sure O -
271
+ they O -
272
+ have O -
273
+ a O -
274
+ copy O -
275
+ of O -
276
+ the O -
277
+ invoice O -
278
+ . O -
279
+
280
+ Texas-born O Instrument-Agency
281
+ virtuoso Agency -
282
+ finds O -
283
+ harmony O -
284
+ , O -
285
+ sophistication O -
286
+ in O -
287
+ Appalachian O -
288
+ instrument Instrument -
289
+ . O -
290
+
291
+ The O Product-Producer
292
+ factory Producer -
293
+ ' O -
294
+ s O -
295
+ products O -
296
+ have O -
297
+ included O -
298
+ flower O -
299
+ pots O -
300
+ , O -
301
+ Finnish O -
302
+ rooster-whistles O -
303
+ , O -
304
+ pans O -
305
+ , O -
306
+ trays Product -
307
+ , O -
308
+ tea O -
309
+ pots O -
310
+ , O -
311
+ ash O -
312
+ trays O -
313
+ and O -
314
+ air O -
315
+ moisturisers O -
316
+ . O -
317
+
318
+ The O Component-Whole
319
+ girl O -
320
+ showed O -
321
+ a O -
322
+ photo O -
323
+ of O -
324
+ apple O -
325
+ tree Whole -
326
+ blossom Component -
327
+ on O -
328
+ a O -
329
+ fruit O -
330
+ tree O -
331
+ in O -
332
+ the O -
333
+ Central O -
334
+ Valley O -
335
+ . O -
336
+
337
+ They O Member-Collection
338
+ tried O -
339
+ an O -
340
+ assault O -
341
+ of O -
342
+ their O -
343
+ own O -
344
+ an O -
345
+ hour O -
346
+ later O -
347
+ , O -
348
+ with O -
349
+ two O -
350
+ columns O -
351
+ of O -
352
+ sixteen O -
353
+ tanks O -
354
+ backed O -
355
+ by O -
356
+ a O -
357
+ battalion Collection -
358
+ of O -
359
+ Panzer O -
360
+ grenadiers Member -
361
+ . O -
362
+
363
+ Their O Entity-Origin
364
+ knowledge Entity -
365
+ of O -
366
+ the O -
367
+ power O -
368
+ and O -
369
+ rank O -
370
+ symbols O -
371
+ of O -
372
+ the O -
373
+ Continental O -
374
+ empires O -
375
+ was O -
376
+ gained O -
377
+ from O -
378
+ the O -
379
+ numerous O -
380
+ Germanic O -
381
+ recruits Origin -
382
+ in O -
383
+ the O -
384
+ Roman O -
385
+ army O -
386
+ , O -
387
+ and O -
388
+ from O -
389
+ the O -
390
+ Roman O -
391
+ practice O -
392
+ of O -
393
+ enfeoffing O -
394
+ various O -
395
+ Germanic O -
396
+ warrior O -
397
+ groups O -
398
+ with O -
399
+ land O -
400
+ in O -
401
+ the O -
402
+ imperial O -
403
+ provinces O -
404
+ . O -
405
+
406
+ She O Member-Collection
407
+ soon O -
408
+ had O -
409
+ a O -
410
+ stable Collection -
411
+ of O -
412
+ her O -
413
+ own O -
414
+ rescued O -
415
+ hounds Member -
416
+ . O -
417
+
418
+ The O Cause-Effect
419
+ singer Cause -
420
+ , O -
421
+ who O -
422
+ performed O -
423
+ three O -
424
+ of O -
425
+ the O -
426
+ nominated O -
427
+ songs O -
428
+ , O -
429
+ also O -
430
+ caused O -
431
+ a O -
432
+ commotion Effect -
433
+ on O -
434
+ the O -
435
+ red O -
436
+ carpet O -
437
+ . O -
438
+
439
+ His O Other
440
+ intellectually O -
441
+ engaging O -
442
+ books O -
443
+ and O -
444
+ essays Orelation1 -
445
+ remain O -
446
+ pertinent O -
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3
+ nltk==3.7
4
+ deep_translator==1.9.1
5
+ gradio == 3.9.1
setup.py ADDED
@@ -0,0 +1,16 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # -*- coding: utf-8 -*-
2
+ """
3
+ Created on Tue Dec 13 18:06:56 2022
4
+
5
+ @author: gita
6
+ """
7
+
8
+ from distutils.core import setup
9
+ import py2exe
10
+
11
+ setup(
12
+ options={"py2exe": {"bundle_files": 1}},
13
+ console=[{
14
+ "script": "execute_GUI.py"
15
+ }]
16
+ )
src/graph/GUI.py ADDED
@@ -0,0 +1,246 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # -*- coding: utf-8 -*-
2
+ """
3
+ Created on Fri Nov 11 16:01:08 2022
4
+
5
+ @author: Santiago Moreno
6
+ """
7
+
8
+ import os
9
+ import gradio as gr
10
+ import sys
11
+ import json
12
+
13
+
14
+ default_path = os.path.dirname(os.path.abspath(__file__))
15
+ #default_path = default_path.replace('\\', '/')
16
+
17
+ os.chdir(default_path)
18
+ sys.path.insert(0, default_path+'/../scripts')
19
+
20
+ from src.scripts.functionsner import use_model, tag_sentence, json_to_txt, training_model, characterize_data, upsampling_data, usage_cuda, copy_data
21
+ from src.scripts.functionsrc import use_model_rc, training_model_rc, usage_cuda_rc
22
+
23
+ models = os.listdir(default_path+'/../../models')
24
+ models.remove('RC')
25
+ models_rc = os.listdir(default_path+'/../../models/RC')
26
+
27
+ #-------------------------------------------Functions-----------------------------------------------
28
+
29
+ #--------------------------------------NER-----------------------------------
30
+ def Trainer(fast, model_name, standard, input_dir, Upsampling, Cuda):
31
+ if fast: epochs = 1
32
+ else: epochs = 20
33
+
34
+ if Cuda:
35
+ cuda_info = usage_cuda(True)
36
+ else:
37
+ cuda_info = usage_cuda(False)
38
+
39
+
40
+ if standard:
41
+ copy_data(input_dir)
42
+ else:
43
+ Error = json_to_txt(input_dir)
44
+ if type(Error)==int:
45
+ yield 'Error processing the input documents, code error {}'.format(Error)
46
+ if Upsampling:
47
+ yield cuda_info+'\n'+'-'*20+'Upsampling'+'-'*20
48
+ entities_dict=characterize_data()
49
+ entities = list(entities_dict.keys())
50
+ entities_to_upsample = [entities[i] for i,value in enumerate(entities_dict.values()) if value < 200]
51
+ upsampling_data(entities_to_upsample, 0.8, entities)
52
+ yield '-'*20+'Training'+'-'*20
53
+ else:
54
+ yield cuda_info+'\n'+'-'*20+'Training'+'-'*20
55
+ Error = training_model(model_name, epochs)
56
+ if type(Error)==int:
57
+ yield 'Error training the model, code error {}'.format(Error)
58
+ else:
59
+ yield 'Training complete, model {} could be found at models/{}'.format(model_name,model_name)
60
+
61
+
62
+ def Tagger_sentence(Model, Sentence, Cuda):
63
+ if Cuda: cuda_info = usage_cuda(True)
64
+ else: cuda_info = usage_cuda(False)
65
+ yield cuda_info+'\n'+'-'*20+'Tagging'+'-'*20
66
+ results = tag_sentence(Sentence, Model)
67
+ if type(results)==int:
68
+ yield "Error {}, see documentation".format(results)
69
+ else:
70
+ yield results['Highligth']
71
+
72
+ def Tagger_json(Model, Input_file, Output_file, Cuda):
73
+ if Cuda: cuda_info = usage_cuda(True)
74
+ else: cuda_info = usage_cuda(False)
75
+
76
+ with open(Output_file, "w", encoding='utf-8') as write_file:
77
+ json.dump({'error':'error'}, write_file)
78
+
79
+ yield cuda_info+'\n'+'-'*20+'Tagging'+'-'*20, {}, Output_file
80
+
81
+ results = use_model(Model, Input_file.name, Output_file)
82
+ if type(results)==int:
83
+ error_dict = {}
84
+ yield "Error {}, see documentation".format(results), error_dict, Output_file
85
+ else:
86
+ yield { "text" : results['text'], 'entities': results['entities']}, results, Output_file
87
+
88
+
89
+ #--------------------RC-------------------------------
90
+ def Trainer_RC(fast, model_name, input_file, rel2id_file, Cuda):
91
+ if fast: epochs = 1
92
+ else: epochs = 200
93
+
94
+ if Cuda:
95
+ cuda_info = usage_cuda_rc(True)
96
+ else:
97
+ cuda_info = usage_cuda_rc(False)
98
+
99
+
100
+ yield cuda_info+'\n'+'-'*20+'Training'+'-'*20
101
+ Error = training_model_rc(model_name, input_file.name, rel2id_file.name ,epochs)
102
+ if type(Error)==int:
103
+ yield 'Error training the model, code error {}'.format(Error)
104
+ else:
105
+ yield 'Training complete, model {} could be found at models/{}'.format(model_name,model_name)
106
+
107
+
108
+ def Tagger_document_RC(Model, Input_file, Output_file, Cuda):
109
+ if Cuda: cuda_info = usage_cuda_rc(True)
110
+ else: cuda_info = usage_cuda_rc(False)
111
+
112
+ with open(Output_file, "w", encoding='utf-8') as write_file:
113
+ json.dump({'error':'error'}, write_file)
114
+
115
+ yield {'cuda':cuda_info}, Output_file
116
+
117
+ results = use_model_rc(Model, Input_file.name, Output_file)
118
+ if type(results)==int:
119
+ error_dict = {}
120
+ yield error_dict, Output_file
121
+ else:
122
+ yield results, Output_file
123
+
124
+
125
+ #---------------------------------GUI-------------------------------------
126
+ def execute_GUI():
127
+ global models
128
+ with gr.Blocks(title='NER', css="#title {font-size: 150% } #sub {font-size: 120% } ") as demo:
129
+
130
+ gr.Markdown("Named Entity Recognition(NER) and Relation Classification (RC) by GITA and Pratec Group S.A.S.",elem_id="title")
131
+ gr.Markdown("Software developed by Santiago Moreno, Daniel Escobar, and Rafael Orozco",elem_id="sub")
132
+ gr.Markdown("Named Entity Recognition(NER) and Relation Classification (RC) System.")
133
+
134
+ with gr.Tab("NER"):
135
+ gr.Markdown("Use Tagger to apply NER from a pretrained model in a sentence or a given document in INPUT (.JSON) format.")
136
+ gr.Markdown("Use Trainer to train a new NER model from a directory of documents in PRATECH (.JSON) format.")
137
+ with gr.Tab("Tagger"):
138
+ with gr.Tab("Sentence"):
139
+ with gr.Row():
140
+ with gr.Column():
141
+ b = gr.Radio(list(models), label='Model')
142
+ inputs =[
143
+ b,
144
+ gr.Textbox(placeholder="Enter sentence here...", label='Sentence'),
145
+ gr.Radio([True,False], label='CUDA', value=False),
146
+ ]
147
+ tagger_sen = gr.Button("Tag")
148
+ output = gr.HighlightedText()
149
+
150
+
151
+
152
+ tagger_sen.click(Tagger_sentence, inputs=inputs, outputs=output)
153
+ b.change(fn=lambda value: gr.update(choices=list(os.listdir('../../models')).remove('RC')), inputs=b, outputs=b)
154
+ gr.Examples(
155
+
156
+ examples=[
157
+ ['CCC',"Camara de comercio de medellín. El ciudadano JAIME JARAMILLO VELEZ identificado con C.C. 12546987 ingresó al plantel el día 1/01/2022"],
158
+ ['CCC',"Razón Social GASEOSAS GLACIAR S.A.S, ACTIVIDAD PRINCIPAL fabricación y distribución de bebidas endulzadas"]
159
+ ],
160
+ inputs=inputs
161
+ )
162
+
163
+
164
+ with gr.Tab("Document"):
165
+ with gr.Row():
166
+ with gr.Column():
167
+ c = gr.Radio(list(models), label='Model')
168
+ inputs =[
169
+ c,
170
+ gr.File(label='Input data file'),
171
+ gr.Textbox(placeholder="Enter path here...", label='Output data file path'), #value='../../data/Tagged/document_tagged.json'),
172
+ gr.Radio([True,False], label='CUDA', value=False),
173
+ ]
174
+ tagger_json = gr.Button("Tag")
175
+ output = [
176
+ gr.HighlightedText(),
177
+ gr.JSON(),
178
+ gr.File(),
179
+ ]
180
+
181
+ models = os.listdir(default_path+'/../../models')
182
+ models.remove('RC')
183
+
184
+ tagger_json.click(Tagger_json, inputs=inputs, outputs=output)
185
+ c.change(fn=lambda value: gr.update(choices=list(os.listdir('../../models')).remove('RC')), inputs=c, outputs=c)
186
+
187
+
188
+ with gr.Tab("Trainer"):
189
+ with gr.Row():
190
+ with gr.Column():
191
+ train_input = inputs =[
192
+ gr.Radio([True,False], label='Fast training', value=True),
193
+ gr.Textbox(placeholder="Enter model name here...", label='New model name'),
194
+ gr.Radio([True,False], label='Standard input', value=False),
195
+ gr.Textbox(placeholder="Enter path here...", label='Input data directory path'),
196
+ gr.Radio([True,False], label='Upsampling', value=False),
197
+ gr.Radio([True,False], label='CUDA', value=False),
198
+ ]
199
+ trainer = gr.Button("Train")
200
+ train_output = gr.TextArea(placeholder="Output information", label='Output')
201
+
202
+
203
+ with gr.Tab("RC"):
204
+ gr.Markdown("Use Tagger to apply RC from a pretrained model in document in (.TXT) CONLL04 format.")
205
+ gr.Markdown("Use Trainer to train a new RC model from a file (.TXT) CONLL04 format and the rel2id file (.JSON).")
206
+ with gr.Tab("Tagger Document"):
207
+
208
+ with gr.Row():
209
+ with gr.Column():
210
+ c = gr.Radio(list(models_rc), label='Model')
211
+ inputs =[
212
+ c,
213
+ gr.File(label='Input data file'),
214
+ gr.Textbox(placeholder="Enter path here...", label='Output data file path (.JSON)'), #value='../../data/Tagged/document_tagged.json'),
215
+ gr.Radio([True,False], label='CUDA', value=False),
216
+ ]
217
+ tagger_json = gr.Button("Tag")
218
+ output = [
219
+ gr.JSON(),
220
+ gr.File(),
221
+ ]
222
+
223
+ tagger_json.click(Tagger_document_RC, inputs=inputs, outputs=output)
224
+ c.change(fn=lambda value: gr.update(choices=list(os.listdir('../../models/RC'))), inputs=c, outputs=c)
225
+
226
+ with gr.Tab("Trainer"):
227
+ with gr.Row():
228
+ with gr.Column():
229
+ train_input = inputs =[
230
+ gr.Radio([True,False], label='Fast training', value=True),
231
+ gr.Textbox(placeholder="Enter model name here...", label='New model name'),
232
+ gr.File(label='Input train file (.TXT)'),
233
+ gr.File(label='Input rel2id file (.JSON)'),
234
+ gr.Radio([True,False], label='CUDA', value=False),
235
+ ]
236
+ trainer = gr.Button("Train")
237
+ train_output = gr.TextArea(placeholder="Output information", label='Output')
238
+
239
+ trainer.click(Trainer_RC, inputs=train_input, outputs=train_output)
240
+
241
+
242
+
243
+ demo.queue()
244
+ demo.launch(server_name="0.0.0.0", server_port=8080,inbrowser=True, share = True)
245
+
246
+
src/graph/__pycache__/GUI.cpython-311.pyc ADDED
Binary file (17.9 kB). View file
 
src/graph/__pycache__/GUI.cpython-39.pyc ADDED
Binary file (7.57 kB). View file
 
src/graph/out ADDED
@@ -0,0 +1 @@
 
 
1
+ {"sentences": {"tokens": [["The", "system", "as", "described", "above", "has", "its", "greatest", "application", "in", "an", "arrayed", "configuration", "of", "antenna", "elements", "."], ["The", "child", "was", "carefully", "wrapped", "and", "bound", "into", "the", "cradle", "by", "means", "of", "a", "cord", "."], ["The", "author", "of", "a", "keygen", "uses", "a", "disassembler", "to", "look", "at", "the", "raw", "assembly", "code", "."], ["A", "misty", "ridge", "uprises", "from", "the", "surge", "."], ["The", "student", "association", "is", "the", "voice", "of", "the", "undergraduate", "student", "population", "of", "the", "State", "University", "of", "New", "York", "at", "Buffalo", "."], ["This", "is", "the", "sprawling", "complex", "that", "is", "Peru", "'s", "largest", "producer", "of", "silver", "."], ["The", "current", "view", "is", "that", "the", "chronic", "inflammation", "in", "the", "distal", "part", "of", "the", "stomach", "caused", "by", "Helicobacter", "pylori", "infection", "results", "in", "an", "increased", "acid", "production", "from", "the", "non-infected", "upper", "corpus", "region", "of", "the", "stomach", "."], ["People", "have", "been", "moving", "back", "into", "downtown", "."], ["The", "lawsonite", "was", "contained", "in", "a", "platinum", "crucible", "and", "the", "counter-weight", "was", "a", "plastic", "crucible", "with", "metal", "pieces", "."], ["The", "solute", "was", "placed", "inside", "a", "beaker", "and", "5", "mL", "of", "the", "solvent", "was", "pipetted", "into", "a", "25", "mL", "glass", "flask", "for", "each", "trial", "."], ["The", "fifty", "essays", "collected", "in", "this", "volume", "testify", "to", "most", "of", "the", "prominent", "themes", "from", "Professor", "Quispel", "'s", "scholarly", "career", "."], ["Their", "composer", "has", "sunk", "into", "oblivion", "."], ["The", "Pulitzer", "Committee", "issues", "an", "official", "citation", "explaining", "the", "reasons", "for", "the", "award", "."], ["The", "burst", "has", "been", "caused", "by", "water", "hammer", "pressure", "."], ["Even", "commercial", "networks", "have", "moved", "into", "high-definition", "broadcast", "."], ["It", "was", "a", "friendly", "call", "to", "remind", "them", "about", "the", "bill", "and", "make", "sure", "they", "have", "a", "copy", "of", "the", "invoice", "."], ["Texas-born", "virtuoso", "finds", "harmony", ",", "sophistication", "in", "Appalachian", "instrument", "."], ["The", "factory", "'", "s", "products", "have", "included", "flower", "pots", ",", "Finnish", "rooster-whistles", ",", "pans", ",", "trays", ",", "tea", "pots", ",", "ash", "trays", "and", "air", "moisturisers", "."], ["The", "girl", "showed", "a", "photo", "of", "apple", "tree", "blossom", "on", "a", "fruit", "tree", "in", "the", "Central", "Valley", "."], ["They", "tried", "an", "assault", "of", "their", "own", "an", "hour", "later", ",", "with", "two", "columns", "of", "sixteen", "tanks", "backed", "by", "a", "battalion", "of", "Panzer", "grenadiers", "."], ["Their", "knowledge", "of", "the", "power", "and", "rank", "symbols", "of", "the", "Continental", "empires", "was", "gained", "from", "the", "numerous", "Germanic", "recruits", "in", "the", "Roman", "army", ",", "and", "from", "the", "Roman", "practice", "of", "enfeoffing", "various", "Germanic", "warrior", "groups", "with", "land", "in", "the", "imperial", "provinces", "."], ["She", "soon", "had", "a", "stable", "of", "her", "own", "rescued", "hounds", "."], ["The", "singer", ",", "who", "performed", "three", "of", "the", "nominated", "songs", ",", "also", "caused", "a", "commotion", "on", "the", "red", "carpet", "."], ["His", "intellectually", "engaging", "books", "and", "essays", "remain", "pertinent", "to", "illuminating", "contemporary", "history", "."], ["Poor", "hygiene", "controls", ",", "reports", "of", "a", "brace", "of", "gamey", "grouse", "and", "what", "looked", "like", "a", "skinned", "fox", "all", "amounted", "to", "a", "pie", "that", "was", "unfit", "for", "human", "consumption", "."], ["This", "sweet", "dress", "is", "made", "with", "a", "blend", "of", "cotton", "and", "silk", ",", "and", "the", "crochet", "flower", "necklace", "is", "the", "perfect", "accessory", "."], ["Suicide", "is", "one", "of", "the", "leading", "causes", "of", "death", "among", "pre-adolescents", "and", "teens", ",", "and", "victims", "of", "bullying", "are", "at", "an", "increased", "risk", "for", "committing", "suicide", "."], ["This", "article", "gives", "details", "on", "2004", "in", "music", "in", "the", "United", "Kingdom", ",", "including", "the", "official", "charts", "from", "that", "year", "."], ["We", "have", "therefore", "taken", "the", "initiative", "to", "convene", "the", "first", "international", "open", "meeting", "dedicated", "solely", "to", "rural", "history", "."], ["The", "timer", "of", "the", "device", "automatically", "eliminates", "wasted", "\"", "standby", "power", "\"", "consumption", "by", "automatically", "turn", "off", "electronics", "plugged", "into", "the", "\"", "auto", "off", "\"", "outlets", "."], ["Bob", "Parks", "made", "a", "similar", "offer", "in", "a", "phone", "call", "made", "earlier", "this", "week", "."], ["He", "had", "chest", "pains", "and", "headaches", "from", "mold", "in", "the", "bedrooms", "."], ["The", "silver-haired", "author", "was", "not", "just", "laying", "India", "'s", "politician", "saint", "to", "rest", "but", "healing", "a", "generations-old", "rift", "in", "the", "family", "of", "the", "country", "'", "s", "founding", "father", "."], ["It", "describes", "a", "method", "for", "loading", "a", "horizontal", "stack", "of", "containers", "into", "a", "carton", "."], ["The", "Foundation", "decided", "to", "repurpose", "the", "building", "in", "order", "to", "reduce", "wear", "and", "tear", "on", "the", "plumbing", "in", "the", "manor", "house", "by", "redirecting", "visitors", "during", "restoration", "projects", "and", "beyond", "."], ["The", "technology", "is", "available", "to", "produce", "and", "transmit", "electricity", "economically", "from", "OTEC", "systems", "."], ["The", "Medicare", "buy-in", "plan", "ran", "into", "Senate", "resistance", "."], ["The", "provinces", "are", "divided", "into", "counties", "(", "shahrestan", ")", ",", "and", "subdivided", "into", "districts", "(", "bakhsh", ")", "and", "sub-districts", "(", "dehestan", ")", "."], ["Financial", "stress", "is", "one", "of", "the", "main", "causes", "of", "divorce", "."], ["Newspapers", "swap", "content", "via", "widgets", "with", "the", "help", "of", "the", "newsgator", "service", "."], ["The", "women", "that", "caused", "the", "accident", "was", "on", "the", "cell", "phone", "and", "ran", "thru", "the", "intersection", "without", "pausing", "on", "the", "median", "."], ["The", "transmitter", "was", "discovered", "inside", "a", "bed", "settee", "suite", "on", "which", "he", "had", "been", "sitting", "."], ["The", "Kerala", "backwaters", "are", "a", "chain", "of", "brackish", "lagoons", "and", "lakes", "lying", "parallel", "to", "the", "Arabian", "Sea", "coast", "of", "Kerala", "state", "in", "southern", "India", "."], ["A", "St.", "Paul", "College", "student", "was", "released", "from", "jail", "Wednesday", "night", ",", "after", "his", "arrest", "Tuesday", "in", "the", "alleged", "rape", "of", "another", "student", "on", "campus", "."], ["Calluses", "are", "caused", "by", "improperly", "fitting", "shoes", "or", "by", "a", "skin", "abnormality", "."], ["Adults", "use", "drugs", "for", "this", "purpose", "."], ["The", "councilor", "proposed", "assessing", "infinitival", "complements", "through", "elicitation", "."], ["As", "in", "the", "popular", "movie", "\"", "Deep", "Impact", "\"", ",", "the", "action", "of", "the", "Perseid", "meteor", "shower", "is", "caused", "by", "a", "comet", ",", "in", "this", "case", "periodic", "comet", "Swift-Tuttle", "."], ["The", "following", "information", "appeared", "in", "the", "notes", "to", "consolidated", "financial", "statements", "of", "some", "corporate", "annual", "reports", "."], ["HipHop", "appropriates", "the", "symbols", "of", "a", "consumer", "society", ":", "oversized", "diamond", "colliers", "are", "worn", "."], ["The", "radiation", "from", "the", "atomic", "bomb", "explosion", "is", "a", "typical", "acute", "radiation", "."], ["The", "ride-on", "boat", "tiller", "was", "developed", "by", "engineers", "Arnold", "S.", "Juliano", "and", "Dr.", "Eulito", "U.", "Bautista", "."], ["A", "neoplastic", "recurrence", "arose", "from", "an", "extensive", "radiation", "induced", "ulceration", "."]], "entities": [["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "Whole", "O", "O", "Component", "O"], ["O", "Orelation1", "O", "O", "O", "O", "O", "O", "O", "Orelation2", "O", "O", "O", "O", "O", "O"], ["O", "Agency", "O", "O", "O", "O", "O", "Instrument", "O", "O", "O", "O", "O", "O", "O", "O"], ["O", "O", "Orelation1", "O", "O", "O", "Orelation2", "O"], ["O", "Member", 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"QUE", "label": "O"}, {"text": "APARECE", "label": "O"}, {"text": "A", "label": "O"}, {"text": "CONTINUACION", "label": "O"}, {"text": "TIENE", "label": "O"}, {"text": "PLENA", "label": "O"}, {"text": "VALIDEZ", "label": "O"}, {"text": "PARA", "label": "O"}, {"text": "TODOS", "label": "O"}, {"text": "LOS", "label": "O"}, {"text": "EFECTOS", "label": "O"}, {"text": "LEGALES", "label": "O"}, {"text": "\"", "label": "O"}]}], "entities": [{"entity": "FCH", "index": 7, "word": "21", "start": 48, "end": 50}, {"entity": "FCH", "index": 8, "word": "DE", "start": 51, "end": 53}, {"entity": "FCH", "index": 9, "word": "NOVIEMBRE", "start": 54, "end": 63}, {"entity": "FCH", "index": 10, "word": "DE", "start": 64, "end": 66}, {"entity": "FCH", "index": 11, "word": "2011", "start": 67, "end": 71}, {"entity": "PER", "index": 43, "word": "RODRIGUEZ", "start": 268, "end": 277}, {"entity": "PER", "index": 44, "word": "MURCIA", "start": 278, "end": 284}, {"entity": "PER", "index": 45, "word": "JULIO", "start": 285, "end": 290}, {"entity": "TDID", "index": 46, "word": "C.C", "start": 291, "end": 294}, {"entity": "DID", "index": 47, "word": "194459", "start": 295, "end": 301}, {"entity": "TDID", "index": 48, "word": "NIT", "start": 302, "end": 305}, {"entity": "DID", "index": 50, "word": "194459-9", "start": 307, "end": 315}, {"entity": "TFMT", "index": 53, "word": "MATRICULA", "start": 327, "end": 336}, {"entity": "FCH", "index": 57, "word": "23", "start": 353, "end": 355}, {"entity": "FCH", "index": 58, "word": "DE", "start": 356, "end": 358}, {"entity": "FCH", "index": 59, "word": "ABRIL", "start": 359, "end": 364}, {"entity": "FCH", "index": 60, "word": "DE", "start": 365, "end": 367}, {"entity": "FCH", "index": 61, "word": "1997", "start": 368, "end": 372}, {"entity": "FCH", "index": 153, "word": "15", "start": 964, "end": 966}, {"entity": "FCH", "index": 154, "word": "DE", "start": 967, "end": 969}, {"entity": "FCH", "index": 155, "word": "JUNIO", "start": 970, "end": 975}, {"entity": "FCH", "index": 156, "word": "DE", "start": 976, "end": 978}, {"entity": "FCH", "index": 157, "word": "2010", "start": 979, "end": 983}, {"entity": "ACT", "index": 170, "word": "ACTIVIDAD", "start": 1073, "end": 1082}, {"entity": "ACT", "index": 171, "word": "ECONOMICA", "start": 1083, "end": 1092}, {"entity": "ACT", "index": 172, "word": "FABRICACION", "start": 1093, "end": 1104}, {"entity": "ACT", "index": 173, "word": "ELEMENTOS", "start": 1105, "end": 1114}, {"entity": "ACT", "index": 174, "word": "METALICOS", "start": 1115, "end": 1124}, {"entity": "ACT", "index": 175, "word": ",", "start": 1124, "end": 1125}, {"entity": "ACT", "index": 176, "word": "ORNAMENTACION", "start": 1126, "end": 1139}, {"entity": "ACT", "index": 177, "word": "PUERTAS", "start": 1140, "end": 1147}, {"entity": "ACT", "index": 178, "word": ",", "start": 1147, "end": 1148}, {"entity": "ACT", "index": 179, "word": "VENTANAS", "start": 1149, "end": 1157}, {"entity": "ACT", "index": 180, "word": ",", "start": 1157, "end": 1158}, {"entity": "ACT", "index": 181, "word": "REJAS", "start": 1159, "end": 1164}, {"entity": "ACT", "index": 182, "word": "Y", "start": 1165, "end": 1166}, {"entity": "ACT", "index": 183, "word": "VERJAS", "start": 1167, "end": 1173}, {"entity": "ACT", "index": 184, "word": "/", "start": 1174, "end": 1175}, {"entity": "ACT", "index": 185, "word": "ALQUILER", "start": 1176, "end": 1184}, {"entity": "ACT", "index": 186, "word": "DE", "start": 1185, "end": 1187}, {"entity": "ACT", "index": 187, "word": "VEHICULOS", "start": 1188, "end": 1197}, {"entity": "ACT", "index": 188, "word": "DE", "start": 1198, "end": 1200}, {"entity": "ACT", "index": 189, "word": "CARGA", "start": 1201, "end": 1206}, {"entity": "ACT", "index": 190, "word": "CON", "start": 1207, "end": 1210}, {"entity": "ACT", "index": 191, "word": "CONDUCTOR", "start": 1211, "end": 1220}, {"entity": "TNRS", "index": 200, "word": "ESTABLECIMIENTOS", "start": 1276, "end": 1292}, {"entity": "TNRS", "index": 201, "word": "DE", "start": 1293, "end": 1295}, {"entity": "TNRS", "index": 203, "word": "NOMBRE", "start": 1305, "end": 1311}, {"entity": "ORG", "index": 204, "word": "CARPINTERIA", "start": 1312, "end": 1323}, {"entity": "ORG", "index": 205, "word": "METALICA", "start": 1324, "end": 1332}, {"entity": "ORG", "index": 206, "word": "RODRIGUEZ", "start": 1333, "end": 1342}, {"entity": "TFMT", "index": 219, "word": "MATRICULA", "start": 1409, "end": 1418}, {"entity": "FCH", "index": 223, "word": "23", "start": 1434, "end": 1436}, {"entity": "FCH", "index": 224, "word": "DE", "start": 1437, "end": 1439}, {"entity": "FCH", "index": 225, "word": "ABRIL", "start": 1440, "end": 1445}, {"entity": "FCH", "index": 226, "word": "DE", "start": 1446, "end": 1448}, {"entity": "FCH", "index": 227, "word": "1997", "start": 1449, "end": 1453}, {"entity": "FCH", "index": 233, "word": "15", "start": 1484, "end": 1486}, {"entity": "FCH", "index": 234, "word": "DE", "start": 1487, "end": 1489}, {"entity": "FCH", "index": 235, "word": "JUNIO", "start": 1490, "end": 1495}, {"entity": "FCH", "index": 236, "word": "DE", "start": 1496, "end": 1498}, {"entity": "FCH", "index": 237, "word": "2010", "start": 1499, "end": 1503}, {"entity": "FCH", "index": 420, "word": "18", "start": 2505, "end": 2507}, {"entity": "FCH", "index": 421, "word": "DE", "start": 2508, "end": 2510}, {"entity": "FCH", "index": 422, "word": "NO", "start": 2511, "end": 2513}, {"entity": "FCH", "index": 423, "word": "IEMBRE", "start": 2514, "end": 2520}, {"entity": "FCH", "index": 424, "word": "DE", "start": 2521, "end": 2523}, {"entity": "FCH", "index": 425, "word": "1996", "start": 2524, "end": 2528}]}
src/scripts/Error_handling.txt ADDED
@@ -0,0 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 1 Error loading tagger
2
+ 2 Error loading document
3
+ 3 Format Error in document
4
+ 4 Empty folder for training
5
+ 5 Error loading embeddings in training
6
+ 6 Error making tagger in training
7
+ 7 Error training the model
8
+ 8 Invalid input document in training
9
+ 9 Document does not exists
10
+ 10 Model does not exists
11
+ 11 Error in output JSON
12
+ 12 Error making up the data
13
+ 13 Error defining the model
14
+
15
+
src/scripts/Json_formats.py ADDED
@@ -0,0 +1,166 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # -*- coding: utf-8 -*-
2
+ """
3
+ Created on Tue Dec 6 11:21:55 2022
4
+
5
+ @author: gita
6
+ """
7
+
8
+ import gradio as gr
9
+
10
+ def image_classifier():
11
+ # j={
12
+ # "sentences":[
13
+ # {"text":"Frase ejemplo"},
14
+ # {"text":"Frase ejemplo"}
15
+ # ]
16
+ # }
17
+
18
+ # j = {
19
+ # 'text':"Frase ejemplo Frase ejemplo ",
20
+
21
+ # 'text_labeled':" \"Frase\"/Entity_Type ejemplo \"Frase\"/Entity_Type ejemplo ",
22
+
23
+ # 'sentences':[
24
+ # {'text':"Frase ejemplo",
25
+ # 'text_labeled':" \"Frase\"/Entity_Type ejemplo",
26
+ # 'tokens':[
27
+ # {'text':"Frase", 'label':"Entity_Type"},
28
+ # {'text':"ejemplo", 'label':"O"}
29
+ # ]},
30
+
31
+ # {'text':"Frase ejemplo",
32
+ # 'text_labeled':" \"Frase\"/Entity_Type ejemplo",
33
+ # 'tokens':[
34
+ # {'text':"Frase", 'label':"Entity_Type"},
35
+ # {'text':"ejemplo", 'label':"O"}
36
+ # ]}
37
+
38
+ # ],
39
+
40
+
41
+ # 'entities': [
42
+ # {
43
+ # 'entity': "Entity_Type" ,
44
+ # 'index' : 0,
45
+ # 'word' : "Frase",
46
+ # 'start': 0,
47
+ # 'end' : 5
48
+
49
+ # },
50
+ # {
51
+ # 'entity': "Entity_Type" ,
52
+ # 'index' : 2,
53
+ # 'word' : "Frase",
54
+ # 'start': 14,
55
+ # 'end' : 19
56
+
57
+ # }
58
+ # ]
59
+
60
+ # }
61
+
62
+
63
+ j = {
64
+
65
+ 'text':"Frase ejemplo Frase ejemplo",
66
+
67
+ 'sentences':[
68
+ {'text':"Frase ejemplo",
69
+ 'id':"s0",
70
+ 'tokens':[
71
+ {'text':"Frase", 'begin':0, 'end':5},
72
+ {'text':"ejemplo", 'begin':6, 'end':13}
73
+ ]},
74
+
75
+ {'text':"Frase ejemplo",
76
+ 'id':"s1",
77
+ 'tokens':[
78
+ {'text':"Frase", 'begin':14, 'end':19},
79
+ {'text':"ejemplo", 'begin':20, 'end':27}
80
+ ]},
81
+
82
+ ],
83
+
84
+
85
+ 'mentions': [
86
+ {
87
+ 'id': "s0-m0" ,
88
+ 'type' : "Entity_type",
89
+ 'begin' : 0,
90
+ 'end': 5,
91
+
92
+ },
93
+
94
+ {
95
+ 'id': "s1-m0" ,
96
+ 'type' : "Entity_type",
97
+ 'begin' : 14,
98
+ 'end': 19,
99
+
100
+ }
101
+
102
+ ]
103
+
104
+ }
105
+
106
+
107
+
108
+ return j
109
+
110
+ demo = gr.Interface(fn=image_classifier, inputs=None, outputs=gr.JSON())
111
+ demo.launch()
112
+
113
+ #%%
114
+ # JSON FORMAT OUTPUT
115
+
116
+ # Document:{ text:"Texto"
117
+
118
+ # text_labeled: "Texto \ENTITY"
119
+
120
+ # sentences:[{ text:"Texto"
121
+
122
+ # text_labeled: "Texto \ENTITY"
123
+
124
+ # tokens: [ {text:"Texto", label : "ENTITY"},
125
+ # {text:"Texto", label : "ENTITY"},
126
+ # {text:"Texto", label : "ENTITY"}
127
+
128
+ # ]
129
+
130
+ # },
131
+
132
+ # { text:"Texto"
133
+
134
+ # text_labeled: "Texto <ENTITY>"
135
+
136
+ # tokens: [ {text:"Texto", label : "ENTITY"},
137
+ # {text:"Texto", label : "ENTITY"},
138
+ # {text:"Texto", label : "ENTITY"}
139
+
140
+ # ]
141
+
142
+ # }
143
+ # ],
144
+ # entities:[
145
+ # {
146
+ # 'entity': "ENTITY",
147
+ # 'index': num,
148
+ # 'word': "Texto",
149
+ # 'start': num,
150
+ # 'end' : num
151
+ # }
152
+ # ]
153
+ # }
154
+
155
+ #%%
156
+
157
+ # JSON FORMAT INPUT
158
+
159
+ # json{...
160
+ # sentences:{
161
+ # s:{
162
+ # text:
163
+ # }
164
+ # }
165
+
166
+ # ...}
src/scripts/Tagged_document.py ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # -*- coding: utf-8 -*-
2
+ """
3
+ Created on Tue Oct 25 00:32:02 2022
4
+
5
+ @author: Santiago Moreno
6
+ """
7
+ import os
8
+ import argparse
9
+ from functions import use_model, str2bool, usage_cuda
10
+
11
+ default_path = os.path.dirname(os.path.abspath(__file__))
12
+ os.chdir(default_path)
13
+ output_dir = "../../data/tagged/document_tagged.json"
14
+
15
+
16
+
17
+ if __name__ == '__main__':
18
+ parser = argparse.ArgumentParser(add_help=True, usage='Tag a document with a pre-trained model (GPU optional)')
19
+ parser.add_argument('-m','--model', default='CCC', type=str, nargs='?', help='New model name', required=True)
20
+ parser.add_argument('-id','--input_data', type=str, nargs='?', help='Absolute path input file', required=True)
21
+ parser.add_argument('-od','--output_data', const=output_dir, default=output_dir, type=str, nargs='?', help='Absolute path output file', required=False)
22
+ parser.add_argument('-cu','--cuda', type=str2bool, nargs='?', const=True, default=False, help='Boolean value for using cuda to Train the model (True). By defaul False.', choices=(True, False), required=False)
23
+ args = parser.parse_args()
24
+
25
+ #print(args.model, args.input_data, args.output_data)
26
+ if args.cuda: cuda_info = usage_cuda(True)
27
+ else: cuda_info = usage_cuda(False)
28
+ print(cuda_info)
29
+ Error = use_model(args.model, args.input_data, args.output_data)
30
+ if type(Error)==int:
31
+ print('Tagged not complete, error code {}'.format(Error))
32
+ else:
33
+ print('Tagged complete')
34
+
35
+ # path_data = "C:/Users/gita/OneDrive - Universidad de Antioquia/GITA/Maestría/Programas/Datasets/camara_comercio_NER/gt/3cb4fa20-89cb-11e8-a485-d149999fe64b-0.json "
36
+ # output_dir = "C:/Users/gita/OneDrive - Universidad de Antioquia/GITA/Maestría/Programas/Software NER/document_tagged.json"
37
+ # sentence = use_model('CCC', path_data, output_dir)
src/scripts/Test.py ADDED
@@ -0,0 +1,20 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # -*- coding: utf-8 -*-
2
+ """
3
+ Created on Thu May 4 18:47:46 2023
4
+
5
+ @author: sanmo
6
+ """
7
+ import os
8
+ default_path = os.path.dirname(os.path.abspath(__file__))
9
+ default_path = default_path.replace('\\', '/')
10
+
11
+ from functionsrc import training_model_rc, usage_cuda_rc, use_model_rc
12
+
13
+ path_data = default_path + '/../../data/RC/test.txt'
14
+ rel2id_data = default_path + '/../../data/RC/rel2id.json'
15
+ print(usage_cuda_rc(True))
16
+ training_model_rc('p', path_data, rel2id_data, 2)
17
+
18
+ # output_dir = default_path + '/../../out_RC.json'
19
+
20
+ # print(use_model_rc('new', path_data, output_dir))
src/scripts/Train_model.py ADDED
@@ -0,0 +1,57 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # -*- coding: utf-8 -*-
2
+ """
3
+ Created on Sat Oct 29 14:56:09 2022
4
+
5
+ @author: Santiago Moreno
6
+ """
7
+ import os
8
+ import argparse
9
+ from functions import json_to_txt, training_model, characterize_data, upsampling_data, str2bool, usage_cuda,copy_data
10
+ default_path = os.path.dirname(os.path.abspath(__file__))
11
+ os.chdir(default_path)
12
+
13
+
14
+
15
+ if __name__ == '__main__':
16
+ parser = argparse.ArgumentParser(add_help=True, usage='Train a new model with given data (GPU optional)')
17
+ parser.add_argument('-f','--fast', type=str2bool, nargs='?',const=True, default=False, help='Training fast option (Only for functioning test)', choices=(True, False), required=False)
18
+ parser.add_argument('-m','--model', type=str, nargs='?', help='New model name', required=True)
19
+ parser.add_argument('-s','--standard', type=str2bool, nargs='?',const=True, default=False, help='Standard CONLL input or not', choices=(True, False), required=False)
20
+ parser.add_argument('-id','--input_dir', type=str, nargs='?', help='Absolute path input directory', required=True)
21
+ parser.add_argument('-u','--up_sample_flag', type=str2bool, nargs='?',const=True, default=False , help='Boolean value to upsampling the data = True or not upsampling = False', required=False, choices=(True, False))
22
+ parser.add_argument('-cu','--cuda', type=str2bool, nargs='?', const=True, default=False, help='Boolean value for using cuda to Train the model (True). By defaul False.', choices=(True, False), required=False)
23
+
24
+ args = parser.parse_args()
25
+
26
+
27
+ if args.fast: epochs = 1
28
+ else: epochs = 20
29
+
30
+ if args.standard:
31
+ copy_data(args.input_dir)
32
+ not_error=True
33
+ else:
34
+ Error = json_to_txt(args.input_dir)
35
+ if type(Error)==int:
36
+ print('Error processing the input documents, code error {}'.format(Error))
37
+ not_error=False
38
+ else:
39
+ not_error=True
40
+
41
+ if not_error:
42
+ if args.up_sample_flag:
43
+ entities_dict=characterize_data()
44
+ entities = list(entities_dict.keys())
45
+ entities_to_upsample = [entities[i] for i,value in enumerate(entities_dict.values()) if value < 200]
46
+ upsampling_data(entities_to_upsample, 0.8, entities)
47
+
48
+ if args.cuda: cuda_info = usage_cuda(True)
49
+ else: cuda_info = usage_cuda(False)
50
+
51
+ print(cuda_info)
52
+
53
+ Error = training_model(args.model,epochs)
54
+ if type(Error)==int:
55
+ print('Error training the model, code error {}'.format(Error))
56
+ else:
57
+ print('Training complete')
src/scripts/__pycache__/functionsner.cpython-311.pyc ADDED
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src/scripts/__pycache__/functionsner.cpython-39.pyc ADDED
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src/scripts/__pycache__/functionsrc.cpython-311.pyc ADDED
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src/scripts/__pycache__/functionsrc.cpython-39.pyc ADDED
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src/scripts/__pycache__/upsampling.cpython-311.pyc ADDED
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src/scripts/__pycache__/upsampling.cpython-39.pyc ADDED
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src/scripts/functionsner.py ADDED
@@ -0,0 +1,467 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # -*- coding: utf-8 -*-
2
+ """
3
+ Created on Tue Oct 11 16:46:45 2022
4
+
5
+ @author: Santiago Moreno
6
+ """
7
+ from upsampling import upsampling_ner
8
+ from flair.datasets import ColumnCorpus
9
+ from flair.data import Corpus
10
+ from flair.trainers import ModelTrainer
11
+ from flair.models import SequenceTagger
12
+ from flair.embeddings import TransformerWordEmbeddings
13
+ from torch.optim.lr_scheduler import OneCycleLR
14
+ from flair.data import Sentence
15
+ from sklearn.model_selection import StratifiedGroupKFold
16
+ from distutils.dir_util import copy_tree
17
+ import numpy as np
18
+ import torch
19
+ import pandas as pd
20
+ import json
21
+ import os
22
+ import operator
23
+ import flair
24
+ import argparse
25
+
26
+ default_path = os.path.dirname(os.path.abspath(__file__))
27
+ tagger_document = 0
28
+ tagger_sentence = 0
29
+ def check_create(path):
30
+ import os
31
+
32
+ if not (os.path.isdir(path)):
33
+ os.makedirs(path)
34
+
35
+ def str2bool(v):
36
+ if isinstance(v, bool):
37
+ return v
38
+ if v.lower() in ('yes', 'True','true', 't', 'y', '1'):
39
+ return True
40
+ elif v.lower() in ('no', 'False', 'false', 'f', 'n', '0'):
41
+ return False
42
+ else:
43
+ raise argparse.ArgumentTypeError('Boolean value expected.')
44
+
45
+
46
+ def copy_data(original_path):
47
+ data_folder = default_path + '/../../data/train'
48
+ copy_tree(original_path, data_folder)
49
+
50
+ def characterize_data():
51
+ data_folder = default_path + '/../../data/train'
52
+ columns = {0: 'text', 1:'ner'}
53
+
54
+ # init a corpus using column format, data folder and the names of the train, dev and test files
55
+
56
+ try:
57
+ corpus: Corpus = ColumnCorpus(data_folder, columns,
58
+ train_file='train.txt',
59
+ test_file='test.txt' )
60
+ #dev_file='dev.txt')
61
+ except:
62
+ print('Invalid input document in training')
63
+ return 8
64
+
65
+ # 2. what tag do we want to predict?
66
+ tag_type = 'ner'
67
+
68
+ #tag_dictionary = corpus.make_label_dictionary(label_type=tag_type)
69
+ tag_dictionary = corpus.get_label_distribution()
70
+ return tag_dictionary
71
+ #return corpus
72
+
73
+
74
+ def upsampling_data(entities_to_upsample, probability, entities):
75
+ print('-'*20,'upsampling','-'*20)
76
+ data_folder = default_path + '/../../data/train'
77
+ columns = {'text':0, 'ner':1}
78
+ for m in ["SiS","LwTR","MR","SR", "MBT"]:
79
+ upsampler = upsampling_ner(data_folder+'/train.txt', entities+['O'], columns)
80
+ data, data_labels = upsampler.get_dataset()
81
+ new_samples, new_labels = upsampler.upsampling(entities_to_upsample,probability,[m])
82
+ data += new_samples
83
+ data_labels += new_labels
84
+
85
+ with open(data_folder+'/train.txt', mode='w', encoding='utf-8') as f:
86
+ for l,sentence in enumerate(data):
87
+ for j,word in enumerate(sentence):
88
+ f.write(word+' '+ data_labels[l][j])
89
+ f.write('\n')
90
+
91
+ if l < (len(data)-1):
92
+ f.write('\n')
93
+
94
+ print('-'*20,'upsampling complete','-'*20)
95
+
96
+
97
+ def usage_cuda(cuda):
98
+ if cuda:
99
+ flair.device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
100
+ if flair.device == torch.device('cpu'): return 'Error handling GPU, CPU will be used'
101
+ elif flair.device == torch.device('cuda:0'): return 'GPU detected, GPU will be used'
102
+ else:
103
+ flair.device = torch.device('cpu')
104
+ return 'CPU will be used'
105
+
106
+
107
+ def training_model(name, epochs=20):
108
+ #FUNCION
109
+
110
+ data_folder = default_path + '/../../data/train'
111
+ path_model = default_path + '/../../models/{}'.format(name)
112
+ if (os.path.isdir(path_model)): print('WARNING, model already exists will be overwritten')
113
+ columns = {0: 'text', 1:'ner'}
114
+ # init a corpus using column format, data folder and the names of the train, dev and test files
115
+
116
+
117
+ try:
118
+ corpus: Corpus = ColumnCorpus(data_folder, columns,
119
+ train_file='train.txt',
120
+ test_file='test.txt' )
121
+ #dev_file='dev.txt')
122
+ except:
123
+ print('Invalid input document in training')
124
+ return 8
125
+
126
+
127
+
128
+
129
+ # 2. what tag do we want to predict?
130
+ tag_type = 'ner'
131
+
132
+ # 3. make the tag dictionary from the corpus
133
+ #tag_dictionary = corpus.make_label_dictionary(label_type=tag_type)
134
+ tag_dictionary = corpus.make_label_dictionary(label_type=tag_type)
135
+
136
+ try:
137
+ embeddings = TransformerWordEmbeddings(
138
+ model='xlm-roberta-large',
139
+ layers="-1",
140
+ subtoken_pooling="first",
141
+ fine_tune=True,
142
+ use_context=True,
143
+ )
144
+ except:
145
+ print('Error while loading embeddings from RoBERTa')
146
+ return 5
147
+
148
+ # 5. initialize bare-bones sequence tagger (no CRF, no RNN, no reprojection)
149
+
150
+ try:
151
+ tagger_train = SequenceTagger(
152
+ hidden_size=256,
153
+ embeddings=embeddings,
154
+ tag_dictionary=tag_dictionary,
155
+ tag_type='ner',
156
+ use_crf=False,
157
+ use_rnn=False,
158
+ reproject_embeddings=False,
159
+ )
160
+ except:
161
+ print('Error making tagger')
162
+ return 6
163
+
164
+ # 6. initialize trainer with AdamW optimizer
165
+
166
+
167
+ trainer = ModelTrainer(tagger_train, corpus)
168
+
169
+ # 7. run training with XLM parameters (20 epochs, small LR)
170
+ try:
171
+ trainer.train(path_model,
172
+ learning_rate=5.0e-6,
173
+ mini_batch_size=1,
174
+ mini_batch_chunk_size=1,
175
+ max_epochs=epochs,
176
+ scheduler=OneCycleLR,
177
+ embeddings_storage_mode='cpu',
178
+ optimizer=torch.optim.AdamW,
179
+ )
180
+ except:
181
+ pass
182
+ print('Error training the model, try setting CUDA False')
183
+ return 7
184
+
185
+ print("Model {} trained and saved in {}".format(name,'models/{}'.format(name)))
186
+
187
+
188
+ def tag_sentence(sentence, name):
189
+
190
+ results={'Sentence_tagged':'', 'Highligth':{}}
191
+ Highligth_dict={"text": "", "entities": []}
192
+
193
+
194
+ #--------------Load the trained model-------------------------
195
+ path_model = default_path + '/../../models/{}'.format(name)
196
+ global tagger_sentence
197
+
198
+ if (not tagger_sentence):
199
+
200
+ try:
201
+ tagger_sentence = SequenceTagger.load(path_model+'/best-model.pt')
202
+ except:
203
+ try:
204
+ tagger_sentence = SequenceTagger.load(path_model+'/final-model.pt')
205
+ except:
206
+ print('Invalid model')
207
+ return 1
208
+
209
+ #------------------Tagged sentence---------------------
210
+ print('-'*20,'Tagging','-'*20)
211
+ sentence_f = Sentence(sentence)
212
+ tagger_sentence.predict(sentence_f)
213
+ sentence_tokenized = []
214
+ Highligth_dict['text'] = sentence_f.to_plain_string()
215
+
216
+ for indx,token in enumerate(sentence_f.tokens):
217
+
218
+ t = token.get_label()
219
+ if t.value == 'O':
220
+ sentence_tokenized += [token.text]
221
+ else:
222
+ sentence_tokenized += [t.shortstring]
223
+ token_info={
224
+ 'entity': t.value ,
225
+ 'index' : indx,
226
+ 'word' : token.text,
227
+ 'start': token.start_position,
228
+ 'end' : token.end_position
229
+
230
+ }
231
+ Highligth_dict["entities"].append(token_info)
232
+ sen_tagged = ' ' .join(sentence_tokenized)
233
+ results['Highligth'] = Highligth_dict
234
+ results['Sentence_tagged'] = sen_tagged
235
+ print('-'*20,'Tagged complete','-'*20)
236
+ return results
237
+
238
+
239
+ def use_model(name, path_data, output_dir):
240
+
241
+ #--------------Load the trained model-------------------------
242
+ path_model = default_path + '/../../models/{}'.format(name)
243
+
244
+ if not (os.path.isdir(path_model)):
245
+ print('Model does not exists')
246
+ return 10
247
+
248
+ if not os.path.isfile(path_data):
249
+ print('Input file is not a file')
250
+ return 9
251
+
252
+ global tagger_document
253
+
254
+ if (not tagger_document):
255
+
256
+ try:
257
+ tagger_document = SequenceTagger.load(path_model+'/best-model.pt')
258
+ except:
259
+ try:
260
+ tagger_document = SequenceTagger.load(path_model+'/final-model.pt')
261
+ except:
262
+ print('Invalid model')
263
+ return 1
264
+
265
+ #-----------------Load the document-------------------------
266
+ try:
267
+ data = pd.read_json(path_data, orient ='index', encoding='utf-8')[0]
268
+ except:
269
+ print('Can\'t open the input file')
270
+ return 2
271
+
272
+ if len(data) <= 0:
273
+ print(f"length of document greater than 0 expected, got: {len(data)}")
274
+ return 2
275
+
276
+ try:
277
+ sentences=data['sentences']
278
+ t = sentences[0]['text']
279
+ except:
280
+ print('Invalid JSON format in document {}'.format(path_data))
281
+ return 3
282
+ print('-'*20,'Tagging','-'*20)
283
+
284
+
285
+
286
+ #-----------------Tagged the document-------------------------
287
+ results = {'text':"", 'text_labeled':"",'sentences':[], 'entities': []}
288
+ indx_prev = 0
289
+ pos_prev = 0
290
+ for s in sentences:
291
+ sentence = Sentence(s['text'])
292
+ tagger_document.predict(sentence, mini_batch_size = 1)
293
+ sen_dict_temp = {'text':sentence.to_plain_string(), 'text_labeled':'', 'tokens':[]}
294
+ #return sentence
295
+ sentence_tokenized = []
296
+ for indx,token in enumerate(sentence.tokens):
297
+ token_dict = {'text':token.text, 'label':token.get_label('ner').value}
298
+ sen_dict_temp['tokens'].append(token_dict)
299
+
300
+ t = token.get_label('ner')
301
+ if t.value == 'O':
302
+ sentence_tokenized += [token.text]
303
+ else:
304
+ sentence_tokenized += [t.shortstring]
305
+ token_info={
306
+ 'entity': t.value ,
307
+ 'index' : indx + indx_prev,
308
+ 'word' : token.text,
309
+ 'start': token.start_position + pos_prev,
310
+ 'end' : token.end_position +pos_prev
311
+
312
+ }
313
+ results["entities"].append(token_info)
314
+ indx_prev += len(sentence.tokens)
315
+ pos_prev += len(sentence.to_plain_string())
316
+ sen_tagged = ' ' .join(sentence_tokenized)
317
+ sen_dict_temp['text_labeled'] = sen_tagged
318
+ results['sentences'].append(sen_dict_temp)
319
+ results['text'] += sentence.to_plain_string()
320
+ #return sentence
321
+ results['text_labeled'] += sen_tagged
322
+
323
+ #-----------------Save the results-------------------------
324
+ try:
325
+ with open(output_dir, "w", encoding='utf-8') as write_file:
326
+ json.dump(results, write_file)
327
+
328
+ print('-'*20,'Tagged complete','-'*20)
329
+ print('Document tagged saved in {}'.format(output_dir))
330
+ except:
331
+ print('Error in output file')
332
+ return 11
333
+
334
+ return results
335
+
336
+ def json_to_txt(path_data_documents):
337
+ #-------------List the documents in the path------------
338
+ documents=os.listdir(path_data_documents)
339
+ if len(documents) <= 0:
340
+ print('There are not documents in the folder')
341
+ return 4
342
+
343
+ data_from_documents={'id':[],'document':[],'sentence':[],'word':[],'tag':[]}
344
+
345
+ #--------------Verify each documment-------------
346
+ for num,doc in enumerate(documents):
347
+ data=path_data_documents+'/'+doc
348
+ df = pd.read_json(data, orient ='index')[0]
349
+ try:
350
+ sentences = df['sentences']
351
+ t = sentences[0]['text']
352
+ t = sentences[0]['id']
353
+ t = sentences[0]['tokens']
354
+ j = t[0]['text']
355
+ j = t[0]['begin']
356
+ j = t[0]['end']
357
+ tags = df['mentions']
358
+ if tags:
359
+ tg = tags[0]['id']
360
+ tg = tags[0]['begin']
361
+ tg = tags[0]['end']
362
+ tg = tags[0]['type']
363
+ except:
364
+ print('Invalid JSON input format in document {}'.format(doc))
365
+ return 3
366
+
367
+
368
+ #-----------------Organize the data----------------
369
+ for s in sentences:
370
+ id_senten=s['id']
371
+ for tk in s['tokens']:
372
+ if len(tk['text'])==1:
373
+ #if ord(tk['text'])>=48 and ord(tk['text'])<=57 and ord(tk['text'])>=65 and ord(tk['text'])<=90 and ord(tk['text'])>=97 and ord(tk['text'])<=122:
374
+ tk_beg=tk['begin']
375
+ tk_end=tk['end']
376
+ data_from_documents['id'].append('d'+str(num)+'_'+id_senten)
377
+ data_from_documents['document'].append(doc)
378
+ data_from_documents['word'].append(tk['text'])
379
+ data_from_documents['sentence'].append(s['text'])
380
+ data_from_documents['tag'].append('O')
381
+ for tg in tags:
382
+ if id_senten == tg['id'].split('-')[0] and tk['begin']>=tg['begin'] and tk['begin']<tg['end']:
383
+ data_from_documents['tag'][-1]=tg['type']
384
+ break
385
+
386
+ else:
387
+ tk_beg=tk['begin']
388
+ tk_end=tk['end']
389
+ data_from_documents['id'].append('d'+str(num)+'_'+id_senten)
390
+ data_from_documents['document'].append(doc)
391
+ data_from_documents['word'].append(tk['text'])
392
+ data_from_documents['sentence'].append(s['text'])
393
+ data_from_documents['tag'].append('O')
394
+ for tg in tags:
395
+ if id_senten == tg['id'].split('-')[0] and tk['begin']>=tg['begin'] and tk['begin']<tg['end']:
396
+ data_from_documents['tag'][-1]=tg['type']
397
+ break
398
+
399
+ X=np.array(data_from_documents['word'])
400
+ y=np.array(data_from_documents['tag'])
401
+ groups=np.array(data_from_documents['id'])
402
+
403
+
404
+ #-------------------Save the data in CONLL format--------------
405
+ group_kfold = StratifiedGroupKFold(n_splits=10, shuffle=True, random_state=42)
406
+ group_kfold.get_n_splits(X, y, groups)
407
+ for train_index, test_index in group_kfold.split(X, y, groups):
408
+ X_train, X_test = X[train_index], X[test_index]
409
+ y_train, y_test = y[train_index], y[test_index]
410
+ groups_train, groups_test = groups[train_index], groups[test_index]
411
+ break
412
+
413
+
414
+
415
+
416
+ X_write=[X_train,X_test]
417
+ y_write=[y_train,y_test]
418
+ groups_write=[groups_train, groups_test]
419
+ archivos=['train','test']
420
+
421
+
422
+ for k in range(2):
423
+ X_temp = X_write[k]
424
+ y_temp = y_write[k]
425
+ groups_temp = groups_write[k]
426
+ arch=archivos[k]
427
+ id_in=groups_temp[0]
428
+
429
+
430
+ data_folder = default_path + '/../../data/train'
431
+ check_create(data_folder)
432
+ count = 0
433
+ with open(data_folder + '/{}.txt'.format(arch), mode='w', encoding='utf-8') as f:
434
+ for i in range(len(X_temp)):
435
+ if groups_temp[i] != id_in:
436
+ id_in=groups_temp[i]
437
+ f.write('\n')
438
+ count = 0
439
+
440
+ count += 1
441
+ f.write(X_temp[i]+' '+ y_temp[i])
442
+ f.write('\n')
443
+
444
+ if count >= 150:
445
+ count = 0
446
+ f.write('\n')
447
+
448
+
449
+
450
+ # print("Before check")
451
+ # checkpoint = "xlm-roberta-large"
452
+ # config = AutoConfig.from_pretrained(checkpoint)
453
+
454
+ # with init_empty_weights():
455
+ # model = AutoModelForSequenceClassification.from_config(config)
456
+
457
+ # print("After check")
458
+ # try:
459
+ # tagger = load_checkpoint_and_dispatch(model, path_model+'/best-model.pt', device_map="auto")
460
+ # except:
461
+ # try:
462
+ # tagger = load_checkpoint_and_dispatch(model, path_model+'/final-model.pt', device_map="auto")
463
+ # except:
464
+ # print('Invalid model')
465
+ # return 1
466
+
467
+
src/scripts/functionsrc.py ADDED
@@ -0,0 +1,718 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # -*- coding: utf-8 -*-
2
+ """
3
+ Created on Mon May 1 20:54:14 2023
4
+
5
+ @author: sanmo
6
+ """
7
+ import numpy as np
8
+ import torch
9
+ import torch.nn as nn
10
+ from torch.utils.data import Dataset, DataLoader, random_split
11
+ import pandas as pd
12
+ import json
13
+ import os
14
+ import gc
15
+ from distutils.dir_util import copy_tree
16
+ import shutil
17
+ import argparse
18
+ import flair
19
+ from flair.embeddings import TokenEmbeddings, WordEmbeddings, StackedEmbeddings, TransformerWordEmbeddings
20
+ from torch import nn, tanh, sigmoid, relu, FloatTensor, rand, stack, optim, cuda, softmax, save, device, tensor, int64, no_grad, concat
21
+ from flair.data import Sentence
22
+
23
+
24
+ default_path = 0
25
+ entities = 0
26
+ tagger_document = 0
27
+ embeddings = 0
28
+ json_data = 0
29
+ train_loader = 0
30
+ val_loader = 0
31
+ test_loader = 0
32
+ cnn = 0
33
+ optimizer = 0
34
+ criterion = 0
35
+ device = 0
36
+ test_sentences = 0
37
+ best_valid_loss = np.inf
38
+
39
+ def check_create(path):
40
+ import os
41
+
42
+ if not (os.path.isdir(path)):
43
+ os.makedirs(path)
44
+
45
+ def str2bool(v):
46
+ if isinstance(v, bool):
47
+ return v
48
+ if v.lower() in ('yes', 'True','true', 't', 'y', '1'):
49
+ return True
50
+ elif v.lower() in ('no', 'False', 'false', 'f', 'n', '0'):
51
+ return False
52
+ else:
53
+ raise argparse.ArgumentTypeError('Boolean value expected.')
54
+
55
+
56
+
57
+ class MyDataset(Dataset):
58
+ def __init__(self, len_c1=7, len_c2=5, len_c3=11):
59
+ global json_data
60
+
61
+ def create_vector(c1,sentence):
62
+ #print("Hola mundo")
63
+ if len(c1): c1 = torch.cat([c1,sentence], dim=0)
64
+ else: c1 = sentence
65
+ return c1
66
+
67
+ def fix_tensor(tensor, size):
68
+
69
+
70
+ while tensor.shape[2] < size:
71
+ tensor = torch.cat([tensor,torch.zeros(1,1,1,1024)], dim=2)
72
+
73
+ tensor = tensor[:,:,:size,:]
74
+ return tensor
75
+
76
+ data = []
77
+ self.targets = []
78
+ self.c1=[]
79
+ self.h1=[]
80
+ self.c2=[]
81
+ self.h2=[]
82
+ self.c3=[]
83
+
84
+ tensor_temp = torch.Tensor(json_data['flat_emb'])
85
+ data = tensor_temp.reshape((tensor_temp.shape[0],1,-1,1024))
86
+
87
+ self.targets = create_vector(self.targets,torch.Tensor(json_data['relation']))
88
+ for n_sen in range(tensor_temp.shape[0]):
89
+
90
+
91
+ tensor_temp = data[n_sen,0,:json_data['h_pos'][n_sen][0],:].reshape((1, 1,-1,1024))
92
+ self.c1 = create_vector(self.c1,fix_tensor(tensor_temp, len_c1))
93
+
94
+ tensor_temp = data[n_sen,0,json_data['h_pos'][n_sen][0]:json_data['h_pos'][n_sen][-1]+1,:].mean(dim=0).reshape((1,1024))
95
+ self.h1 = create_vector(self.h1,tensor_temp)
96
+
97
+ tensor_temp = data[n_sen,0,json_data['h_pos'][n_sen][-1]+1:json_data['t_pos'][n_sen][0],:].reshape((1,1,-1,1024))
98
+ self.c2 = create_vector(self.c2,fix_tensor(tensor_temp, len_c2))
99
+
100
+ tensor_temp = data[n_sen,0,json_data['t_pos'][n_sen][0]:json_data['t_pos'][n_sen][-1]+1,:].mean(dim=0).reshape((1,1024))
101
+ self.h2 = create_vector(self.h2,tensor_temp)
102
+
103
+ tensor_temp = data[n_sen,0,json_data['t_pos'][n_sen][-1]+1:,:].reshape((1, 1,-1,1024))
104
+ self.c3 = create_vector(self.c3,fix_tensor(tensor_temp, len_c3))
105
+ del data
106
+ del tensor_temp
107
+ del json_data
108
+ gc.collect()
109
+ self.targets = self.targets.to(torch.int64)
110
+ #print('Dataset class')
111
+
112
+ def __len__(self):
113
+ return len(self.targets)
114
+
115
+ def __getitem__(self, index):
116
+ c1x = self.c1[index]
117
+ h1x = self.h1[index]
118
+ c2x = self.c2[index]
119
+ h2x = self.h2[index]
120
+ c3x = self.c3[index]
121
+ y = self.targets[index]
122
+ return c1x,h1x,c2x,h2x,c3x, y
123
+
124
+
125
+ def update_step(c1, h1,c2,h2,c3, label):
126
+ global cnn
127
+ global optimizer
128
+ global criterion
129
+ prediction = cnn(c1, h1,c2,h2,c3)
130
+ optimizer.zero_grad()
131
+ loss = criterion(prediction, label)
132
+ loss.backward()
133
+ optimizer.step()
134
+ acc = (nn.Softmax(dim=1)(prediction).detach().argmax(dim=1) == label).type(torch.float).sum().item()
135
+ #print(acc)
136
+ return loss.item(), acc
137
+
138
+ def evaluate_step(c1, h1,c2,h2,c3, label):
139
+ global cnn
140
+ global optimizer
141
+ global criterion
142
+ prediction = cnn(c1, h1,c2,h2,c3)
143
+ loss = criterion(prediction, label)
144
+ acc = (nn.Softmax(dim=1)(prediction).detach().argmax(dim=1) == label).type(torch.float).sum().item()
145
+ return loss.item(), acc
146
+
147
+ def train_one_epoch(epoch, name, rel2id_file):
148
+ global train_loader
149
+ global val_loader
150
+ global device
151
+ global best_valid_loss
152
+ global optimizer
153
+ global cnn
154
+ global default_path
155
+ if (device == torch.device('cuda:0')): cnn.cuda()
156
+
157
+ train_loss, valid_loss, acc_train, acc_valid = 0.0, 0.0, 0.0, 0.0
158
+ for batch_idx, (c1, h1,c2,h2,c3, targets) in enumerate(train_loader):
159
+ train_loss_temp, acc_train_temp = update_step(c1.to(device), h1.to(device),c2.to(device),h2.to(device),c3.to(device), targets.to(device))
160
+ train_loss += train_loss_temp
161
+ acc_train += acc_train_temp
162
+ for batch_idx, (c1, h1,c2,h2,c3, targets) in enumerate(val_loader):
163
+ valid_loss_temp, acc_valid_temp = evaluate_step(c1.to(device), h1.to(device),c2.to(device),h2.to(device),c3.to(device), targets.to(device))
164
+ valid_loss += valid_loss_temp
165
+ acc_valid += acc_valid_temp
166
+ # Guardar modelo si es el mejor hasta ahora
167
+
168
+ if epoch % 10 == 0:
169
+ # path_save = os.path.normpath(default_path +'/../../models/RC/{}/best_model.pt'.format(name))
170
+ # path_save = path_save.replace('\\', '/')
171
+ # print(os.path.abspath(__file__))
172
+ if valid_loss < best_valid_loss:
173
+ best_valid_loss = valid_loss
174
+ torch.save({'epoca': epoch,
175
+ 'model_state_dict': cnn.state_dict(),
176
+ 'optimizer_state_dict': optimizer.state_dict(),
177
+ 'loss': valid_loss},
178
+ '../../models/RC/{}/best_model.pt'.format(name))
179
+
180
+ #path_files = default_path + '/../../data/RC/'
181
+ a=0
182
+ #rel2id_file = path_files + 'rel2id.json'
183
+
184
+ return train_loss/len(train_loader.dataset), valid_loss/len(val_loader.dataset), acc_train/len(train_loader.dataset), acc_valid/len(val_loader.dataset)
185
+
186
+
187
+ def FocalLoss(input, target, gamma=0, alpha=None, size_average=True):
188
+ from torch.autograd import Variable
189
+ if input.dim()>2:
190
+ input = input.view(input.size(0),input.size(1),-1) # N,C,H,W => N,C,H*W
191
+ input = input.transpose(1,2) # N,C,H*W => N,H*W,C
192
+ input = input.contiguous().view(-1,input.size(2)) # N,H*W,C => N*H*W,C
193
+ target = target.view(-1,1)
194
+
195
+ logpt = nn.functional.log_softmax(input)
196
+ logpt = logpt.gather(1,target)
197
+ logpt = logpt.view(-1)
198
+ pt = Variable(logpt.data.exp())
199
+
200
+ if alpha is not None:
201
+ if alpha.type()!=input.data.type():
202
+ alpha = alpha.type_as(input.data)
203
+ at = alpha.gather(0,target.data.view(-1))
204
+ logpt = logpt * Variable(at)
205
+
206
+ loss = -1 * (1-pt)**gamma * logpt
207
+ if size_average: return loss.mean()
208
+ else: return loss.sum()
209
+
210
+
211
+ class EarlyStopping:
212
+ def __init__(self, patience=5, min_delta=0):
213
+
214
+ self.patience = patience
215
+ self.min_delta = min_delta
216
+ self.counter = 0
217
+ self.min_validation_loss = np.inf
218
+ self.early_stop = False
219
+
220
+ def __call__(self, validation_loss):
221
+ if validation_loss < self.min_validation_loss:
222
+ self.min_validation_loss = validation_loss
223
+ self.counter = 0
224
+ self.early_stop = False
225
+
226
+ elif validation_loss > (self.min_validation_loss + self.min_delta):
227
+ print('Less')
228
+ self.counter += 1
229
+ if self.counter >= self.patience:
230
+ self.early_stop = True
231
+
232
+
233
+
234
+
235
+
236
+ def SoftmaxModified(x):
237
+ input_softmax = x.transpose(0,1)
238
+ function_activation = nn.Softmax(dim=1)
239
+ output = function_activation(input_softmax)
240
+ output = output.transpose(0,1)
241
+ return output
242
+
243
+
244
+ class MultiModalGMUAdapted(nn.Module):
245
+
246
+ def __init__(self, input_size_array, hidden_size, dropoutProbability):
247
+ """Initialize params."""
248
+ super(MultiModalGMUAdapted, self).__init__()
249
+ self.input_size_array = input_size_array
250
+ self.hidden_size = hidden_size
251
+ self.dropout = nn.Dropout(dropoutProbability)
252
+
253
+ self.h_1_layer = nn.Linear(input_size_array[0], hidden_size, bias=False)
254
+ self.h_2_layer = nn.Linear(input_size_array[1], hidden_size, bias=False)
255
+ self.h_3_layer = nn.Linear(input_size_array[2], hidden_size, bias=False)
256
+ self.h_4_layer = nn.Linear(input_size_array[3], hidden_size, bias=False)
257
+ self.h_5_layer = nn.Linear(input_size_array[4], hidden_size, bias=False)
258
+
259
+ self.z_1_layer = nn.Linear(input_size_array[0], hidden_size, bias=False)
260
+ self.z_2_layer = nn.Linear(input_size_array[1], hidden_size, bias=False)
261
+ self.z_3_layer = nn.Linear(input_size_array[2], hidden_size, bias=False)
262
+ self.z_4_layer = nn.Linear(input_size_array[3], hidden_size, bias=False)
263
+ self.z_5_layer = nn.Linear(input_size_array[4], hidden_size, bias=False)
264
+
265
+
266
+ #self.z_weights = [nn.Linear(input_size_array[m], hidden_size, bias=False) for m in range(modalities_number)]
267
+ #self.input_weights = [nn.Linear(size, hidden_size, bias=False) for size in input_size_array]
268
+
269
+
270
+ def forward(self, inputModalities):
271
+ """Propogate input through the network."""
272
+ # h_modalities = [self.dropout(self.input_weights[i](i_mod)) for i,i_mod in enumerate(inputModalities)]
273
+ # h_modalities = [tanh(h) for h in h_modalities]
274
+
275
+ h1 = tanh(self.dropout(self.h_1_layer(inputModalities[0])))
276
+ h2 = tanh(self.dropout(self.h_2_layer(inputModalities[1])))
277
+ h3 = tanh(self.dropout(self.h_3_layer(inputModalities[2])))
278
+ h4 = tanh(self.dropout(self.h_4_layer(inputModalities[3])))
279
+ h5 = tanh(self.dropout(self.h_5_layer(inputModalities[4])))
280
+
281
+ z1 = self.dropout(self.z_1_layer(inputModalities[0]))
282
+ z2 = self.dropout(self.z_2_layer(inputModalities[1]))
283
+ z3 = self.dropout(self.z_3_layer(inputModalities[2]))
284
+ z4 = self.dropout(self.z_4_layer(inputModalities[3]))
285
+ z5 = self.dropout(self.z_5_layer(inputModalities[4]))
286
+
287
+
288
+ #z_modalities = [self.dropout(self.z_weights[i](i_mod)) for i,i_mod in enumerate(inputModalities)]
289
+ z_modalities = stack([z1, z2, z3, z4, z5])
290
+ z_normalized = SoftmaxModified(z_modalities)
291
+ final = z_normalized[0] * h1 + z_normalized[1] * h2 + z_normalized[2] * h3 + z_normalized[3] * h4 + z_normalized[4] * h5
292
+
293
+
294
+ return final
295
+
296
+ class MyCNN(nn.Module):
297
+ def __init__(self, num_classes=10, len_c1=7, len_c2=5, len_c3=11):
298
+ super(MyCNN, self).__init__()
299
+ shape1 = (((len_c1-2)))#-2)#//2)-2)//2)
300
+ shape2 = (((len_c2-2)))#-2)#//2)-2)//2)
301
+ shape3 = (((len_c3-2)))#-2)#//2)-2)//2)
302
+
303
+ # Define convolutional layers
304
+ self.conv_layers1 = nn.Sequential(
305
+ nn.Conv2d(in_channels=1, out_channels=1, kernel_size=(3,1)),
306
+ nn.ReLU(),
307
+ nn.MaxPool2d(kernel_size=(shape1,1)),
308
+ )
309
+
310
+ self.conv_layers2 = nn.Sequential(
311
+ nn.Conv2d(in_channels=1, out_channels=1, kernel_size=(3,1)),
312
+ nn.ReLU(),
313
+ nn.MaxPool2d(kernel_size=(shape2,1)),
314
+ )
315
+
316
+ self.conv_layers3 = nn.Sequential(
317
+ nn.Conv2d(in_channels=1, out_channels=1, kernel_size=(3,1)),
318
+ nn.ReLU(),
319
+ nn.MaxPool2d(kernel_size=(shape3,1)),
320
+ )
321
+
322
+
323
+ self.multi_gmu = MultiModalGMUAdapted([1024,1024,1024,1024,1024], 1024, 0.5)
324
+
325
+
326
+
327
+
328
+
329
+ self.fc_simple_layers_multi = nn.Sequential(
330
+ nn.Linear(1024 , 256),
331
+ nn.ReLU(),
332
+ nn.Dropout(0.5),
333
+ nn.Linear(256, num_classes)
334
+ )
335
+
336
+
337
+ def forward(self, c1, h1,c2,h2,c3):
338
+
339
+ # Pass inputs through convolutional layers
340
+
341
+ c1 = self.conv_layers1(c1)
342
+ c2 = self.conv_layers2(c2)
343
+ c3 = self.conv_layers3(c3)
344
+ #print(c1.shape)
345
+
346
+ h1 = tanh(h1)
347
+ h2 = tanh(h2)
348
+ #print(c1.shape)
349
+ c1 = torch.flatten(c1, start_dim=1)
350
+ c2 = torch.flatten(c2, start_dim=1)
351
+ c3 = torch.flatten(c3, start_dim=1)
352
+ #print(c1.shape)
353
+
354
+
355
+ # Multi GMU
356
+ mgmu_out = self.multi_gmu([c1,h1,c2,h2,c3])
357
+ x = self.fc_simple_layers_multi(mgmu_out)
358
+
359
+
360
+ # Return final output
361
+ return x
362
+
363
+ def define_model():
364
+ global cnn
365
+ global optimizer
366
+ global criterion
367
+
368
+ cnn = MyCNN()
369
+ optimizer = torch.optim.Adam(cnn.parameters(), lr=0.001)
370
+ criterion = lambda pred,tar: FocalLoss(input=pred,target=tar,gamma=0.7)
371
+
372
+ def train_model(name, epocs, rel2id_path):
373
+ max_epochs, best_valid_loss = epocs, np.inf
374
+ running_loss = np.zeros(shape=(max_epochs, 4))
375
+ early_stopping = EarlyStopping(patience=10, min_delta=0.01)
376
+
377
+ for epoch in range(max_epochs):
378
+ running_loss[epoch] = train_one_epoch(epoch, name, rel2id_path)
379
+ early_stopping(running_loss[epoch, 1])
380
+ print(f"Epoch {epoch} \t Train_loss = {running_loss[epoch, 0]:.4f} \t Valid_loss = {running_loss[epoch, 1]:.4f} \n\t\t\t Train_acc = {running_loss[epoch, 2]:.4f} \t Valid_acc = {running_loss[epoch, 3]:.4f}")
381
+ if early_stopping.early_stop:
382
+ print("We are at epoch:", epoch)
383
+ break
384
+
385
+
386
+ def usage_cuda_rc(cuda):
387
+ global device
388
+ if cuda:
389
+ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
390
+ flair.device = device
391
+ if flair.device == torch.device('cpu'): return 'Error handling GPU, CPU will be used'
392
+ elif flair.device == torch.device('cuda:0'): return 'GPU detected, GPU will be used'
393
+ else:
394
+ device = torch.device('cpu')
395
+ flair.device = device
396
+ return 'CPU will be used'
397
+
398
+
399
+ def create_embbedings():
400
+ global embeddings
401
+ if (not embeddings):
402
+ embeddings = TransformerWordEmbeddings(
403
+ model='xlm-roberta-large',
404
+ layers="-1",
405
+ subtoken_pooling="first",
406
+ fine_tune=True,
407
+ use_context=True,
408
+ )
409
+
410
+
411
+
412
+
413
+ def prepare_data(rel2id_path, path_data):
414
+ create_embbedings()
415
+ global embeddings
416
+ global json_data
417
+ #Embbeb data
418
+
419
+ global default_path
420
+
421
+
422
+ #path_files
423
+
424
+
425
+ #rel2id_file = path_files + 'rel2id.json'
426
+ #shutil.copy(rel2id_path, rel2id_file)
427
+ with open(rel2id_path, mode='r') as f:
428
+ rel2id = json.load(f)
429
+
430
+ #path_data = path_files+"train.txt"
431
+
432
+ #Json to save the data
433
+ json_data = {'flat_emb':[], 'relation':[], 'h_pos':[], 't_pos':[]}
434
+ PADDING = np.zeros(1024)
435
+ doc=0
436
+ with open(path_data, mode='r', encoding='utf-8') as f:
437
+ sentence_temp = []
438
+ h_pos = []
439
+ t_pos = []
440
+ current_ent=''
441
+ cont=0
442
+
443
+ for n,line in enumerate(f.readlines()):
444
+ if line != '\n':
445
+ sentence_temp.append(line.split('\t')[0])
446
+
447
+ if line.split('\t')[1] != 'O':
448
+ if current_ent == '':
449
+ h_pos.append(cont)
450
+ current_ent = line.split('\t')[1]
451
+
452
+ elif line.split('\t')[1] == current_ent:
453
+ h_pos.append(cont)
454
+
455
+ else:
456
+ t_pos.append(cont)
457
+
458
+ if line.split('\t')[2].replace('\n','') != '-' : relation = line.split('\t')[2].replace('\n','')
459
+
460
+ cont += 1
461
+
462
+ else:
463
+
464
+ #Embbedding sentence
465
+ sentence = Sentence(sentence_temp)
466
+ embeddings.embed(sentence)
467
+
468
+
469
+
470
+ sentence_emb_flatten = []
471
+ for tk in sentence:
472
+ #flatten_embeddings
473
+ if len(sentence_emb_flatten): sentence_emb_flatten = np.hstack((sentence_emb_flatten,
474
+ tk.embedding.detach().to('cpu').numpy()))
475
+ else: sentence_emb_flatten = tk.embedding.detach().to('cpu').numpy()
476
+
477
+ number_padding = 100 - len(sentence)
478
+
479
+ if number_padding > 0:
480
+ for pd in range(number_padding):
481
+ sentence_emb_flatten = np.hstack((sentence_emb_flatten,
482
+ PADDING))
483
+
484
+ #Save embeddings information
485
+ json_data['flat_emb'].append(list(sentence_emb_flatten))
486
+ json_data['h_pos'].append(h_pos)
487
+ json_data['t_pos'].append(t_pos)
488
+ json_data['relation'].append(rel2id[relation])
489
+
490
+ sentence_temp = []
491
+ h_pos = []
492
+ t_pos = []
493
+ current_ent=''
494
+ cont=0
495
+ dataset = MyDataset()
496
+
497
+ train_set_size = int(len(dataset) * 0.9)
498
+ valid_set_size = len(dataset) - train_set_size
499
+
500
+ train_dataset, val_dataset = random_split(dataset, [train_set_size, valid_set_size ])
501
+ del dataset
502
+ global train_loader
503
+ global val_loader
504
+
505
+ train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
506
+ val_loader = DataLoader(val_dataset, batch_size=64, shuffle=True)
507
+
508
+ def prepare_data_test(name, path_data):
509
+ create_embbedings()
510
+ global embeddings
511
+ global json_data
512
+ global test_sentences
513
+ global entities
514
+ #Embbeb data
515
+
516
+ global default_path
517
+
518
+
519
+ test_sentences = []
520
+ entities = []
521
+ #path_files = default_path + '/../../data/RC/'
522
+ # path_model = default_path + '/../../models/RC/{}/'.format(name)
523
+
524
+
525
+
526
+ #path_data = path_files+"test.txt"
527
+
528
+ #Json to save the data
529
+ json_data = {'flat_emb':[], 'relation':[], 'h_pos':[], 't_pos':[]}
530
+ PADDING = np.zeros(1024)
531
+ doc=0
532
+
533
+ with open(path_data, mode='r', encoding='utf-8') as f:
534
+
535
+ sentence_temp = []
536
+ entities_temp = []
537
+ h_pos = []
538
+ t_pos = []
539
+ current_ent=''
540
+ cont=0
541
+ for n,line in enumerate(f.readlines()):
542
+ #print(line)
543
+ if line != '\n':
544
+ sentence_temp.append(line.split('\t')[0])
545
+ entities_temp.append(line.split('\t')[1])
546
+ if line.split('\t')[1] != 'O':
547
+ if current_ent == '':
548
+ h_pos.append(cont)
549
+ current_ent = line.split('\t')[1]
550
+
551
+ elif line.split('\t')[1] == current_ent:
552
+ h_pos.append(cont)
553
+
554
+ else:
555
+ t_pos.append(cont)
556
+
557
+ # if line.split('\t')[2].replace('\n','') != '-' : relation = '-'
558
+
559
+ cont += 1
560
+
561
+ else:
562
+
563
+ #Embbedding sentence
564
+ sentence = Sentence(sentence_temp)
565
+
566
+ test_sentences.append(sentence_temp)
567
+ entities.append(entities_temp)
568
+ #print('mid while')
569
+ embeddings.embed(sentence)
570
+
571
+
572
+ sentence_emb_flatten = []
573
+ for tk in sentence:
574
+ #flatten_embeddings
575
+ if len(sentence_emb_flatten): sentence_emb_flatten = np.hstack((sentence_emb_flatten,
576
+ tk.embedding.detach().to('cpu').numpy()))
577
+ else: sentence_emb_flatten = tk.embedding.detach().to('cpu').numpy()
578
+
579
+ number_padding = 100 - len(sentence)
580
+
581
+ if number_padding > 0:
582
+ for pd in range(number_padding):
583
+ sentence_emb_flatten = np.hstack((sentence_emb_flatten,
584
+ PADDING))
585
+
586
+ #Save embeddings information
587
+ json_data['flat_emb'].append(list(sentence_emb_flatten))
588
+ json_data['h_pos'].append(h_pos)
589
+ json_data['t_pos'].append(t_pos)
590
+ json_data['relation'].append(1)
591
+
592
+ sentence_temp = []
593
+ entities_temp = []
594
+ h_pos = []
595
+ t_pos = []
596
+ current_ent=''
597
+ cont=0
598
+
599
+
600
+ dataset = MyDataset()
601
+ global test_loader
602
+ test_loader = DataLoader(dataset, batch_size=64, shuffle=False)
603
+
604
+
605
+ del dataset
606
+
607
+
608
+
609
+
610
+ #------------------Backend functions----------------------------------------
611
+
612
+ def training_model_rc(name, path_data, rel2id_path, epochs=200):
613
+ global default_path
614
+ default_path = os.path.dirname(os.path.abspath(__file__))
615
+ default_path = default_path.replace('\\', '/')
616
+ print(name)
617
+ #FUNCION
618
+
619
+ try:
620
+ define_model()
621
+ except:
622
+ return 13
623
+ print('Model defined')
624
+ check_create(default_path + '/../../models/RC/{}/'.format(name))
625
+
626
+ try:
627
+ prepare_data(rel2id_path, path_data)
628
+ except:
629
+ return 12
630
+
631
+ print('Data prepared')
632
+ #Train the model
633
+ try:
634
+ train_model(name, epochs, rel2id_path)
635
+ except:
636
+ return 7
637
+ #save the model in
638
+ path_model = default_path + '/../../models/RC/{}/best_model.pt'.format(name)
639
+
640
+ shutil.copy(rel2id_path, default_path + '/../../models/RC/{}/rel2id.json'.format(name))
641
+
642
+ return "model trined and saved at {}".format(path_model)
643
+
644
+
645
+ def use_model_rc(name, path_data, output_dir):
646
+ global default_path
647
+ default_path = os.path.dirname(os.path.abspath(__file__))
648
+ default_path = default_path.replace('\\', '/')
649
+ #--------------Load the trained model-------------------------
650
+ path_model = default_path + '/../../models/RC/{}/best_model.pt'.format(name)
651
+
652
+
653
+ rel2id_file = default_path + '/../../models/RC/{}/rel2id.json'.format(name)
654
+ with open(rel2id_file, mode='r') as f:
655
+ rel2id = json.load(f)
656
+ id2rel = [m for _,m in sorted(zip(list(rel2id.values()),list(rel2id.keys())), key=lambda pair: pair[0])]
657
+
658
+ if not (os.path.isfile(path_model)):
659
+ print('Model does not exists')
660
+ return 10
661
+ print(path_data)
662
+ if not os.path.isfile(path_data):
663
+ print('Input file is not a file')
664
+ return 9
665
+
666
+ global cnn
667
+ try:
668
+ cnn = MyCNN()
669
+ except:
670
+ return 13
671
+
672
+ print('Model defined')
673
+
674
+ try:
675
+ saved_model = torch.load(path_model)
676
+ cnn.load_state_dict(saved_model['model_state_dict'])
677
+ except:
678
+ return 1
679
+ print('Model loaded')
680
+
681
+ #-----------------Load the document-------------------------
682
+ try:
683
+ prepare_data_test(name, path_data)
684
+ except:
685
+ return 12
686
+
687
+ global json_data
688
+ print('Data prepared')
689
+ #-----------------Predict-------------------------
690
+
691
+ global test_loader
692
+ ypred = []
693
+ relations = []
694
+ for batch_idx, (c1, h1,c2,h2,c3, targets) in enumerate(test_loader):
695
+ x = cnn(c1, h1,c2,h2,c3)
696
+ ypred.append(nn.Softmax(dim=1)(x).detach().argmax(dim=1))
697
+ ypred = np.concatenate(ypred)
698
+
699
+ relations = [id2rel[rel] for rel in ypred]
700
+ print('prediction')
701
+ #-----------------Tagged the document-------------------------
702
+ global test_sentences
703
+ global entities
704
+ results = {'sentences':{'tokens':test_sentences, 'entities':entities}, 'relations': relations}
705
+
706
+
707
+ #-----------------Save the results-------------------------
708
+ try:
709
+ with open(output_dir, "w", encoding='utf-8') as write_file:
710
+ json.dump(results, write_file)
711
+
712
+ print('-'*20,'Tagged complete','-'*20)
713
+ print('Document tagged saved in {}'.format(output_dir))
714
+ except:
715
+ print('Error in output file')
716
+ return 11
717
+
718
+ return results
src/scripts/model_rc.py ADDED
@@ -0,0 +1,466 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # -*- coding: utf-8 -*-
2
+ """
3
+ Created on Thu May 4 11:19:59 2023
4
+
5
+ @author: gita
6
+ """
7
+
8
+ import numpy as np
9
+ import torch
10
+ import torch.nn as nn
11
+ from torch.utils.data import Dataset, DataLoader, random_split
12
+ import pandas as pd
13
+ import json
14
+ import os
15
+ import gc
16
+ from distutils.dir_util import copy_tree
17
+ import argparse
18
+ import flair
19
+ from flair.embeddings import TokenEmbeddings, WordEmbeddings, StackedEmbeddings, TransformerWordEmbeddings
20
+ from torch import nn, tanh, sigmoid, relu, FloatTensor, rand, stack, optim, cuda, softmax, save, device, tensor, int64, no_grad, concat
21
+ from flair.data import Sentence
22
+
23
+ default_path = os.path.dirname(os.path.abspath(__file__))
24
+ tagger_document = 0
25
+ embeddings = 0
26
+ json_data = 0
27
+ train_loader = 0
28
+ val_loader = 0
29
+ cnn = 0
30
+ optimizer = 0
31
+ criterion = 0
32
+ device = 0
33
+
34
+
35
+ class MyDataset(Dataset):
36
+ def __init__(self, len_c1=7, len_c2=5, len_c3=11):
37
+ global json_data
38
+
39
+ def create_vector(c1,sentence):
40
+ #print("Hola mundo")
41
+ if len(c1): c1 = torch.cat([c1,sentence], dim=0)
42
+ else: c1 = sentence
43
+ return c1
44
+
45
+ def fix_tensor(tensor, size):
46
+
47
+
48
+ while tensor.shape[2] < size:
49
+ tensor = torch.cat([tensor,torch.zeros(1,1,1,1024)], dim=2)
50
+
51
+ tensor = tensor[:,:,:size,:]
52
+ return tensor
53
+
54
+ tensor_temp = torch.Tensor(json_data['flat_emb'])
55
+
56
+ data = tensor_temp.reshape((tensor_temp.shape[0],1,-1,1024))
57
+
58
+
59
+ self.targets = create_vector(self.targets,torch.Tensor(json_data['relation']))
60
+
61
+
62
+
63
+ for n_sen in range(tensor_temp.shape[0]):
64
+
65
+
66
+ tensor_temp = data[n_sen,0,:json_data['h_pos'][n_sen][0],:].reshape((1, 1,-1,1024))
67
+ self.c1 = create_vector(self.c1,fix_tensor(tensor_temp, len_c1))
68
+
69
+ tensor_temp = data[n_sen,0,json_data['h_pos'][n_sen][0]:json_data['h_pos'][n_sen][-1]+1,:].mean(dim=0).reshape((1,1024))
70
+ self.h1 = create_vector(self.h1,tensor_temp)
71
+
72
+ tensor_temp = data[n_sen,0,json_data['h_pos'][n_sen][-1]+1:json_data['t_pos'][n_sen][0],:].reshape((1,1,-1,1024))
73
+ self.c2 = create_vector(self.c2,fix_tensor(tensor_temp, len_c2))
74
+
75
+ tensor_temp = data[n_sen,0,json_data['t_pos'][n_sen][0]:json_data['t_pos'][n_sen][-1]+1,:].mean(dim=0).reshape((1,1024))
76
+ self.h2 = create_vector(self.h2,tensor_temp)
77
+
78
+ tensor_temp = data[n_sen,0,json_data['t_pos'][n_sen][-1]+1:,:].reshape((1, 1,-1,1024))
79
+ self.c3 = create_vector(self.c3,fix_tensor(tensor_temp, len_c3))
80
+
81
+ del data
82
+ del tensor_temp
83
+ del json_data
84
+ gc.collect()
85
+ self.targets = self.targets.to(torch.int64)
86
+
87
+ def __len__(self):
88
+ return len(self.targets)
89
+
90
+ def __getitem__(self, index):
91
+ c1x = self.c1[index]
92
+ h1x = self.h1[index]
93
+ c2x = self.c2[index]
94
+ h2x = self.h2[index]
95
+ c3x = self.c3[index]
96
+ y = self.targets[index]
97
+ return c1x,h1x,c2x,h2x,c3x, y
98
+
99
+
100
+ def update_step(c1, h1,c2,h2,c3, label):
101
+ global cnn
102
+ global optimizer
103
+ global criterion
104
+ prediction = cnn(c1, h1,c2,h2,c3)
105
+ optimizer.zero_grad()
106
+ loss = criterion(prediction, label)
107
+ loss.backward()
108
+ optimizer.step()
109
+ acc = (nn.Softmax(dim=1)(prediction).detach().argmax(dim=1) == label).type(torch.float).sum().item()
110
+ #print(acc)
111
+ return loss.item(), acc
112
+
113
+ def evaluate_step(c1, h1,c2,h2,c3, label):
114
+ global cnn
115
+ global optimizer
116
+ global criterion
117
+ prediction = cnn(c1, h1,c2,h2,c3)
118
+ loss = criterion(prediction, label)
119
+ acc = (nn.Softmax(dim=1)(prediction).detach().argmax(dim=1) == label).type(torch.float).sum().item()
120
+ return loss.item(), acc
121
+
122
+ def train_one_epoch(epoch):
123
+ global train_loader
124
+ global val_loader
125
+ global device
126
+ if (device == torch.device('cuda:0')): cnn.cuda()
127
+ train_loss, valid_loss, acc_train, acc_valid = 0.0, 0.0, 0.0, 0.0
128
+ for batch_idx, (c1, h1,c2,h2,c3, targets) in enumerate(train_loader):
129
+ train_loss_temp, acc_train_temp = update_step(c1.to(device), h1.to(device),c2.to(device),h2.to(device),c3.to(device), targets.to(device))
130
+ train_loss += train_loss_temp
131
+ acc_train += acc_train_temp
132
+ for batch_idx, (c1, h1,c2,h2,c3, targets) in enumerate(val_loader):
133
+ valid_loss_temp, acc_valid_temp = evaluate_step(c1.to(device), h1.to(device),c2.to(device),h2.to(device),c3.to(device), targets.to(device))
134
+ valid_loss += valid_loss_temp
135
+ acc_valid += acc_valid_temp
136
+ # Guardar modelo si es el mejor hasta ahora
137
+ global best_valid_loss
138
+ if epoch % 10 == 0:
139
+ if valid_loss < best_valid_loss:
140
+ best_valid_loss = valid_loss
141
+ torch.save({'epoca': epoch,
142
+ 'model_state_dict': cnn.state_dict(),
143
+ 'optimizer_state_dict': optimizer.state_dict(),
144
+ 'loss': valid_loss},
145
+ '/../../RC/model/best_model.pt')
146
+
147
+ return train_loss/len(train_loader.dataset), valid_loss/len(val_loader.dataset), acc_train/len(train_loader.dataset), acc_valid/len(val_loader.dataset)
148
+
149
+
150
+ def FocalLoss(input, target, gamma=0, alpha=None, size_average=True):
151
+ from torch.autograd import Variable
152
+ if input.dim()>2:
153
+ input = input.view(input.size(0),input.size(1),-1) # N,C,H,W => N,C,H*W
154
+ input = input.transpose(1,2) # N,C,H*W => N,H*W,C
155
+ input = input.contiguous().view(-1,input.size(2)) # N,H*W,C => N*H*W,C
156
+ target = target.view(-1,1)
157
+
158
+ logpt = nn.functional.log_softmax(input)
159
+ logpt = logpt.gather(1,target)
160
+ logpt = logpt.view(-1)
161
+ pt = Variable(logpt.data.exp())
162
+
163
+ if alpha is not None:
164
+ if alpha.type()!=input.data.type():
165
+ alpha = alpha.type_as(input.data)
166
+ at = alpha.gather(0,target.data.view(-1))
167
+ logpt = logpt * Variable(at)
168
+
169
+ loss = -1 * (1-pt)**gamma * logpt
170
+ if size_average: return loss.mean()
171
+ else: return loss.sum()
172
+
173
+
174
+ class EarlyStopping:
175
+ def __init__(self, patience=5, min_delta=0):
176
+
177
+ self.patience = patience
178
+ self.min_delta = min_delta
179
+ self.counter = 0
180
+ self.min_validation_loss = np.inf
181
+ self.early_stop = False
182
+
183
+ def __call__(self, validation_loss):
184
+ if validation_loss < self.min_validation_loss:
185
+ self.min_validation_loss = validation_loss
186
+ self.counter = 0
187
+ self.early_stop = False
188
+
189
+ elif validation_loss > (self.min_validation_loss + self.min_delta):
190
+ print('Less')
191
+ self.counter += 1
192
+ if self.counter >= self.patience:
193
+ self.early_stop = True
194
+
195
+
196
+
197
+
198
+
199
+ def SoftmaxModified(x):
200
+ input_softmax = x.transpose(0,1)
201
+ function_activation = nn.Softmax(dim=1)
202
+ output = function_activation(input_softmax)
203
+ output = output.transpose(0,1)
204
+ return output
205
+
206
+
207
+ class MultiModalGMUAdapted(nn.Module):
208
+
209
+ def __init__(self, input_size_array, hidden_size, dropoutProbability):
210
+ """Initialize params."""
211
+ super(MultiModalGMUAdapted, self).__init__()
212
+ self.input_size_array = input_size_array
213
+ self.hidden_size = hidden_size
214
+ self.dropout = nn.Dropout(dropoutProbability)
215
+
216
+ self.h_1_layer = nn.Linear(input_size_array[0], hidden_size, bias=False)
217
+ self.h_2_layer = nn.Linear(input_size_array[1], hidden_size, bias=False)
218
+ self.h_3_layer = nn.Linear(input_size_array[2], hidden_size, bias=False)
219
+ self.h_4_layer = nn.Linear(input_size_array[3], hidden_size, bias=False)
220
+ self.h_5_layer = nn.Linear(input_size_array[4], hidden_size, bias=False)
221
+
222
+ self.z_1_layer = nn.Linear(input_size_array[0], hidden_size, bias=False)
223
+ self.z_2_layer = nn.Linear(input_size_array[1], hidden_size, bias=False)
224
+ self.z_3_layer = nn.Linear(input_size_array[2], hidden_size, bias=False)
225
+ self.z_4_layer = nn.Linear(input_size_array[3], hidden_size, bias=False)
226
+ self.z_5_layer = nn.Linear(input_size_array[4], hidden_size, bias=False)
227
+
228
+
229
+ #self.z_weights = [nn.Linear(input_size_array[m], hidden_size, bias=False) for m in range(modalities_number)]
230
+ #self.input_weights = [nn.Linear(size, hidden_size, bias=False) for size in input_size_array]
231
+
232
+
233
+ def forward(self, inputModalities):
234
+ """Propogate input through the network."""
235
+ # h_modalities = [self.dropout(self.input_weights[i](i_mod)) for i,i_mod in enumerate(inputModalities)]
236
+ # h_modalities = [tanh(h) for h in h_modalities]
237
+
238
+ h1 = tanh(self.dropout(self.h_1_layer(inputModalities[0])))
239
+ h2 = tanh(self.dropout(self.h_2_layer(inputModalities[1])))
240
+ h3 = tanh(self.dropout(self.h_3_layer(inputModalities[2])))
241
+ h4 = tanh(self.dropout(self.h_4_layer(inputModalities[3])))
242
+ h5 = tanh(self.dropout(self.h_5_layer(inputModalities[4])))
243
+
244
+ z1 = self.dropout(self.z_1_layer(inputModalities[0]))
245
+ z2 = self.dropout(self.z_2_layer(inputModalities[1]))
246
+ z3 = self.dropout(self.z_3_layer(inputModalities[2]))
247
+ z4 = self.dropout(self.z_4_layer(inputModalities[3]))
248
+ z5 = self.dropout(self.z_5_layer(inputModalities[4]))
249
+
250
+
251
+ #z_modalities = [self.dropout(self.z_weights[i](i_mod)) for i,i_mod in enumerate(inputModalities)]
252
+ z_modalities = stack([z1, z2, z3, z4, z5])
253
+ z_normalized = SoftmaxModified(z_modalities)
254
+ final = z_normalized[0] * h1 + z_normalized[1] * h2 + z_normalized[2] * h3 + z_normalized[3] * h4 + z_normalized[4] * h5
255
+
256
+
257
+ return final
258
+
259
+ class MyCNN(nn.Module):
260
+ def __init__(self, num_classes=10, len_c1=7, len_c2=5, len_c3=11):
261
+ super(MyCNN, self).__init__()
262
+ shape1 = (((len_c1-2)))#-2)#//2)-2)//2)
263
+ shape2 = (((len_c2-2)))#-2)#//2)-2)//2)
264
+ shape3 = (((len_c3-2)))#-2)#//2)-2)//2)
265
+
266
+ # Define convolutional layers
267
+ self.conv_layers1 = nn.Sequential(
268
+ nn.Conv2d(in_channels=1, out_channels=1, kernel_size=(3,1)),
269
+ nn.ReLU(),
270
+ nn.MaxPool2d(kernel_size=(shape1,1)),
271
+ )
272
+
273
+ self.conv_layers2 = nn.Sequential(
274
+ nn.Conv2d(in_channels=1, out_channels=1, kernel_size=(3,1)),
275
+ nn.ReLU(),
276
+ nn.MaxPool2d(kernel_size=(shape2,1)),
277
+ )
278
+
279
+ self.conv_layers3 = nn.Sequential(
280
+ nn.Conv2d(in_channels=1, out_channels=1, kernel_size=(3,1)),
281
+ nn.ReLU(),
282
+ nn.MaxPool2d(kernel_size=(shape3,1)),
283
+ )
284
+
285
+
286
+ self.multi_gmu = MultiModalGMUAdapted([1024,1024,1024,1024,1024], 1024, 0.5)
287
+
288
+
289
+
290
+
291
+
292
+
293
+ self.fc_simple_layers_multi = nn.Sequential(
294
+ nn.Linear(1024 , 256),
295
+ nn.ReLU(),
296
+ nn.Dropout(0.5),
297
+ nn.Linear(256, num_classes)
298
+ )
299
+
300
+
301
+ def forward(self, c1, h1,c2,h2,c3):
302
+
303
+ # Pass inputs through convolutional layers
304
+
305
+ c1 = self.conv_layers1(c1)
306
+ c2 = self.conv_layers2(c2)
307
+ c3 = self.conv_layers3(c3)
308
+ #print(c1.shape)
309
+
310
+ h1 = tanh(h1)
311
+ h2 = tanh(h2)
312
+ #print(c1.shape)
313
+ c1 = torch.flatten(c1, start_dim=1)
314
+ c2 = torch.flatten(c2, start_dim=1)
315
+ c3 = torch.flatten(c3, start_dim=1)
316
+ #print(c1.shape)
317
+
318
+ mgmu_out, mgmu_weigths = self.multi_gmu([c1,h1,c2,h2,c3])
319
+
320
+
321
+ # Multi GMU
322
+ x = self.fc_simple_layers_multi(mgmu_out)
323
+
324
+ # Return final output
325
+ return x
326
+
327
+ def define_model():
328
+ global cnn
329
+ global optimizer
330
+ global criterion
331
+
332
+ cnn = MyCNN()
333
+ optimizer = torch.optim.Adam(cnn.parameters(), lr=0.001)
334
+ criterion = lambda pred,tar: FocalLoss(input=pred,target=tar,gamma=0.7)
335
+
336
+ def train_model():
337
+ max_epochs, best_valid_loss = 200, np.inf
338
+ running_loss = np.zeros(shape=(max_epochs, 4))
339
+ early_stopping = EarlyStopping(patience=10, min_delta=0.01)
340
+
341
+ for epoch in range(max_epochs):
342
+ running_loss[epoch] = train_one_epoch(epoch)
343
+ early_stopping(running_loss[epoch, 1])
344
+ print(f"Epoch {epoch} \t Train_loss = {running_loss[epoch, 0]:.4f} \t Valid_loss = {running_loss[epoch, 1]:.4f} \n\t\t\t Train_acc = {running_loss[epoch, 2]:.4f} \t Valid_acc = {running_loss[epoch, 3]:.4f}")
345
+ if early_stopping.early_stop:
346
+ print("We are at epoch:", epoch)
347
+ break
348
+
349
+
350
+ def usage_cuda_rc(cuda):
351
+ global device
352
+ if cuda:
353
+ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
354
+ flair.device = device
355
+ if flair.device == torch.device('cpu'): return 'Error handling GPU, CPU will be used'
356
+ elif flair.device == torch.device('cuda:0'): return 'GPU detected, GPU will be used'
357
+ else:
358
+ device = torch.device('cpu')
359
+ flair.device = device
360
+ return 'CPU will be used'
361
+
362
+
363
+ def create_embbedings():
364
+ global embeddings
365
+ if (not embeddings):
366
+ embeddings = TransformerWordEmbeddings(
367
+ model='xlm-roberta-large',
368
+ layers="-1",
369
+ subtoken_pooling="first",
370
+ fine_tune=True,
371
+ use_context=True,
372
+ )
373
+
374
+
375
+
376
+
377
+ def prepare_data():
378
+ create_embbedings()
379
+ global embeddings
380
+ global json_data
381
+ #Embbeb data
382
+
383
+ path_files = default_path + '/../../data/RC/'
384
+
385
+ rel2id_file = path_files + 'rel2id.json'
386
+ with open(rel2id_file, mode='r') as f:
387
+ rel2id = json.load(f)
388
+
389
+
390
+ path_data = path_files+"train.txt"
391
+
392
+ #Json to save the data
393
+ json_data = {'flat_emb':[], 'relation':[], 'h_pos':[], 't_pos':[]}
394
+ PADDING = np.zeros(1024)
395
+ doc=0
396
+ with open(path_data, mode='r', encoding='utf-8') as f:
397
+ sentence_temp = []
398
+ h_pos = []
399
+ t_pos = []
400
+ current_ent=''
401
+ cont=0
402
+
403
+ for n,line in enumerate(f.readlines()):
404
+ if line != '\n':
405
+ sentence_temp.append(line.split('\t')[0])
406
+
407
+ if line.split('\t')[1] != 'O':
408
+ if current_ent == '':
409
+ h_pos.append(cont)
410
+ current_ent = line.split('\t')[1]
411
+
412
+ elif line.split('\t')[1] == current_ent:
413
+ h_pos.append(cont)
414
+
415
+ else:
416
+ t_pos.append(cont)
417
+
418
+ if line.split('\t')[2].replace('\n','') != '-' : relation = line.split('\t')[2].replace('\n','')
419
+
420
+ cont += 1
421
+
422
+ else:
423
+
424
+ #Embbedding sentence
425
+ sentence = Sentence(sentence_temp)
426
+ embeddings.embed(sentence)
427
+
428
+
429
+
430
+ sentence_emb_flatten = []
431
+ for tk in sentence:
432
+ #flatten_embeddings
433
+ if len(sentence_emb_flatten): sentence_emb_flatten = np.hstack((sentence_emb_flatten,
434
+ tk.embedding.detach().to('cpu').numpy()))
435
+ else: sentence_emb_flatten = tk.embedding.detach().to('cpu').numpy()
436
+
437
+ number_padding = 100 - len(sentence)
438
+
439
+ if number_padding > 0:
440
+ for pd in range(number_padding):
441
+ sentence_emb_flatten = np.hstack((sentence_emb_flatten,
442
+ PADDING))
443
+
444
+ #Save embeddings information
445
+ json_data['flat_emb'].append(list(sentence_emb_flatten))
446
+ json_data['h_pos'].append(h_pos)
447
+ json_data['t_pos'].append(t_pos)
448
+ json_data['relation'].append(rel2id[relation])
449
+
450
+ sentence_temp = []
451
+ h_pos = []
452
+ t_pos = []
453
+ current_ent=''
454
+ cont=0
455
+ dataset = MyDataset()
456
+
457
+ train_set_size = int(len(dataset) * 0.9)
458
+ valid_set_size = len(dataset) - train_set_size
459
+
460
+ train_dataset, val_dataset = random_split(dataset, [train_set_size, valid_set_size ])
461
+ del dataset
462
+ global train_loader
463
+ global val_loader
464
+
465
+ train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
466
+ val_loader = DataLoader(val_dataset, batch_size=64, shuffle=True)
src/scripts/upsampling.py ADDED
@@ -0,0 +1,517 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # -*- coding: utf-8 -*-
2
+ """
3
+ Created on Tue Oct 11 16:31:58 2022
4
+
5
+ @author: gita
6
+ """
7
+ import random
8
+ import numpy as np
9
+ import copy
10
+
11
+ class upsampling_ner:
12
+
13
+
14
+
15
+ def __init__(self, path_data, entities, pos_labels):
16
+ """
17
+
18
+
19
+ Parameters
20
+ ----------
21
+ path_data : str
22
+ Path of the dataset in format CONLL.
23
+ entities : List
24
+ List of the senten.
25
+ pos_labels : Dict
26
+ Dictionary where the keys are the kind of labels, and the values
27
+ are the position of the labels in one line
28
+
29
+ Returns
30
+ -------
31
+ None.
32
+
33
+ """
34
+ self.__path_data = path_data
35
+ self.__entities = entities
36
+ self.__search_factor = 1000
37
+ self.__pos_labels = pos_labels
38
+ self.__get_data_variables()
39
+
40
+ def __get_data_variables(self):
41
+ """
42
+ Takes the data path and turn the senteces into a matrix of shape
43
+ (Sentences, tokens of each sentence).
44
+ Also executes the __get_total_mentions.
45
+
46
+ Returns
47
+ -------
48
+ None.
49
+
50
+ """
51
+ col = self.__pos_labels['ner']
52
+ self.__dataset = []
53
+ self.__data_labels = []
54
+ data_temp = []
55
+ labels_temp = []
56
+ with open(self.__path_data, mode='r', encoding='utf-8') as f:
57
+ for line in f.readlines():
58
+ if line != '\n':
59
+ data_temp.append(line.split(' ')[0])
60
+ labels_temp.append(line.split(' ')[col][:-1])
61
+ #print('si')
62
+ else:
63
+ self.__dataset.append(data_temp)
64
+ self.__data_labels.append(labels_temp)
65
+ data_temp = []
66
+ labels_temp = []
67
+ self.__get_total_mentions_and_tokens()
68
+
69
+
70
+ def get_mentions(self, sentence, labels):
71
+ """
72
+ Divide sentence to a dictionary of mentions and a dictionary of labels
73
+ of the mentions
74
+
75
+
76
+ Parameters
77
+ ----------
78
+ sentence : List
79
+ List of the tokens of the sentence.
80
+ labels : List
81
+ List of the labels of each token.
82
+
83
+ Returns
84
+ -------
85
+ dict_mentions : Dictionary
86
+ sentece divided by its entities mentions key=number of mention,
87
+ value= set of tokens in the mention.
88
+ dict_label_mentions : Dictionary
89
+ labels corresponding of the mentions in the same order as token
90
+ mentions. key= number of mention, value= label of the mention.
91
+
92
+ """
93
+
94
+ dict_mentions = {}
95
+ dict_label_mentions = {}
96
+ mention = 0
97
+ #print(sentence)
98
+ dict_mentions[mention] = [sentence[0]]
99
+
100
+ dict_label_mentions[mention] = labels[0]
101
+ for i,label in enumerate(labels[1:]):
102
+ if label == labels[i]:
103
+ dict_mentions[mention].append(sentence[i+1])
104
+ else:
105
+ mention += 1
106
+ dict_mentions[mention] = [sentence[i+1]]
107
+ dict_label_mentions[mention] = labels[i+1]
108
+
109
+ return dict_mentions, dict_label_mentions
110
+
111
+
112
+ def __get_total_mentions_and_tokens(self):
113
+ """
114
+ Takes the dataset and divide ach sentence in mentions and it store it
115
+ in __all_mentions
116
+
117
+ Returns
118
+ -------
119
+ None.
120
+
121
+ """
122
+
123
+ self.__all_mentions = {}
124
+ self.__tokens_per_entity = {}
125
+
126
+ for key in self.__entities:
127
+ self.__all_mentions[key] = []
128
+ self.__tokens_per_entity[key] = []
129
+
130
+ for i,sentence in enumerate(self.__dataset):
131
+ if sentence:
132
+ for j,word in enumerate(sentence):
133
+ self.__tokens_per_entity[self.__data_labels[i][j]].append(word)
134
+
135
+ mentions,label_mentions = self.get_mentions(sentence, self.__data_labels[i])
136
+ for n,label in enumerate(label_mentions.values()):
137
+ if mentions[n] not in self.__all_mentions[label]: self.__all_mentions[label].append(mentions[n]);
138
+
139
+
140
+ def get_mentions_dict(self):
141
+ "Return all the mentions in the dataset"
142
+ return self.__all_mentions
143
+
144
+
145
+ def get_dataset(self):
146
+ "Return the dataset"
147
+ return self.__dataset, self.__data_labels
148
+
149
+
150
+ def Label_wise_token_replacement(self, token_mentions, label_mentions, labels, p):
151
+ """
152
+ Do the Label wise token replacement to a sentence divided in mentions
153
+
154
+
155
+ Parameters
156
+ ----------
157
+ token_mentions : Dictionary
158
+ sentece divided by its entities mentions key=number of mention,
159
+ value= set of tokens in the mention.
160
+ label_mentions : Dictionary
161
+ labels corresponding of the mentions in the same order as token
162
+ mentions. key= number of mention, value= label of the mention
163
+ labels : List
164
+ list of entities to be upsampled.
165
+ p : float
166
+ probability upsampled a mention selected.
167
+
168
+ Returns
169
+ -------
170
+ token_mentions : Dictionary
171
+ token mentions but with mention replacement.
172
+
173
+ """
174
+
175
+ p = 1-p
176
+ for i in token_mentions.keys():
177
+ if label_mentions[i] in labels:
178
+ for j,token in enumerate(token_mentions[i]):
179
+ umbral=np.random.uniform(0,1)
180
+ if umbral>=p:
181
+ token_selected = random.choice(self.__tokens_per_entity[label_mentions[i]])
182
+ search = 0
183
+ while token_selected == token and search <= self.__search_factor:
184
+ token_selected = random.choice(self.__tokens_per_entity[label_mentions[i]])
185
+ search += 1
186
+ token_mentions[i][j] = token_selected
187
+
188
+ return token_mentions
189
+
190
+ def synonym_replacement(self, token_mentions, label_mentions, labels, p):
191
+
192
+ """
193
+ Do the synonym_replacement to a sentence divided in mentions
194
+
195
+
196
+ Parameters
197
+ ----------
198
+ token_mentions : Dictionary
199
+ sentece divided by its entities mentions key=number of mention,
200
+ value= set of tokens in the mention.
201
+ label_mentions : Dictionary
202
+ labels corresponding of the mentions in the same order as token
203
+ mentions. key= number of mention, value= label of the mention
204
+ labels : List
205
+ list of entities to be upsampled.
206
+ p : float
207
+ probability upsampled a mention selected.
208
+
209
+ Returns
210
+ -------
211
+ token_mentions : Dictionary
212
+ token mentions but with shuffled.
213
+
214
+ """
215
+
216
+ import requests
217
+ from bs4 import BeautifulSoup
218
+ url='http://www.wordreference.com/sinonimos/'
219
+
220
+ p = 1-p
221
+
222
+ for i in token_mentions.keys():
223
+ if label_mentions[i] in labels:
224
+ for j,token in enumerate(token_mentions[i]):
225
+ umbral=np.random.uniform(0,1)
226
+ if umbral>=p:
227
+
228
+ buscar=url+token
229
+ resp=requests.get(buscar)
230
+ bs=BeautifulSoup(resp.text,'lxml')
231
+ try:
232
+ lista=bs.find(class_='trans clickable')
233
+ sino=lista.find('li')
234
+ list_synonyms = sino.next_element.split(', ')
235
+ except:
236
+ list_synonyms = False
237
+ if list_synonyms:
238
+ synonym_selected = random.choice(list_synonyms)
239
+ search = 0
240
+ while synonym_selected == token_mentions[i][j] and search <= self.__search_factor:
241
+ synonym_selected = random.choice(list_synonyms)
242
+ search += 1
243
+ token_mentions[i][j] = synonym_selected
244
+
245
+ return token_mentions
246
+
247
+
248
+
249
+ def mention_replacement(self, token_mentions, label_mentions, labels, p):
250
+ """
251
+ Do the mentions replacement to a sentence divided in mentions
252
+
253
+
254
+ Parameters
255
+ ----------
256
+ token_mentions : Dictionary
257
+ sentece divided by its entities mentions key=number of mention,
258
+ value= set of tokens in the mention.
259
+ label_mentions : Dictionary
260
+ labels corresponding of the mentions in the same order as token
261
+ mentions. key= number of mention, value= label of the mention
262
+ labels : List
263
+ list of entities to be upsampled.
264
+ p : float
265
+ probability upsampled a mention selected.
266
+
267
+ Returns
268
+ -------
269
+ token_mentions : Dictionary
270
+ token mentions but with mention replacement.
271
+
272
+ """
273
+
274
+ p = 1-p
275
+ for i in token_mentions.keys():
276
+ if label_mentions[i] in labels:
277
+ umbral=np.random.uniform(0,1)
278
+ if umbral>=p:
279
+ set_of_mentions = self.__all_mentions[label_mentions[i]]
280
+ mention_selected = random.choice(set_of_mentions)
281
+ search = 0
282
+ while token_mentions[i] == mention_selected and search <= self.__search_factor:
283
+ mention_selected = random.choice(set_of_mentions)
284
+ search += 1
285
+ token_mentions[i] = mention_selected
286
+ return token_mentions
287
+
288
+
289
+
290
+ def shuffle_within_segments(self, token_mentions, label_mentions, labels, p):
291
+ """
292
+ Do the shuffle within segments to a sentence divided in mentions
293
+
294
+
295
+ Parameters
296
+ ----------
297
+ token_mentions : Dictionary
298
+ sentece divided by its entities mentions key=number of mention,
299
+ value= set of tokens in the mention.
300
+ label_mentions : Dictionary
301
+ labels corresponding of the mentions in the same order as token
302
+ mentions. key= number of mention, value= label of the mention
303
+ labels : List
304
+ list of entities to be upsampled.
305
+ p : float
306
+ probability upsampled a mention selected.
307
+
308
+ Returns
309
+ -------
310
+ token_mentions : Dictionary
311
+ token mentions but with shuffled.
312
+
313
+ """
314
+
315
+ p = 1-p
316
+ for i in token_mentions.keys():
317
+ if label_mentions[i] in labels:
318
+ umbral=np.random.uniform(0,1)
319
+ if umbral>=p: random.shuffle(token_mentions[i])
320
+ return token_mentions
321
+
322
+ def mention_back_traslation(self, token_mentions, label_mentions, labels, p):
323
+ """
324
+ Do the back traslation to each mention in a sentence divided in mentions
325
+
326
+
327
+ Parameters
328
+ ----------
329
+ token_mentions : Dictionary
330
+ sentece divided by its entities mentions key=number of mention,
331
+ value= set of tokens in the mention.
332
+ label_mentions : Dictionary
333
+ labels corresponding of the mentions in the same order as token
334
+ mentions. key= number of mention, value= label of the mention
335
+ labels : List
336
+ list of entities to be upsampled.
337
+ p : float
338
+ probability upsampled a mention selected.
339
+
340
+ Returns
341
+ -------
342
+ token_mentions : Dictionary
343
+ token mentions but with mention brack traslation.
344
+ }
345
+ """
346
+
347
+ from deep_translator import GoogleTranslator
348
+ from nltk.tokenize import word_tokenize
349
+
350
+
351
+ p = 1-p
352
+ for i in token_mentions.keys():
353
+ if label_mentions[i] in labels:
354
+ umbral=np.random.uniform(0,1)
355
+ if umbral>=p:
356
+ try:
357
+ language = random.choice(['en', 'sv', 'fr', 'ja', 'ko', 'af', 'sq', 'cs', 'es', 'el', 'ga'])
358
+ to_translate = " ".join(token_mentions[i])
359
+
360
+ #print("to_trans: ", to_translate[:20])
361
+
362
+ translateden = GoogleTranslator(source='auto', target=language).translate(to_translate)
363
+
364
+ #print("Trans: ",translateden[:20])
365
+
366
+ translatedes = GoogleTranslator(source='auto', target='de').translate(translateden)
367
+
368
+ #print("back Trans: ",translatedes[:20])
369
+
370
+ mention_selected = word_tokenize(translatedes)
371
+ token_mentions[i] = mention_selected
372
+ except:
373
+ pass
374
+ return token_mentions
375
+
376
+
377
+ def upsampling(self, labels, p, methods=None):
378
+
379
+ if methods is None:
380
+ print("Not upsampling required")
381
+ else:
382
+ new_mentions = []
383
+ new_labels = []
384
+ for i,sentence in enumerate(self.__dataset):
385
+ if sentence:
386
+ sentence_mentions,label_mentions = self.get_mentions(sentence, self.__data_labels[i])
387
+
388
+
389
+ if "SiS" in methods:
390
+ new_mentions_temp = self.shuffle_within_segments(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
391
+ if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions:
392
+ new_mentions.append(new_mentions_temp)
393
+ new_labels.append(label_mentions)
394
+
395
+
396
+ if "LwTR" in methods:
397
+ new_mentions_temp = self.Label_wise_token_replacement(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
398
+ if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions:
399
+ new_mentions.append(new_mentions_temp)
400
+ new_labels.append(label_mentions)
401
+
402
+
403
+
404
+
405
+ if "MR" in methods:
406
+ new_mentions_temp = self.mention_replacement(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
407
+ if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions:
408
+ new_mentions.append(new_mentions_temp)
409
+ new_labels.append(label_mentions)
410
+
411
+
412
+
413
+ if "SR" in methods:
414
+ new_mentions_temp = self.synonym_replacement(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
415
+ if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions:
416
+ new_mentions.append(new_mentions_temp)
417
+ new_labels.append(label_mentions)
418
+
419
+
420
+
421
+ if "MBT" in methods:
422
+ new_mentions_temp = self.mention_back_traslation(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
423
+ if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions:
424
+ new_mentions.append(new_mentions_temp)
425
+ new_labels.append(label_mentions)
426
+
427
+
428
+ #Turn the mentions into sentences
429
+ new_samples_generated = []
430
+ new_labels_generated = []
431
+
432
+ for i,mentions in enumerate(new_mentions):
433
+ new_labels_temp = new_labels[i]
434
+ sample_temp = []
435
+ labels_temp = []
436
+ for key in mentions.keys():
437
+ sample_temp += mentions[key]
438
+ labels_temp += [new_labels_temp[key]]*len(mentions[key])
439
+ new_samples_generated.append(sample_temp)
440
+ new_labels_generated.append(labels_temp)
441
+ return new_samples_generated, new_labels_generated
442
+
443
+
444
+
445
+ def mention_to_sentence(self, mentions, labels):
446
+ sample_temp = []
447
+ labels_temp = []
448
+ for key in mentions.keys():
449
+ sample_temp += mentions[key]
450
+ labels_temp += [labels[key]]*len(mentions[key])
451
+
452
+ return sample_temp, labels_temp
453
+
454
+
455
+
456
+ def upsampling_by_sentence(self, labels, p, methods=None):
457
+
458
+ if methods is None:
459
+ print("Not upsampling required")
460
+ else:
461
+ new_mentions = []
462
+ new_labels = []
463
+ map_sentences = []
464
+ map_labels = []
465
+ sentences_upsampled = []
466
+ labels_upsampled = []
467
+
468
+ for i,sentence in enumerate(self.__dataset):
469
+ sentences_upsampled_temp = {}
470
+ labels_upsampled_temp = {}
471
+
472
+ sentences_upsampled_temp["Original"] = sentence
473
+ labels_upsampled_temp["Original"] = self.__data_labels[i]
474
+
475
+ sentence_mentions,label_mentions = self.get_mentions(sentence, self.__data_labels[i])
476
+
477
+
478
+ if "SiS" in methods:
479
+ new_mentions_temp = self.shuffle_within_segments(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
480
+ if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions:
481
+ sentences_upsampled_temp["SiS"], labels_upsampled_temp["SiS"] = self.mention_to_sentence(new_mentions_temp, label_mentions)
482
+
483
+
484
+ if "LwTR" in methods:
485
+ new_mentions_temp = self.Label_wise_token_replacement(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
486
+ if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions:
487
+ sentences_upsampled_temp["LwTR"], labels_upsampled_temp["LwTR"] = self.mention_to_sentence(new_mentions_temp, label_mentions)
488
+
489
+
490
+
491
+
492
+ if "MR" in methods:
493
+ new_mentions_temp = self.mention_replacement(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
494
+ if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions:
495
+ sentences_upsampled_temp["MR"], labels_upsampled_temp["MR"] = self.mention_to_sentence(new_mentions_temp, label_mentions)
496
+
497
+
498
+ if "SR" in methods:
499
+ new_mentions_temp = self.synonym_replacement(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
500
+ if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions:
501
+ sentences_upsampled_temp["SR"], labels_upsampled_temp["SR"] = self.mention_to_sentence(new_mentions_temp, label_mentions)
502
+
503
+
504
+
505
+ if "MBT" in methods:
506
+ new_mentions_temp = self.mention_back_traslation(copy.deepcopy(sentence_mentions), label_mentions,labels ,p)
507
+ if new_mentions_temp not in new_mentions and new_mentions_temp != sentence_mentions:
508
+ sentences_upsampled_temp["MBT"], labels_upsampled_temp["MBT"] = self.mention_to_sentence(new_mentions_temp, label_mentions)
509
+
510
+ if len(sentences_upsampled_temp)>1:
511
+ print(len(sentences_upsampled_temp))
512
+ sentences_upsampled.append(sentences_upsampled_temp)
513
+ labels_upsampled.append(labels_upsampled_temp)
514
+
515
+ return sentences_upsampled, labels_upsampled
516
+
517
+