-
Notifications
You must be signed in to change notification settings - Fork 0
/
train_aug.py
961 lines (799 loc) · 42.6 KB
/
train_aug.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
# coding: utf-8
import time
import torch.optim
from collections import OrderedDict
from attrdict import AttrDict
import pandas as pd
try:
import cPickle as pickle
except ImportError:
import pickle
import pdb
from args import build_parser
from train_and_evaluate import *
from components.models import *
from components.contextual_embeddings import *
from utils.helper import *
from utils.logger import *
from utils.expressions_transfer import *
from utils.augmentation import generation_type_1, generation_type_2, transfer_num_no_tokenize
global log_folder
global model_folder
global result_folder
global data_path
global board_path
log_folder = '/outputs/'
model_folder = 'models'
outputs_folder = '/outputs/'
result_folder = '/outputs/'
data_path = './data/'
board_path = '/outputs/'
def main():
parser = build_parser()
args = parser.parse_args()
config = args
if config.mode == 'train':
is_train = True
else:
is_train = False
''' Set seed for reproducibility'''
np.random.seed(config.seed)
torch.manual_seed(config.seed)
random.seed(config.seed)
'''GPU initialization'''
device = gpu_init_pytorch(config.gpu)
if config.full_cv:
out_file_name = './data/joint_aug.csv'
global data_path
data_name = config.dataset
data_path_cv = data_path
config.val_result_path = os.path.join(result_folder, 'CV_results_{}.json'.format(data_name))
fold_acc_score = 0.0
folds_scores = []
best_acc = []
for z in range(5):
run_name = config.run_name + '_fold' + str(z)
#config.dataset = 'fold' + str(z)
config.log_path = os.path.join(log_folder, run_name)
config.model_path = os.path.join(model_folder, run_name)
config.board_path = os.path.join(board_path, run_name)
config.outputs_path = os.path.join(outputs_folder, run_name)
vocab1_path = os.path.join(config.model_path, 'vocab1.p')
vocab2_path = os.path.join(config.model_path, 'vocab2.p')
config_file = os.path.join(config.model_path, 'config.p')
log_file = os.path.join(config.log_path, 'log.txt')
if config.results:
config.result_path = os.path.join(result_folder, 'val_results_{}.json'.format(config.dataset))
create_save_directories(config.log_path)
create_save_directories(config.model_path)
create_save_directories(config.outputs_path)
logger = get_logger(run_name, log_file, logging.DEBUG)
logger.info('Experiment Name: {}'.format(config.run_name))
logger.debug('Created Relevant Directories')
logger.info('Loading Data...')
train_fold, test_fold = load_cv_data(data_path_cv, 'fold' + str(z), is_train)
pairs_trained, pairs_tested, generate_nums, copy_nums = transfer_num(train_fold, test_fold, config.challenge_disp)
train_problem_descriptions = [' '.join(ii[0]) for ii in pairs_trained]
logger.info('Creating Vocab...')
input_lang = None
output_lang = None
input_lang, output_lang, train_pairs, test_pairs = prepare_data(config, logger, pairs_trained, pairs_tested, config.trim_threshold, generate_nums, copy_nums, input_lang, output_lang, tree=True)
#print(len(svamp_pairs), len(augmented_pairs_1), len(augmented_pairs_2))
#augmented_pairs_1, copy_nums = transfer_num_augmented(augmented_pairs_1, pairs_trained, copy_nums, generate_nums)
#train_ls_aug = load_augmented_data_problemid(data_path, train_problem_descriptions, config.dataset, 'train_aug.csv')
asdiv_mawps_ls_aug = load_augmented_data(data_path, config.dataset, 'additional_val_aug.csv', size = config.val_size)
svamp_ls_aug = load_augmented_data(data_path, config.dataset, 'additional_val_test_aug.csv', size = config.val_size)
keep_val_pairs = []
for p in asdiv_mawps_ls_aug:
if p['Index'] in train_problem_descriptions:
keep_val_pairs.append(p)
for p in svamp_ls_aug:
if p['Index'] in train_problem_descriptions:
keep_val_pairs.append(p)
#print(len(train_ls_aug))
keep_val_pairs, copy_nums = transfer_num_augmented_during_training(keep_val_pairs, train_pairs[0], copy_nums, generate_nums)
#print(len(aug_pairs))
validation_pairs = prepare_data_augmented(config, logger, keep_val_pairs, config.trim_threshold, generate_nums, copy_nums, input_lang, output_lang, tree=True)
remove_duplicate_pairs = []
problem_description_list = []
for pair in validation_pairs:
if pair[0] not in problem_description_list:
remove_duplicate_pairs.append(pair)
problem_description_list.append(pair[0])
validation_pairs = remove_duplicate_pairs
if args.aug_size > 0:
asdiv_mawps_ls_aug = load_augmented_data(data_path, config.dataset, 'train_aug.csv', size = args.aug_size)
svamp_ls_aug = load_augmented_data(data_path, config.dataset, 'test_aug.csv', size = args.aug_size)
keep_val_pairs = []
for p in asdiv_mawps_ls_aug:
if p['Index'] in train_problem_descriptions:
keep_val_pairs.append(p)
for p in svamp_ls_aug:
if p['Index'] in train_problem_descriptions:
keep_val_pairs.append(p)
#print(len(train_ls_aug))
keep_val_pairs, copy_nums = transfer_num_augmented_during_training(keep_val_pairs, train_pairs[0], copy_nums, generate_nums)
#print(len(aug_pairs))
aug_pairs = prepare_data_augmented(config, logger, keep_val_pairs, config.trim_threshold, generate_nums, copy_nums, input_lang, output_lang, tree=True)
remove_duplicate_pairs = []
problem_description_list = []
for pair in aug_pairs:
if pair[0] not in problem_description_list:
remove_duplicate_pairs.append(pair)
problem_description_list.append(pair[0])
aug_pairs = remove_duplicate_pairs
print('number of augs:',len(aug_pairs))
train_pairs = train_pairs + aug_pairs
logger.debug('Data Loaded...')
logger.debug('Number of Training Examples: {}'.format(len(train_pairs)))
logger.debug('Number of Val Examples: {}'.format(len(validation_pairs)))
logger.debug('Number of Testing Examples: {}'.format(len(test_pairs)))
logger.debug('Extra Numbers: {}'.format(generate_nums))
logger.debug('Maximum Number of Numbers: {}'.format(copy_nums))
checkpoint = get_latest_checkpoint(config.model_path, logger)
with open(vocab1_path, 'wb') as f:
pickle.dump(input_lang, f, protocol=pickle.HIGHEST_PROTOCOL)
with open(vocab2_path, 'wb') as f:
pickle.dump(output_lang, f, protocol=pickle.HIGHEST_PROTOCOL)
logger.debug('Vocab saved at {}'.format(vocab1_path))
config.len_generate_nums = len(generate_nums)
config.copy_nums = copy_nums
with open(config_file, 'wb') as f:
pickle.dump(vars(config), f, protocol=pickle.HIGHEST_PROTOCOL)
logger.debug('Config File Saved')
# train_pairs: ([list of token ids of question], len(ques), [list of token ids of equation], len(equation), [list of numbers], [list of indexes of numbers], [number stack])
logger.info('Initializing Models...')
# Initialize models
embedding = None
if config.embedding == 'bert':
embedding = BertEncoder(config.emb_name, device, config.freeze_emb)
elif config.embedding == 'roberta':
embedding = RobertaEncoder(config.emb_name, device, config.freeze_emb)
else:
embedding = Embedding(config, input_lang, input_size=input_lang.n_words, embedding_size=config.embedding_size, dropout=config.dropout)
# encoder = EncoderSeq(input_size=input_lang.n_words, embedding_size=config.embedding_size, hidden_size=config.hidden_size, n_layers=config.depth, dropout=config.dropout)
encoder = EncoderSeq(cell_type=config.cell_type, embedding_size=config.embedding_size, hidden_size=config.hidden_size, n_layers=config.depth, dropout=config.dropout)
predict = Prediction(hidden_size=config.hidden_size, op_nums=output_lang.n_words - copy_nums - 1 - len(generate_nums), input_size=len(generate_nums), dropout=config.dropout)
generate = GenerateNode(hidden_size=config.hidden_size, op_nums=output_lang.n_words - copy_nums - 1 - len(generate_nums), embedding_size=config.embedding_size, dropout=config.dropout)
merge = Merge(hidden_size=config.hidden_size, embedding_size=config.embedding_size, dropout=config.dropout)
# the embedding layer is only for generated number embeddings, operators, and paddings
logger.debug('Models Initialized')
logger.info('Initializing Optimizers...')
embedding_optimizer = torch.optim.Adam(embedding.parameters(), lr=config.emb_lr, weight_decay=config.embedding_decay)
encoder_optimizer = torch.optim.Adam(encoder.parameters(), lr=config.lr, weight_decay=config.weight_decay)
predict_optimizer = torch.optim.Adam(predict.parameters(), lr=config.lr, weight_decay=config.weight_decay)
generate_optimizer = torch.optim.Adam(generate.parameters(), lr=config.lr, weight_decay=config.weight_decay)
merge_optimizer = torch.optim.Adam(merge.parameters(), lr=config.lr, weight_decay=config.weight_decay)
logger.debug('Optimizers Initialized')
logger.info('Initializing Schedulers...')
embedding_scheduler = torch.optim.lr_scheduler.StepLR(embedding_optimizer, step_size=20, gamma=0.5)
encoder_scheduler = torch.optim.lr_scheduler.StepLR(encoder_optimizer, step_size=20, gamma=0.5)
predict_scheduler = torch.optim.lr_scheduler.StepLR(predict_optimizer, step_size=20, gamma=0.5)
generate_scheduler = torch.optim.lr_scheduler.StepLR(generate_optimizer, step_size=20, gamma=0.5)
merge_scheduler = torch.optim.lr_scheduler.StepLR(merge_optimizer, step_size=20, gamma=0.5)
logger.debug('Schedulers Initialized')
logger.info('Loading Models on GPU {}...'.format(config.gpu))
# Move models to GPU
if USE_CUDA:
embedding.to(device)
encoder.to(device)
predict.to(device)
generate.to(device)
merge.to(device)
logger.debug('Models loaded on GPU {}'.format(config.gpu))
generate_num_ids = []
for num in generate_nums:
generate_num_ids.append(output_lang.word2index[num])
max_value_corr = 0
len_total_eval = 0
max_val_acc = 0.0
max_train_acc = 0.0
eq_acc = 0.0
best_epoch = -1
min_train_loss = float('inf')
logger.info('Starting Training Procedure')
for epoch in range(config.epochs):
logger.info('Number of training samples in Epoch {}: {}'.format(str(epoch), str(len(train_pairs))))
loss_total = 0
input_batches, input_lengths, output_batches, output_lengths, nums_batches, num_stack_batches, num_pos_batches, num_size_batches = prepare_train_batch(train_pairs, config.batch_size)
od = OrderedDict()
od['Epoch'] = epoch + 1
print_log(logger, od)
start = time.time()
for idx in range(len(input_lengths)):
# loss = train_tree(
# input_batches[idx], input_lengths[idx], output_batches[idx], output_lengths[idx],
# num_stack_batches[idx], num_size_batches[idx], generate_num_ids, encoder, predict, generate, merge,
# encoder_optimizer, predict_optimizer, generate_optimizer, merge_optimizer, output_lang, num_pos_batches[idx])
loss = train_tree(
config, input_batches[idx], input_lengths[idx], output_batches[idx], output_lengths[idx],
num_stack_batches[idx], num_size_batches[idx], generate_num_ids, embedding, encoder, predict, generate, merge,
embedding_optimizer, encoder_optimizer, predict_optimizer, generate_optimizer, merge_optimizer, input_lang, output_lang,
num_pos_batches[idx])
loss_total += loss
print("Completed {} / {}...".format(idx, len(input_lengths)), end = '\r', flush = True)
embedding_scheduler.step()
encoder_scheduler.step()
predict_scheduler.step()
generate_scheduler.step()
merge_scheduler.step()
logger.debug('Training for epoch {} completed...\nTime Taken: {}'.format(epoch, time_since(time.time() - start)))
if loss_total / len(input_lengths) < min_train_loss:
min_train_loss = loss_total / len(input_lengths)
train_value_ac = 0
train_equation_ac = 0
train_eval_total = 1
if (epoch == 10 or epoch == 20):
train_eval_total = 0
logger.info('Computing Validation Accuracy')
start = time.time()
problem_list = []
with torch.no_grad():
for train_batch in validation_pairs:
# train_res = evaluate_tree(train_batch[0], train_batch[1], generate_num_ids, encoder, predict, generate,
# merge, output_lang, train_batch[5], beam_size=config.beam_size)
train_res = evaluate_tree(config, train_batch[0], train_batch[1], generate_num_ids, embedding, encoder, predict, generate,
merge, input_lang, output_lang, train_batch[5], beam_size=config.beam_size)
train_val_ac, train_equ_ac, _, _ = compute_prefix_tree_result(train_res, train_batch[2], output_lang, train_batch[4], train_batch[6])
if train_val_ac:
train_value_ac += 1
else:
#if (epoch > 5 and epoch < 15 and epoch % 3 == 0) or (epoch > 10 and epoch % 20 ==0):
if config.generation:
random_problem = random.choice(validation_pairs)
try:
continue_flag = False
p = random.random()
if p < config.generation_threshold:
question = [input_lang.index2word[word] for word in train_batch[0]]
answer = [output_lang.index2word[word] for word in train_batch[2]]
else:
question = [input_lang.index2word[word] for word in random_problem[0]]
answer = [output_lang.index2word[word] for word in random_problem[2]]
ops = ['+','-','*','/','(',')','^','x']
for eq_idx in range(len(answer)):
if answer[eq_idx] not in ops and answer[eq_idx][0] != 'N':
continue_flag = True
if continue_flag:
continue
problem_list.append(' '.join(question))
except:
continue
#pair += augmented_pairs prepare_data_augmented
if train_equ_ac:
train_equation_ac += 1
train_eval_total += 1
if config.generation:
logger.debug('Validation Accuracy Computed...\nTime Taken: {}'.format(time_since(time.time() - start)))
augmented_pairs = retrive_augmented_data(out_file_name, problem_list, size = 2)
augmented_pairs, copy_nums = transfer_num_augmented_during_training(augmented_pairs, train_batch, copy_nums, generate_nums)
augmented_pairs = prepare_data_augmented(config, logger, augmented_pairs, config.trim_threshold, generate_nums, copy_nums, input_lang, output_lang, tree=True)
train_pairs += augmented_pairs
logger.info('Starting Tesing')
value_ac = 0
equation_ac = 0
eval_total = 0
start = time.time()
with open(config.outputs_path + '/outputs.txt', 'a') as f_out:
f_out.write('---------------------------------------\n')
f_out.write('Epoch: ' + str(epoch) + '\n')
f_out.write('---------------------------------------\n')
f_out.close()
ex_num = 0
for test_batch in test_pairs:
# test_res = evaluate_tree(test_batch[0], test_batch[1], generate_num_ids, encoder, predict, generate,
# merge, output_lang, test_batch[5], beam_size=config.beam_size)
test_res = evaluate_tree(config, test_batch[0], test_batch[1], generate_num_ids, embedding, encoder, predict, generate,
merge, input_lang, output_lang, test_batch[5], beam_size=config.beam_size)
val_ac, equ_ac, _, _ = compute_prefix_tree_result(test_res, test_batch[2], output_lang, test_batch[4], test_batch[6])
cur_result = 0
if val_ac:
value_ac += 1
cur_result = 1
if equ_ac:
equation_ac += 1
eval_total += 1
with open(config.outputs_path + '/outputs.txt', 'a') as f_out:
f_out.write('Example: ' + str(ex_num) + '\n')
f_out.write('Source: ' + stack_to_string(sentence_from_indexes(input_lang, test_batch[0])) + '\n')
f_out.write('Target: ' + stack_to_string(sentence_from_indexes(output_lang, test_batch[2])) + '\n')
f_out.write('Generated: ' + stack_to_string(sentence_from_indexes(output_lang, test_res)) + '\n')
if config.nums_disp:
src_nums = len(test_batch[4])
tgt_nums = 0
pred_nums = 0
for k_tgt in sentence_from_indexes(output_lang, test_batch[2]):
if k_tgt not in ['+', '-', '*', '/']:
tgt_nums += 1
for k_pred in sentence_from_indexes(output_lang, test_res):
if k_pred not in ['+', '-', '*', '/']:
pred_nums += 1
f_out.write('Numbers in question: ' + str(src_nums) + '\n')
f_out.write('Numbers in Target Equation: ' + str(tgt_nums) + '\n')
f_out.write('Numbers in Predicted Equation: ' + str(pred_nums) + '\n')
f_out.write('Result: ' + str(cur_result) + '\n' + '\n')
f_out.close()
ex_num+=1
if float(train_value_ac) / train_eval_total > max_train_acc:
max_train_acc = float(train_value_ac) / train_eval_total
if float(value_ac) / eval_total > max_val_acc:
max_value_corr = value_ac
len_total_eval = eval_total
max_val_acc = float(value_ac) / eval_total
eq_acc = float(equation_ac) / eval_total
best_epoch = epoch+1
state = {
'epoch' : epoch,
'best_epoch': best_epoch-1,
'embedding_state_dict': embedding.state_dict(),
'encoder_state_dict': encoder.state_dict(),
'predict_state_dict': predict.state_dict(),
'generate_state_dict': generate.state_dict(),
'merge_state_dict': merge.state_dict(),
'embedding_optimizer_state_dict': embedding_optimizer.state_dict(),
'encoder_optimizer_state_dict': encoder_optimizer.state_dict(),
'predict_optimizer_state_dict': predict_optimizer.state_dict(),
'generate_optimizer_state_dict': generate_optimizer.state_dict(),
'merge_optimizer_state_dict': merge_optimizer.state_dict(),
'embedding_scheduler_state_dict': embedding_scheduler.state_dict(),
'encoder_scheduler_state_dict': encoder_scheduler.state_dict(),
'predict_scheduler_state_dict': predict_scheduler.state_dict(),
'generate_scheduler_state_dict': generate_scheduler.state_dict(),
'merge_scheduler_state_dict': merge_scheduler.state_dict(),
'voc1': input_lang,
'voc2': output_lang,
'train_loss_epoch' : loss_total / len(input_lengths),
'min_train_loss' : min_train_loss,
'val_acc_epoch' : float(value_ac) / eval_total,
'max_val_acc' : max_val_acc,
'equation_acc' : eq_acc,
'max_train_acc' : max_train_acc,
'generate_nums' : generate_nums
}
if config.save_model:
save_checkpoint(state, epoch, logger, config.model_path, config.ckpt)
od = OrderedDict()
od['Epoch'] = epoch + 1
od['best_epoch'] = best_epoch
od['train_loss_epoch'] = loss_total / len(input_lengths)
od['min_train_loss'] = min_train_loss
od['train_acc_epoch'] = float(train_value_ac) / train_eval_total
od['max_train_acc'] = max_train_acc
od['val_acc_epoch'] = float(value_ac) / eval_total
od['equation_acc_epoch'] = float(equation_ac) / eval_total
od['max_val_acc'] = max_val_acc
od['equation_acc'] = eq_acc
print_log(logger, od)
logger.debug('Validation Completed...\nTime Taken: {}'.format(time_since(time.time() - start)))
if config.results:
store_results(config, max_train_acc, max_val_acc, eq_acc, min_train_loss, best_epoch)
logger.info('Scores saved at {}'.format(config.result_path))
best_acc.append((max_value_corr, len_total_eval))
total_value_corr = 0
total_len = 0
for w in range(len(best_acc)):
folds_scores.append(float(best_acc[w][0])/best_acc[w][1])
total_value_corr += best_acc[w][0]
total_len += best_acc[w][1]
fold_acc_score = float(total_value_corr)/total_len
store_val_results(config, fold_acc_score, folds_scores)
logger.info('Final Val score: {}'.format(fold_acc_score))
else:
run_name = config.run_name
config.log_path = os.path.join(log_folder, run_name)
config.model_path = os.path.join(model_folder, run_name)
config.board_path = os.path.join(board_path, run_name)
config.outputs_path = os.path.join(outputs_folder, run_name)
vocab1_path = os.path.join(config.model_path, 'vocab1.p')
vocab2_path = os.path.join(config.model_path, 'vocab2.p')
config_file = os.path.join(config.model_path, 'config.p')
log_file = os.path.join(config.log_path, 'log.txt')
if config.results:
config.result_path = os.path.join(result_folder, 'val_results_{}.json'.format(config.dataset))
if is_train:
create_save_directories(config.log_path)
create_save_directories(config.model_path)
create_save_directories(config.outputs_path)
else:
create_save_directories(config.log_path)
create_save_directories(config.result_path)
logger = get_logger(run_name, log_file, logging.DEBUG)
logger.info('Experiment Name: {}'.format(config.run_name))
logger.debug('Created Relevant Directories')
logger.info('Loading Data...')
#print(os.getcwd())
train_ls, dev_ls = load_raw_data(data_path, config.dataset, is_train)
pairs_trained, pairs_tested, generate_nums, copy_nums = transfer_num(train_ls, dev_ls, config.challenge_disp)
augmented_pairs_1 = load_augmented_data(data_path, config.dataset, 'type_1_augmentation.csv')
augmented_pairs_1, copy_nums = transfer_num_augmented(augmented_pairs_1, pairs_trained, copy_nums, generate_nums)
augmented_pairs_2 = load_augmented_data(data_path, config.dataset, 'type_2_augmentation.csv')
augmented_pairs_2, copy_nums = transfer_num_augmented(augmented_pairs_2, pairs_trained, copy_nums, generate_nums)
validation_pairs = augmented_pairs_1 + augmented_pairs_2
remove_duplicate_pairs = []
problem_description_list = []
for pair in validation_pairs:
if pair[0] not in problem_description_list:
remove_duplicate_pairs.append(pair)
problem_description_list.append(pair[0])
validation_pairs = remove_duplicate_pairs
#pairs_trained += augmented_pairs_1
#pairs_trained += augmented_pairs_2
logger.debug('Data Loaded...')
if is_train:
logger.debug('Number of Training Examples: {}'.format(len(pairs_trained)))
logger.debug('Number of Testing Examples: {}'.format(len(pairs_tested)))
logger.debug('Extra Numbers: {}'.format(generate_nums))
logger.debug('Maximum Number of Numbers: {}'.format(copy_nums))
# pairs: ([list of words in question], [list of infix Equation tokens incl brackets and N0, N1], [list of numbers], [list of indexes of numbers])
# generate_nums: Unmentioned numbers used in eqns in atleast 5 examples ['1', '3.14']
# copy_nums: Maximum number of numbers in a single sentence: 15
# pairs: ([list of words in question], [list of prefix Equation tokens w/ metasymbols as N0, N1], [list of numbers], [list of indexes of numbers])
if is_train:
logger.info('Creating Vocab...')
input_lang = None
output_lang = None
else:
logger.info('Loading Vocab File...')
with open(vocab1_path, 'rb') as f:
input_lang = pickle.load(f)
with open(vocab2_path, 'rb') as f:
output_lang = pickle.load(f)
logger.info('Vocab Files loaded from {}\nNumber of Words: {}'.format(vocab1_path, input_lang.n_words))
input_lang, output_lang, train_pairs, test_pairs = prepare_data(config, logger, pairs_trained, pairs_tested, config.trim_threshold, generate_nums, copy_nums, input_lang, output_lang, tree=True)
validation_pairs = prepare_data_augmented(config, logger, validation_pairs, config.trim_threshold, generate_nums, copy_nums, input_lang, output_lang, tree=True)
#augmented_pairs = load_augmented_data('./aug/new_augumentations_epoch_10.csv') + load_augmented_data('./aug/new_augumentations_epoch_8.csv') + load_augmented_data('./aug/new_augumentations_epoch_12.csv') +\
# load_augmented_data('./aug/old_augumentations_epoch_6.csv') + load_augmented_data('./aug/augumentations_epoch_40.csv') + load_augmented_data('./aug/augumentations_epoch_50.csv')\
# + load_augmented_data('./aug/new_augumentations_epoch_20.csv') + load_augmented_data('./aug/augumentations_epoch_10.csv') + load_augmented_data('./aug/augumentations_epoch_20.csv')\
# + load_augmented_data('./aug/augumentations_epoch_30.csv') + load_augmented_data('./aug/augumentations_epoch_60.csv')
augmented_pairs = load_augmented_data_sizes('./aug/chat_augumentations_epoch_0.csv', config.aug_propotion)
augmented_pairs, copy_nums = transfer_num_augmented_during_training(augmented_pairs, train_pairs[0], copy_nums, generate_nums)
augmented_pairs = prepare_data_augmented(config, logger, augmented_pairs, config.trim_threshold, generate_nums, copy_nums, input_lang, output_lang, tree=True)
remove_duplicate_pairs = []
problem_description_list = []
for pair in augmented_pairs:
if pair[0] not in problem_description_list:
remove_duplicate_pairs.append(pair)
problem_description_list.append(pair[0])
train_pairs += remove_duplicate_pairs
checkpoint = get_latest_checkpoint(config.model_path, logger)
if is_train:
with open(vocab1_path, 'wb') as f:
pickle.dump(input_lang, f, protocol=pickle.HIGHEST_PROTOCOL)
with open(vocab2_path, 'wb') as f:
pickle.dump(output_lang, f, protocol=pickle.HIGHEST_PROTOCOL)
logger.debug('Vocab saved at {}'.format(vocab1_path))
generate_num_ids = []
for num in generate_nums:
generate_num_ids.append(output_lang.word2index[num])
config.len_generate_nums = len(generate_nums)
config.copy_nums = copy_nums
with open(config_file, 'wb') as f:
pickle.dump(vars(config), f, protocol=pickle.HIGHEST_PROTOCOL)
logger.debug('Config File Saved')
# train_pairs: ([list of token ids of question], len(ques), [list of token ids of equation], len(equation), [list of numbers], [list of indexes of numbers], [number stack])
logger.info('Initializing Models...')
# Initialize models
embedding = None
if config.embedding == 'bert':
embedding = BertEncoder(config.emb_name, device, config.freeze_emb)
elif config.embedding == 'roberta':
embedding = RobertaEncoder(config.emb_name, device, config.freeze_emb)
elif config.embedding == 'deberta':
embedding = DebertaEncoder(config.emb_name, device, config.freeze_emb)
else:
embedding = Embedding(config, input_lang, input_size=input_lang.n_words, embedding_size=config.embedding_size, dropout=config.dropout)
# encoder = EncoderSeq(input_size=input_lang.n_words, embedding_size=config.embedding_size, hidden_size=config.hidden_size, n_layers=config.depth, dropout=config.dropout)
encoder = EncoderSeq(cell_type=config.cell_type, embedding_size=config.embedding_size, hidden_size=config.hidden_size, n_layers=config.depth, dropout=config.dropout)
predict = Prediction(hidden_size=config.hidden_size, op_nums=output_lang.n_words - copy_nums - 1 - len(generate_nums), input_size=len(generate_nums), dropout=config.dropout)
generate = GenerateNode(hidden_size=config.hidden_size, op_nums=output_lang.n_words - copy_nums - 1 - len(generate_nums), embedding_size=config.embedding_size, dropout=config.dropout)
merge = Merge(hidden_size=config.hidden_size, embedding_size=config.embedding_size, dropout=config.dropout)
# the embedding layer is only for generated number embeddings, operators, and paddings
logger.debug('Models Initialized')
logger.info('Initializing Optimizers...')
embedding_optimizer = torch.optim.Adam(embedding.parameters(), lr=config.emb_lr, weight_decay=config.embedding_decay)
encoder_optimizer = torch.optim.Adam(encoder.parameters(), lr=config.lr, weight_decay=config.weight_decay)
predict_optimizer = torch.optim.Adam(predict.parameters(), lr=config.lr, weight_decay=config.weight_decay)
generate_optimizer = torch.optim.Adam(generate.parameters(), lr=config.lr, weight_decay=config.weight_decay)
merge_optimizer = torch.optim.Adam(merge.parameters(), lr=config.lr, weight_decay=config.weight_decay)
logger.debug('Optimizers Initialized')
logger.info('Initializing Schedulers...')
embedding_scheduler = torch.optim.lr_scheduler.StepLR(embedding_optimizer, step_size=20, gamma=0.5)
encoder_scheduler = torch.optim.lr_scheduler.StepLR(encoder_optimizer, step_size=20, gamma=0.5)
predict_scheduler = torch.optim.lr_scheduler.StepLR(predict_optimizer, step_size=20, gamma=0.5)
generate_scheduler = torch.optim.lr_scheduler.StepLR(generate_optimizer, step_size=20, gamma=0.5)
merge_scheduler = torch.optim.lr_scheduler.StepLR(merge_optimizer, step_size=20, gamma=0.5)
logger.debug('Schedulers Initialized')
logger.info('Loading Models on GPU {}...'.format(config.gpu))
# Move models to GPU
if USE_CUDA:
embedding.to(device)
encoder.to(device)
predict.to(device)
generate.to(device)
merge.to(device)
logger.debug('Models loaded on GPU {}'.format(config.gpu))
# generate_num_ids = []
# for num in generate_nums:
# generate_num_ids.append(output_lang.word2index[num])
max_val_acc = 0.0
max_train_acc = 0.0
eq_acc = 0.0
best_epoch = -1
min_train_loss = float('inf')
logger.info('Starting Training Procedure')
for epoch in range(config.epochs):
logger.info('Number of training samples in Epoch {}: {}'.format(str(epoch), str(len(train_pairs))))
out_file_name = './aug/' + config.out_file_name + 'epoch_'+str(epoch)+'.csv'
loss_total = 0
input_batches, input_lengths, output_batches, output_lengths, nums_batches, num_stack_batches, num_pos_batches, num_size_batches = prepare_train_batch(train_pairs, config.batch_size)
od = OrderedDict()
od['Epoch'] = epoch + 1
print_log(logger, od)
start = time.time()
for idx in range(len(input_lengths)):
# loss = train_tree(
# input_batches[idx], input_lengths[idx], output_batches[idx], output_lengths[idx],
# num_stack_batches[idx], num_size_batches[idx], generate_num_ids, encoder, predict, generate, merge,
# encoder_optimizer, predict_optimizer, generate_optimizer, merge_optimizer, output_lang, num_pos_batches[idx])
loss = train_tree(
config, input_batches[idx], input_lengths[idx], output_batches[idx], output_lengths[idx],
num_stack_batches[idx], num_size_batches[idx], generate_num_ids, embedding, encoder, predict, generate, merge,
embedding_optimizer, encoder_optimizer, predict_optimizer, generate_optimizer, merge_optimizer, input_lang, output_lang,
num_pos_batches[idx])
loss_total += loss
print("Completed {} / {}...".format(idx, len(input_lengths)), end = '\r', flush = True)
embedding_scheduler.step()
encoder_scheduler.step()
predict_scheduler.step()
generate_scheduler.step()
merge_scheduler.step()
logger.debug('Training for epoch {} completed...\nTime Taken: {}'.format(epoch, time_since(time.time() - start)))
if loss_total / len(input_lengths) < min_train_loss:
min_train_loss = loss_total / len(input_lengths)
train_value_ac = 0
train_equation_ac = 0
train_eval_total = 1
if (epoch == 30 or epoch == 10):
train_eval_total = 0
logger.info('Computing Validation Accuracy')
start = time.time()
with torch.no_grad():
for train_batch in validation_pairs:
# train_res = evaluate_tree(train_batch[0], train_batch[1], generate_num_ids, encoder, predict, generate,
# merge, output_lang, train_batch[5], beam_size=config.beam_size)
train_res = evaluate_tree(config, train_batch[0], train_batch[1], generate_num_ids, embedding, encoder, predict, generate,
merge, input_lang, output_lang, train_batch[5], beam_size=config.beam_size)
train_val_ac, train_equ_ac, _, _ = compute_prefix_tree_result(train_res, train_batch[2], output_lang, train_batch[4], train_batch[6])
if train_val_ac:
train_value_ac += 1
else:
#if (epoch > 5 and epoch < 15 and epoch % 3 == 0) or (epoch > 10 and epoch % 20 ==0):
if config.generation:
random_problem = random.choice(train_pairs)
try:
continue_flag = False
p = random.random()
if p < config.generation_threshold:
question = [input_lang.index2word[word] for word in train_batch[0]]
answer = [output_lang.index2word[word] for word in train_batch[2]]
else:
question = [input_lang.index2word[word] for word in random_problem[0]]
answer = [output_lang.index2word[word] for word in random_problem[2]]
ops = ['+','-','*','/','(',')','^','x']
for eq_idx in range(len(answer)):
if answer[eq_idx] not in ops and answer[eq_idx][0] != 'N':
continue_flag = True
if continue_flag:
continue
generation_type_1(' '.join(question), out_file_name, question, answer, 2)
generation_type_2(' '.join(question), out_file_name, question, answer, 2)
except:
continue
#pair += augmented_pairs prepare_data_augmented
if train_equ_ac:
train_equation_ac += 1
train_eval_total += 1
if config.generation:
logger.debug('Validation Accuracy Computed...\nTime Taken: {}'.format(time_since(time.time() - start)))
augmented_pairs = load_augmented_data(out_file_name)
augmented_pairs, copy_nums = transfer_num_augmented_during_training(augmented_pairs, train_batch, copy_nums, generate_nums)
augmented_pairs = prepare_data_augmented(config, logger, augmented_pairs, config.trim_threshold, generate_nums, copy_nums, input_lang, output_lang, tree=True)
train_pairs += augmented_pairs
logger.info('Starting Testing')
value_ac = 0
equation_ac = 0
eval_total = 0
start = time.time()
with open(config.outputs_path + '/outputs.txt', 'a') as f_out:
f_out.write('---------------------------------------\n')
f_out.write('Epoch: ' + str(epoch) + '\n')
f_out.write('---------------------------------------\n')
f_out.close()
ex_num = 0
for test_batch in test_pairs:
# test_res = evaluate_tree(test_batch[0], test_batch[1], generate_num_ids, encoder, predict, generate,
# merge, output_lang, test_batch[5], beam_size=config.beam_size)
#print(test_batch)
test_res = evaluate_tree(config, test_batch[0], test_batch[1], generate_num_ids, embedding, encoder, predict, generate,
merge, input_lang, output_lang, test_batch[5], beam_size=config.beam_size)
val_ac, equ_ac, _, _ = compute_prefix_tree_result(test_res, test_batch[2], output_lang, test_batch[4], test_batch[6])
cur_result = 0
if val_ac:
value_ac += 1
cur_result = 1
if equ_ac:
equation_ac += 1
eval_total += 1
with open(config.outputs_path + '/outputs.txt', 'a') as f_out:
f_out.write('Example: ' + str(ex_num) + '\n')
f_out.write('Source: ' + stack_to_string(sentence_from_indexes(input_lang, test_batch[0])) + '\n')
f_out.write('Target: ' + stack_to_string(sentence_from_indexes(output_lang, test_batch[2])) + '\n')
f_out.write('Generated: ' + stack_to_string(sentence_from_indexes(output_lang, test_res)) + '\n')
if config.challenge_disp:
f_out.write('Type: ' + test_batch[7] + '\n')
f_out.write('Variation Type: ' + test_batch[8] + '\n')
f_out.write('Annotator: ' + test_batch[9] + '\n')
f_out.write('Alternate: ' + str(test_batch[10]) + '\n')
if config.nums_disp:
src_nums = len(test_batch[4])
tgt_nums = 0
pred_nums = 0
for k_tgt in sentence_from_indexes(output_lang, test_batch[2]):
if k_tgt not in ['+', '-', '*', '/']:
tgt_nums += 1
for k_pred in sentence_from_indexes(output_lang, test_res):
if k_pred not in ['+', '-', '*', '/']:
pred_nums += 1
f_out.write('Numbers in question: ' + str(src_nums) + '\n')
f_out.write('Numbers in Target Equation: ' + str(tgt_nums) + '\n')
f_out.write('Numbers in Predicted Equation: ' + str(pred_nums) + '\n')
f_out.write('Result: ' + str(cur_result) + '\n' + '\n')
f_out.close()
ex_num+=1
if float(train_value_ac) / train_eval_total > max_train_acc:
max_train_acc = float(train_value_ac) / train_eval_total
if float(value_ac) / eval_total > max_val_acc:
max_val_acc = float(value_ac) / eval_total
eq_acc = float(equation_ac) / eval_total
best_epoch = epoch+1
state = {
'epoch' : epoch,
'best_epoch': best_epoch-1,
'embedding_state_dict': embedding.state_dict(),
'encoder_state_dict': encoder.state_dict(),
'predict_state_dict': predict.state_dict(),
'generate_state_dict': generate.state_dict(),
'merge_state_dict': merge.state_dict(),
'embedding_optimizer_state_dict': embedding_optimizer.state_dict(),
'encoder_optimizer_state_dict': encoder_optimizer.state_dict(),
'predict_optimizer_state_dict': predict_optimizer.state_dict(),
'generate_optimizer_state_dict': generate_optimizer.state_dict(),
'merge_optimizer_state_dict': merge_optimizer.state_dict(),
'embedding_scheduler_state_dict': embedding_scheduler.state_dict(),
'encoder_scheduler_state_dict': encoder_scheduler.state_dict(),
'predict_scheduler_state_dict': predict_scheduler.state_dict(),
'generate_scheduler_state_dict': generate_scheduler.state_dict(),
'merge_scheduler_state_dict': merge_scheduler.state_dict(),
'voc1': input_lang,
'voc2': output_lang,
'train_loss_epoch' : loss_total / len(input_lengths),
'min_train_loss' : min_train_loss,
'val_acc_epoch' : float(value_ac) / eval_total,
'max_val_acc' : max_val_acc,
'equation_acc' : eq_acc,
'max_train_acc' : max_train_acc,
'generate_nums' : generate_nums
}
if config.save_model:
save_checkpoint(state, epoch, logger, config.model_path, config.ckpt)
od = OrderedDict()
od['Epoch'] = epoch + 1
od['best_epoch'] = best_epoch
od['train_loss_epoch'] = loss_total / len(input_lengths)
od['min_train_loss'] = min_train_loss
od['train_acc_epoch'] = float(train_value_ac) / train_eval_total
od['max_train_acc'] = max_train_acc
od['val_acc_epoch'] = float(value_ac) / eval_total
od['equation_acc_epoch'] = float(equation_ac) / eval_total
od['max_val_acc'] = max_val_acc
od['equation_acc'] = eq_acc
print_log(logger, od)
logger.debug('Validation Completed...\nTime Taken: {}'.format(time_since(time.time() - start)))
if config.results:
store_results(config, max_train_acc, max_val_acc, eq_acc, min_train_loss, best_epoch)
logger.info('Scores saved at {}'.format(config.result_path))
else:
gpu = config.gpu
mode = config.mode
dataset = config.dataset
batch_size = config.batch_size
old_run_name = config.run_name
with open(config_file, 'rb') as f:
config = AttrDict(pickle.load(f))
config.gpu = gpu
config.mode = mode
config.dataset = dataset
config.batch_size = batch_size
logger.info('Initializing Models...')
# Initialize models
embedding = None
if config.embedding == 'bert':
embedding = BertEncoder(config.emb_name, device, config.freeze_emb)
elif config.embedding == 'roberta':
embedding = RobertaEncoder(config.emb_name, device, config.freeze_emb)
else:
embedding = Embedding(config, input_lang, input_size=input_lang.n_words, embedding_size=config.embedding_size, dropout=config.dropout)
# encoder = EncoderSeq(input_size=input_lang.n_words, embedding_size=config.embedding_size, hidden_size=config.hidden_size, n_layers=config.depth, dropout=config.dropout)
encoder = EncoderSeq(cell_type=config.cell_type, embedding_size=config.embedding_size, hidden_size=config.hidden_size, n_layers=config.depth, dropout=config.dropout)
predict = Prediction(hidden_size=config.hidden_size, op_nums=output_lang.n_words - config.copy_nums - 1 - config.len_generate_nums, input_size=config.len_generate_nums, dropout=config.dropout)
generate = GenerateNode(hidden_size=config.hidden_size, op_nums=output_lang.n_words - config.copy_nums - 1 - config.len_generate_nums, embedding_size=config.embedding_size, dropout=config.dropout)
merge = Merge(hidden_size=config.hidden_size, embedding_size=config.embedding_size, dropout=config.dropout)
# the embedding layer is only for generated number embeddings, operators, and paddings
logger.debug('Models Initialized')
epoch_offset, min_train_loss, max_train_acc, max_val_acc, equation_acc, best_epoch, generate_nums = load_checkpoint(config, embedding, encoder, predict, generate, merge, config.mode, checkpoint, logger, device)
logger.info('Prediction from')
od = OrderedDict()
od['epoch'] = epoch_offset
od['min_train_loss'] = min_train_loss
od['max_train_acc'] = max_train_acc
od['max_val_acc'] = max_val_acc
od['equation_acc'] = equation_acc
od['best_epoch'] = best_epoch
print_log(logger, od)
generate_num_ids = []
for num in generate_nums:
generate_num_ids.append(output_lang.word2index[num])
value_ac = 0
equation_ac = 0
eval_total = 0
start = time.time()
with open(config.outputs_path + '/outputs.txt', 'a') as f_out:
f_out.write('---------------------------------------\n')
f_out.write('Test Name: ' + old_run_name + '\n')
f_out.write('---------------------------------------\n')
f_out.close()
test_res_ques, test_res_act, test_res_gen, test_res_scores = [], [], [], []
ex_num = 0
for test_batch in test_pairs:
test_res = evaluate_tree(config, test_batch[0], test_batch[1], generate_num_ids, embedding, encoder, predict, generate,
merge, input_lang, output_lang, test_batch[5], beam_size=config.beam_size)
val_ac, equ_ac, _, _ = compute_prefix_tree_result(test_res, test_batch[2], output_lang, test_batch[4], test_batch[6])
cur_result = 0
if val_ac:
value_ac += 1
cur_result = 1
if equ_ac:
equation_ac += 1
eval_total += 1
test_res_ques.append(stack_to_string(sentence_from_indexes(input_lang, test_batch[0])))
test_res_act.append(stack_to_string(sentence_from_indexes(output_lang, test_batch[2])))
test_res_gen.append(stack_to_string(sentence_from_indexes(output_lang, test_res)))
test_res_scores.append(cur_result)
with open(config.outputs_path + '/outputs.txt', 'a') as f_out:
f_out.write('Example: ' + str(ex_num) + '\n')
f_out.write('Source: ' + stack_to_string(sentence_from_indexes(input_lang, test_batch[0])) + '\n')
f_out.write('Target: ' + stack_to_string(sentence_from_indexes(output_lang, test_batch[2])) + '\n')
f_out.write('Generated: ' + stack_to_string(sentence_from_indexes(output_lang, test_res)) + '\n')
if config.nums_disp:
src_nums = len(test_batch[4])
tgt_nums = 0
pred_nums = 0
for k_tgt in sentence_from_indexes(output_lang, test_batch[2]):
if k_tgt not in ['+', '-', '*', '/']:
tgt_nums += 1
for k_pred in sentence_from_indexes(output_lang, test_res):
if k_pred not in ['+', '-', '*', '/']:
pred_nums += 1
f_out.write('Numbers in question: ' + str(src_nums) + '\n')
f_out.write('Numbers in Target Equation: ' + str(tgt_nums) + '\n')
f_out.write('Numbers in Predicted Equation: ' + str(pred_nums) + '\n')
f_out.write('Result: ' + str(cur_result) + '\n' + '\n')
f_out.close()
ex_num+=1
results_df = pd.DataFrame([test_res_ques, test_res_act, test_res_gen, test_res_scores]).transpose()
results_df.columns = ['Question', 'Actual Equation', 'Generated Equation', 'Score']
csv_file_path = os.path.join(config.outputs_path, config.dataset+'.csv')
results_df.to_csv(csv_file_path, index = False)
logger.info('Accuracy: {}'.format(sum(test_res_scores)/len(test_res_scores)))
if __name__ == '__main__':
main()