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main.py
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from data_loader import Data, CustomData
from config import NUM_RUNS, TRAIN_DATASETS, TEST_DATASETS, SUBTASK, TASK, CUE_MODEL, SCOPE_MODEL, SCOPE_METHOD, INITIAL_LEARNING_RATE, EPOCHS, PATIENCE
from neg_model import CueModel, ScopeModel
def main():
bioscope_full_papers_data = Data('bioscope/full_papers.xml', dataset_name='bioscope')
sfu_data = Data('SFU_Review_Corpus_Negation_Speculation', dataset_name='sfu')
bioscope_abstracts_data = Data('bioscope/abstracts.xml', dataset_name='bioscope')
if TASK == 'negation':
sherlock_train_data = Data('starsem-st-2012-data/cd-sco/corpus/training/SEM-2012-SharedTask-CD-SCO-training-09032012.txt', dataset_name='starsem')
sherlock_dev_data = Data('starsem-st-2012-data/cd-sco/corpus/dev/SEM-2012-SharedTask-CD-SCO-dev-09032012.txt', dataset_name='starsem')
sherlock_test_gold_cardboard_data = Data('starsem-st-2012-data/cd-sco/corpus/test-gold/SEM-2012-SharedTask-CD-SCO-test-cardboard-GOLD.txt', dataset_name='starsem')
sherlock_test_gold_circle_data = Data('starsem-st-2012-data/cd-sco/corpus/test-gold/SEM-2012-SharedTask-CD-SCO-test-circle-GOLD.txt', dataset_name='starsem')
vetcompass_train_data = Data('vetcompass_subsets/train/negspec', dataset_name='vetcompass')
vetcompass_dev_data = Data('vetcompass_subsets/dev/negspec', dataset_name='vetcompass')
vetcompass_test_data = Data('vetcompass_subsets/test/negspec', dataset_name='vetcompass')
cue_list = []
cont = False
for i,sent in enumerate(sherlock_test_gold_circle_data.cue_data.cues):
for j,tok in enumerate(sent):
if tok == 1:
if cont == True:
cue = cue.strip()
cue_list.append(cue)
cue = ''
cont = False
cue = sherlock_test_gold_circle_data.cue_data.sentences[i][j]
cue = cue.strip()
cue_list.append(cue)
cue = ''
cont = False
elif tok == 2:
if cont == True:
cue+= ' ' + sherlock_test_gold_circle_data.cue_data.sentences[i][j]
else:
cue = sherlock_test_gold_circle_data.cue_data.sentences[i][j]
elif tok == 3:
if cont == True:
cue = cue.strip()
cue_list.append(cue)
cue = ''
cont = False
cue_list = set(cue_list)
for cue in cue_list:
print(cue)
# print(vetcompass_dev_data.cue_data.)
# return 0
# print('HERE')
# print(vetcompass_train_data)
# print(vetcompass_dev_data.cue_data.sentences)
# print(vetcompass_test_data)
sent_list = []
cue_list = []
# for i,sent in enumerate(vetcompass_dev_data.cue_data.sentences):
# sent_text = ' '.join(sent)
# sent_list.append(sent_text)
# cue_list.append(vetcompass_dev_data.cue_data.cues[i])
# # mydata = CustomData(["Hi there this might be good"], cues = [[3,3,3,1,3,3]])
# custom_data = CustomData(sent_list, cues = cue_list)
# print(custom_data.sentences[-1])
# print(custom_data.cues[-1])
scope_list = []
for i,sent in enumerate(vetcompass_dev_data.scope_data.sentences):
sent_text = ' '.join(sent)
sent_list.append(sent_text)
cue_list.append(vetcompass_dev_data.scope_data.cues[i])
# mydata = CustomData(["Hi there this might be good"], cues = [[3,3,3,1,3,3]])
custom_data = CustomData(sent_list, cues = cue_list)
# return 0
for run_num in range(NUM_RUNS):
first_dataset = None
other_datasets = []
if 'sfu' in TRAIN_DATASETS:
first_dataset = sfu_data
if 'bioscope_full_papers' in TRAIN_DATASETS:
if first_dataset == None:
first_dataset = bioscope_full_papers_data
else:
other_datasets.append(bioscope_full_papers_data)
if 'bioscope_abstracts' in TRAIN_DATASETS:
if first_dataset == None:
first_dataset = bioscope_abstracts_data
else:
other_datasets.append(bioscope_abstracts_data)
if 'sherlock' in TRAIN_DATASETS:
if first_dataset == None:
first_dataset = sherlock_train_data
else:
other_datasets.append(sherlock_train_data)
if 'vetcompass' in TRAIN_DATASETS:
if first_dataset == None:
first_dataset = vetcompass_train_data
else:
other_datasets.append(vetcompass_train_data)
if SUBTASK == 'cue_detection':
train_dl, val_dls, test_dls = first_dataset.get_cue_dataloader(other_datasets = other_datasets)
if 'sherlock' in TRAIN_DATASETS:
val_dls = val_dls[:-1]
append_dl, _, _ = sherlock_dev_data.get_cue_dataloader(test_size = 0.00000001, val_size = 0.00000001)
val_dls.append(append_dl)
test_dls = test_dls[:-1]
sherlock_dl, _, _ = sherlock_test_gold_cardboard_data.get_cue_dataloader(test_size = 0.00000001, val_size = 0.00000001, other_datasets = [sherlock_test_gold_circle_data])
test_dls.append(sherlock_dl)
if 'vetcompass' in TRAIN_DATASETS:
val_dls = val_dls[:-1]
append_dl, _, _ = vetcompass_dev_data.get_cue_dataloader(test_size = 0.00000001, val_size = 0.00000001)
val_dls.append(append_dl)
test_dls = test_dls[:-1]
vetcompass_dl, _, _ = vetcompass_test_data.get_cue_dataloader(test_size = 0.00000001, val_size = 0.00000001)
test_dls.append(vetcompass_dl)
test_dataloaders = {}
val_dataloaders = {}
idx = 0
if 'sfu' in TRAIN_DATASETS:
if 'sfu' in TEST_DATASETS:
test_dataloaders['sfu'] = test_dls[idx]
val_dataloaders['sfu'] = val_dls[idx]
idx+=1
elif 'sfu' in TEST_DATASETS:
sfu_dl, _, _ = sfu_data.get_cue_dataloader(test_size = 0.00000001, val_size = 0.00000001)
test_dataloaders['sfu'] = sfu_dl
val_dataloaders['sfu'] = sfu_dl
if 'bioscope_full_papers' in TRAIN_DATASETS:
if 'bioscope_full_papers' in TEST_DATASETS:
test_dataloaders['bioscope_full_papers'] = test_dls[idx]
val_dataloaders['bioscope_full_papers'] = val_dls[idx]
idx+=1
elif 'bioscope_full_papers' in TEST_DATASETS:
bioscope_full_papers_dl, _, _ = bioscope_full_papers_data.get_cue_dataloader(test_size = 0.00000001, val_size = 0.00000001)
test_dataloaders['bioscope_full_papers'] = bioscope_full_papers_dl
val_dataloaders['bioscope_full_papers'] = bioscope_full_papers_dl
if 'bioscope_abstracts' in TRAIN_DATASETS:
if 'bioscope_abstracts' in TEST_DATASETS:
test_dataloaders['bioscope_abstracts'] = test_dls[idx]
val_dataloaders['bioscope_abstracts'] = val_dls[idx]
idx+=1
elif 'bioscope_abstracts' in TEST_DATASETS:
bioscope_abstracts_dl, _, _ = bioscope_abstracts_data.get_cue_dataloader(test_size = 0.00000001, val_size = 0.00000001)
test_dataloaders['bioscope_abstracts'] = bioscope_abstracts_dl
val_dataloaders['bioscope_abstracts'] = bioscope_abstracts_dl
if 'sherlock' in TRAIN_DATASETS:
if 'sherlock' in TEST_DATASETS:
test_dataloaders['sherlock'] = test_dls[idx]
val_dataloaders['sherlock'] = val_dls[idx]
idx+=1
elif 'sherlock' in TEST_DATASETS:
sherlock_dl, _, _ = sherlock_test_gold_cardboard_data.get_cue_dataloader(test_size = 0.00000001, val_size = 0.00000001, other_datasets = [sherlock_test_gold_circle_data])
test_dataloaders['sherlock'] = sherlock_dl
val_dataloaders['sherlock'] = sherlock_dl
if 'vetcompass' in TRAIN_DATASETS:
if 'vetcompass' in TEST_DATASETS:
test_dataloaders['vetcompass'] = test_dls[idx]
val_dataloaders['vetcompass'] = val_dls[idx]
idx+=1
elif 'vetcompass' in TEST_DATASETS:
vetcompass_dl, _, _ = vetcompass_test_data.get_cue_dataloader(test_size = 0.00000001, val_size = 0.00000001)
test_dataloaders['vetcompass'] = vetcompass_dl
val_dataloaders['vetcompass'] = vetcompass_dl
elif SUBTASK == 'scope_resolution':
train_dl, val_dls, test_dls = first_dataset.get_scope_dataloader(other_datasets = other_datasets)
if 'sherlock' in TRAIN_DATASETS:
val_dls = val_dls[:-1]
append_dl, _, _ = sherlock_dev_data.get_scope_dataloader(test_size = 0.00000001, val_size = 0.00000001)
val_dls.append(append_dl)
test_dls = test_dls[:-1]
sherlock_dl, _, _ = sherlock_test_gold_cardboard_data.get_scope_dataloader(test_size = 0.00000001, val_size = 0.00000001, other_datasets = [sherlock_test_gold_circle_data])
test_dls.append(sherlock_dl)
if 'vetcompass' in TRAIN_DATASETS:
val_dls = val_dls[:-1]
append_dl, _, _ = vetcompass_dev_data.get_scope_dataloader(test_size = 0.00000001, val_size = 0.00000001)
val_dls.append(append_dl)
test_dls = test_dls[:-1]
vetcompass_dl, _, _ = vetcompass_test_data.get_scope_dataloader(test_size = 0.00000001, val_size = 0.00000001)
test_dls.append(vetcompass_dl)
test_dataloaders = {}
val_dataloaders = {}
idx = 0
if 'sfu' in TRAIN_DATASETS:
if 'sfu' in TEST_DATASETS:
test_dataloaders['sfu'] = test_dls[idx]
val_dataloaders['sfu'] = val_dls[idx]
idx+=1
elif 'sfu' in TEST_DATASETS:
sfu_dl, _, _ = sfu_data.get_scope_dataloader(test_size = 0.00000001, val_size = 0.00000001)
test_dataloaders['sfu'] = sfu_dl
val_dataloaders['sfu'] = sfu_dl
if 'bioscope_full_papers' in TRAIN_DATASETS:
if 'bioscope_full_papers' in TEST_DATASETS:
test_dataloaders['bioscope_full_papers'] = test_dls[idx]
val_dataloaders['bioscope_full_papers'] = val_dls[idx]
idx+=1
elif 'bioscope_full_papers' in TEST_DATASETS:
bioscope_full_papers_dl, _, _ = bioscope_full_papers_data.get_scope_dataloader(test_size = 0.00000001, val_size = 0.00000001)
test_dataloaders['bioscope_full_papers'] = bioscope_full_papers_dl
val_dataloaders['bioscope_full_papers'] = bioscope_full_papers_dl
if 'bioscope_abstracts' in TRAIN_DATASETS:
if 'bioscope_abstracts' in TEST_DATASETS:
test_dataloaders['bioscope_abstracts'] = test_dls[idx]
val_dataloaders['bioscope_abstracts'] = val_dls[idx]
idx+=1
elif 'bioscope_abstracts' in TEST_DATASETS:
bioscope_abstracts_dl, _, _ = bioscope_abstracts_data.get_scope_dataloader(test_size = 0.00000001, val_size = 0.00000001)
test_dataloaders['bioscope_abstracts'] = bioscope_abstracts_dl
val_dataloaders['bioscope_abstracts'] = bioscope_abstracts_dl
if 'sherlock' in TRAIN_DATASETS:
if 'sherlock' in TEST_DATASETS:
test_dataloaders['sherlock'] = test_dls[idx]
val_dataloaders['sherlock'] = val_dls[idx]
idx+=1
elif 'sherlock' in TEST_DATASETS:
sherlock_dl, _, _ = sherlock_test_gold_cardboard_data.get_scope_dataloader(test_size = 0.00000001, val_size = 0.00000001, other_datasets = [sherlock_test_gold_circle_data])
test_dataloaders['sherlock'] = sherlock_dl
val_dataloaders['sherlock'] = sherlock_dl
if 'vetcompass' in TRAIN_DATASETS:
if 'vetcompass' in TEST_DATASETS:
test_dataloaders['vetcompass'] = test_dls[idx]
val_dataloaders['vetcompass'] = val_dls[idx]
idx+=1
elif 'vetcompass' in TEST_DATASETS:
vetcompass_dl, _, _ = vetcompass_test_data.get_scope_dataloader(test_size = 0.00000001, val_size = 0.00000001)
test_dataloaders['vetcompass'] = vetcompass_dl
val_dataloaders['vetcompass'] = vetcompass_dl
else:
raise ValueError("Unsupported subtask. Supported values are: cue_detection, scope_resolution")
if SUBTASK == 'cue_detection':
model = CueModel(full_finetuning=True, train=True, learning_rate = INITIAL_LEARNING_RATE)
elif SUBTASK == 'scope_resolution':
model = ScopeModel(full_finetuning=True, train=True, learning_rate = INITIAL_LEARNING_RATE)
else:
raise ValueError("Unsupported subtask. Supported values are: cue_detection, scope_resolution")
model.train(train_dl, val_dls, epochs=EPOCHS, patience=PATIENCE, train_dl_name = ','.join(TRAIN_DATASETS), val_dl_name = ','.join(TRAIN_DATASETS))
# for k in test_dataloaders.keys():
# print(f"Evaluate on {k}:")
# # model.evaluate(test_dataloaders[k], test_dl_name = k)
# model.evaluate(val_dataloaders[k], test_dl_name = k)
# custom_data_loader = custom_data.get_cue_dataloader()
custom_data_loader = custom_data.get_scope_dataloader()
pred = model.predict(custom_data_loader)
print(len(pred))
flat_pred = [item for sublist in pred for item in sublist]
# print(pred)
for i,sent in enumerate(vetcompass_dev_data.scope_data.sentences):
# if flat_pred[i] != vetcompass_dev_data.cue_data.cues[i]:
print(sent)
print(flat_pred[i])
print(vetcompass_dev_data.scope_data.scopes[i])
print('=============================================')
print(f"\n\n************ RUN {run_num+1} DONE! **************\n\n")
if __name__ == "__main__":
main()