test_avgloss, test_pred_res, _ = evaluate(model, test_data, loss_Function, word_to_ix, all_losses_test, 'test')
return test_pred_res, all_losses, all_losses_dev
if __name__ == '__main__':
emotionlist = ['joy', 'anger', 'fear', 'sadness']
#emotionlist = ['joy', 'anger', 'fear', 'sadness']
#emotionlist = ['joy']
seedlist = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
for emotion in emotionlist:
trainfile = 'data/EI-reg-En-train/EI-reg-En-' + emotion + '-train.txt'
devfile = 'data/2018-EI-reg-En-dev/2018-EI-reg-En-' + emotion + '-dev.txt'
# testfile = 'data/2018-EI-reg-En-test/2018-EI-reg-En-'+emotion+'-test.txt'
testfile = 'data/SemEval2018-Task1-AIT-Test-gold/EI-reg/2018-EI-reg-En-' + emotion + '-test-gold.txt'
#train_data, dev_data, test_data, word_to_ix = dataLoaderRegresser.loadData(trainfile, devfile, testfile)
train_data, dev_data, test_data, word_to_ix, char_to_ix = dataLoaderRegresser.loadDataChar(trainfile, devfile, testfile)
for SEED in seedlist:
torch.manual_seed(SEED)
random.seed(SEED)
print('EMOTION:', emotion, 'SEED:', SEED)
train_dev_data = train_data + dev_data
random.shuffle(train_dev_data)
train_data = train_dev_data[:int(len(train_dev_data)*0.9)]
dev_data = train_dev_data[int(len(train_dev_data)*0.9):]
#test_data = test_data[:1106]
print('-> len(test_data): ', len(test_data))
print('-> test_data example:', test_data[0])
print('-> test_data example: ', test_data[-1])
#EMBEDDING_DIM = 50
if __name__ == '__main__':
emotionlist = ['joy', 'anger', 'fear', 'sadness']
#emotionlist = ['joy', 'anger', 'fear', 'sadness']
#emotionlist = ['joy']
seedlist = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
for emotion in emotionlist:
print('English data:')
trainfile = 'data/EI-reg-En-train/EI-reg-En-' + emotion + '-train.txt'
devfile = 'data/2018-EI-reg-En-dev/2018-EI-reg-En-' + emotion + '-dev.txt'
# testfile = 'data/2018-EI-reg-En-test/2018-EI-reg-En-'+emotion+'-test.txt'
testfile = 'data/SemEval2018-Task1-AIT-Test-gold/EI-reg/2018-EI-reg-En-' + emotion + '-test-gold.txt'
#train_data, dev_data, test_data, word_to_ix = dataLoaderRegresser.loadData(trainfile, devfile, testfile)
train_data, dev_data, test_data, word_to_ix, char_to_ix = dataLoaderRegresser.loadDataChar(
trainfile, devfile, testfile)
print('Arabic translated data:')
trainfileAr = 'translated/Ar_' + emotion + '_train.txt'
devfileAr = 'translated/Ar_' + emotion + '_dev.txt'
testfileAr = 'translated/Ar_' + emotion + '_test.txt'
train_dataAr, dev_dataAr, test_dataAr, word_to_ixAr, char_to_ixAr = dataLoaderRegresser.loadDataChar(
trainfileAr, devfileAr, testfileAr)
train_dev_dataAr = train_dataAr + dev_dataAr + test_dataAr
random.shuffle(train_dev_dataAr)
train_dataAr = train_dev_dataAr[:int(len(train_dev_dataAr) * 0.9)]
dev_dataAr = train_dev_dataAr[int(len(train_dev_dataAr) * 0.9):]
print('Spanish translated data:')
trainfileEs = 'translated/Es_' + emotion + '_train.txt'
devfileEs = 'translated/Es_' + emotion + '_dev.txt'