def train(model, loss_Function, optimizer, train_data, dev_data, test_data, pretrainedWE, word2id, emotion, seed, testfile, predpath, best_char_dev_avgloss=1.0, EPOCH=100):
#best_dev_acc = 0.0
#print('->dev_data:', dev_data)
best_dev_avgloss = best_char_dev_avgloss
#no_up = 0
no_drop = 0
all_losses = []
all_losses_dev = []
for i in range(EPOCH):
random.shuffle(train_data)
print('epoch: %d start!' % i)
#train_epoch(model, train_data, loss_Function, optimizer, word_to_ix, label_to_ix, i)
all_losses, model = train_epoch(model, train_data, loss_Function, optimizer, pretrainedWE, word2id, i, all_losses)
#print('now best dev acc:', best_dev_acc)
print('now best dev loss:', best_dev_avgloss)
#dev_acc = evaluate(model, dev_data, loss_Function, word_to_ix, label_to_ix, 'dev')
#test_acc = evaluate(model, test_data, loss_Function, word_to_ix, label_to_ix, 'test')
dev_avgloss, dev_pred_res, all_losses_dev = evaluate(model, dev_data, loss_Function, pretrainedWE, word2id, all_losses_dev, 'dev')
#test_avgloss = evaluate(model, test_data, loss_Function, word_to_ix, 'test')
#if dev_acc > best_dev_acc:
if dev_avgloss < best_dev_avgloss:
#best_dev_acc = dev_acc
best_dev_avgloss = dev_avgloss
os.system('rm best_models/best_model_avgloss_*.model')
print('New Best Dev!!!', best_dev_avgloss)
#torch.save(model.state_dict(), 'best_models/mr_best_model_acc_'+str(int(test_acc*10000)) + '.model')
torch.save(model.state_dict(), 'best_models/best_model_avgloss_'+str(int(dev_avgloss*10000))+'.model')
#no_up = 0.0
no_drop = 0.0
all_losses_test = []
test_avgloss, test_pred_res, _ = evaluate(model, test_data, loss_Function, pretrainedWE, word2id, all_losses_test, 'test')
predpathnow = predpath+emotion+str(seed)+'/'
if not os.path.exists(predpathnow):
os.makedirs(predpathnow)
# predfile = 'predictions/en-'+emotion+str(SEED)+'.txt'
predfile = predpathnow + 'epoch'+str(i)+'loss'+str(round(best_dev_avgloss, 4))+ '.txt'
dataLoaderRegresser.outputPred(testfile, predfile, test_pred_res)
else:
#no_up += 1
no_drop += 1
#if no_up >= 10:
if no_drop >= 20:
#exit()
break
all_losses_test = []
test_avgloss, test_pred_res, _ = evaluate(model, test_data, loss_Function, pretrainedWE, word2id, all_losses_test, 'test')
return test_pred_res, all_losses, all_losses_dev
#best_dev_acc = 0.0
#model = LSTMClassifier(embedding_dim=EMBEDDING_DIM, hidden_dim=HIDDEN_DIM,
# vocab_size=len(word_to_ix), label_size=len(label_to_ix))
model = LSTMRegresser(embedding_dim=EMBEDDING_DIM,
hidden_dim=HIDDEN_DIM,
vocab_size=len(word_to_ix),
n_output=1)
model = model.to(device)
#loss_Function = nn.NLLLoss()
loss_Function = nn.MSELoss()
loss_Function = loss_Function.to(device)
optimizer = optim.Adam(model.parameters(), lr=0.0001)
#optimizer = optim.SGD(model.parameters(), lr=0.01)
#train(model, loss_Function, optimizer, train_data, dev_data, test_data, word_to_ix, label_to_ix, EPOCH=100)
#test_pred_res = train(model, loss_Function, optimizer, train_data, dev_data, test_data, word_to_ix, EPOCH=2)
test_pred_res = train(model,
loss_Function,
optimizer,
train_data,
dev_data,
test_data,
word_to_ix,
EPOCH=EPOCH)
predfile = 'predictions/en-' + emotion + str(SEED) + '.txt'
dataLoaderRegresser.outputPred(testfile, predfile, test_pred_res)
#for text, preddgr in zip(test_data, test_pred_res):
# print(text, '\t', preddgr.item())