def test(model, data_loader, criterion, posp, max_fpr):
model.eval()
targets, predicts = list(), list()
loss = Averager()
posp = torch.FloatTensor([posp]).cuda()
one = torch.FloatTensor([1]).cuda()
with torch.no_grad():
for j, (ids, values, seqlength, label,
seq_mask) in enumerate(data_loader):
ids, values = ids.cuda(), values.cuda()
label = label.cuda().float()
seq_mask = seq_mask.cuda()
y, _ = model(ids, values, seqlength, seq_mask, 'tgt')
p = posp * label + (one - posp) * (one - label)
loss.add(torch.mean(p * criterion(y, label)).item())
targets.extend(label.tolist())
predicts.extend(y.tolist())
model.train()
return roc_auc_score(targets, predicts,
max_fpr=max_fpr), loss.item(), roc_auc_score(
targets, predicts)
def validation(model, ctc_criterion, attn_criterion, evaluation_loader, ctc_converter, attn_converter, opt):
""" validation or evaluation """
for p in model.parameters():
p.requires_grad = False
n_correct = 0
norm_ED = 0
length_of_data = 0
infer_time = 0
valid_loss_avg = Averager()
ctc_correct = 0
for i, (image_tensors, labels) in enumerate(evaluation_loader):
batch_size = image_tensors.size(0)
length_of_data = length_of_data + batch_size
#image = image_tensors.cuda()
image = image_tensors.to(device)
length_for_pred = torch.IntTensor([opt.batch_max_length] * batch_size).to(device)
text_for_pred = torch.LongTensor(batch_size, opt.batch_max_length + 1).fill_(0).to(device)
ctc_text_for_loss, ctc_length_for_loss = ctc_converter.encode(labels)
attn_text_for_loss, attn_length_for_loss = attn_converter.encode(labels)
start_time = time.time()
ctc_preds, attn_preds = model(image, text_for_pred)
forward_time = time.time() - start_time
# ctc
ctc_preds = ctc_preds.log_softmax(2)
# Calculate evaluation loss for CTC deocder.
preds_size = torch.IntTensor([ctc_preds.size(1)] * batch_size)
ctc_preds = ctc_preds.permute(1, 0, 2) # to use CTCloss format
ctc_cost = ctc_criterion(ctc_preds, ctc_text_for_loss, preds_size, ctc_length_for_loss)
# Select max probabilty (greedy decoding) then decode index to character
_, preds_index = ctc_preds.max(2)
preds_index = preds_index.transpose(1, 0).contiguous().view(-1)
ctc_preds_str = ctc_converter.decode(preds_index.data, preds_size.data)
# attention
attn_preds = attn_preds[:, :attn_text_for_loss.shape[1] - 1, :]
target = attn_text_for_loss[:, 1:] # without [GO] Symbol
attn_cost = attn_criterion(attn_preds.contiguous().view(-1, attn_preds.shape[-1]), target.contiguous().view(-1))
# select max probabilty (greedy decoding) then decode index to character
_, attn_preds_index = attn_preds.max(2)
attn_preds_str = attn_converter.decode(attn_preds_index, length_for_pred)
attn_labels = attn_converter.decode(attn_text_for_loss[:, 1:], attn_length_for_loss)
cost = opt.ctc_weight * ctc_cost + (1.0 - opt.ctc_weight) * attn_cost
infer_time += forward_time
valid_loss_avg.add(cost)
# calculate accuracy.
#for attn_pred, attn_gt in zip(attn_preds_str, attn_labels):
for pred, gt, attn_pred, attn_gt in zip(ctc_preds_str, labels, attn_preds_str, attn_labels):
attn_pred = attn_pred[:attn_pred.find('[s]')] # prune after "end of sentence" token ([s])
attn_gt = attn_gt[:attn_gt.find('[s]')]
if pred == gt:
ctc_correct += 1
if attn_pred == attn_gt:
n_correct += 1
norm_ED += edit_distance(attn_pred, attn_gt) / len(attn_gt)
accuracy = n_correct / float(length_of_data) * 100
ctc_accuracy = ctc_correct / float(length_of_data) * 100
return valid_loss_avg.val(), accuracy, ctc_accuracy, norm_ED, attn_preds_str, attn_labels, infer_time, length_of_data
propagate_loader = DataLoader(dataset=trainset,
batch_size=1280,
shuffle=True,
num_workers=24,
pin_memory=True)
premodel = torch.load(cfg.load)
# model = Convnet()
model = copyModel(torch.load(cfg.load), Convnet()).cuda()
# model = copyModel(Convnet(), torch.load(cfg.oad)).cuda()
model.eval()
allacc = []
ave_acc = Averager()
allExtraData = []
with torch.no_grad():
for i, batch in enumerate(propagate_loader, 1):
ext_data, _ = [_.cuda() for _ in batch]
allExtraData.append(ext_data)
allExtraData = torch.cat(allExtraData)
if cfg.progalambda > 0:
with torch.no_grad():
n = 1280
allExtraproto = []
index = torch.randperm(
allExtraData.shape[0])[:int(allExtraData.shape[0] / 10)]
extraDatatemp = allExtraData[index]
def train(opt):
""" dataset preparation """
opt.select_data = opt.select_data.split('-')
opt.batch_ratio = opt.batch_ratio.split('-')
train_dataset = Batch_Balanced_Dataset(opt)
AlignCollate_valid = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD)
valid_dataset = hierarchical_dataset(root=opt.valid_data, opt=opt)
valid_loader = torch.utils.data.DataLoader(
valid_dataset, batch_size=opt.batch_size,
shuffle=True, # 'True' to check training progress with validation function.
num_workers=int(opt.workers),
collate_fn=AlignCollate_valid, pin_memory=True)
print('-' * 80)
""" model configuration """
if 'CTC' in opt.Prediction:
converter = CTCLabelConverter(opt.character)
else:
converter = AttnLabelConverter(opt.character)
opt.num_class = len(converter.character)
if opt.rgb:
opt.input_channel = 3
model = Model(opt)
print('model input parameters', opt.imgH, opt.imgW, opt.num_fiducial, opt.input_channel, opt.output_channel,
opt.hidden_size, opt.num_class, opt.batch_max_length, opt.Transformation, opt.FeatureExtraction,
opt.SequenceModeling, opt.Prediction)
# weight initialization
for name, param in model.named_parameters():
if 'localization_fc2' in name:
print(f'Skip {name} as it is already initialized')
continue
try:
if 'bias' in name:
init.constant_(param, 0.0)
elif 'weight' in name:
init.kaiming_normal_(param)
except Exception as e: # for batchnorm.
if 'weight' in name:
param.data.fill_(1)
continue
# data parallel for multi-GPU
model = torch.nn.DataParallel(model).cuda()
model.train()
if opt.continue_model != '':
if opt.without_prediction:
load_model_without_prediction(opt.continue_model, model)
print(f'loading pretrained model from {opt.continue_model}, without prediction layer')
else:
print(f'loading pretrained model from {opt.continue_model}')
model.load_state_dict(torch.load(opt.continue_model))
print("Model:")
print(model)
""" setup loss """
if 'CTC' in opt.Prediction:
criterion = torch.nn.CTCLoss(zero_infinity=True).cuda()
else:
criterion = torch.nn.CrossEntropyLoss(ignore_index=0).cuda() # ignore [GO] token = ignore index 0
# loss averager
loss_avg = Averager()
# filter that only require gradient decent
filtered_parameters = []
params_num = []
for p in filter(lambda p: p.requires_grad, model.parameters()):
filtered_parameters.append(p)
params_num.append(np.prod(p.size()))
print('Trainable params num : ', sum(params_num))
# [print(name, p.numel()) for name, p in filter(lambda p: p[1].requires_grad, model.named_parameters())]
# setup optimizer
if opt.adam:
optimizer = optim.Adam(filtered_parameters, lr=opt.lr, betas=(opt.beta1, 0.999))
else:
optimizer = optim.Adadelta(filtered_parameters, lr=opt.lr, rho=opt.rho, eps=opt.eps)
print("Optimizer:")
print(optimizer)
""" final options """
# print(opt)
with open(f'./saved_models/{opt.experiment_name}/opt.txt', 'a') as opt_file:
opt_log = '------------ Options -------------\n'
args = vars(opt)
for k, v in args.items():
opt_log += f'{str(k)}: {str(v)}\n'
opt_log += '---------------------------------------\n'
print(opt_log)
opt_file.write(opt_log)
""" start training """
start_iter = 0
if opt.continue_model != '':
print(f'continue to train, start_iter: {start_iter}')
start_time = time.time()
best_accuracy = -1
best_norm_ED = 1e+6
i = start_iter
while True:
# train part
for p in model.parameters():
p.requires_grad = True
image_tensors, labels = train_dataset.get_batch()
image = image_tensors.cuda()
text, length = converter.encode(labels)
batch_size = image.size(0)
if 'CTC' in opt.Prediction:
preds = model(image, text).log_softmax(2)
preds_size = torch.IntTensor([preds.size(1)] * batch_size)
preds = preds.permute(1, 0, 2) # to use CTCLoss format
cost = criterion(preds, text, preds_size, length)
else:
preds = model(image, text)
target = text[:, 1:] # without [GO] Symbol
cost = criterion(preds.view(-1, preds.shape[-1]), target.contiguous().view(-1))
model.zero_grad()
cost.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip) # gradient clipping with 5 (Default)
optimizer.step()
loss_avg.add(cost)
# validation part
if i % opt.valInterval == 0:
elapsed_time = time.time() - start_time
logging.info(f'[{i}/{opt.num_iter}] Loss: {loss_avg.val():0.5f} elapsed_time: {elapsed_time:0.5f}')
# for log
with open(f'./saved_models/{opt.experiment_name}/log_train.txt', 'a') as log:
log.write(f'[{i}/{opt.num_iter}] Loss: {loss_avg.val():0.5f} elapsed_time: {elapsed_time:0.5f}\n')
loss_avg.reset()
model.eval()
valid_loss, current_accuracy, current_norm_ED, preds, labels, infer_time, length_of_data = validation(
model, criterion, valid_loader, converter, opt)
model.train()
for pred, gt in zip(preds[:5], labels[:5]):
if 'Attn' in opt.Prediction:
pred = pred[:pred.find('[s]')]
gt = gt[:gt.find('[s]')]
print(f'{pred:20s}, gt: {gt:20s}, {str(pred == gt)}')
log.write(f'{pred:20s}, gt: {gt:20s}, {str(pred == gt)}\n')
valid_log = f'[{i}/{opt.num_iter}] valid loss: {valid_loss:0.5f}'
valid_log += f' accuracy: {current_accuracy:0.3f}, norm_ED: {current_norm_ED:0.2f}'
log.write(valid_log + '\n')
# keep best accuracy model
if current_accuracy > best_accuracy:
best_accuracy = current_accuracy
torch.save(model.state_dict(), f'./saved_models/{opt.experiment_name}/mtl_best_accuracy.pth')
if current_norm_ED < best_norm_ED:
best_norm_ED = current_norm_ED
torch.save(model.state_dict(), f'./saved_models/{opt.experiment_name}/best_norm_ED.pth')
best_model_log = f'best_accuracy: {best_accuracy:0.3f}, best_norm_ED: {best_norm_ED:0.2f}'
logging.info(best_model_log)
log.write(best_model_log + '\n')
# save model per 1e+5 iter.
if (i + 1) % 50000 == 0:
torch.save(
model.state_dict(), f'./saved_models/{opt.experiment_name}/iter_{i+1}.pth')
if i == opt.num_iter:
logging.info('end the training')
sys.exit()
i += 1
def train(opt):
#logging.info(opt)
train_dataset = Batch_Dataset(opt)
AlignCollate_valid = AlignCollate(imgH=opt.imgH,
imgW=opt.imgW,
keep_ratio_with_pad=opt.PAD)
valid_dataset = LmdbDataset(root=opt.valid_data, opt=opt)
valid_loader = torch.utils.data.DataLoader(valid_dataset,
batch_size=opt.batch_size,
shuffle=True,
num_workers=int(opt.workers),
collate_fn=AlignCollate_valid,
pin_memory=True)
print('-' * 80)
""" model configuration """
ctc_converter = CTCLabelConverter(opt.character, opt.subword)
attn_converter = AttnLabelConverter(opt.character, opt.subword,
opt.batch_max_length)
opt.num_class = len(attn_converter.character)
opt.ctc_num_class = len(ctc_converter.character)
print("ctc num class {}".format(len(ctc_converter.character)))
print("attention num class {}".format(len(attn_converter.character)))
if opt.rgb:
opt.input_channel = 3
model = MyModel(opt)
# weight initialization
for name, param in model.named_parameters():
if 'localization_fc2' in name:
print('Skip {name} as it is already initialized'.format(name))
continue
try:
if 'bias' in name:
init.constant_(param, 0.0)
elif 'weight' in name:
init.kaiming_normal_(param)
except Exception as e:
if 'weight' in name:
param.data.fill_(1)
continue
model = torch.nn.DataParallel(model).to(device)
model.train()
if opt.continue_model != '':
print('loading pretrained model from {}'.format(opt.continue_model))
model.load_state_dict(torch.load(opt.continue_model))
""" setup loss """
ctc_criterion = torch.nn.CTCLoss(zero_infinity=True).to(device)
attn_criterion = torch.nn.CrossEntropyLoss(ignore_index=0).to(device)
loss_avg = Averager()
filtered_parameters = []
params_num = []
for p in filter(lambda p: p.requires_grad, model.parameters()):
filtered_parameters.append(p)
params_num.append(np.prod(p.size()))
print('Trainable params num : ', sum(params_num))
if opt.adam:
optimizer = optim.Adam(filtered_parameters,
lr=opt.lr,
betas=(opt.beta1, 0.999))
else:
optimizer = optim.Adadelta(filtered_parameters,
lr=opt.lr,
rho=opt.rho,
eps=opt.eps)
print("Optimizer:")
print(optimizer)
""" final options """
with open(osj(opt.outPath, '{}/opt.txt'.format(opt.experiment_name)),
'a') as opt_file:
opt_log = '------------ Options -------------\n'
args = vars(opt)
for k, v in args.items():
opt_log += '{}: {}\n'.format(str(k), str(v))
opt_log += '---------------------------------------\n'
print(opt_log)
opt_file.write(opt_log)
""" start training """
start_iter = 0
if opt.continue_model != '':
print('continue to train, start_iter: {}'.format(start_iter))
start_time = time.time()
best_accuracy = -1
i = start_iter
while True:
# train part
for p in model.parameters():
p.requires_grad = True
image_tensors, labels = train_dataset.get_batch()
image = image_tensors.to(device)
ctc_text, ctc_length = ctc_converter.encode(labels)
attn_text, attn_length = attn_converter.encode(labels)
batch_size = image.size(0)
# ctc loss
ctc_preds, attn_preds = model(image, attn_text)
ctc_preds = ctc_preds.log_softmax(2)
preds_size = torch.IntTensor([ctc_preds.size(1)] * batch_size)
ctc_preds = ctc_preds.permute(1, 0, 2)
ctc_cost = ctc_criterion(ctc_preds, ctc_text, preds_size, ctc_length)
# attn loss
target = attn_text[:, 1:]
attn_cost = attn_criterion(attn_preds.view(-1, attn_preds.shape[-1]),
target.contiguous().view(-1))
cost = opt.ctc_weight * ctc_cost + (1.0 - opt.ctc_weight) * attn_cost
model.zero_grad()
cost.backward()
torch.nn.utils.clip_grad_norm_(
model.parameters(),
opt.grad_clip) # gradient clipping with 5 (Default)
optimizer.step()
loss_avg.add(cost)
# validation part
if i % opt.valInterval == 0:
elapsed_time = time.time() - start_time
logging.info('[{}/{}] Loss: {:0.5f} elapsed_time: {:0.5f}'.format(
i, opt.num_iter, loss_avg.val(), elapsed_time))
# for log
with open(
osj(opt.outPath,
'{}/log_train.txt'.format(opt.experiment_name)),
'a') as log:
log.write(
'[{}/{}] Loss: {:0.5f} elapsed_time: {:0.5f}\n'.format(
i, opt.num_iter, loss_avg.val(), elapsed_time))
loss_avg.reset()
model.eval()
with torch.no_grad():
valid_loss, current_accuracy, ctc_accuracy, current_norm_ED, preds, labels, infer_time, length_of_data \
= mtl_validation(model, ctc_criterion, attn_criterion, valid_loader, ctc_converter, attn_converter, opt)
model.train()
for pred, gt in zip(preds[:5], labels[:5]):
pred = pred[:pred.find('[s]')]
gt = gt[:gt.find('[s]')]
print('{:20s}, gt: {:20s}, {}'.format(
pred, gt, str(pred == gt)))
log.write('{:20s}, gt: {:20s}, {}\n'.format(
pred, gt, str(pred == gt)))
valid_log = '[{}/{}] valid loss: {:0.5f}'.format(
i, opt.num_iter, valid_loss)
valid_log += ' accuracy: {:0.3f}'.format(current_accuracy)
log.write(valid_log + '\n')
# save best accuracy model
if current_accuracy > best_accuracy:
best_accuracy = current_accuracy
torch.save(
model.state_dict(),
osj(opt.outPath, '{}/best_accuracy.pth'.format(
opt.experiment_name)))
best_model_log = 'best_accuracy: {:0.3f}'.format(best_accuracy)
logging.info(best_model_log)
log.write(best_model_log + '\n')
if (i + 1) % 50000 == 0:
torch.save(
model.state_dict(),
osj(opt.outPath,
'{}/iter_{}.pth'.format(opt.experiment_name, i + 1)))
if i == opt.num_iter:
logging.info('end the training')
sys.exit()
i += 1
def validation(model, criterion, evaluation_loader, converter, opt):
""" validation or evaluation """
for p in model.parameters():
p.requires_grad = False
n_correct = 0
norm_ED = 0
length_of_data = 0
infer_time = 0
valid_loss_avg = Averager()
for i, (image_tensors, labels) in enumerate(evaluation_loader):
batch_size = image_tensors.size(0)
length_of_data = length_of_data + batch_size
with torch.no_grad():
image = image_tensors.cuda()
# For max length prediction
length_for_pred = torch.cuda.IntTensor([opt.batch_max_length] *
batch_size)
text_for_pred = torch.cuda.LongTensor(
batch_size, opt.batch_max_length + 1).fill_(0)
text_for_loss, length_for_loss = converter.encode(labels)
start_time = time.time()
if 'CTC' in opt.Prediction:
preds = model(image, text_for_pred).log_softmax(2)
forward_time = time.time() - start_time
# Calculate evaluation loss for CTC deocder.
preds_size = torch.IntTensor([preds.size(1)] * batch_size)
preds = preds.permute(1, 0, 2) # to use CTCloss format
cost = criterion(preds, text_for_loss, preds_size, length_for_loss)
# Select max probabilty (greedy decoding) then decode index to character
_, preds_index = preds.max(2)
preds_index = preds_index.transpose(1, 0).contiguous().view(-1)
preds_str = converter.decode(preds_index.data, preds_size.data)
else:
preds = model(image, text_for_pred, is_train=False)
forward_time = time.time() - start_time
preds = preds[:, :text_for_loss.shape[1] - 1, :]
target = text_for_loss[:, 1:] # without [GO] Symbol
cost = criterion(preds.contiguous().view(-1, preds.shape[-1]),
target.contiguous().view(-1))
# select max probabilty (greedy decoding) then decode index to character
_, preds_index = preds.max(2)
preds_str = converter.decode(preds_index, length_for_pred)
labels = converter.decode(text_for_loss[:, 1:], length_for_loss)
infer_time += forward_time
valid_loss_avg.add(cost)
# calculate accuracy.
for pred, gt in zip(preds_str, labels):
if 'Attn' in opt.Prediction:
pred = pred[:pred.find(
'[s]')] # prune after "end of sentence" token ([s])
gt = gt[:gt.find('[s]')]
if pred == gt:
n_correct += 1
norm_ED += edit_distance(pred, gt) / len(gt)
accuracy = n_correct / float(length_of_data) * 100
return valid_loss_avg.val(
), accuracy, norm_ED, preds_str, labels, infer_time, length_of_data
allExtraData = []
with torch.no_grad():
for i, batch in enumerate(propagate_loader, 1):
ext_data, _ = [_.cuda() for _ in batch]
allExtraData.append(ext_data)
allExtraData = torch.cat(allExtraData)
timer = Timer()
for epoch in range(1, cfg.max_epoch + 1):
# torch.cuda.empty_cache()
lr_scheduler.step()
model.train()
tl = Averager()
ta = Averager()
for i, batch in enumerate(train_loader, 1):
# time.sleep(100)
torch.cuda.empty_cache()
data, _ = [_.cuda() for _ in batch]
p = cfg.shot * cfg.train_way
data_shot, data_query = data[:p], data[p:]
proto = model(data_shot)
proto = proto.reshape(cfg.shot, cfg.train_way, -1).mean(dim=0)
query_proto = model(data_query)
p = (1 - cfg.progalambda) * proto
def train(model,
optimizer,
src_loader,
tgt_loader,
valid_loader,
criterion,
log_interval=1000,
val_interval=50,
posp=1,
nagp=0.5,
params_cls=0.5,
params_da=0.5,
da_type='cmmd',
max_fpr=0.01):
global max_auc
global max_auchead
global min_loss
posp = torch.FloatTensor([posp]).cuda()
nagp = torch.FloatTensor([nagp]).cuda()
one = torch.FloatTensor([1]).cuda()
iter_src = iter(src_loader)
iter_tgt = iter(tgt_loader)
num_iter = len(src_loader)
stoper = Stoper()
avg_all_loss = Averager()
avg_src_loss = Averager()
avg_tgt_loss = Averager()
avg_da_loss = Averager()
start_time = time.time()
for i in range(1, num_iter * 20):
model.train()
src_ids, src_values, src_seqlength, src_label, src_seq_mask = iter_src.next(
)
src_ids, src_values, src_label = src_ids.cuda(), src_values.cuda(
), src_label.cuda().float()
src_seq_mask = src_seq_mask.cuda()
if i % len(src_loader) == 0:
iter_src = iter(src_loader)
if i % len(tgt_loader) == 0:
iter_tgt = iter(tgt_loader)
src_p = posp * src_label + nagp * (one - src_label)
src_y, src_fea_LSTM = model(src_ids, src_values, src_seqlength,
src_seq_mask, 'src')
src_loss = torch.mean(
src_p * criterion(src_y, src_label)
) # + torch.mean(src_p * criterion(src_spey, src_label))
tgt_ids, tgt_values, tgt_seqlength, tgt_label, tgt_seq_mask = iter_tgt.next(
)
tgt_ids, tgt_values, tgt_label = tgt_ids.cuda(), tgt_values.cuda(
), tgt_label.cuda().float()
tgt_seq_mask = tgt_seq_mask.cuda()
# print(tgt_seqlength, tgt_label)
tgt_p = posp * tgt_label + nagp * (one - tgt_label)
tgt_y, tgt_fea_LSTM, tgt_spey = model(tgt_ids, tgt_values,
tgt_seqlength, tgt_seq_mask,
'tgt')
tgt_loss = torch.mean(
tgt_p * criterion(tgt_y, tgt_label)
) # + 0.5 * torch.mean(tgt_p * criterion(tgt_spey, tgt_label))
if da_type == 'cmmd':
da_loss = cmmd(src_fea_LSTM, tgt_fea_LSTM, src_label.long(),
tgt_label.long())
elif da_type == 'mmd':
da_loss = mmd_rbf_noaccelerate(src_fea_LSTM, tgt_fea_LSTM)
elif da_type == 'coral':
da_loss = coral(src_fea_LSTM, tgt_fea_LSTM)
elif da_type == 'euclidian':
da_loss = euclidian(src_fea_LSTM, tgt_fea_LSTM)
elif da_type == 'c_euclidian':
da_loss = c_euclidian(src_fea_LSTM, tgt_fea_LSTM, src_label.long(),
tgt_label.long())
elif da_type == 'nometric':
da_loss = nometric(src_fea_LSTM, tgt_fea_LSTM)
elif da_type == 'ced':
da_loss = ced(src_fea_LSTM, tgt_fea_LSTM, src_label.long(),
tgt_label.long())
lambd = 2 / (1 + math.exp((-5 * i) / (len(src_loader)))) - 1
loss = params_cls * src_loss + tgt_loss + params_da * lambd * da_loss
model.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)
optimizer.step()
avg_all_loss.add(loss.item())
avg_src_loss.add(src_loss.item())
avg_tgt_loss.add(tgt_loss.item())
avg_da_loss.add(da_loss.item())
if (i + 1) % log_interval == 0:
print(
'step: {}, loss: {:.4f}, src_loss: {:.4f}, tgt_loss: {:.4f}, {}_loss:, {:.4f}, lambda: {}'
.format(i + 1, avg_all_loss.item(), avg_src_loss.item(),
avg_tgt_loss.item(), da_type, avg_da_loss.item(),
lambd))
avg_all_loss = Averager()
avg_src_loss = Averager()
avg_tgt_loss = Averager()
avg_da_loss = Averager()
if (i + 1) % val_interval == 0:
end_time = time.time()
print('train time (s):', end_time - start_time)
start_time = time.time()
auc_head, loss, auc = test(model, valid_loader, criterion, posp,
max_fpr)
if loss < min_loss:
min_loss = loss
if auc > max_auc:
max_auc = auc
if auc_head > max_auchead:
torch.save(model, f'{save_dir}/tmp.pt')
max_auchead = auc_head
print(
'dev --- auchead: {:.4f}, max_auchead: {:.4f}, auc: {:.4f}, max_auc: {:.4f}, loss: {:.4f}, minloss: {:.4f}'
.format(auc_head, max_auchead, auc, max_auc, loss, min_loss))
end_time = time.time()
print('dev time (s):', end_time - start_time)
start_time = time.time()
if stoper.add(auc_head):
print('training end')
break
def transform(self, stu, que, ans) -> pd.DataFrame:
"""
Main method to calculate, preprocess students's features and append textual embeddings
:param stu: students dataframe with preprocessed textual columns
:param que: questions dataframe with preprocessed textual columns
:param ans: answers dataframe with preprocessed textual columns
:return: dataframe of students's id, timestamp and model-friendly students's features after that timestamp
"""
stu['students_state'] = stu['students_location'].apply(
lambda s: str(s).split(', ')[-1])
que['questions_body_length'] = que['questions_body'].apply(
lambda s: len(str(s)))
ans['answers_body_length'] = ans['answers_body'].apply(
lambda s: len(str(s)))
# prepare all the dataframes needed for iteration
que_change = stu.merge(que,
left_on='students_id',
right_on='questions_author_id')
ans_change = que_change.merge(ans, left_on='questions_id', right_on='answers_question_id') \
.rename(columns={'answers_date_added': 'students_time'})
# add new columns which will be used to determine to which change corressponds stacked DataFrame row
ans_change['change_type'] = 'answer'
que_change['change_type'] = 'question'
que_change = que_change.rename(
columns={'questions_date_added': 'students_time'})
# stack two DataFrame to form resulting one for iteration
df = pd.concat([que_change, ans_change], ignore_index=True,
sort=True).sort_values('students_time')
# data is a dist with mapping from student's id to his list of features
# each list contains dicts with mapping from feature name to its value on a particular moment
data = {}
avgs = {}
for i, row in stu.iterrows():
cur_stu = row['students_id']
# DEFAULT CASE
# student's feature values before he left any questions
if cur_stu not in data:
new = {
'students_questions_asked': 0,
'students_previous_question_time':
row['students_date_joined']
}
for feature in ['students_time'
] + self.features['numerical']['mean']:
new[feature] = None
data[cur_stu] = [new]
avgs[cur_stu] = {
feature: Averager()
for feature in self.features['numerical']['mean']
}
for i, row in df.iterrows():
cur_stu = row['students_id']
# features on previous timestamp
prv = data[cur_stu][-1]
new = prv.copy()
new['students_time'] = row['students_time']
# UPDATE RULES
# if current change is new question, update question-depended features
if row['change_type'] == 'question':
new['students_questions_asked'] += 1
new['students_previous_question_time'] = row[
'questions_date_added']
new['students_average_question_body_length'] = row[
'questions_body_length']
# if new answer is added, update answer-depended features
else:
new['students_average_answer_body_length'] = row[
'answers_body_length']
new['students_average_answer_amount'] = new['students_average_answer_amount'] + 1 \
if new['students_average_answer_amount'] is not None else 1
# NORMALIZE AVERAGE FEATURES
for feature in ['students_average_question_body_length'] if row['change_type'] == 'question' else \
['students_average_answer_body_length', 'students_average_answer_amount']:
avgs[cur_stu][feature].upd(new[feature])
new[feature] = avgs[cur_stu][feature].get()
data[cur_stu].append(new)
# construct a DataFrame out of dict of list of feature dicts
df = pd.DataFrame([{
**f,
**{
'students_id': id
}
} for (id, fs) in data.items() for f in fs])
df = df.merge(stu, on='students_id')
# launch feature pre-processing
self.preprocess(df)
# re-order the columns
df = df[['students_id', 'students_time'] + self.features['all']]
return df