def train_model(train, test, dictionary, params):
ids_train, labels_train, msg_train, code_train = train
ids_test, labels_test, msg_test, code_test = test
dict_msg, dict_code = dictionary
print('Dictionary message: %i -- Dictionary code: %i' % (len(dict_msg), len(dict_code)))
print('Training data')
info_label(labels_train)
pad_msg_train = padding_data(data=msg_train, dictionary=dict_msg, params=params, type='msg')
pad_code_train = padding_data(data=code_train, dictionary=dict_code, params=params, type='code')
print('Testing data')
info_label(labels_test)
pad_msg_test = padding_data(data=msg_test, dictionary=dict_msg, params=params, type='msg')
pad_code_test = padding_data(data=code_test, dictionary=dict_code, params=params, type='code')
# building batches
batches_train = mini_batches(X_msg=pad_msg_train, X_code=pad_code_train, Y=labels_train)
batches_test = mini_batches(X_msg=pad_msg_test, X_code=pad_code_test, Y=labels_test)
# set up parameters
params.cuda = (not params.no_cuda) and torch.cuda.is_available()
del params.no_cuda
params.filter_sizes = [int(k) for k in params.filter_sizes.split(',')]
params.save_dir = os.path.join(params.save_dir, datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S'))
params.vocab_msg, params.vocab_code = len(dict_msg), len(dict_code)
if len(labels_train.shape) == 1:
params.class_num = 1
else:
params.class_num = labels_train.shape[1]
params.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# create and train the defect model
model = DefectNet(args=params)
if torch.cuda.is_available():
model = model.cuda()
running_train(batches_train=batches_train, batches_test=batches_test, model=model, params=params)
def construct_model(data, params):
training, testing, dictionary = data
ids_train, labels_train, msg_train, code_train = training
ids_test, labels_test, msg_test, code_test = testing
dict_msg, dict_code = dictionary
pad_msg_train = padding_data(data=msg_train,
dictionary=dict_msg,
params=params,
type='msg')
pad_code_train = padding_data(data=code_train,
dictionary=dict_code,
params=params,
type='code')
pad_msg_test = padding_data(data=msg_test,
dictionary=dict_msg,
params=params,
type='msg')
pad_code_test = padding_data(data=code_test,
dictionary=dict_code,
params=params,
type='code')
batches_train = mini_batches(X_msg=pad_msg_train,
X_code=pad_code_train,
Y=labels_train)
batches_test = mini_batches(X_msg=pad_msg_test,
X_code=pad_code_test,
Y=labels_test)
# set up parameters
params.cuda = (not params.no_cuda) and torch.cuda.is_available()
del params.no_cuda
params.vocab_msg, params.vocab_code = len(dict_msg), len(dict_code)
if len(labels_train.shape) == 1:
params.class_num = 1
else:
params.class_num = labels_train.shape[1]
params.device = torch.device(
'cuda' if torch.cuda.is_available() else 'cpu')
# create and train the defect model
model = DefectNet(params)
if torch.cuda.is_available():
model = model.cuda()
return model, batches_test, batches_train
def train_model_loss_undersampling(project, train, test, dictionary, params):
#####################################################################################################
# training model using penalized classification technique (modify loss function) and under sampling technique
#####################################################################################################
ids_train, labels_train, msg_train, code_train = train
ids_test, labels_test, msg_test, code_test = test
dict_msg, dict_code = dictionary
print('Dictionary message: %i -- Dictionary code: %i' %
(len(dict_msg), len(dict_code)))
print('Training data')
info_label(labels_train)
pad_msg_train = padding_data(data=msg_train,
dictionary=dict_msg,
params=params,
type='msg')
pad_code_train = padding_data(data=code_train,
dictionary=dict_code,
params=params,
type='code')
print('Testing data')
info_label(labels_test)
pad_msg_test = padding_data(data=msg_test,
dictionary=dict_msg,
params=params,
type='msg')
pad_code_test = padding_data(data=code_test,
dictionary=dict_code,
params=params,
type='code')
# set up parameters
params.cuda = (not params.no_cuda) and torch.cuda.is_available()
del params.no_cuda
params.filter_sizes = [int(k) for k in params.filter_sizes.split(',')]
params.save_dir = os.path.join(
params.save_dir,
datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S'))
params.vocab_msg, params.vocab_code = len(dict_msg), len(dict_code)
if len(labels_train.shape) == 1:
params.class_num = 1
else:
params.class_num = labels_train.shape[1]
params.device = torch.device(
'cuda' if torch.cuda.is_available() else 'cpu')
# create and train the defect model
model = DefectNet(args=params)
if torch.cuda.is_available():
model = model.cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=params.l2_reg_lambda)
steps = 0
batches_test = mini_batches(X_msg=pad_msg_test,
X_code=pad_code_test,
Y=labels_test)
for epoch in range(1, params.num_epochs + 1):
# building batches for training model
batches_train = mini_batches_undersampling(X_msg=pad_msg_train,
X_code=pad_code_train,
Y=labels_train)
for batch in batches_train:
pad_msg, pad_code, labels = batch
if torch.cuda.is_available():
pad_msg, pad_code, labels = torch.tensor(
pad_msg).cuda(), torch.tensor(
pad_code).cuda(), torch.cuda.FloatTensor(labels)
else:
pad_msg, pad_code, labels = torch.tensor(pad_msg).long(
), torch.tensor(pad_code).long(), torch.tensor(labels).float()
optimizer.zero_grad()
predict = model.forward(pad_msg, pad_code)
if project == 'openstack':
loss = custom_loss(y_pred=predict,
y_true=labels,
weights=[0.1, 1])
loss.backward()
optimizer.step()
elif project == 'qt':
print(
'We need to find the weights for negative and positive labels later'
)
exit()
else:
loss = nn.BCELoss()
loss = loss(predict, labels)
loss.backward()
optimizer.step()
steps += 1
if steps % params.log_interval == 0:
print('\rEpoch: {} step: {} - loss: {:.6f}'.format(
epoch, steps, loss.item()))
print('Epoch: %i ---Training data' % (epoch))
acc, prc, rc, f1, auc_ = eval(data=batches_train, model=model)
print(
'Accuracy: %f -- Precision: %f -- Recall: %f -- F1: %f -- AUC: %f'
% (acc, prc, rc, f1, auc_))
print('Epoch: %i ---Testing data' % (epoch))
acc, prc, rc, f1, auc_ = eval(data=batches_test, model=model)
print(
'Accuracy: %f -- Precision: %f -- Recall: %f -- F1: %f -- AUC: %f'
% (acc, prc, rc, f1, auc_))
if epoch % 5 == 0:
save(model, params.save_dir, 'epoch', epoch)
def train_model_mini_batches_update(train, test, dictionary, params):
#####################################################################################################
# training model using 50% of positive and 50% of negative data in mini batch
#####################################################################################################
ids_train, labels_train, msg_train, code_train = train
ids_test, labels_test, msg_test, code_test = test
dict_msg, dict_code = dictionary
print('Dictionary message: %i -- Dictionary code: %i' %
(len(dict_msg), len(dict_code)))
print('Training data')
info_label(labels_train)
pad_msg_train = padding_data(data=msg_train,
dictionary=dict_msg,
params=params,
type='msg')
pad_code_train = padding_data(data=code_train,
dictionary=dict_code,
params=params,
type='code')
print('Testing data')
info_label(labels_test)
pad_msg_test = padding_data(data=msg_test,
dictionary=dict_msg,
params=params,
type='msg')
pad_code_test = padding_data(data=code_test,
dictionary=dict_code,
params=params,
type='code')
# set up parameters
params.cuda = (not params.no_cuda) and torch.cuda.is_available()
del params.no_cuda
params.filter_sizes = [int(k) for k in params.filter_sizes.split(',')]
params.save_dir = os.path.join(
params.save_dir,
datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S'))
params.vocab_msg, params.vocab_code = len(dict_msg), len(dict_code)
if len(labels_train.shape) == 1:
params.class_num = 1
else:
params.class_num = labels_train.shape[1]
params.device = torch.device(
'cuda' if torch.cuda.is_available() else 'cpu')
# create and train the defect model
model = DefectNet(args=params)
if torch.cuda.is_available():
model = model.cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=params.l2_reg_lambda)
steps = 0
batches_test = mini_batches(X_msg=pad_msg_test,
X_code=pad_code_test,
Y=labels_test)
write_log = list()
for epoch in range(1, params.num_epochs + 1):
# building batches for training model
batches_train = mini_batches_update(X_msg=pad_msg_train,
X_code=pad_code_train,
Y=labels_train)
for batch in batches_train:
pad_msg, pad_code, labels = batch
if torch.cuda.is_available():
pad_msg, pad_code, labels = torch.tensor(
pad_msg).cuda(), torch.tensor(
pad_code).cuda(), torch.cuda.FloatTensor(labels)
else:
pad_msg, pad_code, labels = torch.tensor(pad_msg).long(
), torch.tensor(pad_code).long(), torch.tensor(labels).float()
optimizer.zero_grad()
ftr, predict = model.forward(pad_msg, pad_code)
loss = nn.BCELoss()
loss = loss(predict, labels)
loss.backward()
optimizer.step()
steps += 1
if steps % params.log_interval == 0:
print('\rEpoch: {} step: {} - loss: {:.6f}'.format(
epoch, steps, loss.item()))
print('Epoch: %i ---Training data' % (epoch))
acc, prc, rc, f1, auc_ = eval(data=batches_train, model=model)
print(
'Accuracy: %f -- Precision: %f -- Recall: %f -- F1: %f -- AUC: %f'
% (acc, prc, rc, f1, auc_))
print('Epoch: %i ---Testing data' % (epoch))
acc, prc, rc, f1, auc_ = eval(data=batches_test, model=model)
print(
'Accuracy: %f -- Precision: %f -- Recall: %f -- F1: %f -- AUC: %f'
% (acc, prc, rc, f1, auc_))
write_log.append(
'Epoch - testing: %i --- Accuracy: %f -- Precision: %f -- Recall: %f -- F1: %f -- AUC: %f'
% (epoch, acc, prc, rc, f1, auc_))
if epoch % 5 == 0:
save(model, params.save_dir, 'epoch', epoch)
write_file(params.save_dir + '/log.txt', write_log)