def pretrain_dsm(model, t_train, e_train, t_valid, e_valid,
n_iter=10000, lr=1e-2, thres=1e-4):
premodel = DeepSurvivalMachinesTorch(1, 1,
dist=model.dist)
premodel.double()
optimizer = torch.optim.Adam(premodel.parameters(), lr=lr)
oldcost = float('inf')
patience = 0
costs = []
for _ in tqdm(range(n_iter)):
optimizer.zero_grad()
loss = unconditional_loss(premodel, t_train, e_train)
loss.backward()
optimizer.step()
valid_loss = unconditional_loss(premodel, t_valid, e_valid)
valid_loss = valid_loss.detach().cpu().numpy()
costs.append(valid_loss)
if np.abs(costs[-1] - oldcost) < thres:
patience += 1
if patience == 3:
break
oldcost = costs[-1]
return premodel
def pretrain_dsm(model,
t_train,
e_train,
t_valid,
e_valid,
n_iter=10000,
lr=1e-2,
thres=1e-4,
cuda=False):
premodel = DeepSurvivalMachinesTorch(1,
1,
dist=model.dist,
risks=model.risks,
optimizer=model.optimizer).double()
if cuda:
premodel.cuda()
t_train, e_train = t_train.cuda(), e_train.cuda()
t_valid, e_valid = t_valid.cuda(), e_valid.cuda()
optimizer = get_optimizer(premodel, lr)
oldcost = float('inf')
patience = 0
costs = []
for _ in tqdm(range(n_iter)):
optimizer.zero_grad()
loss = 0
for r in range(model.risks):
loss += unconditional_loss(premodel, t_train, e_train, str(r + 1))
loss.backward()
optimizer.step()
valid_loss = 0
for r in range(model.risks):
valid_loss += unconditional_loss(premodel, t_valid, e_valid,
str(r + 1))
valid_loss = valid_loss.detach().cpu().numpy()
costs.append(valid_loss)
#print(valid_loss)
if np.abs(costs[-1] - oldcost) < thres:
patience += 1
if patience == 3:
break
oldcost = costs[-1]
return premodel