def test_submodular(net,
epoch,
sample_instance,
dataset,
device='cpu',
evaluate=True):
net.eval()
# loss_fn = torch.nn.BCELoss()
loss_fn = torch.nn.MSELoss()
test_losses, test_objs = [], []
n, m, d, f, budget = sample_instance.n, sample_instance.m, torch.Tensor(
sample_instance.d), torch.Tensor(
sample_instance.f), sample_instance.budget
A, b, G, h = createConstraintMatrix(m, n, budget)
with tqdm.tqdm(dataset) as tqdm_loader:
for batch_idx, (features, labels) in enumerate(tqdm_loader):
features, labels = features.to(device), labels.to(device)
if epoch >= 0:
outputs = net(features)
else:
outputs = labels
# two-stage loss
loss = loss_fn(outputs, labels)
# decision-focused loss
objective_value_list = []
batch_size = len(labels)
for (label, output) in zip(labels, outputs):
if evaluate:
optimize_result = getOptimalDecision(n,
m,
output,
d,
f,
budget=budget)
optimal_x = torch.Tensor(optimize_result.x)
obj = getObjective(optimal_x, n, m, label, d, f)
else:
obj = torch.Tensor([0])
objective_value_list.append(obj)
objective = sum(objective_value_list) / batch_size
test_losses.append(loss.item())
test_objs.append(objective.item())
average_loss = np.mean(test_losses)
average_obj = np.mean(test_objs)
tqdm_loader.set_postfix(loss=f'{average_loss:.3f}',
obj=f'{average_obj:.3f}')
average_loss = np.mean(test_losses)
average_obj = np.mean(test_objs)
return average_loss, average_obj
optimizer = torch.optim.SGD(net.parameters(), lr=lr)
# surrogate setup
if training_method == 'surrogate':
# A, b, G, h = LPCreateSurrogateConstraintMatrix(m, n)
variable_size = n
T_size = 8
# init_T = normalize_matrix(torch.rand(variable_size, T_size))
init_T = normalize_matrix_positive(torch.rand(variable_size, T_size))
T = torch.tensor(init_T, requires_grad=True)
T_lr = lr
T_optimizer = torch.optim.Adam([T], lr=T_lr)
optimize_result = getOptimalDecision(n,
m,
torch.Tensor(sample_instance.c),
sample_instance.d,
sample_instance.f,
budget=budget)
optimal_x = torch.Tensor(optimize_result.x)
xx = torch.autograd.Variable(optimal_x, requires_grad=True)
d, f = sample_instance.d, sample_instance.f
c = torch.Tensor(
sample_instance.c
) # torch.autograd.Variable(torch.Tensor(sample_instance.c), requires_grad=True)
obj = getObjective(xx, n, m, c, d, f)
jac_torch = torch.autograd.grad(obj, xx)
jac_manual = getManualDerivative(xx.detach(), n, m, c, d, f)
print('torch grad:', jac_torch)
print('hand grad:', jac_manual)
hessian = getHessian(optimal_x, n, m, torch.Tensor(c), d, f)
def train_submodular(net,
optimizer,
epoch,
sample_instance,
dataset,
lr=0.1,
training_method='two-stage',
device='cpu',
evaluate=True):
net.train()
# loss_fn = torch.nn.BCELoss()
loss_fn = torch.nn.MSELoss()
train_losses, train_objs = [], []
n, m, d, f, budget = sample_instance.n, sample_instance.m, torch.Tensor(
sample_instance.d), torch.Tensor(
sample_instance.f), sample_instance.budget
A, b, G, h = createConstraintMatrix(m, n, budget)
forward_time, inference_time, qp_time, backward_time = 0, 0, 0, 0
REG = 0.0
with tqdm.tqdm(dataset) as tqdm_loader:
for batch_idx, (features, labels) in enumerate(tqdm_loader):
net_start_time = time.time()
features, labels = features.to(device), labels.to(device)
if epoch >= 0:
outputs = net(features)
else:
outputs = labels
# two-stage loss
loss = loss_fn(outputs, labels)
forward_time += time.time() - net_start_time
# decision-focused loss
objective_value_list = []
batch_size = len(labels)
for (label, output) in zip(labels, outputs):
forward_start_time = time.time()
if training_method == 'decision-focused':
inference_start_time = time.time()
min_fun = -np.inf
for _ in range(1):
tmp_result = getOptimalDecision(n,
m,
output,
d,
f,
budget=budget,
REG=REG)
if tmp_result.fun > min_fun:
optimize_result = tmp_result
min_fun = tmp_result.fun
inference_time += time.time() - inference_start_time
optimal_x = torch.Tensor(optimize_result.x)
if optimize_result.success:
qp_start_time = time.time()
newA, newb = torch.Tensor(), torch.Tensor()
newG = torch.cat((A, G))
newh = torch.cat((b, h))
Q = getHessian(optimal_x, n, m, output, d, f,
REG=REG) + torch.eye(n) * 10
L = torch.cholesky(Q)
jac = -getDerivative(optimal_x,
n,
m,
output,
d,
f,
create_graph=True,
REG=REG)
p = jac - Q @ optimal_x
qp_solver = qpth.qp.QPFunction()
x = qp_solver(Q, p, G, h, A, b)[0]
# if True:
# # =============== solving QP using CVXPY ===============
# x_default = cp.Variable(n)
# G_default, h_default = cp.Parameter(newG.shape), cp.Parameter(newh.shape)
# L_default = cp.Parameter((n,n))
# p_default = cp.Parameter(n)
# constraints = [G_default @ x_default <= h_default]
# objective = cp.Minimize(0.5 * cp.sum_squares(L_default @ x_default) + p_default.T @ x_default)
# problem = cp.Problem(objective, constraints)
# cvxpylayer = CvxpyLayer(problem, parameters=[G_default, h_default, L_default, p_default], variables=[x_default])
# coverage_qp_solution, = cvxpylayer(newG, newh, L, p)
# x = coverage_qp_solution
# except:
# print("CVXPY solver fails... Usually because Q is not PSD")
# x = optimal_x
else:
print('Optimization failed...')
x = optimal_x
obj = getObjective(x, n, m, label, d, f, REG=0)
qp_time += time.time() - qp_start_time
elif training_method == 'two-stage':
if evaluate:
inference_start_time = time.time()
optimize_result = getOptimalDecision(n,
m,
output,
d,
f,
budget=budget,
REG=REG)
x = torch.Tensor(optimize_result.x)
obj = getObjective(x, n, m, label, d, f, REG=0)
inference_time += time.time() - inference_start_time
qp_time = 0
else:
obj = torch.Tensor([0])
qp_time = 0
else:
raise ValueError('Not implemented method!')
objective_value_list.append(obj)
objective = sum(objective_value_list) / batch_size
optimizer.zero_grad()
backward_start_time = time.time()
try:
if training_method == 'two-stage':
loss.backward()
elif training_method == 'decision-focused':
# (-objective).backward()
(-objective * 0.5 + loss * 0.5).backward() # TODO
for parameter in net.parameters():
parameter.grad = torch.clamp(parameter.grad,
min=-MAX_NORM,
max=MAX_NORM)
else:
raise ValueError('Not implemented method')
except:
print("no grad is backpropagated...")
pass
optimizer.step()
backward_time += time.time() - backward_start_time
train_losses.append(loss.item())
train_objs.append(objective.item())
average_loss = np.mean(train_losses)
average_obj = np.mean(train_objs)
# Print status
tqdm_loader.set_postfix(loss=f'{average_loss:.6f}',
obj=f'{average_obj:.6f}')
average_loss = np.mean(train_losses)
average_obj = np.mean(train_objs)
return average_loss, average_obj, (forward_time, inference_time, qp_time,
backward_time)
