def evaluatePoint(self, sol):
criterion = MyLossReg(self.regCoe, self.costs, self.xhat, self.budget)
sol = torch.tensor(sol, dtype=torch.float)
y = self.model(sol)
y = y.squeeze()
self.loss, self.actualCost = criterion(y, self.us, self.x)
return self.loss
def evaluateSolution(self, trueGame):
true_model = trueGame.f
criterion = MyLossReg(self.regCoe, self.costs, self.xhat, self.budget)
y = true_model(self.xalter)
y = y.squeeze()
self.trueoptVal, self.trueActualCost = criterion(y, self.us, self.xalter)
self.trueoptVal = self.trueoptVal.item()
return self.trueoptVal
def evaluateSolution(self, trueGame):
true_model = trueGame.f
self.regCoe = 1
criterion = MyLossReg(self.regCoe, torch.tensor(self.costs, dtype=torch.float), torch.tensor(self.xhat, dtype=torch.float)
, torch.tensor(self.budget, dtype=torch.float))
self.xsol = torch.tensor(self.xsol, dtype=torch.float)
y = true_model(self.xsol)
y = y.squeeze()
self.trueoptVal, self.trueActualCost = criterion(y, torch.tensor(self.us, dtype=torch.float), self.xsol)
self.trueoptVal = self.trueoptVal.item()
return self.trueoptVal
def evaluateSolution(self, trueGame):
true_model = copy.deepcopy(trueGame.f)
criterion = MyLossReg(self.regCoe, self.costs, self.xhat, self.budget)
y = true_model(self.x)
y = y.squeeze()
self.trueoptVal, self.trueActualCost = criterion(y, self.us, self.x)
self.trueoptVal = self.trueoptVal.item()
y = true_model(self.xhat)
y = y.squeeze()
self.trueoriVal, self.trueoriCost = criterion(y, self.us, self.xhat)
self.trueoriVal = self.trueoriVal.item()
print('optimized loss: ', self.trueoptVal, ', initial loss: ',
self.trueoriVal)
return self.trueoptVal
def solve(self):
self.solveStart = time.time()
criterion = MyLossReg(self.regCoe, self.costs, self.xhat, self.budget)
optimizer = torch.optim.RMSprop([self.x], lr=self.lr)
for t in range(10000):
y = self.model(self.x)
y = y.squeeze()
self.loss, self.actualCost = criterion(y, self.us, self.x)
optimizer.zero_grad()
self.loss.backward()
optimizer.step()
self.x.data.clamp_(min=0, max=1)
self.extractSolution()
self.solveTime = time.time() - self.solveStart
self.optVal = self.loss.item()
return self.optVal
def solve(self):
criterion = MyLossReg(self.regCoe, self.costs, self.xhat, self.budget)
optimizer = torch.optim.RMSprop([self.x], lr=self.lr)
for t in range(10000):
y = self.model(self.x)
y = y.squeeze()
self.loss, self.actualCost = criterion(y, self.us, self.x)
if t % 1000 == 0:
print("t = ", t, self.loss.item(), self.actualCost,
self.budget)
optimizer.zero_grad()
self.loss.backward()
optimizer.step()
self.x.data.clamp_(min=0, max=1)
self.extractSolution()
return self.loss.data
