def get_kcenter_test_loss(model,
adj,
bin_adj,
train_objectives,
test_objectives,
instances,
features,
num_reps=10,
hardmax=False,
update=False,
algoname=None):
loss = 0
for i in instances:
best_loss = 100
x_best = None
for _ in range(num_reps):
x = model(features[i], adj[i])
if x.sum() > args.K:
x = args.K * x / x.sum()
train_loss = train_objectives[i](x)
if train_loss.item() < best_loss:
best_loss = train_loss.item()
x_best = x
testvals = []
trainvals = []
for _ in range(50):
y = rounding(x_best)
testvals.append(test_objectives[i](y).item())
trainvals.append(train_objectives[i](y).item())
loss += testvals[np.argmin(trainvals)]
if update:
vals[algoname][test_instances.index(i)] = testvals[
np.argmin(trainvals)]
return loss / (len(instances))
def get_kcenter_test_loss(model,
adj,
bin_adj,
train_objectives,
test_objectives,
instances,
features,
num_reps=10,
hardmax=False,
update=False,
algoname=None):
loss = 0
for idx, i in enumerate(instances):
best_loss = 100
x_best = None
for _ in range(num_reps):
mu, r, embeds, dist = model(features[i], adj[i],
num_cluster_iter)
x = torch.softmax(dist * args.kcentertemp, 0).sum(dim=1)
x = 2 * (torch.sigmoid(4 * x) - 0.5)
if x.sum() > args.K:
x = args.K * x / x.sum()
train_loss = loss_fn(mu, r, embeds, dist, bin_adj[i],
train_objectives[i], args)
if train_loss.item() < best_loss:
best_loss = train_loss.item()
x_best = x
testvals = []
trainvals = []
for _ in range(50):
y = rounding(x_best)
testvals.append(test_objectives[i](y).item())
trainvals.append(train_objectives[i](y).item())
loss += testvals[np.argmin(trainvals)]
if update:
vals[algoname][test_instances.index(i)] = testvals[np.argmin(
trainvals)]
return loss / (len(instances))
curr_test_loss = loss_test.item()
#convert distances into a feasible (fractional x)
x_best = torch.softmax(dist * args.kcentertemp, 0).sum(dim=1)
x_best = 2 * (torch.sigmoid(4 * x_best) - 0.5)
if x_best.sum() > K:
x_best = K * x_best / x_best.sum()
losses.append(loss.item())
optimizer.step()
#for k-center: round 50 times and take the solution with best training
#value
if args.objective == 'kcenter':
testvals = []
trainvals = []
for _ in range(50):
y = rounding(x_best)
testvals.append(obj_test(y).item())
trainvals.append(obj_train(y).item())
print('ClusterNet value', testvals[np.argmin(trainvals)])
if args.objective == 'modularity':
print('ClusterNet value', curr_test_loss)
##############################################################################
#TRAIN TWO-STAGE
##############################################################################
def train_twostage(model_ts):
optimizer_ts = optim.Adam(model_ts.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)