def sgcn_plus_v2(feat_data,
labels,
lap_matrix,
train_nodes,
valid_nodes,
test_nodes,
args,
device,
calculate_grad_vars=False):
# use multiprocess sample data
process_ids = np.arange(args.batch_num)
pool = mp.Pool(args.pool_num)
lap_matrix_sq = lap_matrix.multiply(lap_matrix)
jobs = prepare_data(pool, sampler, process_ids, train_nodes, samp_num_list,
len(feat_data), lap_matrix, lap_matrix_sq,
args.n_layers)
susage = GCN(nfeat=feat_data.shape[1],
nhid=args.nhid,
num_classes=num_classes,
layers=args.n_layers,
dropout=args.dropout).to(device)
susage.to(device)
print(susage)
adjs_full, input_nodes_full, sampled_nodes_full = full_batch_sampler(
train_nodes, len(feat_data), lap_matrix, args.n_layers)
adjs_full = package_mxl(adjs_full, device)
# this stupid wrapper is only used for sgcn++
forward_wrapper = ForwardWrapperMomentum(len(feat_data), args.nhid,
args.n_layers, num_classes)
optimizer = optim.SGD(filter(lambda p: p.requires_grad,
susage.parameters()),
lr=0.1)
loss_train = []
loss_test = []
grad_variance_all = []
loss_train_all = []
best_model = copy.deepcopy(susage)
best_val_loss = 10 # randomly pick a large number is fine
best_val_index = 0
best_val_cnt = 0
for epoch in np.arange(args.epoch_num):
# fetch train data
train_data = [job.get() for job in jobs]
pool.close()
pool.join()
# prepare next epoch train data
pool = mp.Pool(args.pool_num)
jobs = prepare_data(pool, sampler,
process_ids, train_nodes, samp_num_list,
len(feat_data), lap_matrix, lap_matrix_sq,
args.n_layers)
inner_loop_num = args.batch_num
# compare with sgcn_plus, the only difference is we use multi_level_spider_step_v1 here
cur_train_loss, cur_train_loss_all, grad_variance = multi_level_momentum_step(
susage,
optimizer,
feat_data,
labels,
train_nodes,
valid_nodes,
adjs_full,
sampled_nodes_full,
train_data,
inner_loop_num,
forward_wrapper,
device,
calculate_grad_vars=calculate_grad_vars)
loss_train_all.extend(cur_train_loss_all)
grad_variance_all.extend(grad_variance)
# calculate validate loss
susage.eval()
susage.zero_grad()
val_loss, _ = susage.calculate_loss_grad(feat_data, adjs_full, labels,
valid_nodes)
if val_loss + 0.01 < best_val_loss:
best_val_loss = val_loss
del best_model
best_model = copy.deepcopy(susage)
best_val_index = epoch
best_val_cnt = 0
cur_test_loss = val_loss
best_val_cnt += 1
loss_train.append(cur_train_loss)
loss_test.append(cur_test_loss)
# print progress
print('Epoch: ', epoch, '| train loss: %.8f' % cur_train_loss,
'| test loss: %.8f' % cur_test_loss)
if best_val_cnt > 10:
break
f1_score_test = best_model.calculate_f1(feat_data, adjs_full, labels,
test_nodes)
return best_model, loss_train[:
best_val_index], loss_test[:
best_val_index], loss_train_all, f1_score_test, grad_variance_all
def sgcn(feat_data,
labels,
lap_matrix,
train_nodes,
valid_nodes,
test_nodes,
args,
device,
calculate_grad_vars=False,
full_batch=False):
# use multiprocess sample data
process_ids = np.arange(args.batch_num)
pool = mp.Pool(args.pool_num)
lap_matrix_sq = lap_matrix.multiply(lap_matrix)
jobs = prepare_data(pool, sampler, process_ids, train_nodes, samp_num_list,
len(feat_data), lap_matrix, lap_matrix_sq,
args.n_layers)
susage = GCN(nfeat=feat_data.shape[1],
nhid=args.nhid,
num_classes=num_classes,
layers=args.n_layers,
dropout=args.dropout).to(device)
susage.to(device)
print(susage)
adjs_full, input_nodes_full, sampled_nodes_full = full_batch_sampler(
train_nodes, len(feat_data), lap_matrix, args.n_layers)
adjs_full = package_mxl(adjs_full, device)
optimizer = optim.SGD(filter(lambda p: p.requires_grad,
susage.parameters()),
lr=args.lr)
loss_train = []
loss_test = []
grad_variance_all = []
loss_train_all = []
best_model = copy.deepcopy(susage)
best_val_loss = 10 # randomly pick a large number is fine
best_val_index = 0
best_val_cnt = 0
for epoch in np.arange(args.epoch_num):
# fetch train data
train_data = [job.get() for job in jobs]
pool.close()
pool.join()
# prepare next epoch train data
pool = mp.Pool(args.pool_num)
jobs = prepare_data(pool, sampler,
process_ids, train_nodes, samp_num_list,
len(feat_data), lap_matrix, lap_matrix_sq,
args.n_layers)
# it can also run full-batch GD by ignoring all the samplings
if full_batch:
inner_loop_num = args.batch_num
cur_train_loss, cur_train_loss_all, grad_variance = full_step(
susage,
optimizer,
feat_data,
labels,
train_nodes,
valid_nodes,
adjs_full,
train_data,
inner_loop_num,
device,
calculate_grad_vars=calculate_grad_vars)
else:
inner_loop_num = args.batch_num
cur_train_loss, cur_train_loss_all, grad_variance = sgd_step(
susage,
optimizer,
feat_data,
labels,
train_nodes,
valid_nodes,
adjs_full,
train_data,
inner_loop_num,
device,
calculate_grad_vars=calculate_grad_vars)
loss_train_all.extend(cur_train_loss_all)
grad_variance_all.extend(grad_variance)
# calculate test loss
susage.eval()
susage.zero_grad()
val_loss, _ = susage.calculate_loss_grad(feat_data, adjs_full, labels,
valid_nodes)
if val_loss + 5e-4 < best_val_loss:
best_val_loss = val_loss
del best_model
best_model = copy.deepcopy(susage)
best_val_index = epoch
best_val_cnt = 0
cur_test_loss = val_loss
best_val_cnt += 1
loss_train.append(cur_train_loss)
loss_test.append(cur_test_loss)
# print progress
print('Epoch: ', epoch, '| train loss: %.8f' % cur_train_loss,
'| test loss: %.8f' % cur_test_loss)
if best_val_cnt > 10:
break
f1_score_test = best_model.calculate_f1(feat_data, adjs_full, labels,
test_nodes)
return best_model, loss_train, loss_test, loss_train_all, f1_score_test, grad_variance_all, best_val_index
