y = le.fit_transform(labels)
print("Building vocabulary")
# Build vocabulary
max_document_length = max([len(x.split(" ")) for x in subgraphs])
x = np.zeros((len(subgraphs), max_document_length), dtype=np.int32)
for i in range(len(subgraphs)):
# print(i, "/", len(subgraphs))
communities = subgraphs[i].split()
for j in range(len(communities)):
x[i,j] = int(communities[j])
pkl.dump(x, open('x_news.pkl', 'wb'))
kf = KFold(n_splits=10, random_state=None)
kf.shuffle=True
train_accs = []
test_accs = []
it = 0
print("Starting cross-validation...")
for train_index, test_index in kf.split(x):
it += 1
x_train, x_test = x[train_index], x[test_index]
y_train, y_test = y[train_index], y[test_index]
train_loader, test_loader = create_train_test_loaders(Q, x_train, x_test, y_train, y_test, batch_size)
cnn = CNN(input_size=num_filters, hidden_size=hidden_size, num_classes=np.unique(y).size, dim=dim, num_kernels=num_kernels, max_document_length=max_document_length)
if torch.cuda.is_available():
def main():
global args
args = parser.parse_args()
# Check if CUDA is enabled
args.cuda = not args.no_cuda and torch.cuda.is_available()
unlabeled_datasets = [
"IMDB-BINARY", "IMDB-MULTI", "REDDIT-BINARY", "REDDIT-MULTI-5K",
"COLLAB", "SYNTHETIC"
]
if args.dataset in unlabeled_datasets:
use_node_labels = False
from graph_kernels import sp_kernel, wl_kernel
else:
use_node_labels = True
from graph_kernels_labeled import sp_kernel, wl_kernel
kernels = [wl_kernel]
n_kernels = len(kernels)
print('Computing graph maps')
Q, subgraphs, labels, shapes = compute_nystrom(args.dataset,
use_node_labels, args.d,
args.community_detection,
kernels)
M = np.zeros((shapes[0], shapes[1], n_kernels))
for idx, k in enumerate(kernels):
M[:, :, idx] = Q[idx]
Q = M
# Binarize labels
le = LabelEncoder()
y = le.fit_transform(labels)
# Build vocabulary
max_n_communities = max([len(x.split(" ")) for x in subgraphs])
x = np.zeros((len(subgraphs), max_n_communities), dtype=np.int32)
for i in range(len(subgraphs)):
communities = subgraphs[i].split()
for j in range(len(communities)):
x[i, j] = int(communities[j])
kf = KFold(n_splits=10, random_state=None)
kf.shuffle = True
accs = []
it = 0
print('Starting cross-validation')
for train_index, test_index in kf.split(x):
it += 1
best_acc1 = 0
x_train, x_test = x[train_index], x[test_index]
y_train, y_test = y[train_index], y[test_index]
x_train, x_val, y_train, y_val = train_test_split(x_train,
y_train,
test_size=0.1)
train_loader, val_loader, test_loader = create_train_val_test_loaders(
Q, x_train, x_val, x_test, y_train, y_val, y_test, args.batch_size)
print('\tCreate model')
model = CNN(input_size=args.n_filters,
hidden_size=args.hidden_size,
n_classes=np.unique(y).size,
d=args.d,
n_kernels=n_kernels,
max_n_communities=max_n_communities)
print('Optimizer')
optimizer = optim.Adam(model.parameters(), lr=args.lr)
criterion = nn.CrossEntropyLoss()
evaluation = lambda output, target: torch.sum(output.eq(target)
) / target.size()[0]
#print('Logger')
#logger = Logger(args.logPath)
lr = args.lr
lr_step = (args.lr - args.lr * args.lr_decay) / (
args.epochs * args.schedule[1] - args.epochs * args.schedule[0])
if os.path.isdir(args.checkpoint_dir):
shutil.rmtree(args.checkpoint_dir)
os.makedirs(args.checkpoint_dir)
print('Check cuda')
if args.cuda:
print('\t* Cuda')
model = model.cuda()
criterion = criterion.cuda()
# Epoch for loop
for epoch in range(0, args.epochs):
if epoch > args.epochs * args.schedule[
0] and epoch < args.epochs * args.schedule[1]:
lr -= lr_step
#args.lr -= lr_step
for param_group in optimizer.param_groups:
param_group['lr'] = lr
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, evaluation)
# evaluate on test set
acc1 = validate(val_loader, model, criterion, evaluation)
is_best = acc1 > best_acc1
best_acc1 = max(acc1, best_acc1)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_acc1': best_acc1,
'optimizer': optimizer.state_dict(),
},
is_best=is_best,
directory=args.checkpoint_dir)
# Logger step
#logger.log_value('learning_rate', args.lr).step()
# get the best checkpoint and test it with test set
best_model_file = os.path.join(args.checkpoint_dir, 'model_best.pth')
if not os.path.isdir(args.checkpoint_dir):
os.makedirs(args.checkpoint_dir)
if os.path.isfile(best_model_file):
print("=> loading best model '{}'".format(best_model_file))
checkpoint = torch.load(best_model_file)
args.start_epoch = checkpoint['epoch']
best_acc1 = checkpoint['best_acc1']
model.load_state_dict(checkpoint['state_dict'])
if args.cuda:
model.cuda()
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded best model '{}' (epoch {})".format(
best_model_file, checkpoint['epoch']))
else:
print("=> no best model found at '{}'".format(best_model_file))
# For testing
acc = validate(test_loader, model, criterion, evaluation)
print("Accuracy at iteration " + str(it) + ": " + str(acc))
accs.append(acc)
print("Average accuracy: ", np.mean(accs))
print("std: ", np.std(accs))
