import matplotlib.pyplot as plt
import networkx as nx
import gcn_utils
adj, _, labels, _, _, _ = gcn_utils.load_data('cora')
# Pre-processing for Networkx
nx_adj = nx.from_numpy_array(adj.ceil().numpy())
node_colour = labels.numpy() / max(labels)
labels = dict(
(node, label) for node, label in zip(range(len(labels)), labels.numpy()))
# Set options
options = {
'node_size': 35,
'font_size': 3,
'width': 1.0,
'alpha': 0.3,
'labels': labels,
'node_color': node_colour
}
# Draw graph
nx.draw_spectral(nx_adj, **options)
# nx.draw(nx_adj, **options)
plt.show()
help='Alpha for the leaky_relu.')
parser.add_argument('--patience', type=int, default=100, help='Patience')
# args = parser.parse_args([])
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
# Load data
adj, features, labels, idx_train, idx_val, idx_test = gcn_utils.load_data(
args.dataset)
# Model and optimizer
if args.model == 'GAT':
model = GAT(nfeat=features.shape[1],
nhid=args.hidden,
nclass=int(labels.max()) + 1,
dropout=args.dropout,
nheads=args.nb_heads,
alpha=args.alpha)
elif args.model == 'GCN':
model = GCN(nfeat=features.shape[1],
nhid=args.hidden,
nclass=int(labels.max()) + 1,
dropout=args.dropout)
else:
help='Number of hidden units.')
parser.add_argument('--dropout',
type=float,
default=0.5,
help='Dropout rate (1 - keep probability).')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
# Load data
adj, features, labels, idx_train, idx_val, idx_test = load_data()
# Model and optimizer
model = GCN(nfeat=features.shape[1],
nhid=args.hidden,
nclass=labels.max().item() + 1,
dropout=args.dropout)
optimizer = optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
if args.cuda:
model.cuda()
features = features.cuda()
adj = adj.cuda()
labels = labels.cuda()