help='Number of clusters.')
parser.add_argument('--dropout',
type=float,
default=0.,
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)
adj, features = load_gene_data(use_features=True,
_adj='homology',
_feat='ontology')
# create graph from adjacency matrix
G = nx.from_numpy_matrix(adj.toarray())
# print(adj.shape)
#
# print(G.number_of_edges())
# print(G.number_of_nodes())
adj_label = adj
adj_label = torch.FloatTensor(adj_label.toarray())
adj_train = preprocess_graph(adj)
default=0.,
help='Dropout rate (1 - keep probability).')
parser.add_argument('--saved-model',
type=str,
default='models/gae',
help='Saved model')
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)
adj, features = load_gene_data()
G = nx.from_numpy_matrix(adj.toarray())
adj_train = preprocess_graph(adj)
model = GCNModelAE(nfeat=features.shape[1],
nhid=args.hidden,
nclass=args.ndim,
dropout=args.dropout)
model.load_state_dict(torch.load(args.saved_model))
model.eval()
model(features, adj_train)
output = model.mu.data
# Normalize the output data
data = output