def __init__(self, graphs, max_nodes, normalize=False):
self.adjs = []
self.features = []
self.rwrs = []
self.labels = []
self.max_nodes = max_nodes
self.feat_dim = len(graphs[0].nodes[list(
graphs[0].nodes.keys())[0]]['feat'])
for graph in graphs:
num_nodes = graph.number_of_nodes()
adj = np.array(nx.to_numpy_matrix(graph))
if normalize:
adj = normalize_adjacency(adj)
self.adjs.append(adj)
self.labels.append(graph.graph['label'])
feat = np.zeros((self.max_nodes, self.feat_dim), dtype=float)
feat[:num_nodes] = np.array(
list(nx.get_node_attributes(graph, 'feat').values()))[:]
self.features.append(feat)
rwr = np.zeros((self.max_nodes, self.max_nodes), dtype=float)
src_array = np.array(
list(nx.get_node_attributes(graph, 'rwr').values()))[:]
rwr[:src_array.shape[0], :src_array.shape[0]] = src_array
self.rwrs.append(-np.sort(-rwr, axis=1))
self.feat_dim = self.features[0].shape[1]
from utils import load_data, accuracy, normalize_adjacency from models import GNN # Hyperparameters epochs = 100 n_hidden_1 = 64 n_hidden_2 = 32 learning_rate = 0.01 dropout_rate = 0.5 # Read data features, adj, class_labels = load_data() n = adj.shape[0] # Number of nodes n_class = class_labels.shape[1] adj = normalize_adjacency(adj) # Normalize adjacency matrix # Yields indices to split data into training, validation and test sets idx = np.random.permutation(n) idx_train = idx[:int(0.6 * n)] idx_val = idx[int(0.6 * n):int(0.8 * n)] idx_test = idx[int(0.8 * n):] # Transform the numpy matrices/vectors to torch tensors features = torch.FloatTensor(features) y = torch.LongTensor(np.argmax(class_labels, axis=1)) adj = torch.FloatTensor(adj) idx_train = torch.LongTensor(idx_train) idx_val = torch.LongTensor(idx_val) idx_test = torch.LongTensor(idx_test)
