def read_gtn_data(self, folder):
data = sio.loadmat(osp.join(folder, 'data.mat'))
if self.name == 'han-acm' or self.name == 'han-imdb':
truelabels, truefeatures = data['label'], data['feature'].astype(float)
elif self.name == 'han-dblp':
truelabels, truefeatures = data['label'], data['features'].astype(float)
num_nodes = truefeatures.shape[0]
if self.name == 'han-acm':
rownetworks = [data['PAP'] - np.eye(num_nodes), data['PLP'] - np.eye(num_nodes)]
elif self.name == 'han-dblp':
rownetworks = [data['net_APA'] - np.eye(num_nodes), data['net_APCPA'] - np.eye(num_nodes), data['net_APTPA'] - np.eye(num_nodes)]
elif self.name == 'han-imdb':
rownetworks = [data['MAM'] - np.eye(num_nodes), data['MDM'] - np.eye(num_nodes), data['MYM'] - np.eye(num_nodes)]
y = truelabels
train_idx = data['train_idx']
val_idx = data['val_idx']
test_idx = data['test_idx']
train_mask = sample_mask(train_idx, y.shape[0])
val_mask = sample_mask(val_idx, y.shape[0])
test_mask = sample_mask(test_idx, y.shape[0])
y_train = np.argmax(y[train_mask, :], axis=1)
y_val = np.argmax(y[val_mask, :], axis=1)
y_test = np.argmax(y[test_mask, :], axis=1)
data = Data()
A = []
for i, edge in enumerate(rownetworks):
edge_tmp = torch.from_numpy(np.vstack((edge.nonzero()[0], edge.nonzero()[1]))).type(torch.LongTensor)
value_tmp = torch.ones(edge_tmp.shape[1]).type(torch.FloatTensor)
A.append((edge_tmp, value_tmp))
edge_tmp = torch.stack((torch.arange(0,num_nodes), torch.arange(0,num_nodes))).type(torch.LongTensor)
value_tmp = torch.ones(num_nodes).type(torch.FloatTensor)
A.append((edge_tmp, value_tmp))
data.adj = A
data.x = torch.from_numpy(truefeatures).type(torch.FloatTensor)
data.train_node = torch.from_numpy(train_idx[0]).type(torch.LongTensor)
data.train_target = torch.from_numpy(y_train).type(torch.LongTensor)
data.valid_node = torch.from_numpy(val_idx[0]).type(torch.LongTensor)
data.valid_target = torch.from_numpy(y_val).type(torch.LongTensor)
data.test_node = torch.from_numpy(test_idx[0]).type(torch.LongTensor)
data.test_target = torch.from_numpy(y_test).type(torch.LongTensor)
self.data = data
def read_gtn_data(self, folder):
edges = pickle.load(open(osp.join(folder, 'edges.pkl'), 'rb'))
labels = pickle.load(open(osp.join(folder, 'labels.pkl'), 'rb'))
node_features = pickle.load(
open(osp.join(folder, 'node_features.pkl'), 'rb'))
data = Data()
data.x = torch.from_numpy(node_features).type(torch.FloatTensor)
num_nodes = edges[0].shape[0]
A = []
for i, edge in enumerate(edges):
edge_tmp = torch.from_numpy(
np.vstack((edge.nonzero()[0],
edge.nonzero()[1]))).type(torch.LongTensor)
value_tmp = torch.ones(edge_tmp.shape[1]).type(torch.FloatTensor)
A.append((edge_tmp, value_tmp))
edge_tmp = torch.stack(
(torch.arange(0, num_nodes),
torch.arange(0, num_nodes))).type(torch.LongTensor)
value_tmp = torch.ones(num_nodes).type(torch.FloatTensor)
A.append((edge_tmp, value_tmp))
data.adj = A
data.train_node = torch.from_numpy(np.array(labels[0])[:, 0]).type(
torch.LongTensor)
data.train_target = torch.from_numpy(np.array(labels[0])[:, 1]).type(
torch.LongTensor)
data.valid_node = torch.from_numpy(np.array(labels[1])[:, 0]).type(
torch.LongTensor)
data.valid_target = torch.from_numpy(np.array(labels[1])[:, 1]).type(
torch.LongTensor)
data.test_node = torch.from_numpy(np.array(labels[2])[:, 0]).type(
torch.LongTensor)
data.test_target = torch.from_numpy(np.array(labels[2])[:, 1]).type(
torch.LongTensor)
self.data = data
def read_gtn_data(self, folder):
edges = pickle.load(open(osp.join(folder, 'edges.pkl'), 'rb'))
labels = pickle.load(open(osp.join(folder, 'labels.pkl'), 'rb'))
node_features = pickle.load(
open(osp.join(folder, 'node_features.pkl'), 'rb'))
data = Data()
data.x = torch.from_numpy(node_features).type(torch.FloatTensor)
num_nodes = edges[0].shape[0]
node_type = np.zeros((num_nodes), dtype=int)
assert len(edges) == 4
assert len(edges[0].nonzero()) == 2
node_type[edges[0].nonzero()[0]] = 0
node_type[edges[0].nonzero()[1]] = 1
node_type[edges[1].nonzero()[0]] = 1
node_type[edges[1].nonzero()[1]] = 0
node_type[edges[2].nonzero()[0]] = 0
node_type[edges[2].nonzero()[1]] = 2
node_type[edges[3].nonzero()[0]] = 2
node_type[edges[3].nonzero()[1]] = 0
print(node_type)
data.pos = torch.from_numpy(node_type)
edge_list = []
for i, edge in enumerate(edges):
edge_tmp = torch.from_numpy(
np.vstack((edge.nonzero()[0],
edge.nonzero()[1]))).type(torch.LongTensor)
edge_list.append(edge_tmp)
data.edge_index = torch.cat(edge_list, 1)
A = []
for i, edge in enumerate(edges):
edge_tmp = torch.from_numpy(
np.vstack((edge.nonzero()[0],
edge.nonzero()[1]))).type(torch.LongTensor)
value_tmp = torch.ones(edge_tmp.shape[1]).type(torch.FloatTensor)
A.append((edge_tmp, value_tmp))
edge_tmp = torch.stack(
(torch.arange(0, num_nodes),
torch.arange(0, num_nodes))).type(torch.LongTensor)
value_tmp = torch.ones(num_nodes).type(torch.FloatTensor)
A.append((edge_tmp, value_tmp))
data.adj = A
data.train_node = torch.from_numpy(np.array(labels[0])[:, 0]).type(
torch.LongTensor)
data.train_target = torch.from_numpy(np.array(labels[0])[:, 1]).type(
torch.LongTensor)
data.valid_node = torch.from_numpy(np.array(labels[1])[:, 0]).type(
torch.LongTensor)
data.valid_target = torch.from_numpy(np.array(labels[1])[:, 1]).type(
torch.LongTensor)
data.test_node = torch.from_numpy(np.array(labels[2])[:, 0]).type(
torch.LongTensor)
data.test_target = torch.from_numpy(np.array(labels[2])[:, 1]).type(
torch.LongTensor)
y = np.zeros((num_nodes), dtype=int)
x_index = torch.cat((data.train_node, data.valid_node, data.test_node))
y_index = torch.cat(
(data.train_target, data.valid_target, data.test_target))
y[x_index.numpy()] = y_index.numpy()
data.y = torch.from_numpy(y)
self.data = data
def read_gtn_data(self, folder):
data = sio.loadmat(osp.join(folder, "data.mat"))
if self.name == "han-acm" or self.name == "han-imdb":
truelabels, truefeatures = data["label"], data["feature"].astype(
float)
elif self.name == "han-dblp":
truelabels, truefeatures = data["label"], data["features"].astype(
float)
num_nodes = truefeatures.shape[0]
if self.name == "han-acm":
rownetworks = [
data["PAP"] - np.eye(num_nodes),
data["PLP"] - np.eye(num_nodes)
]
elif self.name == "han-dblp":
rownetworks = [
data["net_APA"] - np.eye(num_nodes),
data["net_APCPA"] - np.eye(num_nodes),
data["net_APTPA"] - np.eye(num_nodes),
]
elif self.name == "han-imdb":
rownetworks = [
data["MAM"] - np.eye(num_nodes),
data["MDM"] - np.eye(num_nodes),
data["MYM"] - np.eye(num_nodes),
]
y = truelabels
train_idx = data["train_idx"]
val_idx = data["val_idx"]
test_idx = data["test_idx"]
train_mask = sample_mask(train_idx, y.shape[0])
val_mask = sample_mask(val_idx, y.shape[0])
test_mask = sample_mask(test_idx, y.shape[0])
y_train = np.argmax(y[train_mask, :], axis=1)
y_val = np.argmax(y[val_mask, :], axis=1)
y_test = np.argmax(y[test_mask, :], axis=1)
data = Data()
A = []
for i, edge in enumerate(rownetworks):
edge_tmp = torch.from_numpy(
np.vstack((edge.nonzero()[0],
edge.nonzero()[1]))).type(torch.LongTensor)
value_tmp = torch.ones(edge_tmp.shape[1]).type(torch.FloatTensor)
A.append((edge_tmp, value_tmp))
edge_tmp = torch.stack(
(torch.arange(0, num_nodes),
torch.arange(0, num_nodes))).type(torch.LongTensor)
value_tmp = torch.ones(num_nodes).type(torch.FloatTensor)
A.append((edge_tmp, value_tmp))
data.adj = A
data.x = torch.from_numpy(truefeatures).type(torch.FloatTensor)
data.train_node = torch.from_numpy(train_idx[0]).type(torch.LongTensor)
data.train_target = torch.from_numpy(y_train).type(torch.LongTensor)
data.valid_node = torch.from_numpy(val_idx[0]).type(torch.LongTensor)
data.valid_target = torch.from_numpy(y_val).type(torch.LongTensor)
data.test_node = torch.from_numpy(test_idx[0]).type(torch.LongTensor)
data.test_target = torch.from_numpy(y_test).type(torch.LongTensor)
self.data = data
