def __call__(self, data):
x = data.x
data.x = data.x[:, :5]
data = TwoMalkin()(data)
data = ConnectedThreeMalkin()(data)
data.x = x
return data
def __call__(self,data):
data.x = torch.zeros((data.num_nodes, 1), dtype=torch.float)
data = TwoMalkin()(data)
data = ConnectedThreeMalkin()(data)
data.x = degree(data.edge_index[0], data.num_nodes, dtype=torch.long)
data.x = F.one_hot(data.x//self.div, num_classes=(degrees[self.args.dataset])//self.div+1).to(torch.float)
return data
def __call__(self, data):
data.x = torch.zeros((data.num_nodes, 1), dtype=torch.float)
data = TwoMalkin()(data)
data = ConnectedThreeMalkin()(data)
data.x = degree(data.edge_index[0], data.num_nodes, dtype=torch.long)
data.x = one_hot(data.x, 136, torch.float)
return data
def __call__(self, data):
if data.x is None:
data.x = torch.zeros((data.num_nodes, 1), dtype=torch.float)
data = TwoMalkin()(data)
data = ConnectedThreeMalkin()(data)
data.x = degree(data.edge_index[0],
data.num_nodes,
dtype=torch.long)
data.x = F.one_hot(data.x,
num_classes=int(degrees[self.args.dataset])).to(
torch.float)
else:
x = data.x
data = TwoMalkin()(data)
data = ConnectedThreeMalkin()(data)
data.x = x
return data
args = parser.parse_args()
class MyFilter(object):
def __call__(self, data):
return not (data.num_nodes == 7 and data.num_edges == 12) and \
data.num_nodes < 450
BATCH = 20
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data',
'1-2-3-PROTEINS')
dataset = TUDataset(path,
name='PROTEINS',
pre_transform=T.Compose(
[TwoMalkin(), ConnectedThreeMalkin()]),
pre_filter=MyFilter())
perm = torch.randperm(len(dataset), dtype=torch.long)
dataset = dataset[perm]
dataset.data.iso_type_2 = torch.unique(dataset.data.iso_type_2, True, True)[1]
num_i_2 = dataset.data.iso_type_2.max().item() + 1
dataset.data.iso_type_2 = F.one_hot(dataset.data.iso_type_2,
num_classes=num_i_2).to(torch.float)
dataset.data.iso_type_3 = torch.unique(dataset.data.iso_type_3, True, True)[1]
num_i_3 = dataset.data.iso_type_3.max().item() + 1
dataset.data.iso_type_3 = F.one_hot(dataset.data.iso_type_3,
num_classes=num_i_3).to(torch.float)
class MyPreTransform(object):
def __call__(self, data):
return data
BATCH = 32
path = osp.join(
osp.dirname(osp.realpath(__file__)), '..', 'data', '1-2-3-MUTAG')
dataset = TUDataset(
path,
name='MUTAG',
pre_transform=T.Compose(
[MyPreTransform(),
TwoMalkin(), ConnectedThreeLocal()]),
pre_filter=MyFilter())
perm = torch.randperm(len(dataset), dtype=torch.long)
torch.save(perm, 'mutag_perm.pt')
perm = torch.load('mutag_perm.pt')
dataset = dataset[perm]
dataset.data.iso_type_2 = torch.unique(dataset.data.iso_type_2, True, True)[1]
num_i_2 = dataset.data.iso_type_2.max().item() + 1
dataset.data.iso_type_2 = F.one_hot(
dataset.data.iso_type_2, num_classes=num_i_2).to(torch.float)
dataset.data.iso_type_3 = torch.unique(dataset.data.iso_type_3, True, True)[1]
num_i_3 = dataset.data.iso_type_3.max().item() + 1
dataset.data.iso_type_3 = F.one_hot(
class MyPreTransform(object):
def __call__(self, data):
data.x = data.x[:, -3:] # Only use node attributes.
return data
BATCH = 20
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data',
'1-2-3-PROTEINS')
dataset = TUDataset(path,
name='PROTEINS',
pre_transform=T.Compose([
MyPreTransform(),
TwoMalkin(),
ConnectedThreeMalkin()
]),
pre_filter=MyFilter())
perm = torch.randperm(len(dataset), dtype=torch.long)
dataset = dataset[perm]
dataset.data.iso_type_2 = torch.unique(dataset.data.iso_type_2, True, True)[1]
num_i_2 = dataset.data.iso_type_2.max().item() + 1
dataset.data.iso_type_2 = F.one_hot(dataset.data.iso_type_2,
num_classes=num_i_2).to(torch.float)
dataset.data.iso_type_3 = torch.unique(dataset.data.iso_type_3, True, True)[1]
num_i_3 = dataset.data.iso_type_3.max().item() + 1
dataset.data.iso_type_3 = F.one_hot(dataset.data.iso_type_3,