def test_to_undirected_with_duplicates():
edge_index = torch.tensor([[0, 0, 1, 1], [0, 1, 0, 2]])
edge_weight = torch.ones(4)
data = Data(edge_index=edge_index, edge_weight=edge_weight, num_nodes=3)
data = ToUndirected()(data)
assert len(data) == 3
assert data.edge_index.tolist() == [[0, 0, 1, 1, 2], [0, 1, 0, 2, 1]]
assert data.edge_weight.tolist() == [2, 2, 2, 1, 1]
assert data.num_nodes == 3
def test_to_undirected():
assert ToUndirected().__repr__() == 'ToUndirected()'
edge_index = torch.tensor([[2, 0, 2], [3, 1, 0]])
edge_weight = torch.randn(edge_index.size(1))
edge_attr = torch.randn(edge_index.size(1), 8)
perm = torch.tensor([1, 2, 1, 2, 0, 0])
data = Data(edge_index=edge_index,
edge_weight=edge_weight,
edge_attr=edge_attr,
num_nodes=4)
data = ToUndirected()(data)
assert len(data) == 4
assert data.edge_index.tolist() == [[0, 0, 1, 2, 2, 3], [1, 2, 0, 0, 3, 2]]
assert data.edge_weight.tolist() == edge_weight[perm].tolist()
assert data.edge_attr.tolist() == edge_attr[perm].tolist()
assert data.num_nodes == 4
dst_mapping=movie_mapping,
encoders={'rating': IdentityEncoder(dtype=torch.long)},
)
data = HeteroData()
data['user'].num_nodes = len(user_mapping) # Users do not have any features.
data['movie'].x = movie_x
data['user', 'rates', 'movie'].edge_index = edge_index
data['user', 'rates', 'movie'].edge_label = edge_label
print(data)
# We can now convert `data` into an appropriate format for training a
# graph-based machine learning model:
# 1. Add a reverse ('movie', 'rev_rates', 'user') relation for message passing.
data = ToUndirected()(data)
del data['movie', 'rev_rates', 'user'].edge_label # Remove "reverse" label.
# 2. Perform a link-level split into training, validation, and test edges.
transform = RandomLinkSplit(
num_val=0.05,
num_test=0.1,
neg_sampling_ratio=0.0,
edge_types=[('user', 'rates', 'movie')],
rev_edge_types=[('movie', 'rev_rates', 'user')],
)
train_data, val_data, test_data = transform(data)
print(train_data)
print(val_data)
print(test_data)
parser.add_argument('--dataset',
type=str,
default='politifact',
choices=['politifact', 'gossipcop'])
parser.add_argument('--feature',
type=str,
default='spacy',
choices=['profile', 'spacy', 'bert', 'content'])
parser.add_argument('--model',
type=str,
default='GCN',
choices=['GCN', 'GAT', 'SAGE'])
args = parser.parse_args()
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'UPFD')
train_dataset = UPFD(path, args.dataset, args.feature, 'train', ToUndirected())
val_dataset = UPFD(path, args.dataset, args.feature, 'val', ToUndirected())
test_dataset = UPFD(path, args.dataset, args.feature, 'test', ToUndirected())
train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=128, shuffle=False)
test_loader = DataLoader(test_dataset, batch_size=128, shuffle=False)
class Net(torch.nn.Module):
def __init__(self,
model,
in_channels,
hidden_channels,
out_channels,
concat=False):