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import pytest
import torch
import torch_geometric.typing
from torch_geometric.nn import Node2Vec
from torch_geometric.testing import (
has_package,
is_full_test,
withDevice,
withPackage,
)
@withDevice
@withPackage('pyg_lib|torch_cluster')
@pytest.mark.parametrize('p', [1.0])
@pytest.mark.parametrize('q', [1.0, 0.5])
def test_node2vec(device, p, q):
edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]], device=device)
kwargs = dict(embedding_dim=16, walk_length=2, context_size=2, p=p, q=q)
if not torch_geometric.typing.WITH_TORCH_CLUSTER and q != 1.0:
with pytest.raises(ImportError, match="requires the 'torch-cluster'"):
model = Node2Vec(edge_index, **kwargs)
return
model = Node2Vec(edge_index, **kwargs).to(device)
assert str(model) == 'Node2Vec(3, 16)'
assert model(torch.arange(3, device=device)).size() == (3, 16)
pos_rw, neg_rw = model.sample(torch.arange(3))
assert float(model.loss(pos_rw.to(device), neg_rw.to(device))) >= 0
if has_package('sklearn'):
acc = model.test(torch.ones(20, 16), torch.randint(10, (20, )),
torch.ones(20, 16), torch.randint(10, (20, )))
assert 0 <= acc and acc <= 1
if is_full_test():
jit = torch.jit.script(model)
assert jit(torch.arange(3, device=device)).size() == (3, 16)
pos_rw, neg_rw = jit.sample(torch.arange(3))
assert float(jit.loss(pos_rw.to(device), neg_rw.to(device))) >= 0
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