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import torch
from torch_geometric.utils import (
batched_negative_sampling,
contains_self_loops,
is_undirected,
negative_sampling,
structured_negative_sampling,
structured_negative_sampling_feasible,
to_undirected,
)
from torch_geometric.utils._negative_sampling import (
edge_index_to_vector,
vector_to_edge_index,
)
def is_negative(edge_index, neg_edge_index, size, bipartite):
adj = torch.zeros(size, dtype=torch.bool)
neg_adj = torch.zeros(size, dtype=torch.bool)
adj[edge_index[0], edge_index[1]] = True
neg_adj[neg_edge_index[0], neg_edge_index[1]] = True
if not bipartite:
arange = torch.arange(size[0])
assert neg_adj[arange, arange].sum() == 0
return (adj & neg_adj).sum() == 0
def test_edge_index_to_vector_and_vice_versa():
# Create a fully-connected graph:
N = 10
row = torch.arange(N).view(-1, 1).repeat(1, N).view(-1)
col = torch.arange(N).view(1, -1).repeat(N, 1).view(-1)
edge_index = torch.stack([row, col], dim=0)
idx, population = edge_index_to_vector(edge_index, (N, N), bipartite=True)
assert population == N * N
assert idx.tolist() == list(range(population))
edge_index2 = vector_to_edge_index(idx, (N, N), bipartite=True)
assert is_undirected(edge_index2)
assert edge_index.tolist() == edge_index2.tolist()
idx, population = edge_index_to_vector(edge_index, (N, N), bipartite=False)
assert population == N * N - N
assert idx.tolist() == list(range(population))
mask = edge_index[0] != edge_index[1] # Remove self-loops.
edge_index2 = vector_to_edge_index(idx, (N, N), bipartite=False)
assert is_undirected(edge_index2)
assert edge_index[:, mask].tolist() == edge_index2.tolist()
idx, population = edge_index_to_vector(edge_index, (N, N), bipartite=False,
force_undirected=True)
assert population == (N * (N + 1)) / 2 - N
assert idx.tolist() == list(range(population))
mask = edge_index[0] != edge_index[1] # Remove self-loops.
edge_index2 = vector_to_edge_index(idx, (N, N), bipartite=False,
force_undirected=True)
assert is_undirected(edge_index2)
assert edge_index[:, mask].tolist() == to_undirected(edge_index2).tolist()
def test_negative_sampling():
edge_index = torch.as_tensor([[0, 0, 1, 2], [0, 1, 2, 3]])
neg_edge_index = negative_sampling(edge_index)
assert neg_edge_index.size(1) == edge_index.size(1)
assert is_negative(edge_index, neg_edge_index, (4, 4), bipartite=False)
neg_edge_index = negative_sampling(edge_index, method='dense')
assert neg_edge_index.size(1) == edge_index.size(1)
assert is_negative(edge_index, neg_edge_index, (4, 4), bipartite=False)
neg_edge_index = negative_sampling(edge_index, num_neg_samples=2)
assert neg_edge_index.size(1) == 2
assert is_negative(edge_index, neg_edge_index, (4, 4), bipartite=False)
edge_index = to_undirected(edge_index)
neg_edge_index = negative_sampling(edge_index, force_undirected=True)
assert neg_edge_index.size(1) == edge_index.size(1) - 1
assert is_undirected(neg_edge_index)
assert is_negative(edge_index, neg_edge_index, (4, 4), bipartite=False)
def test_bipartite_negative_sampling():
edge_index = torch.as_tensor([[0, 0, 1, 2], [0, 1, 2, 3]])
neg_edge_index = negative_sampling(edge_index, num_nodes=(3, 4))
assert neg_edge_index.size(1) == edge_index.size(1)
assert is_negative(edge_index, neg_edge_index, (3, 4), bipartite=True)
neg_edge_index = negative_sampling(edge_index, num_nodes=(3, 4),
num_neg_samples=2)
assert neg_edge_index.size(1) == 2
assert is_negative(edge_index, neg_edge_index, (3, 4), bipartite=True)
def test_batched_negative_sampling():
edge_index = torch.as_tensor([[0, 0, 1, 2], [0, 1, 2, 3]])
edge_index = torch.cat([edge_index, edge_index + 4], dim=1)
batch = torch.tensor([0, 0, 0, 0, 1, 1, 1, 1])
neg_edge_index = batched_negative_sampling(edge_index, batch)
assert neg_edge_index.size(1) <= edge_index.size(1)
adj = torch.zeros(8, 8, dtype=torch.bool)
adj[edge_index[0], edge_index[1]] = True
neg_adj = torch.zeros(8, 8, dtype=torch.bool)
neg_adj[neg_edge_index[0], neg_edge_index[1]] = True
assert (adj & neg_adj).sum() == 0
assert (adj | neg_adj).sum() == edge_index.size(1) + neg_edge_index.size(1)
assert neg_adj[:4, 4:].sum() == 0
assert neg_adj[4:, :4].sum() == 0
def test_bipartite_batched_negative_sampling():
edge_index1 = torch.as_tensor([[0, 0, 1, 1], [0, 1, 2, 3]])
edge_index2 = edge_index1 + torch.tensor([[2], [4]])
edge_index3 = edge_index2 + torch.tensor([[2], [4]])
edge_index = torch.cat([edge_index1, edge_index2, edge_index3], dim=1)
src_batch = torch.tensor([0, 0, 1, 1, 2, 2])
dst_batch = torch.tensor([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2])
neg_edge_index = batched_negative_sampling(edge_index,
(src_batch, dst_batch))
assert neg_edge_index.size(1) <= edge_index.size(1)
adj = torch.zeros(6, 12, dtype=torch.bool)
adj[edge_index[0], edge_index[1]] = True
neg_adj = torch.zeros(6, 12, dtype=torch.bool)
neg_adj[neg_edge_index[0], neg_edge_index[1]] = True
assert (adj & neg_adj).sum() == 0
assert (adj | neg_adj).sum() == edge_index.size(1) + neg_edge_index.size(1)
def test_structured_negative_sampling():
edge_index = torch.as_tensor([[0, 0, 1, 2], [0, 1, 2, 3]])
i, j, k = structured_negative_sampling(edge_index)
assert i.size(0) == edge_index.size(1)
assert j.size(0) == edge_index.size(1)
assert k.size(0) == edge_index.size(1)
adj = torch.zeros(4, 4, dtype=torch.bool)
adj[i, j] = 1
neg_adj = torch.zeros(4, 4, dtype=torch.bool)
neg_adj[i, k] = 1
assert (adj & neg_adj).sum() == 0
# Test with no self-loops:
edge_index = torch.LongTensor([[0, 0, 1, 1, 2], [1, 2, 0, 2, 1]])
i, j, k = structured_negative_sampling(edge_index, num_nodes=4,
contains_neg_self_loops=False)
neg_edge_index = torch.vstack([i, k])
assert not contains_self_loops(neg_edge_index)
def test_structured_negative_sampling_feasible():
edge_index = torch.LongTensor([[0, 0, 1, 1, 2, 2, 2],
[1, 2, 0, 2, 0, 1, 1]])
assert not structured_negative_sampling_feasible(edge_index, 3, False)
assert structured_negative_sampling_feasible(edge_index, 3, True)
assert structured_negative_sampling_feasible(edge_index, 4, False)
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