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import torch
from torch_geometric.nn import HypergraphConv
def test_hypergraph_conv_with_more_nodes_than_edges():
in_channels, out_channels = (16, 32)
hyperedge_index = torch.tensor([[0, 0, 1, 1, 2, 3], [0, 1, 0, 1, 0, 1]])
num_nodes = hyperedge_index[0].max().item() + 1
num_edges = hyperedge_index[1].max().item() + 1
x = torch.randn((num_nodes, in_channels))
hyperedge_weight = torch.tensor([1.0, 0.5])
hyperedge_attr = torch.randn((num_edges, in_channels))
conv = HypergraphConv(in_channels, out_channels)
assert str(conv) == 'HypergraphConv(16, 32)'
out = conv(x, hyperedge_index)
assert out.size() == (num_nodes, out_channels)
out = conv(x, hyperedge_index, hyperedge_weight)
assert out.size() == (num_nodes, out_channels)
conv = HypergraphConv(in_channels, out_channels, use_attention=True,
heads=2)
out = conv(x, hyperedge_index, hyperedge_attr=hyperedge_attr)
assert out.size() == (num_nodes, 2 * out_channels)
out = conv(x, hyperedge_index, hyperedge_weight, hyperedge_attr)
assert out.size() == (num_nodes, 2 * out_channels)
conv = HypergraphConv(in_channels, out_channels, use_attention=True,
heads=2, concat=False, dropout=0.5)
out = conv(x, hyperedge_index, hyperedge_weight, hyperedge_attr)
assert out.size() == (num_nodes, out_channels)
def test_hypergraph_conv_with_more_edges_than_nodes():
in_channels, out_channels = (16, 32)
hyperedge_index = torch.tensor([[0, 0, 1, 1, 2, 3, 3, 3, 2, 1, 2],
[0, 1, 2, 1, 2, 1, 0, 3, 3, 4, 4]])
hyperedge_weight = torch.tensor([1.0, 0.5, 0.8, 0.2, 0.7])
num_nodes = hyperedge_index[0].max().item() + 1
x = torch.randn((num_nodes, in_channels))
conv = HypergraphConv(in_channels, out_channels)
assert str(conv) == 'HypergraphConv(16, 32)'
out = conv(x, hyperedge_index)
assert out.size() == (num_nodes, out_channels)
out = conv(x, hyperedge_index, hyperedge_weight)
assert out.size() == (num_nodes, out_channels)
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