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import pytest
import torch
import torch_geometric
from torch_geometric.data.hypergraph_data import HyperGraphData
from torch_geometric.loader import DataLoader
def test_hypergraph_data():
torch_geometric.set_debug(True)
x = torch.tensor([[1, 3, 5, 7], [2, 4, 6, 8], [7, 8, 9, 10]],
dtype=torch.float).t()
edge_index = torch.tensor([[0, 1, 2, 1, 2, 3, 0, 2, 3],
[0, 0, 0, 1, 1, 1, 2, 2, 2]])
data = HyperGraphData(x=x, edge_index=edge_index).to(torch.device('cpu'))
data.validate(raise_on_error=True)
assert data.num_nodes == 4
assert data.num_edges == 3
assert data.node_attrs() == ['x']
assert data.edge_attrs() == ['edge_index']
assert data.x.tolist() == x.tolist()
assert data['x'].tolist() == x.tolist()
assert data.get('x').tolist() == x.tolist()
assert data.get('y', 2) == 2
assert data.get('y', None) is None
assert sorted(data.keys()) == ['edge_index', 'x']
assert len(data) == 2
assert 'x' in data and 'edge_index' in data and 'pos' not in data
D = data.to_dict()
assert len(D) == 2
assert 'x' in D and 'edge_index' in D
D = data.to_namedtuple()
assert len(D) == 2
assert D.x is not None and D.edge_index is not None
assert data.__cat_dim__('x', data.x) == 0
assert data.__cat_dim__('edge_index', data.edge_index) == -1
assert data.__inc__('x', data.x) == 0
assert torch.equal(data.__inc__('edge_index', data.edge_index),
torch.tensor([[data.num_nodes], [data.num_edges]]))
data_list = [data, data]
loader = DataLoader(data_list, batch_size=2)
batch = next(iter(loader))
batched_edge_index = batch.edge_index
assert batched_edge_index.tolist() == [[
0, 1, 2, 1, 2, 3, 0, 2, 3, 4, 5, 6, 5, 6, 7, 4, 6, 7
], [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5]]
assert not data.x.is_contiguous()
data.contiguous()
assert data.x.is_contiguous()
assert not data.is_coalesced()
data = data.coalesce()
assert data.is_coalesced()
clone = data.clone()
assert clone != data
assert len(clone) == len(data)
assert clone.x.data_ptr() != data.x.data_ptr()
assert clone.x.tolist() == data.x.tolist()
assert clone.edge_index.data_ptr() != data.edge_index.data_ptr()
assert clone.edge_index.tolist() == data.edge_index.tolist()
data['x'] = x + 1
assert data.x.tolist() == (x + 1).tolist()
assert str(data) == 'HyperGraphData(x=[4, 3], edge_index=[2, 9])'
dictionary = {'x': data.x, 'edge_index': data.edge_index}
data = HyperGraphData.from_dict(dictionary)
assert sorted(data.keys()) == ['edge_index', 'x']
assert not data.has_isolated_nodes()
# assert not data.has_self_loops()
# assert data.is_undirected()
# assert not data.is_directed()
assert data.num_nodes == 4
assert data.num_edges == 3
with pytest.warns(UserWarning, match='deprecated'):
assert data.num_faces is None
assert data.num_node_features == 3
assert data.num_features == 3
data.edge_attr = torch.randn(data.num_edges, 2)
assert data.num_edge_features == 2
assert data.is_edge_attr('edge_attr')
data.edge_attr = None
data.x = None
with pytest.warns(UserWarning, match='Unable to accurately infer'):
assert data.num_nodes == 4
data.edge_index = None
with pytest.warns(UserWarning, match='Unable to accurately infer'):
assert data.num_nodes is None
assert data.num_edges == 0
data.num_nodes = 4
assert data.num_nodes == 4
data = HyperGraphData(x=x, attribute=x)
assert len(data) == 2
assert data.x.tolist() == x.tolist()
assert data.attribute.tolist() == x.tolist()
face = torch.tensor([[0, 1], [1, 2], [2, 3]])
data = HyperGraphData(num_nodes=4, face=face)
with pytest.warns(UserWarning, match='deprecated'):
assert data.num_faces == 2
assert data.num_nodes == 4
data = HyperGraphData(title='test')
assert str(data) == "HyperGraphData(title='test')"
assert data.num_node_features == 0
# assert data.num_edge_features == 0
key = value = 'test_value'
data[key] = value
assert data[key] == value
del data[value]
del data[value] # Deleting unset attributes should work as well.
assert data.get(key) is None
assert data.get('title') == 'test'
torch_geometric.set_debug(False)
def test_hypergraphdata_subgraph():
x = torch.arange(5)
y = torch.tensor([0.])
edge_index = torch.tensor([[0, 1, 3, 2, 4, 0, 3, 4, 2, 1, 2, 3],
[0, 0, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3]])
edge_attr = torch.rand(4, 2)
data = HyperGraphData(x=x, y=y, edge_index=edge_index, edge_attr=edge_attr,
num_nodes=5)
out = data.subgraph(torch.tensor([1, 2, 4]))
assert len(out) == 5
assert torch.equal(out.x, torch.tensor([1, 2, 4]))
assert torch.equal(out.y, data.y)
assert out.edge_index.tolist() == [[1, 2, 2, 1, 0, 1], [0, 0, 1, 1, 2, 2]]
assert torch.equal(out.edge_attr, edge_attr[[1, 2, 3]])
assert out.num_nodes == 3
# Test unordered selection:
out = data.subgraph(torch.tensor([3, 1, 2]))
assert len(out) == 5
assert torch.equal(out.x, torch.tensor([3, 1, 2]))
assert torch.equal(out.y, data.y)
assert out.edge_index.tolist() == [[0, 2, 0, 2, 1, 2, 0],
[0, 0, 1, 1, 2, 2, 2]]
assert torch.equal(out.edge_attr, edge_attr[[1, 2, 3]])
assert out.num_nodes == 3
out = data.subgraph(torch.tensor([False, False, False, True, True]))
assert len(out) == 5
assert torch.equal(out.x, torch.arange(3, 5))
assert torch.equal(out.y, data.y)
assert out.edge_index.tolist() == [[0, 1, 0, 1], [0, 0, 1, 1]]
assert torch.equal(out.edge_attr, edge_attr[[1, 2]])
assert out.num_nodes == 2
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