def test_citeseer():
root = osp.join('/', 'tmp', str(random.randrange(sys.maxsize)))
dataset = Planetoid(root, 'Citeseer')
loader = DataLoader(dataset, batch_size=len(dataset))
assert len(dataset) == 1
assert dataset.__repr__() == 'Citeseer()'
for data in loader:
assert data.num_graphs == 1
assert data.num_nodes == 3327
assert data.num_edges / 2 == 4552
assert len(data) == 7
assert list(data.x.size()) == [data.num_nodes, 3703]
assert list(data.y.size()) == [data.num_nodes]
assert data.y.max() + 1 == 6
assert data.train_mask.sum() == 6 * 20
assert data.val_mask.sum() == 500
assert data.test_mask.sum() == 1000
assert (data.train_mask & data.val_mask & data.test_mask).sum() == 0
assert list(data.batch.size()) == [data.num_nodes]
assert data.contains_isolated_nodes()
assert not data.contains_self_loops()
assert data.is_undirected()
shutil.rmtree(root)
def test_citeseer():
root = osp.join('/', 'tmp', str(random.randrange(sys.maxsize)))
dataset = Planetoid(root, 'Citeseer')
loader = DataLoader(dataset, batch_size=len(dataset))
assert len(dataset) == 1
assert dataset.__repr__() == 'Citeseer()'
for data in loader:
assert data.num_graphs == 1
assert data.num_nodes == 3327
assert data.num_edges / 2 == 4552
assert len(data) == 8
assert list(data.x.size()) == [data.num_nodes, 3703]
assert list(data.y.size()) == [data.num_nodes]
assert data.y.max() + 1 == 6
assert data.train_mask.sum() == 6 * 20
assert data.val_mask.sum() == 500
assert data.test_mask.sum() == 1000
assert (data.train_mask & data.val_mask & data.test_mask).sum() == 0
assert list(data.batch.size()) == [data.num_nodes]
assert data.ptr.tolist() == [0, data.num_nodes]
assert data.has_isolated_nodes()
assert not data.has_self_loops()
assert data.is_undirected()
dataset = Planetoid(root, 'Citeseer', split='full')
data = dataset[0]
assert data.val_mask.sum() == 500
assert data.test_mask.sum() == 1000
assert data.train_mask.sum() == data.num_nodes - 1500
assert (data.train_mask & data.val_mask & data.test_mask).sum() == 0
dataset = Planetoid(root,
'Citeseer',
split='random',
num_train_per_class=11,
num_val=29,
num_test=41)
data = dataset[0]
assert data.train_mask.sum() == dataset.num_classes * 11
assert data.val_mask.sum() == 29
assert data.test_mask.sum() == 41
assert (data.train_mask & data.val_mask & data.test_mask).sum() == 0
shutil.rmtree(root)