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import pytest
import torch
from torch_geometric.data import Data, HeteroData
from torch_geometric.testing import (
get_random_edge_index,
onlyFullTest,
onlyOnline,
)
from torch_geometric.transforms import RandomLinkSplit, ToSparseTensor
from torch_geometric.utils import is_undirected, to_undirected
def test_random_link_split():
assert str(RandomLinkSplit()) == ('RandomLinkSplit('
'num_val=0.1, num_test=0.2)')
edge_index = torch.tensor([[0, 1, 1, 2, 2, 3, 3, 4, 4, 5],
[1, 0, 2, 1, 3, 2, 4, 3, 5, 4]])
edge_attr = torch.randn(edge_index.size(1), 3)
data = Data(edge_index=edge_index, edge_attr=edge_attr, num_nodes=100)
# No test split:
transform = RandomLinkSplit(num_val=2, num_test=0, is_undirected=True)
train_data, val_data, test_data = transform(data)
assert len(train_data) == 5
assert train_data.num_nodes == 100
assert train_data.edge_index.size() == (2, 6)
assert train_data.edge_attr.size() == (6, 3)
assert train_data.edge_label_index.size(1) == 6
assert train_data.edge_label.size(0) == 6
assert len(val_data) == 5
assert val_data.num_nodes == 100
assert val_data.edge_index.size() == (2, 6)
assert val_data.edge_attr.size() == (6, 3)
assert val_data.edge_label_index.size(1) == 4
assert val_data.edge_label.size(0) == 4
assert len(test_data) == 5
assert test_data.num_nodes == 100
assert test_data.edge_index.size() == (2, 10)
assert test_data.edge_attr.size() == (10, 3)
assert test_data.edge_label_index.size() == (2, 0)
assert test_data.edge_label.size() == (0, )
# Percentage split:
transform = RandomLinkSplit(num_val=0.2, num_test=0.2,
neg_sampling_ratio=2.0, is_undirected=False)
train_data, val_data, test_data = transform(data)
assert len(train_data) == 5
assert train_data.num_nodes == 100
assert train_data.edge_index.size() == (2, 6)
assert train_data.edge_attr.size() == (6, 3)
assert train_data.edge_label_index.size(1) == 18
assert train_data.edge_label.size(0) == 18
assert len(val_data) == 5
assert val_data.num_nodes == 100
assert val_data.edge_index.size() == (2, 6)
assert val_data.edge_attr.size() == (6, 3)
assert val_data.edge_label_index.size(1) == 6
assert val_data.edge_label.size(0) == 6
assert len(test_data) == 5
assert test_data.num_nodes == 100
assert test_data.edge_index.size() == (2, 8)
assert test_data.edge_attr.size() == (8, 3)
assert test_data.edge_label_index.size(1) == 6
assert test_data.edge_label.size(0) == 6
# Disjoint training split:
transform = RandomLinkSplit(num_val=0.2, num_test=0.2, is_undirected=False,
disjoint_train_ratio=0.5)
train_data, val_data, test_data = transform(data)
assert len(train_data) == 5
assert train_data.num_nodes == 100
assert train_data.edge_index.size() == (2, 3)
assert train_data.edge_attr.size() == (3, 3)
assert train_data.edge_label_index.size(1) == 6
assert train_data.edge_label.size(0) == 6
def test_random_link_split_with_to_sparse_tensor():
edge_index = torch.tensor([[0, 1, 1, 2, 2, 3, 3, 4, 4, 5],
[1, 0, 2, 1, 3, 2, 4, 3, 5, 4]])
data = Data(edge_index=edge_index, num_nodes=6)
transform = RandomLinkSplit(num_val=2, num_test=2, neg_sampling_ratio=0.0)
train_data1, _, _ = transform(data)
assert train_data1.edge_index.size(1) == train_data1.edge_label.size(0)
train_data2 = ToSparseTensor()(train_data1)
assert train_data1.edge_label.equal(train_data2.edge_label)
assert train_data1.edge_label_index.equal(train_data2.edge_label_index)
def test_random_link_split_with_label():
edge_index = torch.tensor([[0, 1, 1, 2, 2, 3, 3, 4, 4, 5],
[1, 0, 2, 1, 3, 2, 4, 3, 5, 4]])
edge_label = torch.tensor([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
data = Data(edge_index=edge_index, edge_label=edge_label, num_nodes=6)
transform = RandomLinkSplit(num_val=0.2, num_test=0.2,
neg_sampling_ratio=0.0)
train_data, _, _ = transform(data)
assert len(train_data) == 4
assert train_data.num_nodes == 6
assert train_data.edge_index.size() == (2, 6)
assert train_data.edge_label_index.size() == (2, 6)
assert train_data.edge_label.size() == (6, )
assert train_data.edge_label.min() == 0
assert train_data.edge_label.max() == 1
transform = RandomLinkSplit(num_val=0.2, num_test=0.2,
neg_sampling_ratio=1.0)
train_data, _, _ = transform(data)
assert len(train_data) == 4
assert train_data.num_nodes == 6
assert train_data.edge_index.size() == (2, 6)
assert train_data.edge_label_index.size() == (2, 12)
assert train_data.edge_label.size() == (12, )
assert train_data.edge_label.min() == 0
assert train_data.edge_label.max() == 2
assert train_data.edge_label[6:].sum() == 0
def test_random_link_split_increment_label():
edge_index = torch.tensor([[0, 1, 1, 2, 2, 3, 3, 4, 4, 5],
[1, 0, 2, 1, 3, 2, 4, 3, 5, 4]])
edge_label = torch.tensor([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
data = Data(edge_index=edge_index, edge_label=edge_label, num_nodes=6)
transform = RandomLinkSplit(num_val=0, num_test=0, neg_sampling_ratio=0.0)
train_data, _, _ = transform(data)
assert train_data.edge_label.numel() == edge_index.size(1)
assert train_data.edge_label.min() == 0
assert train_data.edge_label.max() == 1
transform = RandomLinkSplit(num_val=0, num_test=0, neg_sampling_ratio=1.0)
train_data, _, _ = transform(data)
assert train_data.edge_label.numel() == 2 * edge_index.size(1)
assert train_data.edge_label.min() == 0
assert train_data.edge_label.max() == 2
assert train_data.edge_label[edge_index.size(1):].sum() == 0
def test_random_link_split_on_hetero_data():
data = HeteroData()
data['p'].x = torch.arange(100)
data['a'].x = torch.arange(100, 300)
data['p', 'p'].edge_index = get_random_edge_index(100, 100, 500)
data['p', 'p'].edge_index = to_undirected(data['p', 'p'].edge_index)
data['p', 'p'].edge_attr = torch.arange(data['p', 'p'].num_edges)
data['p', 'a'].edge_index = get_random_edge_index(100, 200, 1000)
data['p', 'a'].edge_attr = torch.arange(500, 1500)
data['a', 'p'].edge_index = data['p', 'a'].edge_index.flip([0])
data['a', 'p'].edge_attr = torch.arange(1500, 2500)
transform = RandomLinkSplit(num_val=0.2, num_test=0.2, is_undirected=True,
edge_types=('p', 'p'))
train_data, val_data, test_data = transform(data)
assert len(train_data['p']) == 1
assert len(train_data['a']) == 1
assert len(train_data['p', 'p']) == 4
assert len(train_data['p', 'a']) == 2
assert len(train_data['a', 'p']) == 2
assert is_undirected(train_data['p', 'p'].edge_index,
train_data['p', 'p'].edge_attr)
assert is_undirected(val_data['p', 'p'].edge_index,
val_data['p', 'p'].edge_attr)
assert is_undirected(test_data['p', 'p'].edge_index,
test_data['p', 'p'].edge_attr)
transform = RandomLinkSplit(num_val=0.2, num_test=0.2,
edge_types=('p', 'a'),
rev_edge_types=('a', 'p'))
train_data, val_data, test_data = transform(data)
assert len(train_data['p']) == 1
assert len(train_data['a']) == 1
assert len(train_data['p', 'p']) == 2
assert len(train_data['p', 'a']) == 4
assert len(train_data['a', 'p']) == 2
assert train_data['p', 'a'].edge_index.size() == (2, 600)
assert train_data['p', 'a'].edge_attr.size() == (600, )
assert train_data['p', 'a'].edge_attr.min() >= 500
assert train_data['p', 'a'].edge_attr.max() <= 1500
assert train_data['a', 'p'].edge_index.size() == (2, 600)
assert train_data['a', 'p'].edge_attr.size() == (600, )
assert train_data['a', 'p'].edge_attr.min() >= 500
assert train_data['a', 'p'].edge_attr.max() <= 1500
assert train_data['p', 'a'].edge_label_index.size() == (2, 1200)
assert train_data['p', 'a'].edge_label.size() == (1200, )
assert val_data['p', 'a'].edge_index.size() == (2, 600)
assert val_data['p', 'a'].edge_attr.size() == (600, )
assert val_data['p', 'a'].edge_attr.min() >= 500
assert val_data['p', 'a'].edge_attr.max() <= 1500
assert val_data['a', 'p'].edge_index.size() == (2, 600)
assert val_data['a', 'p'].edge_attr.size() == (600, )
assert val_data['a', 'p'].edge_attr.min() >= 500
assert val_data['a', 'p'].edge_attr.max() <= 1500
assert val_data['p', 'a'].edge_label_index.size() == (2, 400)
assert val_data['p', 'a'].edge_label.size() == (400, )
assert test_data['p', 'a'].edge_index.size() == (2, 800)
assert test_data['p', 'a'].edge_attr.size() == (800, )
assert test_data['p', 'a'].edge_attr.min() >= 500
assert test_data['p', 'a'].edge_attr.max() <= 1500
assert test_data['a', 'p'].edge_index.size() == (2, 800)
assert test_data['a', 'p'].edge_attr.size() == (800, )
assert test_data['a', 'p'].edge_attr.min() >= 500
assert test_data['a', 'p'].edge_attr.max() <= 1500
assert test_data['p', 'a'].edge_label_index.size() == (2, 400)
assert test_data['p', 'a'].edge_label.size() == (400, )
transform = RandomLinkSplit(num_val=0.2, num_test=0.2, is_undirected=True,
edge_types=[('p', 'p'), ('p', 'a')],
rev_edge_types=[None, ('a', 'p')])
train_data, val_data, test_data = transform(data)
assert len(train_data['p']) == 1
assert len(train_data['a']) == 1
assert len(train_data['p', 'p']) == 4
assert len(train_data['p', 'a']) == 4
assert len(train_data['a', 'p']) == 2
assert is_undirected(train_data['p', 'p'].edge_index,
train_data['p', 'p'].edge_attr)
assert train_data['p', 'a'].edge_index.size() == (2, 600)
assert train_data['a', 'p'].edge_index.size() == (2, 600)
# No reverse edge types specified:
transform = RandomLinkSplit(edge_types=[('p', 'p'), ('p', 'a')])
train_data, val_data, test_data = transform(data)
assert train_data['p', 'p'].num_edges < data['p', 'p'].num_edges
assert train_data['p', 'a'].num_edges < data['p', 'a'].num_edges
assert train_data['a', 'p'].num_edges == data['a', 'p'].num_edges
def test_random_link_split_on_undirected_hetero_data():
data = HeteroData()
data['p'].x = torch.arange(100)
data['p', 'p'].edge_index = get_random_edge_index(100, 100, 500)
data['p', 'p'].edge_index = to_undirected(data['p', 'p'].edge_index)
transform = RandomLinkSplit(is_undirected=True, edge_types=('p', 'p'))
train_data, val_data, test_data = transform(data)
assert train_data['p', 'p'].is_undirected()
transform = RandomLinkSplit(is_undirected=True, edge_types=('p', 'p'),
rev_edge_types=('p', 'p'))
train_data, val_data, test_data = transform(data)
assert train_data['p', 'p'].is_undirected()
transform = RandomLinkSplit(is_undirected=True, edge_types=('p', 'p'),
rev_edge_types=('p', 'p'))
train_data, val_data, test_data = transform(data)
assert train_data['p', 'p'].is_undirected()
def test_random_link_split_insufficient_negative_edges():
edge_index = torch.tensor([[0, 0, 1, 1, 2, 2], [1, 3, 0, 2, 0, 1]])
data = Data(edge_index=edge_index, num_nodes=4)
transform = RandomLinkSplit(num_val=0.34, num_test=0.34,
is_undirected=False, neg_sampling_ratio=2,
split_labels=True)
with pytest.warns(UserWarning, match="not enough negative edges"):
train_data, val_data, test_data = transform(data)
assert train_data.neg_edge_label_index.size() == (2, 2)
assert val_data.neg_edge_label_index.size() == (2, 2)
assert test_data.neg_edge_label_index.size() == (2, 2)
def test_random_link_split_non_contiguous():
edge_index = get_random_edge_index(40, 40, num_edges=150)
edge_index = edge_index[:, :100]
assert not edge_index.is_contiguous()
data = Data(edge_index=edge_index, num_nodes=40)
transform = RandomLinkSplit(num_val=0.2, num_test=0.2)
train_data, val_data, test_data = transform(data)
assert train_data.num_edges == 60
assert train_data.edge_index.is_contiguous()
data = HeteroData()
data['p'].num_nodes = 40
data['p', 'p'].edge_index = edge_index
transform = RandomLinkSplit(num_val=0.2, num_test=0.2,
edge_types=('p', 'p'))
train_data, val_data, test_data = transform(data)
assert train_data['p', 'p'].num_edges == 60
assert train_data['p', 'p'].edge_index.is_contiguous()
@onlyOnline
@onlyFullTest
def test_random_link_split_on_dataset(get_dataset):
dataset = get_dataset(name='MUTAG')
dataset.transform = RandomLinkSplit(
num_val=0.1,
num_test=0.1,
disjoint_train_ratio=0.3,
add_negative_train_samples=False,
)
train_dataset, val_dataset, test_dataset = zip(*dataset)
assert len(train_dataset) == len(dataset)
assert len(val_dataset) == len(dataset)
assert len(test_dataset) == len(dataset)
assert isinstance(train_dataset[0], Data)
assert train_dataset[0].edge_label.min() == 1.0
assert train_dataset[0].edge_label.max() == 1.0
assert isinstance(val_dataset[0], Data)
assert val_dataset[0].edge_label.min() == 0.0
assert val_dataset[0].edge_label.max() == 1.0
assert isinstance(test_dataset[0], Data)
assert test_dataset[0].edge_label.min() == 0.0
assert test_dataset[0].edge_label.max() == 1.0
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