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import torch
from torch import tensor
from torch_geometric.data import HeteroData
from torch_geometric.transforms import AddMetaPaths, AddRandomMetaPaths
from torch_geometric.utils import coalesce
def generate_data() -> HeteroData:
data = HeteroData()
data['p'].x = torch.ones(5)
data['a'].x = torch.ones(6)
data['c'].x = torch.ones(3)
data['p', 'p'].edge_index = tensor([[0, 1, 2, 3], [1, 2, 4, 2]])
data['p', 'a'].edge_index = tensor([[0, 1, 2, 3, 4], [2, 2, 5, 2, 5]])
data['a', 'p'].edge_index = data['p', 'a'].edge_index.flip([0])
data['c', 'p'].edge_index = tensor([[0, 0, 1, 2, 2], [0, 1, 2, 3, 4]])
data['p', 'c'].edge_index = data['c', 'p'].edge_index.flip([0])
return data
def test_add_metapaths() -> None:
data = generate_data()
# Test transform options:
metapaths = [[('p', 'c'), ('c', 'p')]]
transform = AddMetaPaths(metapaths)
assert str(transform) == 'AddMetaPaths()'
meta1 = transform(data)
transform = AddMetaPaths(metapaths, drop_orig_edge_types=True)
assert str(transform) == 'AddMetaPaths()'
meta2 = transform(data)
transform = AddMetaPaths(metapaths, drop_orig_edge_types=True,
keep_same_node_type=True)
assert str(transform) == 'AddMetaPaths()'
meta3 = transform(data)
transform = AddMetaPaths(metapaths, drop_orig_edge_types=True,
keep_same_node_type=True,
drop_unconnected_node_types=True)
assert str(transform) == 'AddMetaPaths()'
meta4 = transform(data)
assert meta1['metapath_0'].edge_index.size() == (2, 9)
assert meta2['metapath_0'].edge_index.size() == (2, 9)
assert meta3['metapath_0'].edge_index.size() == (2, 9)
assert meta4['metapath_0'].edge_index.size() == (2, 9)
assert all([i in meta1.edge_types for i in data.edge_types])
assert meta2.edge_types == [('p', 'metapath_0', 'p')]
assert meta3.edge_types == [('p', 'to', 'p'), ('p', 'metapath_0', 'p')]
assert meta4.edge_types == [('p', 'to', 'p'), ('p', 'metapath_0', 'p')]
assert meta3.node_types == ['p', 'a', 'c']
assert meta4.node_types == ['p']
# Test 4-hop metapath:
metapaths = [
[('a', 'p'), ('p', 'c')],
[('a', 'p'), ('p', 'c'), ('c', 'p'), ('p', 'a')],
]
transform = AddMetaPaths(metapaths)
meta = transform(data)
new_edge_types = [('a', 'metapath_0', 'c'), ('a', 'metapath_1', 'a')]
assert meta['metapath_0'].edge_index.size() == (2, 4)
assert meta['metapath_1'].edge_index.size() == (2, 4)
# Test `metapath_dict` information:
assert list(meta.metapath_dict.values()) == metapaths
assert list(meta.metapath_dict.keys()) == new_edge_types
def test_add_metapaths_max_sample() -> None:
torch.manual_seed(12345)
data = generate_data()
metapaths = [[('p', 'c'), ('c', 'p')]]
transform = AddMetaPaths(metapaths, max_sample=1)
meta = transform(data)
assert meta['metapath_0'].edge_index.size(1) < 9
def test_add_weighted_metapaths() -> None:
torch.manual_seed(12345)
data = HeteroData()
data['a'].num_nodes = 2
data['b'].num_nodes = 3
data['c'].num_nodes = 2
data['d'].num_nodes = 2
data['a', 'b'].edge_index = tensor([[0, 1, 1], [0, 1, 2]])
data['b', 'a'].edge_index = data['a', 'b'].edge_index.flip([0])
data['b', 'c'].edge_index = tensor([[0, 1, 2], [0, 1, 1]])
data['c', 'b'].edge_index = data['b', 'c'].edge_index.flip([0])
data['c', 'd'].edge_index = tensor([[0, 1], [0, 0]])
data['d', 'c'].edge_index = data['c', 'd'].edge_index.flip([0])
metapaths = [
[('a', 'b'), ('b', 'c')],
[('a', 'b'), ('b', 'c'), ('c', 'd')],
[('a', 'b'), ('b', 'c'), ('c', 'd'), ('d', 'c'), ('c', 'b'),
('b', 'a')],
]
transform = AddMetaPaths(metapaths, weighted=True)
out = transform(data)
# Make sure manually added metapaths compute the correct number of edges:
edge_index = out['a', 'a'].edge_index
edge_weight = out['a', 'a'].edge_weight
edge_index, edge_weight = coalesce(edge_index, edge_weight)
assert edge_index.tolist() == [[0, 0, 1, 1], [0, 1, 0, 1]]
assert edge_weight.tolist() == [1, 2, 2, 4]
edge_index = out['a', 'c'].edge_index
edge_weight = out['a', 'c'].edge_weight
edge_index, edge_weight = coalesce(edge_index, edge_weight)
assert edge_index.tolist() == [[0, 1], [0, 1]]
assert edge_weight.tolist() == [1, 2]
edge_index = out['a', 'd'].edge_index
edge_weight = out['a', 'd'].edge_weight
edge_index, edge_weight = coalesce(edge_index, edge_weight)
assert edge_index.tolist() == [[0, 1], [0, 0]]
assert edge_weight.tolist() == [1, 2]
# Compute intra-table metapaths efficiently:
metapaths = [[('a', 'b'), ('b', 'c'), ('c', 'd')]]
out = AddMetaPaths(metapaths, weighted=True)(data)
out['d', 'a'].edge_index = out['a', 'd'].edge_index.flip([0])
out['d', 'a'].edge_weight = out['a', 'd'].edge_weight
metapaths = [[('a', 'd'), ('d', 'a')]]
out = AddMetaPaths(metapaths, weighted=True)(out)
edge_index = out['a', 'a'].edge_index
edge_weight = out['a', 'a'].edge_weight
edge_index, edge_weight = coalesce(edge_index, edge_weight)
assert edge_index.tolist() == [[0, 0, 1, 1], [0, 1, 0, 1]]
assert edge_weight.tolist() == [1, 2, 2, 4]
def test_add_random_metapaths() -> None:
data = generate_data()
# Test transform options:
metapaths = [[('p', 'c'), ('c', 'p')]]
torch.manual_seed(12345)
transform = AddRandomMetaPaths(metapaths)
assert str(transform) == ('AddRandomMetaPaths(sample_ratio=1.0, '
'walks_per_node=[1])')
meta1 = transform(data)
transform = AddRandomMetaPaths(metapaths, drop_orig_edge_types=True)
assert str(transform) == ('AddRandomMetaPaths(sample_ratio=1.0, '
'walks_per_node=[1])')
meta2 = transform(data)
transform = AddRandomMetaPaths(metapaths, drop_orig_edge_types=True,
keep_same_node_type=True)
assert str(transform) == ('AddRandomMetaPaths(sample_ratio=1.0, '
'walks_per_node=[1])')
meta3 = transform(data)
transform = AddRandomMetaPaths(metapaths, drop_orig_edge_types=True,
keep_same_node_type=True,
drop_unconnected_node_types=True)
assert str(transform) == ('AddRandomMetaPaths(sample_ratio=1.0, '
'walks_per_node=[1])')
meta4 = transform(data)
transform = AddRandomMetaPaths(metapaths, sample_ratio=0.8,
drop_orig_edge_types=True,
keep_same_node_type=True,
drop_unconnected_node_types=True)
assert str(transform) == ('AddRandomMetaPaths(sample_ratio=0.8, '
'walks_per_node=[1])')
meta5 = transform(data)
transform = AddRandomMetaPaths(metapaths, walks_per_node=5,
drop_orig_edge_types=True,
keep_same_node_type=True,
drop_unconnected_node_types=True)
assert str(transform) == ('AddRandomMetaPaths(sample_ratio=1.0, '
'walks_per_node=[5])')
meta6 = transform(data)
assert meta1['metapath_0'].edge_index.size() == (2, 5)
assert meta2['metapath_0'].edge_index.size() == (2, 5)
assert meta3['metapath_0'].edge_index.size() == (2, 5)
assert meta4['metapath_0'].edge_index.size() == (2, 5)
assert meta5['metapath_0'].edge_index.size() == (2, 4)
assert meta6['metapath_0'].edge_index.size() == (2, 7)
assert all([i in meta1.edge_types for i in data.edge_types])
assert meta2.edge_types == [('p', 'metapath_0', 'p')]
assert meta3.edge_types == [('p', 'to', 'p'), ('p', 'metapath_0', 'p')]
assert meta4.edge_types == [('p', 'to', 'p'), ('p', 'metapath_0', 'p')]
assert meta3.node_types == ['p', 'a', 'c']
assert meta4.node_types == ['p']
# Test 4-hop metapath:
metapaths = [
[('a', 'p'), ('p', 'c')],
[('a', 'p'), ('p', 'c'), ('c', 'p'), ('p', 'a')],
]
transform = AddRandomMetaPaths(metapaths)
assert str(transform) == ('AddRandomMetaPaths(sample_ratio=1.0, '
'walks_per_node=[1, 1])')
meta1 = transform(data)
new_edge_types = [('a', 'metapath_0', 'c'), ('a', 'metapath_1', 'a')]
assert meta1['metapath_0'].edge_index.size() == (2, 2)
assert meta1['metapath_1'].edge_index.size() == (2, 2)
# Test `metapath_dict` information:
assert list(meta1.metapath_dict.values()) == metapaths
assert list(meta1.metapath_dict.keys()) == new_edge_types
transform = AddRandomMetaPaths(metapaths, walks_per_node=[2, 5])
assert str(transform) == ('AddRandomMetaPaths(sample_ratio=1.0, '
'walks_per_node=[2, 5])')
meta2 = transform(data)
new_edge_types = [('a', 'metapath_0', 'c'), ('a', 'metapath_1', 'a')]
assert meta2['metapath_0'].edge_index.size() == (2, 2)
assert meta2['metapath_1'].edge_index.size() == (2, 3)
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