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Add SubgraphType with bidirectional sampling support (2/2) #7200

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merged 6 commits into from
Apr 18, 2023
Merged

Add SubgraphType with bidirectional sampling support (2/2) #7200

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rusty1s Apr 18, 2023
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2 changes: 1 addition & 1 deletion CHANGELOG.md
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).

### Added

- Added graph mode sampling option with bidirectional sampling support ([#7199](https://github.com/pyg-team/pytorch_geometric/pull/7199))
- Added subgraph type sampling option with bidirectional edge support ([#7199](https://github.com/pyg-team/pytorch_geometric/pull/7199), [#7200](https://github.com/pyg-team/pytorch_geometric/pull/7200))
- Added support for `"any"`-reductions in `scatter` ([#7198](https://github.com/pyg-team/pytorch_geometric/pull/7198))
- Added manual sampling interface to `NodeLoader` and `LinkLoader` ([#7197](https://github.com/pyg-team/pytorch_geometric/pull/7197))
- Extending `torch.sparse` support ([#7155](https://github.com/pyg-team/pytorch_geometric/pull/7155))
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37 changes: 18 additions & 19 deletions test/loader/test_link_neighbor_loader.py
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Expand Up @@ -17,14 +17,14 @@ def unique_edge_pairs(edge_index):


@onlyNeighborSampler
@pytest.mark.parametrize('directed', [True]) # TODO re-enable undirected mode
@pytest.mark.parametrize('subgraph_type', ['directional', 'bidirectional'])
@pytest.mark.parametrize('neg_sampling_ratio', [None, 1.0])
@pytest.mark.parametrize('filter_per_worker', [True, False])
def test_homo_link_neighbor_loader_basic(directed, neg_sampling_ratio,
def test_homo_link_neighbor_loader_basic(subgraph_type, neg_sampling_ratio,
filter_per_worker):
pos_edge_index = get_random_edge_index(100, 50, 500)
neg_edge_index = get_random_edge_index(100, 50, 500)
neg_edge_index[1, :] += 50
pos_edge_index = get_random_edge_index(50, 50, 500)
neg_edge_index = get_random_edge_index(50, 50, 500)
neg_edge_index += 50

edge_label_index = torch.cat([pos_edge_index, neg_edge_index], dim=-1)
edge_label = torch.cat([torch.ones(500), torch.zeros(500)], dim=0)
Expand All @@ -41,7 +41,7 @@ def test_homo_link_neighbor_loader_basic(directed, neg_sampling_ratio,
batch_size=20,
edge_label_index=edge_label_index,
edge_label=edge_label if neg_sampling_ratio is None else None,
directed=directed,
subgraph_type=subgraph_type,
neg_sampling_ratio=neg_sampling_ratio,
shuffle=True,
filter_per_worker=filter_per_worker,
Expand Down Expand Up @@ -91,9 +91,9 @@ def test_homo_link_neighbor_loader_basic(directed, neg_sampling_ratio,


@onlyNeighborSampler
@pytest.mark.parametrize('directed', [True]) # TODO re-enable undirected mode
@pytest.mark.parametrize('subgraph_type', ['directional', 'bidirectional'])
@pytest.mark.parametrize('neg_sampling_ratio', [None, 1.0])
def test_hetero_link_neighbor_loader_basic(directed, neg_sampling_ratio):
def test_hetero_link_neighbor_loader_basic(subgraph_type, neg_sampling_ratio):
data = HeteroData()

data['paper'].x = torch.arange(100)
Expand All @@ -111,7 +111,7 @@ def test_hetero_link_neighbor_loader_basic(directed, neg_sampling_ratio):
num_neighbors=[-1] * 2,
edge_label_index=('paper', 'author'),
batch_size=20,
directed=directed,
subgraph_type=subgraph_type,
neg_sampling_ratio=neg_sampling_ratio,
shuffle=True,
)
Expand All @@ -121,7 +121,6 @@ def test_hetero_link_neighbor_loader_basic(directed, neg_sampling_ratio):

for batch in loader:
assert isinstance(batch, HeteroData)
assert len(batch) == 7 + (1 if neg_sampling_ratio is not None else 0)
if neg_sampling_ratio is None:
# Assert only positive samples are present in the original graph:
edge_index = unique_edge_pairs(batch['paper', 'author'].edge_index)
Expand All @@ -136,8 +135,8 @@ def test_hetero_link_neighbor_loader_basic(directed, neg_sampling_ratio):


@onlyNeighborSampler
@pytest.mark.parametrize('directed', [True]) # TODO re-enable undirected mode
def test_hetero_link_neighbor_loader_loop(directed):
@pytest.mark.parametrize('subgraph_type', ['directional', 'bidirectional'])
def test_hetero_link_neighbor_loader_loop(subgraph_type):
data = HeteroData()

data['paper'].x = torch.arange(100)
Expand All @@ -147,9 +146,13 @@ def test_hetero_link_neighbor_loader_loop(directed):
data['paper', 'author'].edge_index = get_random_edge_index(100, 200, 1000)
data['author', 'paper'].edge_index = get_random_edge_index(200, 100, 1000)

loader = LinkNeighborLoader(data, num_neighbors=[-1] * 2,
edge_label_index=('paper', 'paper'),
batch_size=20, directed=directed)
loader = LinkNeighborLoader(
data,
num_neighbors=[-1] * 2,
edge_label_index=('paper', 'paper'),
batch_size=20,
subgraph_type=subgraph_type,
)

for batch in loader:
assert batch['paper'].x.size(0) <= 100
Expand Down Expand Up @@ -279,15 +282,13 @@ def test_custom_hetero_link_neighbor_loader():
num_neighbors=[-1] * 2,
edge_label_index=('paper', 'to', 'author'),
batch_size=20,
directed=True,
)

loader2 = LinkNeighborLoader(
(feature_store, graph_store),
num_neighbors=[-1] * 2,
edge_label_index=('paper', 'to', 'author'),
batch_size=20,
directed=True,
)

assert str(loader1) == str(loader2)
Expand Down Expand Up @@ -377,7 +378,6 @@ def test_homo_link_neighbor_loader_triplet(disjoint, temporal, amount):
edge_label_index=data.edge_label_index,
edge_label_time=edge_label_time,
time_attr=time_attr,
directed=True,
disjoint=disjoint,
neg_sampling=dict(mode='triplet', amount=amount),
shuffle=True,
Expand Down Expand Up @@ -472,7 +472,6 @@ def test_hetero_link_neighbor_loader_triplet(disjoint, temporal, amount):
edge_label_index=index,
edge_label_time=edge_label_time,
time_attr=time_attr,
directed=True,
disjoint=disjoint,
neg_sampling=dict(mode='triplet', amount=amount, weight=weight),
shuffle=True,
Expand Down
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