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[Code Coverage] data/graph_store.py #6720

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Feb 15, 2023
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[Code Coverage] data/graph_store.py #6720

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36debdf
Improve code cov for graph store
zechengz Feb 15, 2023
feb347a
Update CHANGELOG.md
zechengz Feb 15, 2023
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2 changes: 1 addition & 1 deletion CHANGELOG.md
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Expand Up @@ -91,7 +91,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
- Properly reset the `data_list` cache of an `InMemoryDataset` when accessing `dataset.data` ([#6685](https://github.com/pyg-team/pytorch_geometric/pull/6685))
- Fixed a bug in `Data.subgraph()` and `HeteroData.subgraph()` ([#6613](https://github.com/pyg-team/pytorch_geometric/pull/6613))
- Fixed a bug in `PNAConv` and `DegreeScalerAggregation` to correctly incorporate degree statistics of isolated nodes ([#6609](https://github.com/pyg-team/pytorch_geometric/pull/6609))
- Improved code coverage ([#6523](https://github.com/pyg-team/pytorch_geometric/pull/6523), [#6538](https://github.com/pyg-team/pytorch_geometric/pull/6538), [#6555](https://github.com/pyg-team/pytorch_geometric/pull/6555), [#6558](https://github.com/pyg-team/pytorch_geometric/pull/6558), [#6568](https://github.com/pyg-team/pytorch_geometric/pull/6568), [#6573](https://github.com/pyg-team/pytorch_geometric/pull/6573), [#6578](https://github.com/pyg-team/pytorch_geometric/pull/6578), [#6597](https://github.com/pyg-team/pytorch_geometric/pull/6597), [#6600](https://github.com/pyg-team/pytorch_geometric/pull/6600), [#6618](https://github.com/pyg-team/pytorch_geometric/pull/6618), [#6619](https://github.com/pyg-team/pytorch_geometric/pull/6619), [#6621](https://github.com/pyg-team/pytorch_geometric/pull/6621), [#6623](https://github.com/pyg-team/pytorch_geometric/pull/6623), [#6637](https://github.com/pyg-team/pytorch_geometric/pull/6637), [#6638](https://github.com/pyg-team/pytorch_geometric/pull/6638), [#6640](https://github.com/pyg-team/pytorch_geometric/pull/6640), [#6645](https://github.com/pyg-team/pytorch_geometric/pull/6645), [#6648](https://github.com/pyg-team/pytorch_geometric/pull/6648), [#6647](https://github.com/pyg-team/pytorch_geometric/pull/6647), [#6657](https://github.com/pyg-team/pytorch_geometric/pull/6657), [#6662](https://github.com/pyg-team/pytorch_geometric/pull/6662), [#6664](https://github.com/pyg-team/pytorch_geometric/pull/6664), [#6667](https://github.com/pyg-team/pytorch_geometric/pull/6667), [#6668](https://github.com/pyg-team/pytorch_geometric/pull/6668), [#6669](https://github.com/pyg-team/pytorch_geometric/pull/6669), [#6670](https://github.com/pyg-team/pytorch_geometric/pull/6670), [#6671](https://github.com/pyg-team/pytorch_geometric/pull/6671), [#6673](https://github.com/pyg-team/pytorch_geometric/pull/6673), [#6675](https://github.com/pyg-team/pytorch_geometric/pull/6675), [#6676](https://github.com/pyg-team/pytorch_geometric/pull/6676), [#6677](https://github.com/pyg-team/pytorch_geometric/pull/6677), [#6678](https://github.com/pyg-team/pytorch_geometric/pull/6678), [#6681](https://github.com/pyg-team/pytorch_geometric/pull/6681), [#6683](https://github.com/pyg-team/pytorch_geometric/pull/6683), [#6703](https://github.com/pyg-team/pytorch_geometric/pull/6703))
- Improved code coverage ([#6523](https://github.com/pyg-team/pytorch_geometric/pull/6523), [#6538](https://github.com/pyg-team/pytorch_geometric/pull/6538), [#6555](https://github.com/pyg-team/pytorch_geometric/pull/6555), [#6558](https://github.com/pyg-team/pytorch_geometric/pull/6558), [#6568](https://github.com/pyg-team/pytorch_geometric/pull/6568), [#6573](https://github.com/pyg-team/pytorch_geometric/pull/6573), [#6578](https://github.com/pyg-team/pytorch_geometric/pull/6578), [#6597](https://github.com/pyg-team/pytorch_geometric/pull/6597), [#6600](https://github.com/pyg-team/pytorch_geometric/pull/6600), [#6618](https://github.com/pyg-team/pytorch_geometric/pull/6618), [#6619](https://github.com/pyg-team/pytorch_geometric/pull/6619), [#6621](https://github.com/pyg-team/pytorch_geometric/pull/6621), [#6623](https://github.com/pyg-team/pytorch_geometric/pull/6623), [#6637](https://github.com/pyg-team/pytorch_geometric/pull/6637), [#6638](https://github.com/pyg-team/pytorch_geometric/pull/6638), [#6640](https://github.com/pyg-team/pytorch_geometric/pull/6640), [#6645](https://github.com/pyg-team/pytorch_geometric/pull/6645), [#6648](https://github.com/pyg-team/pytorch_geometric/pull/6648), [#6647](https://github.com/pyg-team/pytorch_geometric/pull/6647), [#6657](https://github.com/pyg-team/pytorch_geometric/pull/6657), [#6662](https://github.com/pyg-team/pytorch_geometric/pull/6662), [#6664](https://github.com/pyg-team/pytorch_geometric/pull/6664), [#6667](https://github.com/pyg-team/pytorch_geometric/pull/6667), [#6668](https://github.com/pyg-team/pytorch_geometric/pull/6668), [#6669](https://github.com/pyg-team/pytorch_geometric/pull/6669), [#6670](https://github.com/pyg-team/pytorch_geometric/pull/6670), [#6671](https://github.com/pyg-team/pytorch_geometric/pull/6671), [#6673](https://github.com/pyg-team/pytorch_geometric/pull/6673), [#6675](https://github.com/pyg-team/pytorch_geometric/pull/6675), [#6676](https://github.com/pyg-team/pytorch_geometric/pull/6676), [#6677](https://github.com/pyg-team/pytorch_geometric/pull/6677), [#6678](https://github.com/pyg-team/pytorch_geometric/pull/6678), [#6681](https://github.com/pyg-team/pytorch_geometric/pull/6681), [#6683](https://github.com/pyg-team/pytorch_geometric/pull/6683), [#6703](https://github.com/pyg-team/pytorch_geometric/pull/6703), [#6720](https://github.com/pyg-team/pytorch_geometric/pull/6720)))
- Fixed a bug in which `data.to_heterogeneous()` filtered attributs in the wrong dimension ([#6522](https://github.com/pyg-team/pytorch_geometric/pull/6522))
- Breaking Change: Temporal sampling will now also sample nodes with an equal timestamp to the seed time (requires `pyg-lib>0.1.0`) ([#6517](https://github.com/pyg-team/pytorch_geometric/pull/6517))
- Changed `DataLoader` workers with affinity to start at `cpu0` ([#6512](https://github.com/pyg-team/pytorch_geometric/pull/6512))
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21 changes: 21 additions & 0 deletions test/data/test_graph_store.py
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Expand Up @@ -2,6 +2,7 @@
import torch
from torch_sparse import SparseTensor

from torch_geometric.data.graph_store import EdgeAttr, EdgeLayout
from torch_geometric.testing import MyGraphStore


Expand All @@ -14,6 +15,8 @@ def get_edge_index(num_src_nodes, num_dst_nodes, num_edges):
def test_graph_store():
graph_store = MyGraphStore()

assert str(graph_store) == 'MyGraphStore()'

coo = torch.tensor([0, 1]), torch.tensor([1, 2])
csr = torch.tensor([0, 1, 2]), torch.tensor([1, 2])
csc = torch.tensor([0, 1]), torch.tensor([0, 0, 1, 2])
Expand All @@ -31,6 +34,10 @@ def test_graph_store():

assert len(graph_store.get_all_edge_attrs()) == 3

del graph_store['edge_type', 'coo']
with pytest.raises(KeyError):
graph_store['edge_type', 'coo']

with pytest.raises(KeyError):
graph_store['edge_type_2', 'coo']

Expand Down Expand Up @@ -73,3 +80,17 @@ def test_graph_store_conversion():
out = graph_store.coo([('v', '1', 'v')])
assert torch.equal(list(out[0].values())[0], coo[0])
assert torch.equal(list(out[1].values())[0], coo[1])

# Ensure that 'store' parameter works as intended:
key = EdgeAttr(edge_type=('v', '1', 'v'), layout=EdgeLayout.CSR,
is_sorted=False, size=(100, 100))
with pytest.raises(KeyError):
graph_store[key]

out = graph_store.csr([('v', '1', 'v')], store=True)
assert torch.equal(list(out[0].values())[0], csr[0])
assert torch.equal(list(out[1].values())[0].sort()[0], csr[1].sort()[0])

out = graph_store[key]
assert torch.equal(out[0], csr[0])
assert torch.equal(out[1].sort()[0], csr[1].sort()[0])
5 changes: 3 additions & 2 deletions torch_geometric/data/graph_store.py
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Expand Up @@ -126,8 +126,9 @@ def put_edge_index(self, edge_index: EdgeTensorType, *args,
Returns whether insertion was successful.

Args:
tensor (Tuple[torch.Tensor, torch.Tensor]): The :obj:`edge_index`
tuple in a format specified in :class:`EdgeAttr`.
edge_index (Tuple[torch.Tensor, torch.Tensor]): The
:obj:`edge_index` tuple in a format specified in
:class:`EdgeAttr`.
**kwargs (EdgeAttr): Any relevant edge attributes that
correspond to the :obj:`edge_index` tuple. See the
:class:`EdgeAttr` documentation for required and optional
Expand Down