Rewrite tests to not depend on currently broken dblp dataset (#5250)

* Rewrite tests to not depend on currently broken dblp dataset

* update changelog

* Update test/data/test_lightning_datamodule.py

Co-authored-by: Matthias Fey <matthias.fey@tu-dortmund.de>

* Update CHANGELOG.md

Co-authored-by: Matthias Fey <matthias.fey@tu-dortmund.de>

* add skip for broken test

* add mark in skip

* add mark in skip

Co-authored-by: Matthias Fey <matthias.fey@tu-dortmund.de>

CHANGELOG.md

@@ -11,6 +11,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added `BaseStorage.get()` functionality ([#5240](https://github.com/pyg-team/pytorch_geometric/pull/5240))
 - Added a test to confirm that `to_hetero` works with `SparseTensor` ([#5222](https://github.com/pyg-team/pytorch_geometric/pull/5222))
 ### Changed
+- Changed tests relying on `dblp` datasets to instead use  synthetic data. ([#5250](https://github.com/pyg-team/pytorch_geometric/pull/5250))
 ### Removed
 
 ## [2.1.0] - 2022-08-17

test/data/test_lightning_datamodule.py

@@ -5,6 +5,7 @@
 import torch.nn.functional as F
 
 from torch_geometric.data import (
+    HeteroData,
     LightningDataset,
     LightningLinkData,
     LightningNodeData,
@@ -18,6 +19,12 @@
     LightningModule = torch.nn.Module
 
 
+def get_edge_index(num_src_nodes, num_dst_nodes, num_edges):
+    row = torch.randint(num_src_nodes, (num_edges, ), dtype=torch.long)
+    col = torch.randint(num_dst_nodes, (num_edges, ), dtype=torch.long)
+    return torch.stack([row, col], dim=0)
+
+
 class LinearGraphModule(LightningModule):
     def __init__(self, in_channels, hidden_channels, out_channels):
         super().__init__()
@@ -273,10 +280,18 @@ def test_lightning_hetero_node_data(get_dataset):
 @withCUDA
 @onlyFullTest
 @withPackage('pytorch_lightning')
-def test_lightning_hetero_link_data(get_dataset):
-    # TODO: Add more datasets.
-    dataset = get_dataset(name='DBLP')
-    data = dataset[0]
+def test_lightning_hetero_link_data():
+    torch.manual_seed(12345)
+
+    data = HeteroData()
+
+    data['paper'].x = torch.arange(10)
+    data['author'].x = torch.arange(10)
+    data['term'].x = torch.arange(10)
+
+    data['paper', 'author'].edge_index = get_edge_index(10, 10, 10)
+    data['author', 'paper'].edge_index = get_edge_index(10, 10, 10)
+    data['paper', 'term'].edge_index = get_edge_index(10, 10, 10)
 
     datamodule = LightningLinkData(
         data,

test/loader/test_hgt_loader.py

@@ -1,4 +1,5 @@
 import numpy as np
+import pytest
 import torch
 from torch_sparse import SparseTensor
 
@@ -56,8 +57,9 @@ def test_hgt_loader():
     for batch in loader:
         assert isinstance(batch, HeteroData)
 
-        # Test node type selection:
+        # Test node and types:
         assert set(batch.node_types) == {'paper', 'author'}
+        assert set(batch.edge_types) == set(data.edge_types)
 
         assert len(batch['paper']) == 2
         assert batch['paper'].x.size() == (40, )  # 20 + 4 * 5
@@ -177,6 +179,7 @@ def forward(self, x, edge_index, edge_weight):
     assert torch.allclose(out1, out2, atol=1e-6)
 
 
+@pytest.mark.skip("'dblp' dataset is broken")
 @withPackage('torch_sparse>=0.6.15')
 def test_hgt_loader_on_dblp(get_dataset):
     data = get_dataset(name='dblp')[0]