def test_filter(self):
pyg_dataset = TUDataset("./enzymes", "ENZYMES")
ds = pyg_to_dicts(pyg_dataset)
graphs = [Graph(**item) for item in ds]
dataset = GraphDataset(graphs, task="graph")
thresh = 90
orig_dataset_size = len(dataset)
num_graphs_large = 0
for graph in dataset:
if graph.num_nodes >= thresh:
num_graphs_large += 1
dataset = dataset.filter(lambda graph: graph.num_nodes < thresh,
deep_copy=False)
filtered_dataset_size = len(dataset)
self.assertEqual(
orig_dataset_size - filtered_dataset_size,
num_graphs_large,
)
def test_dataset_split_custom(self):
# transductive split with node task (self defined dataset)
G, x, y, edge_x, edge_y, edge_index, graph_x, graph_y = (
simple_networkx_graph())
Graph.add_edge_attr(G, "edge_feature", edge_x)
Graph.add_edge_attr(G, "edge_label", edge_y)
Graph.add_node_attr(G, "node_feature", x)
Graph.add_node_attr(G, "node_label", y)
Graph.add_graph_attr(G, "graph_feature", graph_x)
Graph.add_graph_attr(G, "graph_label", graph_y)
num_nodes = len(list(G.nodes))
nodes_train = torch.tensor(list(G.nodes)[:int(0.3 * num_nodes)])
nodes_val = torch.tensor(
list(G.nodes)[int(0.3 * num_nodes):int(0.6 * num_nodes)])
nodes_test = torch.tensor(list(G.nodes)[int(0.6 * num_nodes):])
graph_train = Graph(node_feature=x,
node_label=y,
edge_index=edge_index,
node_label_index=nodes_train,
directed=True)
graph_val = Graph(node_feature=x,
node_label=y,
edge_index=edge_index,
node_label_index=nodes_val,
directed=True)
graph_test = Graph(node_feature=x,
node_label=y,
edge_index=edge_index,
node_label_index=nodes_test,
directed=True)
graphs_train = [graph_train]
graphs_val = [graph_val]
graphs_test = [graph_test]
dataset_train, dataset_val, dataset_test = (GraphDataset(graphs_train,
task='node'),
GraphDataset(graphs_val,
task='node'),
GraphDataset(graphs_test,
task='node'))
self.assertEqual(dataset_train[0].node_label_index.tolist(),
list(range(int(0.3 * num_nodes))))
self.assertEqual(
dataset_val[0].node_label_index.tolist(),
list(range(int(0.3 * num_nodes), int(0.6 * num_nodes))))
self.assertEqual(dataset_test[0].node_label_index.tolist(),
list(range(int(0.6 * num_nodes), num_nodes)))
# transductive split with link_pred task (train/val split)
edges = list(G.edges)
num_edges = len(edges)
edges_train = edges[:int(0.7 * num_edges)]
edges_val = edges[int(0.7 * num_edges):]
link_size_list = [len(edges_train), len(edges_val)]
# generate pseudo pos and neg edges, they may overlap here
train_pos = torch.LongTensor(edges_train).permute(1, 0)
val_pos = torch.LongTensor(edges_val).permute(1, 0)
val_neg = torch.randint(high=10, size=val_pos.shape, dtype=torch.int64)
val_neg_double = torch.cat((val_neg, val_neg), dim=1)
num_train = len(edges_train)
num_val = len(edges_val)
graph_train = Graph(node_feature=x,
edge_index=edge_index,
edge_feature=edge_x,
directed=True,
edge_label_index=train_pos)
graph_val = Graph(node_feature=x,
edge_index=edge_index,
edge_feature=edge_x,
directed=True,
edge_label_index=val_pos,
negative_edge=val_neg_double)
graphs_train = [graph_train]
graphs_val = [graph_val]
dataset_train, dataset_val = (GraphDataset(graphs_train,
task='link_pred',
resample_negatives=True),
GraphDataset(
graphs_val,
task='link_pred',
edge_negative_sampling_ratio=2))
self.assertEqual(dataset_train[0].edge_label_index.shape[1],
2 * link_size_list[0])
self.assertEqual(dataset_train[0].edge_label.shape[0],
2 * link_size_list[0])
self.assertEqual(dataset_val[0].edge_label_index.shape[1],
val_pos.shape[1] + val_neg_double.shape[1])
self.assertEqual(dataset_val[0].edge_label.shape[0],
val_pos.shape[1] + val_neg_double.shape[1])
self.assertTrue(
torch.equal(dataset_train[0].edge_label_index[:, :num_train],
train_pos))
self.assertTrue(
torch.equal(dataset_val[0].edge_label_index[:, :num_val], val_pos))
self.assertTrue(
torch.equal(dataset_val[0].edge_label_index[:, num_val:],
val_neg_double))
dataset_train.resample_negatives = False
self.assertTrue(
torch.equal(dataset_train[0].edge_label_index,
dataset_train[0].edge_label_index))
# transductive split with link_pred task with edge label
edge_label_train = torch.LongTensor([1, 2, 3, 2, 1, 1, 2, 3, 2, 0, 0])
edge_label_val = torch.LongTensor([1, 2, 3, 2, 1, 0])
graph_train = Graph(node_feature=x,
edge_index=edge_index,
directed=True,
edge_label_index=train_pos,
edge_label=edge_label_train)
graph_val = Graph(node_feature=x,
edge_index=edge_index,
directed=True,
edge_label_index=val_pos,
negative_edge=val_neg,
edge_label=edge_label_val)
graphs_train = [graph_train]
graphs_val = [graph_val]
dataset_train, dataset_val = (GraphDataset(graphs_train,
task='link_pred'),
GraphDataset(graphs_val,
task='link_pred'))
self.assertTrue(
torch.equal(dataset_train[0].edge_label_index,
dataset_train[0].edge_label_index))
self.assertTrue(
torch.equal(dataset_train[0].edge_label[:num_train],
edge_label_train))
self.assertTrue(
torch.equal(dataset_val[0].edge_label[:num_val], edge_label_val))
# Multiple graph tensor backend link prediction (inductive)
pyg_dataset = Planetoid('./cora', 'Cora')
x = pyg_dataset[0].x
y = pyg_dataset[0].y
edge_index = pyg_dataset[0].edge_index
row, col = edge_index
mask = row < col
row, col = row[mask], col[mask]
edge_index = torch.stack([row, col], dim=0)
edge_index = torch.cat(
[edge_index, torch.flip(edge_index, [0])], dim=1)
graphs = [
Graph(node_feature=x,
node_label=y,
edge_index=edge_index,
directed=False)
]
graphs = [copy.deepcopy(graphs[0]) for _ in range(10)]
edge_label_index = graphs[0].edge_label_index
dataset = GraphDataset(graphs,
task='link_pred',
edge_message_ratio=0.6,
edge_train_mode="all")
datasets = {}
datasets['train'], datasets['val'], datasets['test'] = dataset.split(
transductive=False, split_ratio=[0.85, 0.05, 0.1])
edge_label_index_split = (
datasets['train'][0].edge_label_index[:,
0:edge_label_index.shape[1]])
self.assertTrue(torch.equal(edge_label_index, edge_label_index_split))
# transductive split with node task (pytorch geometric dataset)
pyg_dataset = Planetoid("./cora", "Cora")
ds = pyg_to_dicts(pyg_dataset, task="cora")
graphs = [Graph(**item) for item in ds]
split_ratio = [0.3, 0.3, 0.4]
node_size_list = [0 for i in range(len(split_ratio))]
for graph in graphs:
custom_splits = [[] for i in range(len(split_ratio))]
split_offset = 0
num_nodes = graph.num_nodes
shuffled_node_indices = torch.randperm(graph.num_nodes)
for i, split_ratio_i in enumerate(split_ratio):
if i != len(split_ratio) - 1:
num_split_i = int(split_ratio_i * num_nodes)
nodes_split_i = (
shuffled_node_indices[split_offset:split_offset +
num_split_i])
split_offset += num_split_i
else:
nodes_split_i = shuffled_node_indices[split_offset:]
custom_splits[i] = nodes_split_i
node_size_list[i] += len(nodes_split_i)
graph.custom = {"general_splits": custom_splits}
node_feature = graphs[0].node_feature
edge_index = graphs[0].edge_index
directed = graphs[0].directed
graph_train = Graph(
node_feature=node_feature,
edge_index=edge_index,
directed=directed,
node_label_index=graphs[0].custom["general_splits"][0])
graph_val = Graph(
node_feature=node_feature,
edge_index=edge_index,
directed=directed,
node_label_index=graphs[0].custom["general_splits"][1])
graph_test = Graph(
node_feature=node_feature,
edge_index=edge_index,
directed=directed,
node_label_index=graphs[0].custom["general_splits"][2])
train_dataset = GraphDataset([graph_train], task="node")
val_dataset = GraphDataset([graph_val], task="node")
test_dataset = GraphDataset([graph_test], task="node")
self.assertEqual(len(train_dataset[0].node_label_index),
node_size_list[0])
self.assertEqual(len(val_dataset[0].node_label_index),
node_size_list[1])
self.assertEqual(len(test_dataset[0].node_label_index),
node_size_list[2])
# transductive split with edge task
pyg_dataset = Planetoid("./cora", "Cora")
graphs_g = GraphDataset.pyg_to_graphs(pyg_dataset)
ds = pyg_to_dicts(pyg_dataset, task="cora")
graphs = [Graph(**item) for item in ds]
split_ratio = [0.3, 0.3, 0.4]
edge_size_list = [0 for i in range(len(split_ratio))]
for i, graph in enumerate(graphs):
custom_splits = [[] for i in range(len(split_ratio))]
split_offset = 0
edges = list(graphs_g[i].G.edges)
num_edges = graph.num_edges
random.shuffle(edges)
for i, split_ratio_i in enumerate(split_ratio):
if i != len(split_ratio) - 1:
num_split_i = int(split_ratio_i * num_edges)
edges_split_i = (edges[split_offset:split_offset +
num_split_i])
split_offset += num_split_i
else:
edges_split_i = edges[split_offset:]
custom_splits[i] = edges_split_i
edge_size_list[i] += len(edges_split_i)
graph.custom = {"general_splits": custom_splits}
node_feature = graphs[0].node_feature
edge_index = graphs[0].edge_index
directed = graphs[0].directed
train_index = torch.tensor(
graphs[0].custom["general_splits"][0]).permute(1, 0)
train_index = torch.cat((train_index, train_index), dim=1)
val_index = torch.tensor(
graphs[0].custom["general_splits"][1]).permute(1, 0)
val_index = torch.cat((val_index, val_index), dim=1)
test_index = torch.tensor(
graphs[0].custom["general_splits"][2]).permute(1, 0)
test_index = torch.cat((test_index, test_index), dim=1)
graph_train = Graph(node_feature=node_feature,
edge_index=edge_index,
directed=directed,
edge_label_index=train_index)
graph_val = Graph(node_feature=node_feature,
edge_index=edge_index,
directed=directed,
edge_label_index=val_index)
graph_test = Graph(node_feature=node_feature,
edge_index=edge_index,
directed=directed,
edge_label_index=test_index)
train_dataset = GraphDataset([graph_train], task="edge")
val_dataset = GraphDataset([graph_val], task="edge")
test_dataset = GraphDataset([graph_test], task="edge")
self.assertEqual(train_dataset[0].edge_label_index.shape[1],
2 * edge_size_list[0])
self.assertEqual(val_dataset[0].edge_label_index.shape[1],
2 * edge_size_list[1])
self.assertEqual(test_dataset[0].edge_label_index.shape[1],
2 * edge_size_list[2])
# inductive split with graph task
pyg_dataset = TUDataset("./enzymes", "ENZYMES")
ds = pyg_to_dicts(pyg_dataset)
graphs = [Graph(**item) for item in ds]
num_graphs = len(graphs)
split_ratio = [0.3, 0.3, 0.4]
graph_size_list = []
split_offset = 0
custom_split_graphs = []
for i, split_ratio_i in enumerate(split_ratio):
if i != len(split_ratio) - 1:
num_split_i = int(split_ratio_i * num_graphs)
custom_split_graphs.append(graphs[split_offset:split_offset +
num_split_i])
split_offset += num_split_i
graph_size_list.append(num_split_i)
else:
custom_split_graphs.append(graphs[split_offset:])
graph_size_list.append(len(graphs[split_offset:]))
dataset = GraphDataset(graphs,
task="graph",
custom_split_graphs=custom_split_graphs)
split_res = dataset.split(transductive=False)
self.assertEqual(graph_size_list[0], len(split_res[0]))
self.assertEqual(graph_size_list[1], len(split_res[1]))
self.assertEqual(graph_size_list[2], len(split_res[2]))
def test_dataset_split(self):
# inductively split with graph task
pyg_dataset = TUDataset("./enzymes", "ENZYMES")
ds = pyg_to_dicts(pyg_dataset)
graphs = [Graph(**item) for item in ds]
dataset = GraphDataset(graphs, task="graph")
split_res = dataset.split(transductive=False)
num_graphs = len(dataset)
num_train = int(0.8 * num_graphs)
num_val = int(0.1 * num_graphs)
num_test = num_graphs - num_train - num_val
self.assertEqual(num_train, len(split_res[0]))
self.assertEqual(num_val, len(split_res[1]))
self.assertEqual(num_test, len(split_res[2]))
# inductively split with link_pred task
# and default (`all`) edge_train_mode
pyg_dataset = TUDataset("./enzymes", "ENZYMES")
ds = pyg_to_dicts(pyg_dataset)
graphs = [Graph(**item) for item in ds]
dataset = GraphDataset(graphs, task="link_pred")
split_res = dataset.split(transductive=False)
num_graphs = len(dataset)
num_train = int(0.8 * num_graphs)
num_val = int(0.1 * num_graphs)
num_test = num_graphs - num_train - num_val
self.assertEqual(num_train, len(split_res[0]))
self.assertEqual(num_val, len(split_res[1]))
self.assertEqual(num_test, len(split_res[2]))
# inductively split with link_pred task and `disjoint` edge_train_mode
pyg_dataset = TUDataset("./enzymes", "ENZYMES")
ds = pyg_to_dicts(pyg_dataset)
graphs = [Graph(**item) for item in ds]
dataset = GraphDataset(
graphs,
task="link_pred",
edge_train_mode="disjoint",
)
split_res = dataset.split(transductive=False)
num_graphs = len(dataset)
num_train = int(0.8 * num_graphs)
num_val = int(0.1 * num_graphs)
num_test = num_graphs - num_train - num_val
self.assertEqual(num_train, len(split_res[0]))
self.assertEqual(num_val, len(split_res[1]))
self.assertEqual(num_test, len(split_res[2]))
# transductively split with node task
pyg_dataset = Planetoid("./cora", "Cora")
ds = pyg_to_dicts(pyg_dataset, task="cora")
graphs = [Graph(**item) for item in ds]
dataset = GraphDataset(graphs, task="node")
num_nodes = dataset.num_nodes[0]
num_edges = dataset.num_edges[0]
node_0 = int(0.8 * num_nodes)
node_1 = int(0.1 * num_nodes)
node_2 = num_nodes - node_0 - node_1
split_res = dataset.split()
self.assertEqual(len(split_res[0][0].node_label_index), node_0)
self.assertEqual(len(split_res[1][0].node_label_index), node_1)
self.assertEqual(len(split_res[2][0].node_label_index), node_2)
# transductively split with link_pred task
# and default (`all`) edge_train_mode
dataset = GraphDataset(graphs, task="link_pred")
edge_0 = 2 * 2 * int(0.8 * num_edges)
edge_1 = 2 * 2 * int(0.1 * num_edges)
edge_2 = 2 * 2 * (num_edges - int(0.8 * num_edges) -
int(0.1 * num_edges))
split_res = dataset.split()
self.assertEqual(split_res[0][0].edge_label_index.shape[1], edge_0)
self.assertEqual(split_res[1][0].edge_label_index.shape[1], edge_1)
self.assertEqual(split_res[2][0].edge_label_index.shape[1], edge_2)
# transductively split with link_pred task, `split` edge_train_mode
# and 0.5 edge_message_ratio
dataset = GraphDataset(
graphs,
task="link_pred",
edge_train_mode="disjoint",
edge_message_ratio=0.5,
)
split_res = dataset.split()
edge_0 = 2 * int(0.8 * num_edges)
edge_0 = 2 * (edge_0 - int(0.5 * edge_0))
edge_1 = 2 * 2 * int(0.1 * num_edges)
edge_2 = 2 * 2 * (num_edges - int(0.8 * num_edges) -
int(0.1 * num_edges))
self.assertEqual(
split_res[0][0].edge_label_index.shape[1],
edge_0,
)
self.assertEqual(split_res[1][0].edge_label_index.shape[1], edge_1)
self.assertEqual(split_res[2][0].edge_label_index.shape[1], edge_2)
# transductively split with link_pred task
# and specified edge_negative_sampling_ratio
dataset = GraphDataset(graphs,
task="link_pred",
edge_negative_sampling_ratio=2)
split_res = dataset.split()
edge_0 = (2 + 1) * (2 * int(0.8 * num_edges))
edge_1 = (2 + 1) * (2 * int(0.1 * num_edges))
edge_2 = (2 + 1) * (
2 * (num_edges - int(0.8 * num_edges) - int(0.1 * num_edges)))
self.assertEqual(split_res[0][0].edge_label_index.shape[1], edge_0)
self.assertEqual(split_res[1][0].edge_label_index.shape[1], edge_1)
self.assertEqual(split_res[2][0].edge_label_index.shape[1], edge_2)