def prepare_gnn_training(self):
if verbose:
print("\n\n==>> Clustering the graph and preparing dataloader....")
self.data = Data(x=self.x_data.float(), edge_index = self.edge_index_data.long(), edge_attr = self.edge_type_data, y=self.y_data)
new_num_nodes, _ = self.data.x.shape
self.data.train_mask = torch.FloatTensor(self.split_masks['train_mask'])
self.data.val_mask = torch.FloatTensor(self.split_masks['val_mask'])
self.data.representation_mask = torch.FloatTensor(self.split_masks['repr_mask'])
self.data.node2id = torch.tensor(list(self.node2id.values()))
# self.data.node_type = self.node_type
if not self.config['full_graph']:
if self.config['cluster'] :
cluster_data = ClusterData(self.data, num_parts=self.config['clusters'], recursive=False)
self.loader = ClusterLoader(cluster_data, batch_size=self.config['batch_size'], shuffle=self.config['shuffle'], num_workers=0)
elif self.config['saint'] == 'random_walk':
self.loader = GraphSAINTRandomWalkSampler(self.data, batch_size=6000, walk_length=2, num_steps=5, sample_coverage=100, num_workers=0)
elif self.config['saint'] == 'node':
self.loader = GraphSAINTNodeSampler(self.data, batch_size=6000, num_steps=5, sample_coverage=100, num_workers=0)
elif self.config['saint'] == 'edge':
self.loader = GraphSAINTEdgeSampler(self.data, batch_size=6000, num_steps=5, sample_coverage=100, num_workers=0)
else:
self.loader=None
return self.loader, self.vocab_size, self.data
def build_sampler(args, data, save_dir):
if args.sampler == 'rw-my':
msg = 'Use GraphSaint randomwalk sampler(mysaint sampler)'
loader = MySAINTSampler(data, batch_size=args.batch_size, sample_type='random_walk',
walk_length=2, sample_coverage=1000, save_dir=save_dir)
elif args.sampler == 'node-my':
msg = 'Use random node sampler(mysaint sampler)'
loader = MySAINTSampler(data, sample_type='node', batch_size=args.batch_size * 3,
walk_length=2, sample_coverage=1000, save_dir=save_dir)
elif args.sampler == 'rw':
msg = 'Use GraphSaint randomwalk sampler'
loader = GraphSAINTRandomWalkSampler(data, batch_size=args.batch_size, walk_length=2,
num_steps=5, sample_coverage=1000,
save_dir=save_dir)
elif args.sampler == 'node':
msg = 'Use GraphSaint node sampler'
loader = GraphSAINTNodeSampler(data, batch_size=args.batch_size * 3,
num_steps=5, sample_coverage=1000, num_workers=0, save_dir=save_dir)
elif args.sampler == 'edge':
msg = 'Use GraphSaint edge sampler'
loader = GraphSAINTEdgeSampler(data, batch_size=args.batch_size,
num_steps=5, sample_coverage=1000,
save_dir=save_dir, num_workers=0)
elif args.sampler == 'cluster':
msg = 'Use cluster sampler'
cluster_data = ClusterData(data, num_parts=args.num_parts, save_dir=save_dir)
loader = ClusterLoader(cluster_data, batch_size=20, shuffle=True,
num_workers=0)
else:
raise KeyError('Sampler type error')
return loader, msg
def test_graph_saint():
adj = torch.tensor([
[1, 1, 1, 0, 1, 0],
[1, 1, 0, 1, 0, 1],
[1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1],
[1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1],
])
edge_index = adj.nonzero().t()
x = torch.Tensor([[0, 0], [1, 1], [2, 2], [3, 3], [4, 4], [5, 5]])
data = Data(edge_index=edge_index, x=x, num_nodes=6)
torch.manual_seed(12345)
loader = GraphSAINTNodeSampler(data,
batch_size=2,
num_steps=4,
sample_coverage=10,
log=False)
for sample in loader:
assert len(sample) == 4
assert sample.num_nodes <= 2
assert sample.num_edges <= 3 * 2
assert sample.node_norm.numel() == sample.num_nodes
assert sample.edge_norm.numel() == sample.num_edges
torch.manual_seed(12345)
loader = GraphSAINTEdgeSampler(data,
batch_size=2,
num_steps=4,
sample_coverage=10,
log=False)
for sample in loader:
assert len(sample) == 4
assert sample.num_nodes <= 4
assert sample.num_edges <= 3 * 4
assert sample.node_norm.numel() == sample.num_nodes
assert sample.edge_norm.numel() == sample.num_edges
torch.manual_seed(12345)
loader = GraphSAINTRandomWalkSampler(data,
batch_size=2,
walk_length=1,
num_steps=4,
sample_coverage=10,
log=False)
for sample in loader:
assert len(sample) == 4
assert sample.num_nodes <= 4
assert sample.num_edges <= 3 * 4
assert sample.node_norm.numel() == sample.num_nodes
assert sample.edge_norm.numel() == sample.num_edges
def build_sampler(args, data, save_dir):
if args.sampler == 'rw-my':
msg = 'Use GraphSaint randomwalk sampler(mysaint sampler)'
loader = MySAINTSampler(data,
batch_size=args.batch_size,
sample_type='random_walk',
walk_length=2,
sample_coverage=1000,
save_dir=save_dir)
elif args.sampler == 'node-my':
msg = 'Use random node sampler(mysaint sampler)'
loader = MySAINTSampler(data,
sample_type='node',
batch_size=args.batch_size * 3,
walk_length=2,
sample_coverage=1000,
save_dir=save_dir)
elif args.sampler == 'rw':
msg = 'Use GraphSaint randomwalk sampler'
loader = GraphSAINTRandomWalkSampler(data,
batch_size=args.batch_size,
walk_length=2,
num_steps=5,
sample_coverage=1000,
save_dir=save_dir)
elif args.sampler == 'node':
msg = 'Use GraphSaint node sampler'
loader = GraphSAINTNodeSampler(data,
batch_size=args.batch_size * 3,
num_steps=5,
sample_coverage=1000,
num_workers=0,
save_dir=save_dir)
elif args.sampler == 'edge':
msg = 'Use GraphSaint edge sampler'
loader = GraphSAINTEdgeSampler(data,
batch_size=args.batch_size,
num_steps=5,
sample_coverage=1000,
save_dir=save_dir,
num_workers=0)
# elif args.sampler == 'cluster':
# logger.info('Use cluster sampler')
# cluster_data = ClusterData(data, num_parts=args.num_parts, save_dir=dataset.processed_dir)
# raise NotImplementedError('Cluster loader not implement yet')
else:
raise KeyError('Sampler type error')
return loader, msg
splitted_idx = dataset.get_idx_split()
data = dataset[0]
data.n_id = torch.arange(data.num_nodes)
data.node_species = None
data.y = data.y.float()
# Initialize features of nodes by aggregating edge features.
row, col = data.edge_index
#Set split indices to masks.
for split in ['train', 'valid', 'test']:
mask = torch.zeros(data.num_nodes, dtype=torch.bool)
mask[splitted_idx[split]] = True
data[f'{split}_mask'] = mask
train_loader = GraphSAINTNodeSampler(data,
batch_size=args.batch_size,
num_steps=args.num_steps,
sample_coverage=0,
save_dir=dataset.processed_dir)
test_loader = GraphSAINTNodeSampler(data,
batch_size=args.batch_size,
num_steps=args.num_steps,
sample_coverage=0,
save_dir=dataset.processed_dir)
p_train_loader = GraphSAINTNodeSampler(data,
batch_size=args.batch_size * 2,
num_steps=args.num_steps,
sample_coverage=0,
save_dir=dataset.processed_dir)
subgraph_loader = NeighborSampler(data.edge_index,
def test_graph_saint():
adj = torch.tensor([
[+1, +2, +3, +0, +4, +0],
[+5, +6, +0, +7, +0, +8],
[+9, +0, 10, +0, 11, +0],
[+0, 12, +0, 13, +0, 14],
[15, +0, 16, +0, 17, +0],
[+0, 18, +0, 19, +0, 20],
])
edge_index = adj.nonzero(as_tuple=False).t()
edge_type = adj[edge_index[0], edge_index[1]]
x = torch.Tensor([[0, 0], [1, 1], [2, 2], [3, 3], [4, 4], [5, 5]])
data = Data(edge_index=edge_index, x=x, edge_type=edge_type, num_nodes=6)
torch.manual_seed(12345)
loader = GraphSAINTNodeSampler(data, batch_size=3, num_steps=4,
sample_coverage=10, log=False)
sample = next(iter(loader))
assert sample.x.tolist() == [[2, 2], [4, 4], [5, 5]]
assert sample.edge_index.tolist() == [[0, 0, 1, 1, 2], [0, 1, 0, 1, 2]]
assert sample.edge_type.tolist() == [10, 11, 16, 17, 20]
assert len(loader) == 4
for sample in loader:
assert len(sample) == 5
assert sample.num_nodes <= 3
assert sample.num_edges <= 3 * 4
assert sample.node_norm.numel() == sample.num_nodes
assert sample.edge_norm.numel() == sample.num_edges
torch.manual_seed(12345)
loader = GraphSAINTEdgeSampler(data, batch_size=2, num_steps=4,
sample_coverage=10, log=False)
sample = next(iter(loader))
assert sample.x.tolist() == [[0, 0], [2, 2], [3, 3]]
assert sample.edge_index.tolist() == [[0, 0, 1, 1, 2], [0, 1, 0, 1, 2]]
assert sample.edge_type.tolist() == [1, 3, 9, 10, 13]
assert len(loader) == 4
for sample in loader:
assert len(sample) == 5
assert sample.num_nodes <= 4
assert sample.num_edges <= 4 * 4
assert sample.node_norm.numel() == sample.num_nodes
assert sample.edge_norm.numel() == sample.num_edges
torch.manual_seed(12345)
loader = GraphSAINTRandomWalkSampler(data, batch_size=2, walk_length=1,
num_steps=4, sample_coverage=10,
log=False)
sample = next(iter(loader))
assert sample.x.tolist() == [[1, 1], [2, 2], [4, 4]]
assert sample.edge_index.tolist() == [[0, 1, 1, 2, 2], [0, 1, 2, 1, 2]]
assert sample.edge_type.tolist() == [6, 10, 11, 16, 17]
assert len(loader) == 4
for sample in loader:
assert len(sample) == 5
assert sample.num_nodes <= 4
assert sample.num_edges <= 4 * 4
assert sample.node_norm.numel() == sample.num_nodes
assert sample.edge_norm.numel() == sample.num_edges
def test_graph_saint():
adj = torch.tensor([
[+1, +2, +3, +0, +4, +0],
[+5, +6, +0, +7, +0, +8],
[+9, +0, 10, +0, 11, +0],
[+0, 12, +0, 13, +0, 14],
[15, +0, 16, +0, 17, +0],
[+0, 18, +0, 19, +0, 20],
])
edge_index = adj.nonzero(as_tuple=False).t()
edge_id = adj[edge_index[0], edge_index[1]]
x = torch.Tensor([[0, 0], [1, 1], [2, 2], [3, 3], [4, 4], [5, 5]])
n_id = torch.arange(6)
data = Data(edge_index=edge_index,
x=x,
n_id=n_id,
edge_id=edge_id,
num_nodes=6)
loader = GraphSAINTNodeSampler(data,
batch_size=3,
num_steps=4,
sample_coverage=10,
log=False)
assert len(loader) == 4
for sample in loader:
assert sample.num_nodes <= data.num_nodes
assert sample.n_id.min() >= 0 and sample.n_id.max() < 6
assert sample.num_nodes == sample.n_id.numel()
assert sample.x.tolist() == x[sample.n_id].tolist()
assert sample.edge_index.min() >= 0
assert sample.edge_index.max() < sample.num_nodes
assert sample.edge_id.min() >= 1 and sample.edge_id.max() <= 21
assert sample.edge_id.numel() == sample.num_edges
assert sample.node_norm.numel() == sample.num_nodes
assert sample.edge_norm.numel() == sample.num_edges
loader = GraphSAINTEdgeSampler(data,
batch_size=2,
num_steps=4,
sample_coverage=10,
log=False)
assert len(loader) == 4
for sample in loader:
assert sample.num_nodes <= data.num_nodes
assert sample.n_id.min() >= 0 and sample.n_id.max() < 6
assert sample.num_nodes == sample.n_id.numel()
assert sample.x.tolist() == x[sample.n_id].tolist()
assert sample.edge_index.min() >= 0
assert sample.edge_index.max() < sample.num_nodes
assert sample.edge_id.min() >= 1 and sample.edge_id.max() <= 21
assert sample.edge_id.numel() == sample.num_edges
assert sample.node_norm.numel() == sample.num_nodes
assert sample.edge_norm.numel() == sample.num_edges
loader = GraphSAINTRandomWalkSampler(data,
batch_size=2,
walk_length=1,
num_steps=4,
sample_coverage=10,
log=False)
assert len(loader) == 4
for sample in loader:
assert sample.num_nodes <= data.num_nodes
assert sample.n_id.min() >= 0 and sample.n_id.max() < 6
assert sample.num_nodes == sample.n_id.numel()
assert sample.x.tolist() == x[sample.n_id].tolist()
assert sample.edge_index.min() >= 0
assert sample.edge_index.max() < sample.num_nodes
assert sample.edge_id.min() >= 1 and sample.edge_id.max() <= 21
assert sample.edge_id.numel() == sample.num_edges
assert sample.node_norm.numel() == sample.num_nodes
assert sample.edge_norm.numel() == sample.num_edges