def process(self):
add_inverse_edge = self.meta_info[
self.name]["add_inverse_edge"] == "True"
if self.meta_info[self.name]["additional node files"] == 'None':
additional_node_files = []
else:
additional_node_files = self.meta_info[
self.name]["additional node files"].split(',')
if self.meta_info[self.name]["additional edge files"] == 'None':
additional_edge_files = []
else:
additional_edge_files = self.meta_info[
self.name]["additional edge files"].split(',')
if self.is_hetero:
data = read_csv_heterograph_pyg(
self.raw_dir,
add_inverse_edge=add_inverse_edge,
additional_node_files=additional_node_files,
additional_edge_files=additional_edge_files)[0]
else:
data = read_csv_graph_pyg(
self.raw_dir,
add_inverse_edge=add_inverse_edge,
additional_node_files=additional_node_files,
additional_edge_files=additional_edge_files)[0]
data = data if self.pre_transform is None else self.pre_transform(data)
print('Saving...')
torch.save(self.collate([data]), self.processed_paths[0])
def process(self):
add_inverse_edge = self.meta_info[
self.name]["add_inverse_edge"] == "True"
if self.meta_info[self.name]["additional node files"] == 'None':
additional_node_files = []
else:
additional_node_files = self.meta_info[
self.name]["additional node files"].split(',')
if self.meta_info[self.name]["additional edge files"] == 'None':
additional_edge_files = []
else:
additional_edge_files = self.meta_info[
self.name]["additional edge files"].split(',')
if self.is_hetero:
data = read_csv_heterograph_pyg(
self.raw_dir,
add_inverse_edge=add_inverse_edge,
additional_node_files=additional_node_files,
additional_edge_files=additional_edge_files)[0]
node_label_dict = read_node_label_hetero(self.raw_dir)
data.y_dict = {}
if "classification" in self.task_type:
for nodetype, node_label in node_label_dict.items():
data.y_dict[nodetype] = torch.from_numpy(node_label).to(
torch.long)
else:
for nodetype, node_label in node_label_dict.items():
data.y_dict[nodetype] = torch.from_numpy(node_label).to(
torch.float32)
else:
data = read_csv_graph_pyg(
self.raw_dir,
add_inverse_edge=add_inverse_edge,
additional_node_files=additional_node_files,
additional_edge_files=additional_edge_files)[0]
### adding prediction target
node_label = pd.read_csv(osp.join(self.raw_dir,
'node-label.csv.gz'),
compression="gzip",
header=None).values
if "classification" in self.task_type:
data.y = torch.from_numpy(node_label).to(torch.long)
else:
data.y = torch.from_numpy(node_label).to(torch.float32)
data if self.pre_transform is None else self.pre_transform(data)
print('Saving...')
torch.save(self.collate([data]), self.processed_paths[0])