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"
data = read_csv_graph_pyg(self.raw_dir,
add_inverse_edge=add_inverse_edge)[0]
data = data if self.pre_transform is None else self.pre_transform(data)
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])
def process(self):
### read pyg graph list
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(',')
data_list = 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)
if self.task_type == "sequence prediction":
graph_label_notparsed = pd.read_csv(osp.join(
self.raw_dir, "graph-label.csv.gz"),
compression="gzip",
header=None).values
graph_label = [
str(graph_label_notparsed[i][0]).split(' ')
for i in range(len(graph_label_notparsed))
]
for i, g in enumerate(data_list):
g.y = graph_label[i]
else:
graph_label = pd.read_csv(osp.join(self.raw_dir,
"graph-label.csv.gz"),
compression="gzip",
header=None).values
for i, g in enumerate(data_list):
g.y = torch.tensor(graph_label[i]).view(1, -1)
if self.pre_transform is not None:
data_list = [self.pre_transform(data) for data in data_list]
data, slices = self.collate(data_list)
print('Saving...')
torch.save((data, slices), self.processed_paths[0])
def process(self):
### read pyg graph list
add_inverse_edge = self.meta_info[
self.name]["add_inverse_edge"] == "True"
data_list = read_csv_graph_pyg(self.raw_dir,
add_inverse_edge=add_inverse_edge)
graph_label = pd.read_csv(osp.join(self.raw_dir, "graph-label.csv.gz"),
compression="gzip",
header=None).values
### add target labels
for i, g in enumerate(data_list):
g.y = torch.tensor(graph_label[i]).view(1, -1)
data, slices = self.collate(data_list)
torch.save((data, slices), self.processed_paths[0])
def process(self):
add_inverse_edge = self.meta_info[
self.name]["add_inverse_edge"] == "True"
data = read_csv_graph_pyg(self.raw_dir,
add_inverse_edge=add_inverse_edge)[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.tensor(node_label, dtype=torch.long)
else:
data.y = torch.tensor(node_label, dtype=torch.float32)
data.__num_nodes__ = int(self.meta_info[self.name]["num nodes"])
data = data if self.pre_transform is None else self.pre_transform(data)
torch.save(self.collate([data]), self.processed_paths[0])
