def pre_process(self):
processed_dir = osp.join(self.root, 'processed')
pre_processed_file_path = osp.join(processed_dir, 'dgl_data_processed')
if osp.exists(pre_processed_file_path):
self.graph, label_dict = load_graphs(pre_processed_file_path)
self.labels = label_dict['labels']
else:
### check download
if not osp.exists(osp.join(self.root, "raw", "edge.csv.gz")):
url = self.meta_info[self.name]["url"]
if decide_download(url):
path = download_url(url, self.original_root)
extract_zip(path, self.original_root)
os.unlink(path)
# delete folder if there exists
try:
shutil.rmtree(self.root)
except:
pass
shutil.move(osp.join(self.original_root, self.download_name), self.root)
else:
print("Stop download.")
exit(-1)
raw_dir = osp.join(self.root, "raw")
### pre-process and save
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(',')
graph = read_csv_graph_dgl(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(raw_dir, 'node-label.csv.gz'), compression="gzip", header = None).values
if "classification" in self.task_type:
node_label = torch.tensor(node_label, dtype = torch.long)
else:
node_label = torch.tensor(node_label, dtype = torch.float32)
label_dict = {"labels": node_label}
print('Saving...')
save_graphs(pre_processed_file_path, graph, label_dict)
self.graph, label_dict = load_graphs(pre_processed_file_path)
self.labels = label_dict['labels']
def pre_process(self):
processed_dir = osp.join(self.root, 'processed')
pre_processed_file_path = osp.join(processed_dir, 'dgl_data_processed')
if osp.exists(pre_processed_file_path):
self.graph, _ = load_graphs(pre_processed_file_path)
else:
### check download
if not osp.exists(osp.join(self.root, "raw", "edge.csv.gz")):
url = self.meta_info[self.name]["url"]
if decide_download(url):
path = download_url(url, self.original_root)
extract_zip(path, self.original_root)
os.unlink(path)
# delete folder if there exists
try:
shutil.rmtree(self.root)
except:
pass
shutil.move(
osp.join(self.original_root, self.download_name),
self.root)
else:
print("Stop download.")
exit(-1)
raw_dir = osp.join(self.root, "raw")
add_inverse_edge = self.meta_info[
self.name]["add_inverse_edge"] == "True"
### pre-process and save
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(',')
graph = read_csv_graph_dgl(
raw_dir,
add_inverse_edge=add_inverse_edge,
additional_node_files=additional_node_files,
additional_edge_files=additional_edge_files)[0]
print('Saving...')
save_graphs(pre_processed_file_path, graph, {})
self.graph, _ = load_graphs(pre_processed_file_path)
def pre_process(self):
processed_dir = osp.join(self.root, 'processed')
raw_dir = osp.join(self.root, 'raw')
pre_processed_file_path = osp.join(processed_dir, 'dgl_data_processed')
### download
url = 'https://snap.stanford.edu/ogb/data/graphproppred/csv_mol_download/pcba.zip'
if decide_download(url):
path = download_url(url, self.original_root)
extract_zip(path, self.original_root)
os.unlink(path)
# delete folder if there exists
try:
shutil.rmtree(self.root)
except:
pass
shutil.move(osp.join(self.original_root, self.download_name),
self.root)
else:
print("Stop download.")
exit(-1)
### preprocess
add_inverse_edge = True
additional_node_files = []
additional_edge_files = []
graphs = read_csv_graph_dgl(
raw_dir,
add_inverse_edge=add_inverse_edge,
additional_node_files=additional_node_files,
additional_edge_files=additional_edge_files)
labels = pd.read_csv(osp.join(raw_dir, "graph-label.csv.gz"),
compression="gzip",
header=None).values
has_nan = np.isnan(labels).any()
if "classification" in self.task_type:
if has_nan:
labels = torch.from_numpy(labels)
else:
labels = torch.from_numpy(labels).to(torch.long)
else:
labels = torch.from_numpy(labels)
print('Not Saving...')
# save_graphs(pre_processed_file_path, graphs, labels={'labels': labels})
### load preprocessed files
self.graphs = graphs
self.labels = labels
def pre_process(self):
processed_dir = osp.join(self.root, 'processed')
pre_processed_file_path = osp.join(processed_dir, 'dgl_data_processed')
if osp.exists(pre_processed_file_path):
self.graph, _ = load_graphs(pre_processed_file_path)
else:
### check download
if not osp.exists(osp.join(self.root, "raw", "edge.csv.gz")):
url = self.meta_info[self.name]["url"]
if decide_download(url):
path = download_url(url, self.original_root)
extract_zip(path, self.original_root)
os.unlink(path)
# delete folder if there exists
try:
shutil.rmtree(self.root)
except:
pass
shutil.move(
osp.join(self.original_root, self.download_name),
self.root)
else:
print("Stop download.")
exit(-1)
raw_dir = osp.join(self.root, "raw")
file_names = ["edge"]
if self.meta_info[self.name]["has_node_attr"] == "True":
file_names.append("node-feat")
if self.meta_info[self.name]["has_edge_attr"] == "True":
file_names.append("edge-feat")
raw_file_names = [
file_name + ".csv.gz" for file_name in file_names
]
### pre-process and save
add_inverse_edge = self.meta_info[
self.name]["add_inverse_edge"] == "True"
graph = read_csv_graph_dgl(raw_dir,
raw_file_names,
add_inverse_edge=add_inverse_edge)
save_graphs(pre_processed_file_path, graph, {})
self.graph, _ = load_graphs(pre_processed_file_path)
def pre_process(self):
processed_dir = osp.join(self.root, 'processed')
raw_dir = osp.join(self.root, 'raw')
pre_processed_file_path = osp.join(processed_dir, 'dgl_data_processed')
if os.path.exists(pre_processed_file_path):
self.graphs, label_dict = load_graphs(pre_processed_file_path)
self.labels = label_dict['labels']
else:
### download
url = self.meta_info[self.name]["url"]
if decide_download(url):
path = download_url(url, self.original_root)
extract_zip(path, self.original_root)
os.unlink(path)
# delete folder if there exists
try:
shutil.rmtree(self.root)
except:
pass
shutil.move(osp.join(self.original_root, self.download_name),
self.root)
else:
print("Stop download.")
exit(-1)
### preprocess
add_inverse_edge = self.meta_info[
self.name]["add_inverse_edge"] == "True"
graphs = read_csv_graph_dgl(raw_dir,
add_inverse_edge=add_inverse_edge)
labels = torch.tensor(
pd.read_csv(osp.join(raw_dir, "graph-label.csv.gz"),
compression="gzip",
header=None).values)
print('Saving...')
save_graphs(pre_processed_file_path,
graphs,
labels={'labels': labels})
### load preprocessed files
self.graphs, label_dict = load_graphs(pre_processed_file_path)
self.labels = label_dict['labels']
def pre_process(self):
processed_dir = osp.join(self.root, 'processed')
raw_dir = osp.join(self.root, 'raw')
pre_processed_file_path = osp.join(processed_dir, 'dgl_data_processed')
if self.task_type == 'sequence prediction':
target_sequence_file_path = osp.join(processed_dir,
'target_sequence')
if os.path.exists(pre_processed_file_path):
if self.task_type == "sequence prediction":
self.graphs, _ = load_graphs(pre_processed_file_path)
self.labels = torch.load(target_sequence_file_path)
else:
self.graphs, label_dict = load_graphs(pre_processed_file_path)
self.labels = label_dict['labels']
else:
### download
url = self.meta_info[self.name]["url"]
if decide_download(url):
path = download_url(url, self.original_root)
extract_zip(path, self.original_root)
os.unlink(path)
# delete folder if there exists
try:
shutil.rmtree(self.root)
except:
pass
shutil.move(osp.join(self.original_root, self.download_name),
self.root)
else:
print("Stop download.")
exit(-1)
### preprocess
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(',')
graphs = read_csv_graph_dgl(
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":
# the downloaded labels are initially joined by ' '
labels_joined = pd.read_csv(osp.join(raw_dir,
"graph-label.csv.gz"),
compression="gzip",
header=None).values
# need to split each element into subtokens
labels = [
str(labels_joined[i][0]).split(' ')
for i in range(len(labels_joined))
]
print('Saving...')
save_graphs(pre_processed_file_path, graphs)
torch.save(labels, target_sequence_file_path)
### load preprocessed files
self.graphs, _ = load_graphs(pre_processed_file_path)
self.labels = torch.load(target_sequence_file_path)
else:
labels = torch.tensor(
pd.read_csv(osp.join(raw_dir, "graph-label.csv.gz"),
compression="gzip",
header=None).values)
print('Saving...')
save_graphs(pre_processed_file_path,
graphs,
labels={'labels': labels})
### load preprocessed files
self.graphs, label_dict = load_graphs(pre_processed_file_path)
self.labels = label_dict['labels']
def pre_process(self):
processed_dir = osp.join(self.root, 'processed')
pre_processed_file_path = osp.join(processed_dir, 'dgl_data_processed')
if osp.exists(pre_processed_file_path):
if not self.is_hetero:
self.graph, label_dict = load_graphs(pre_processed_file_path)
self.labels = label_dict['labels']
else:
with open(pre_processed_file_path, 'rb') as f:
self.graph, self.labels = pickle.load(f)
else:
### check if the downloaded file exists
has_necessary_file_simple = osp.exists(
osp.join(self.root, "raw",
"edge.csv.gz")) and (not self.is_hetero)
has_necessary_file_hetero = osp.exists(
osp.join(self.root, "raw",
"triplet-type-list.csv.gz")) and self.is_hetero
has_necessary_file = has_necessary_file_simple or has_necessary_file_hetero
if not has_necessary_file:
url = self.meta_info[self.name]["url"]
if decide_download(url):
path = download_url(url, self.original_root)
extract_zip(path, self.original_root)
os.unlink(path)
# delete folder if there exists
try:
shutil.rmtree(self.root)
except:
pass
shutil.move(
osp.join(self.original_root, self.download_name),
self.root)
else:
print("Stop download.")
exit(-1)
raw_dir = osp.join(self.root, "raw")
### pre-process and save
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:
graph = read_csv_heterograph_dgl(
raw_dir,
add_inverse_edge=add_inverse_edge,
additional_node_files=additional_node_files,
additional_edge_files=additional_edge_files)[0]
label_dict = read_node_label_hetero(raw_dir)
# convert into torch tensor
if "classification" in self.task_type:
for nodetype in label_dict.keys():
# detect if there is any nan
node_label = label_dict[nodetype]
if np.isnan(node_label).any():
label_dict[nodetype] = torch.from_numpy(
node_label).to(torch.float32)
else:
label_dict[nodetype] = torch.from_numpy(
node_label).to(torch.long)
else:
for nodetype in label_dict.keys():
node_label = label_dict[nodetype]
label_dict[nodetype] = torch.from_numpy(node_label).to(
torch.float32)
with open(pre_processed_file_path, 'wb') as f:
pickle.dump(([graph], label_dict), f)
with open(pre_processed_file_path, 'rb') as f:
self.graph, self.labels = pickle.load(f)
else:
graph = read_csv_graph_dgl(
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(raw_dir,
'node-label.csv.gz'),
compression="gzip",
header=None).values
if "classification" in self.task_type:
# detect if there is any nan
if np.isnan(node_label).any():
node_label = torch.from_numpy(node_label).to(
torch.float32)
else:
node_label = torch.from_numpy(node_label).to(
torch.long)
else:
node_label = torch.from_numpy(node_label).to(torch.float32)
label_dict = {"labels": node_label}
save_graphs(pre_processed_file_path, graph, label_dict)
self.graph, label_dict = load_graphs(pre_processed_file_path)
self.labels = label_dict['labels']
def pre_process(self):
processed_dir = osp.join(self.root, 'processed')
pre_processed_file_path = osp.join(processed_dir, 'dgl_data_processed')
if osp.exists(pre_processed_file_path):
if not self.is_hetero:
self.graph, _ = load_graphs(pre_processed_file_path)
else:
with open(pre_processed_file_path, 'rb') as f:
self.graph = pickle.load(f)
else:
### check if the downloaded file exists
has_necessary_file_simple = osp.exists(
osp.join(self.root, "raw",
"edge.csv.gz")) and (not self.is_hetero)
has_necessary_file_hetero = osp.exists(
osp.join(self.root, "raw",
"triplet-type-list.csv.gz")) and self.is_hetero
has_necessary_file = has_necessary_file_simple or has_necessary_file_hetero
if not has_necessary_file:
url = self.meta_info[self.name]["url"]
if decide_download(url):
path = download_url(url, self.original_root)
extract_zip(path, self.original_root)
os.unlink(path)
# delete folder if there exists
try:
shutil.rmtree(self.root)
except:
pass
shutil.move(
osp.join(self.original_root, self.download_name),
self.root)
else:
print("Stop download.")
exit(-1)
raw_dir = osp.join(self.root, "raw")
add_inverse_edge = self.meta_info[
self.name]["add_inverse_edge"] == "True"
### pre-process and save
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:
graph = read_csv_heterograph_dgl(
raw_dir,
add_inverse_edge=add_inverse_edge,
additional_node_files=additional_node_files,
additional_edge_files=additional_edge_files)[0]
with open(pre_processed_file_path, 'wb') as f:
pickle.dump([graph], f)
with open(pre_processed_file_path, 'rb') as f:
self.graph = pickle.load(f)
else:
graph = read_csv_graph_dgl(
raw_dir,
add_inverse_edge=add_inverse_edge,
additional_node_files=additional_node_files,
additional_edge_files=additional_edge_files)[0]
print('Saving...')
save_graphs(pre_processed_file_path, graph, {})
self.graph, _ = load_graphs(pre_processed_file_path)