def test_unsupervised_node_classification():
sys.argv = [sys.argv[0], "-m", "prone", "-dt", "ppi"]
parser = options.get_training_parser()
args, _ = parser.parse_known_args()
args = options.parse_args_and_arch(parser, args)
print(args)
assert args.model[0] == "prone"
assert args.dataset[0] == "ppi"
def test_unsupervised_graph_classification():
sys.argv = [sys.argv[0], "-m", "infograph", "-dt", "mutag"]
parser = options.get_training_parser()
args, _ = parser.parse_known_args()
args = options.parse_args_and_arch(parser, args)
print(args)
assert args.model[0] == "infograph"
assert args.dataset[0] == "mutag"
def test_training_options():
sys.argv = [sys.argv[0], "-m", "gcn", "-dt", "cora"]
parser = options.get_training_parser()
args, _ = parser.parse_known_args()
args = options.parse_args_and_arch(parser, args)
print(args)
assert args.model[0] == "gcn"
assert args.dataset[0] == "cora"
def test_attributed_graph_clustering():
sys.argv = [sys.argv[0], "-m", "daegc", "-dt", "cora"]
parser = options.get_training_parser()
args, _ = parser.parse_known_args()
args = options.parse_args_and_arch(parser, args)
print(args)
assert args.model[0] == "daegc"
assert args.dataset[0] == "cora"
assert args.num_clusters == 7
def test_multiplex_link_prediction():
sys.argv = [sys.argv[0], "-m", "gatne", "-dt", "amazon"]
parser = options.get_training_parser()
args, _ = parser.parse_known_args()
args = options.parse_args_and_arch(parser, args)
print(args)
assert args.model[0] == "gatne"
assert args.dataset[0] == "amazon"
assert args.eval_type == "all"
def test_link_prediction():
sys.argv = [sys.argv[0], "-t", "link_prediction", "-m", "prone", "-dt", "ppi"]
parser = options.get_training_parser()
args, _ = parser.parse_known_args()
args = options.parse_args_and_arch(parser, args)
print(args)
assert args.task == "link_prediction"
assert args.model[0] == "prone"
assert args.dataset[0] == "ppi"
assert args.evaluate_interval == 30
def test_graph_classification():
sys.argv = [sys.argv[0], "-t", "graph_classification", "-m", "gin", "-dt", "mutag"]
parser = options.get_training_parser()
args, _ = parser.parse_known_args()
args = options.parse_args_and_arch(parser, args)
print(args)
assert args.task == "graph_classification"
assert args.model[0] == "gin"
assert args.dataset[0] == "mutag"
assert args.degree_feature is False
def test_unsupervised_graph_classification():
sys.argv = [sys.argv[0], "-t", "unsupervised_graph_classification", "-m", "infograph", "-dt", "mutag"]
parser = options.get_training_parser()
args, _ = parser.parse_known_args()
args = options.parse_args_and_arch(parser, args)
print(args)
assert args.task == "unsupervised_graph_classification"
assert args.model[0] == "infograph"
assert args.dataset[0] == "mutag"
assert args.num_shuffle == 10
assert args.degree_feature is False
def test_link_prediction():
sys.argv = [sys.argv[0], "-m", "prone", "-dt", "ppi"]
sys.argv += [
"--mw", "embedding_link_prediction_mw", "--dw",
"embedding_link_prediction_dw"
]
parser = options.get_training_parser()
args, _ = parser.parse_known_args()
args = options.parse_args_and_arch(parser, args)
print(args)
assert args.model[0] == "prone"
assert args.dataset[0] == "ppi"
assert args.mw == "embedding_link_prediction_mw"
assert args.dw == "embedding_link_prediction_dw"
def gen_variants(**items):
Variant = namedtuple("Variant", items.keys())
return itertools.starmap(Variant, itertools.product(*items.values()))
def getpid(_):
# HACK to get different pids
time.sleep(1)
return mp.current_process().pid
if __name__ == "__main__":
# Magic for making multiprocessing work for PyTorch
mp.set_start_method("spawn", force=True)
parser = options.get_training_parser()
args, _ = parser.parse_known_args()
args = options.parse_args_and_arch(parser, args)
# Make sure datasets are downloaded first
datasets = args.dataset
for dataset in datasets:
args.dataset = dataset
_ = build_dataset(args)
args.dataset = datasets
print(args)
variants = list(
gen_variants(dataset=args.dataset, model=args.model, seed=args.seed))
device_ids = args.device_id
