def eval(chromosome):
args = build_args()
dataset_list = ["Citeseer", "Pubmed", "cora"]
base_list = [
"pyg",
"pyg",
"dgl",
]
for dataset, actions, base in zip(dataset_list, chromosome, base_list):
# if dataset == "cora":
# continue
args.dataset = dataset
if base == "dgl":
manager = CitationGNNManager(args)
else:
manager = GeoCitationManager(args)
val_acc, test_acc = manager.evaluate(actions)
print(test_acc)
print("_" * 80)
return test_acc
if __name__ == "__main__":
args = build_args()
gnn_list = [
['gat', 'sum', 'linear', 4, 128, 'linear', 'sum', 'elu', 8, 6],
['gcn', 'sum', 'tanh', 6, 64, 'cos', 'sum', 'tanh', 6, 3],
['const', 'sum', 'relu6', 2, 128, 'gat', 'sum', 'linear', 2, 7],
]
dataset_list = ["Citeseer", "Pubmed", "cora"]
base_list = [
"pyg",
"pyg",
"dgl",
]
for dataset, actions, base in zip(dataset_list, gnn_list, base_list):
# if dataset == "cora":
# continue
args.dataset = dataset
if base == "dgl":
manager = CitationGNNManager(args)
else:
manager = GeoCitationManager(args)
test_scores_list = []
for i in range(100):
val_acc, test_acc = manager.evaluate(actions)
test_scores_list.append(test_acc)
print("_" * 80)
test_scores_list.sort()
print(dataset, np.mean(test_scores_list[5:-5]),
np.std(test_scores_list[5:-5]))