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run_gc.py
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run_gc.py
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import sys
from torch_geometric.data import DataLoader
from torch_geometric.datasets import TUDataset
import os.path as osp
import trainer
import time
import ssl
import utils.gsn_argparse as gap
import numpy as np
ssl._create_default_https_context = ssl._create_unverified_context
def load_data(dataset_name, val_idx):
bs = 600
path = osp.join(osp.dirname(osp.realpath(__file__)), '.', 'data', dataset_name)
dataset = TUDataset(path, dataset_name, use_node_attr=True)
dataset = dataset.shuffle()
if dataset_name == "DD" or dataset_name == "MUTAG":
# remove node label
dataset.data.x = dataset.data.x[:, :-3]
val_size = len(dataset) // 10
if val_idx == 0:
train_data = dataset[val_size:]
elif 0 < val_idx < 9:
train_data = dataset[:(val_idx * val_size)] + dataset[((val_idx + 1) * val_size):]
elif val_idx == 9:
train_data = dataset[:(val_idx * val_size)]
else:
raise AttributeError("val index must in [0,9]")
train_loader = DataLoader(train_data, batch_size=bs)
val_loader = DataLoader(dataset[(val_idx * val_size): ((val_idx + 1) * val_size)], batch_size=bs)
test_loader = DataLoader(dataset[(val_idx * val_size): ((val_idx + 1) * val_size)], batch_size=bs)
return dataset, train_loader, val_loader, test_loader
def main(_args):
args = gap.parser.parse_args(_args)
val_accs = []
all_gc_accs = []
for i in range(10):
start_time = time.perf_counter()
dataset, train_loader, val_loader, test_loader = load_data(args.dataset, i)
val_acc, gc_accs = trainer.trainer(args, args.dataset, train_loader, val_loader, test_loader,
num_features=dataset.num_features,
num_graph_class=dataset.num_classes,
max_epoch=args.epochs,
node_multi_label=False,
graph_multi_label=False)
val_accs.append(val_acc)
all_gc_accs.append(np.array(gc_accs))
end_time = time.perf_counter()
spent_time = (end_time - start_time) / 60
print(" It took: {:2f} minutes to complete one round....".format(spent_time))
print("\033[1;32m Best graph classification accuracy in {}th round is: {:4f} \033[0m".format((i + 1), val_acc))
all_gc_accs = np.vstack(all_gc_accs)
all_gc_accs = np.mean(all_gc_accs, axis=0)
final_gc = np.mean(val_accs)
print("\n\n\033[1;32m Average over 10 best results: {:.4f} \033[0m".format(final_gc))
val_accs = ['{:.4f}'.format(i) for i in val_accs]
print(" 10 Best results: ", np.asfarray(val_accs, float))
print(" DiffPoll cross val: {:.4f} ".format(np.max(all_gc_accs)))
print(" DiffPoll argmax pos: ", np.argmax(all_gc_accs))
if __name__ == '__main__':
main(sys.argv[1:])