]
idx = np.random.permutation(len(files))
idx = idx.tolist()
valid_ids = [files[i] for i in idx[0:10000]]
test_ids = [files[i] for i in idx[10000:20000]]
train_ids = [files[i] for i in idx[20000:]]
data_train = Qm9(root,
train_ids,
vertex_transform=utils.qm9_nodes,
edge_transform=lambda g: utils.qm9_edges(
g, e_representation='raw_distance'))
data_valid = Qm9(root, valid_ids)
data_test = Qm9(root, test_ids)
print(len(data_train))
print(len(data_valid))
print(len(data_test))
print(data_train[1])
print(data_valid[1])
print(data_test[1])
start = time.time()
print(utils.get_graph_stats(data_valid, 'degrees'))
end = time.time()
print('Time Statistics Par')
print(end - start)
import os, sys
import torch
parser = argparse.ArgumentParser(description='QM9 Object.')
# Optional argument
parser.add_argument('--root',
nargs=1,
help='Specify the data directory.',
default=['GraphReader/'])
args = parser.parse_args()
root = args.root[0]
files = [f for f in os.listdir(root) \
if os.path.isfile(os.path.join(root, f)) \
and os.path.splitext(f)[-1] == ".json"]
test = AIChemy(root,
files,
vertex_transform=utils.qm9_nodes,
e_representation="raw_distance")
print(len(test))
start = time.time()
print(utils.get_graph_stats(test, ['target_mean', 'target_std']))
end = time.time()
print('Time Statistics Par')
print(end - start)