timenow = time.strftime("%Y-%m-%d_%H:%M:%S", time.localtime(time.time()))
fh = logging.FileHandler('log/{}.log'.format(str(args.prefix) + timenow))
fh.setLevel(logging.DEBUG)
ch = logging.StreamHandler()
ch.setLevel(logging.WARN)
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
fh.setFormatter(formatter)
ch.setFormatter(formatter)
logger.addHandler(fh)
logger.addHandler(ch)
logger.info(args)
### Extract data for training, validation and testing
node_features, edge_features, full_data, train_data, val_data, test_data, new_node_val_data, \
new_node_test_data = get_data(DATA,
different_new_nodes_between_val_and_test=args.different_new_nodes)
# Initialize training neighbor finder to retrieve temporal graph
train_ngh_finder = get_neighbor_finder(train_data, args.uniform)
# Initialize validation and test neighbor finder to retrieve temporal graph
full_ngh_finder = get_neighbor_finder(full_data, args.uniform)
# Initialize negative samplers. Set seeds for validation and testing so negatives are the same
# across different runs
# NB: in the inductive setting, negatives are sampled only amongst other new nodes
train_rand_sampler = RandEdgeSampler(train_data.sources,
train_data.destinations)
val_rand_sampler = RandEdgeSampler(full_data.sources,
full_data.destinations,
seed=0)
logger.setLevel(logging.DEBUG)
Path("log/").mkdir(parents=True, exist_ok=True)
fh = logging.FileHandler('log/{}.log'.format(str(time.time())))
fh.setLevel(logging.DEBUG)
ch = logging.StreamHandler()
ch.setLevel(logging.WARN)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
fh.setFormatter(formatter)
ch.setFormatter(formatter)
logger.addHandler(fh)
logger.addHandler(ch)
logger.info(args)
### Extract data for training, validation and testing
node_features, edge_features, full_data, train_data, val_data, test_data, new_node_val_data, \
new_node_test_data = get_data(DATA,
different_new_nodes_between_val_and_test=args.different_new_nodes, randomize_features=args.randomize_features)
# Initialize training neighbor finder to retrieve temporal graph
train_ngh_finder = get_neighbor_finder(train_data, args.uniform)
# Initialize validation and test neighbor finder to retrieve temporal graph
full_ngh_finder = get_neighbor_finder(full_data, args.uniform)
# Initialize negative samplers. Set seeds for validation and testing so negatives are the same
# across different runs
# NB: in the inductive setting, negatives are sampled only amongst other new nodes
train_rand_sampler = RandEdgeSampler(train_data.sources, train_data.destinations)
val_rand_sampler = RandEdgeSampler(full_data.sources, full_data.destinations, seed=0)
nn_val_rand_sampler = RandEdgeSampler(new_node_val_data.sources, new_node_val_data.destinations,
seed=1)
test_rand_sampler = RandEdgeSampler(full_data.sources, full_data.destinations, seed=2)