def main():
"""
Parsing command line parameters, reading data, fitting an NGCN and scoring the model.
"""
args = parameter_parser()
torch.manual_seed(args.seed)
tab_printer(args)
graph = graph_reader(args.edge_path)
features = feature_reader(args.features_path)
target = target_reader(args.target_path)
trainer = Trainer(args, graph, features, target)
trainer.fit()
def main():
"""
Parsing command line parameters, reading data, graph decomposition, fitting a ClusterGCN and scoring the model.
"""
args = parameter_parser()
torch.manual_seed(args.seed)
tab_printer(args)
graph = graph_reader(args.edge_path)
features = feature_reader(args.features_path)
target = target_reader(args.target_path)
clustering_machine = ClusteringMachine(args, graph, features, target)
clustering_machine.decompose()
gcn_trainer = ClusterGCNTrainer(args, clustering_machine)
gcn_trainer.train()
gcn_trainer.test()
def main():
"""
Parsing command line parameters, reading data.
Doing sparsification, fitting a GWNN and saving the logs.
"""
args = parameter_parser()
tab_printer(args)
graph = graph_reader(args.edge_path)
features = feature_reader(args.features_path)
target = target_reader(args.target_path)
sparsifier = WaveletSparsifier(graph, args.scale, args.approximation_order, args.tolerance)
sparsifier.calculate_all_wavelets()
trainer = GWNNTrainer(args, sparsifier, features, target)
trainer.fit()
trainer.score()
save_logs(args, trainer.logs)
def main():
"""
Parsing command line parameters, reading data, fitting an NGCN and scoring the model.
"""
args = parameter_parser()
torch.manual_seed(args.seed)
tab_printer(args)
graph = graph_reader(args.edge_path)
features = feature_reader(args.features_path)
target = target_reader(args.target_path)
trainer = Trainer(args, graph, features, target, True)
trainer.fit()
if args.model == "mixhop":
trainer.evaluate_architecture()
args = trainer.reset_architecture()
trainer = Trainer(args, graph, features, target, False)
trainer.fit()