def setFileName(self, dataset: GraphDataset, args: ArgumentParser):
"""
sets the generic name for the output file
Parameters
----------
dataset: GraphDataset
args: ArgumentParser - command line inputs
"""
if self.singleGNN is None:
self.file_name = fileNamer(
dataset_name=dataset.name,
l_inf=args.l_inf,
l_0=args.l_0,
num_layers=args.num_layers,
seed=args.seed,
targeted=args.targeted,
continuous_epochs=args.continuous_epochs,
start=self.start_to_file,
end=self.end_to_file)
else:
self.file_name = fileNamer(
dataset_name=dataset.name,
model_name=args.singleGNN.string(),
l_inf=args.l_inf,
l_0=args.l_0,
num_layers=args.num_layers,
seed=args.seed,
targeted=args.targeted,
continuous_epochs=args.continuous_epochs,
start=self.start_to_file,
end=self.end_to_file)
def setFileName(self, dataset: GraphDataset, args: ArgumentParser):
"""
information at the generic base class oneGNNSAttack
"""
if self.singleGNN is None:
self.file_name = fileNamer(
dataset_name=dataset.name,
l_inf=args.l_inf,
num_layers=args.num_layers,
seed=args.seed,
targeted=args.targeted,
continuous_epochs=args.continuous_epochs,
start=self.start_to_file,
end=self.end_to_file)
else:
self.file_name = fileNamer(
dataset_name=dataset.name,
model_name=args.singleGNN.string(),
l_inf=args.l_inf,
num_layers=args.num_layers,
seed=args.seed,
targeted=args.targeted,
continuous_epochs=args.continuous_epochs,
start=self.start_to_file,
end=self.end_to_file)
def train(self, dataset, attack=None):
model = self.model
folder_name = osp.join(getGitPath(), 'models')
if attack is None:
folder_name = osp.join(folder_name, 'basic_models')
targeted, attack_epochs = None, None
else:
folder_name = osp.join(folder_name, 'adversarial_models')
targeted, attack_epochs = attack.targeted, attack.attack_epochs
file_name = fileNamer(node_model=self.node_model,
dataset_name=dataset.name,
model_name=model.name,
num_layers=model.num_layers,
patience=self.patience,
seed=self.seed,
targeted=targeted,
attack_epochs=attack_epochs,
end='.pt')
model_path = osp.join(folder_name, file_name)
# load model and optimizer
if not osp.exists(model_path):
# train model
model, model_log, test_acc = self.useTrainer(data=dataset.data,
attack=attack)
torch.save((model.state_dict(), model_log, test_acc), model_path)
else:
model_state_dict, model_log, test_acc = torch.load(model_path)
model.load_state_dict(model_state_dict)
print(model_log + '\n')
self.basic_log = model_log
self.clean = test_acc
def setFileName(self, dataset, args):
if self.singleGNN is None:
self.file_name = fileNamer(dataset_name=dataset.name,
num_layers=args.num_layers,
seed=args.seed,
targeted=args.targeted,
attack_epochs=args.attEpochs,
start=self.start_to_file,
end=self.end_to_file)
else:
self.file_name = fileNamer(dataset_name=dataset.name,
model_name=args.singleGNN.string(),
num_layers=args.num_layers,
seed=args.seed,
targeted=args.targeted,
attack_epochs=args.attEpochs,
start=self.start_to_file,
end=self.end_to_file)