def run_exp_lib(dataset_feat_net_triples,
get_model=get_model_with_default_configs):
results = []
exp_nums = len(dataset_feat_net_triples)
print("-----\nTotal %d experiments in this run:" % exp_nums)
for exp_id, (dataset_name, feat_str,
net) in enumerate(dataset_feat_net_triples):
print('{}/{} - {} - {} - {}'.format(exp_id + 1, exp_nums, dataset_name,
feat_str, net))
print("Here we go..")
sys.stdout.flush()
for exp_id, (dataset_name, feat_str,
net) in enumerate(dataset_feat_net_triples):
print('-----\n{}/{} - {} - {} - {}'.format(exp_id + 1, exp_nums,
dataset_name, feat_str,
net))
sys.stdout.flush()
dataset = get_dataset(dataset_name,
sparse=True,
feat_str=feat_str,
root=args.data_root)
model_func = get_model(net)
if 'MNIST' in dataset_name or 'CIFAR' in dataset_name:
train_dataset, test_dataset = dataset
train_acc, acc, duration = single_train_test(
train_dataset,
test_dataset,
model_func,
epochs=args.epochs,
batch_size=args.batch_size,
lr=args.lr,
lr_decay_factor=args.lr_decay_factor,
lr_decay_step_size=args.lr_decay_step_size,
weight_decay=0,
epoch_select=args.epoch_select,
with_eval_mode=args.with_eval_mode)
std = 0
else:
cross_validation_with_val_set(
dataset,
model_func,
folds=10,
epochs=args.epochs,
batch_size=args.batch_size,
lr=args.lr,
lr_decay_factor=args.lr_decay_factor,
lr_decay_step_size=args.lr_decay_step_size,
weight_decay=0,
epoch_select=args.epoch_select,
with_eval_mode=args.with_eval_mode,
logger=logger,
dataset_name=args.dataset,
aug1=args.aug1,
aug_ratio1=args.aug_ratio1,
aug2=args.aug2,
aug_ratio2=args.aug_ratio2,
suffix=args.suffix)
"""
def run_exp_lib(dataset_feat_net_triples):
results = []
sys.stdout.flush()
for (dataset_name, feat_str) in dataset_feat_net_triples:
sys.stdout.flush()
dataset = get_dataset(dataset_name,
sparse=True,
feat_str=feat_str,
root=args.data_root)
max_node_num = max(dataset.data.num_nodes)
print('Data: {}, Max Node Num: {}'.format(dataset_name, max_node_num))
train_acc, acc, std, duration = cross_validation_with_val_set(
args,
dataset,
max_node_num=max_node_num,
folds=10,
epochs=args.epochs,
batch_size=args.batch_size,
lr=args.lr,
lr_decay_factor=args.lr_decay_factor,
lr_decay_step_size=args.lr_decay_step_size,
weight_decay=args.weight_decay,
epoch_select=args.epoch_select,
with_eval_mode=args.with_eval_mode,
logger=logger)
val_loss, test_acc = info['val_loss'], info['test_acc']
print('{:02d}/{:03d}: Val Loss: {:.4f}, Test Accuracy: {:.3f}'.format(
fold, epoch, val_loss, test_acc))
results = []
for dataset_name, Net in product(datasets, nets):
best_result = (float('inf'), 0, 0) # (loss, acc, std)
print('-----\n{} - {}'.format(dataset_name, Net.__name__))
for num_layers, hidden in product(layers, hiddens):
dataset = get_dataset(dataset_name, sparse=Net != DiffPool)
model = Net(dataset, num_layers, hidden)
loss, acc, std = cross_validation_with_val_set(
dataset,
model,
folds=10,
epochs=args.epochs,
batch_size=args.batch_size,
lr=args.lr,
lr_decay_factor=args.lr_decay_factor,
lr_decay_step_size=args.lr_decay_step_size,
weight_decay=0,
logger=None)
if loss < best_result[0]:
best_result = (loss, acc, std)
desc = '{:.3f} + {:.3f}'.format(best_result[1], best_result[2])
print('Best result - {}'.format(desc))
results += ['{} - {}: {}'.format(dataset_name, model, desc)]
print('-----\n{}'.format('\n'.join(results)))
def run_exp_lib(dataset_feat_net_triples,
get_model=get_model_with_default_configs):
results = []
exp_nums = len(dataset_feat_net_triples)
print("-----\nTotal %d experiments in this run:" % exp_nums)
for exp_id, (dataset_name, feat_str,
net) in enumerate(dataset_feat_net_triples):
print('{}/{} - {} - {} - {}'.format(exp_id + 1, exp_nums, dataset_name,
feat_str, net))
print("Here we go..")
sys.stdout.flush()
for exp_id, (dataset_name, feat_str,
net) in enumerate(dataset_feat_net_triples):
print('-----\n{}/{} - {} - {} - {}'.format(exp_id + 1, exp_nums,
dataset_name, feat_str,
net))
sys.stdout.flush()
dataset = get_dataset(dataset_name,
sparse=True,
feat_str=feat_str,
root=args.data_root)
model_func = get_model(net)
if 'MNIST' in dataset_name or 'CIFAR' in dataset_name:
train_dataset, test_dataset = dataset
train_acc, acc, duration = single_train_test(
train_dataset,
test_dataset,
model_func,
epochs=args.epochs,
batch_size=args.batch_size,
lr=args.lr,
lr_decay_factor=args.lr_decay_factor,
lr_decay_step_size=args.lr_decay_step_size,
weight_decay=0,
epoch_select=args.epoch_select,
with_eval_mode=args.with_eval_mode)
std = 0
else:
train_acc, acc, std, duration = cross_validation_with_val_set(
dataset,
model_func,
folds=10,
epochs=args.epochs,
batch_size=args.batch_size,
lr=args.lr,
lr_decay_factor=args.lr_decay_factor,
lr_decay_step_size=args.lr_decay_step_size,
weight_decay=0,
epoch_select=args.epoch_select,
with_eval_mode=args.with_eval_mode,
logger=logger)
summary1 = 'data={}, model={}, feat={}, eval={}'.format(
dataset_name, net, feat_str, args.epoch_select)
summary2 = 'train_acc={:.2f}, test_acc={:.2f} ± {:.2f}, sec={}'.format(
train_acc * 100, acc * 100, std * 100, round(duration, 2))
results += ['{}: {}, {}'.format('fin-result', summary1, summary2)]
print('{}: {}, {}'.format('mid-result', summary1, summary2))
sys.stdout.flush()
print('-----\n{}'.format('\n'.join(results)))
sys.stdout.flush()
