# Validate args
args = parser.parse_args()
args.cuda = not args.no_cuda
assert args.batch_size > 1, 'parse_args: batch_size must be > 1'
return args
if __name__ == "__main__":
args = parse_args()
set_seeds(args.seed)
# --
# Load problem
problem = NodeProblem(problem_path=args.problem_path, cuda=args.cuda)
# --
# Define model
model = GSSupervised(
**{
"n_nodes": problem.n_nodes,
"lr_init": args.lr_init,
"weight_decay": args.weight_decay,
})
if args.cuda:
model = model.cuda()
print(model, file=sys.stderr)
return args
if __name__ == "__main__":
args = parse_args()
set_seeds(args.seed)
# --
# Load problem
s = {'dblp': ['APA'], 'yelp': ['BRURB'], 'yago': ['MAM']}
schemes = s[args.problem]
device = torch.device(
"cuda:0" if torch.cuda.is_available() and args.cuda else "cpu")
problem = NodeProblem(problem_path=args.problem_path,
problem=args.problem,
device=device,
schemes=schemes,
input_edge_dims=18,
train_per=args.train_per)
n_targets = problem.targets.shape[0]
#load embeddings as features
features, n_nodes, n_feature = read_embed(n_targets,
path=args.problem_path,
emb_file=args.feat,
mp2vec=args.mp2vec)
features = torch.FloatTensor(features)
# --
# Define model
n_train_samples = list(map(int, args.n_train_samples.split(',')))
n_val_samples = list(map(int, args.n_val_samples.split(',')))
def main(args):
# Load problem
mp_index = {
'dblp': ['APA', 'APAPA', 'APCPA'],
'yelp': ['BRURB', 'BRKRB'],
'yago': ['MAM', 'MDM', 'MWM'],
'dblp2': ['APA', 'APAPA', 'APCPA'],
}
schemes = mp_index[args.problem]
device = torch.device(
"cuda:0" if torch.cuda.is_available() and args.cuda else "cpu")
problem = NodeProblem(problem_path=args.problem_path,
problem=args.problem,
device=device,
schemes=schemes,
train_per=args.train_per,
K=args.K,
input_edge_dims=args.in_edge_len,
emb_len=args.in_node_len)
# --
# Define model
n_train_samples = list(map(int, args.n_train_samples.split(',')))
n_val_samples = list(map(int, args.n_val_samples.split(',')))
output_dims = list(map(int, args.output_dims.split(',')))
model = CLING(
**{
"problem":
problem,
"n_mp":
len(schemes),
"sampler_class":
sampler_lookup[args.sampler_class],
"prep_class":
prep_lookup[args.prep_class],
"prep_len":
args.prep_len,
"aggregator_class":
aggregator_lookup[args.aggregator_class],
"mpaggr_class":
metapath_aggregator_lookup[args.mpaggr_class],
"edgeupt_class":
edge_aggregator_lookup[args.edgeupt_class],
"n_head":
args.n_head,
"layer_specs": [
{
"n_train_samples": n_train_samples[0],
"n_val_samples": n_val_samples[0],
"output_dim": output_dims[0],
"activation": F.relu,
"concat_node": args.concat_node,
"concat_edge": args.concat_edge,
'n_hid': args.n_hid,
},
{
"n_train_samples": n_train_samples[1],
"n_val_samples": n_val_samples[1],
"output_dim": output_dims[1],
"activation": F.relu, # lambda x: x
"concat_node": args.concat_node,
"concat_edge": args.concat_edge,
'n_hid': args.n_hid,
},
# {
# "n_train_samples": n_train_samples[2],
# "n_val_samples": n_val_samples[2],
# "output_dim": output_dims[2],
# "activation": lambda x: x, # lambda x: x
# "concat_node": args.concat_node,
# "concat_edge": args.concat_edge,
# },
][:args.n_layer],
#
# "lr_init" : args.lr_init,
# "lr_schedule" : args.lr_schedule,
# "weight_decay" : args.weight_decay,
"dropout":
args.dropout,
"input_dropout":
args.input_dropout,
"batchnorm":
args.batchnorm,
"attn_dropout":
args.attn_dropout,
"concat_node":
True,
})
if args.cuda:
print("Let's use", torch.cuda.device_count(), "GPUs!")
model = torch.nn.DataParallel(model)
model = model.to(device)
# --
# Define optimizer
lr = args.lr_init
if args.optimizer == 'adam':
optimizer = torch.optim.Adam(model.parameters(),
lr=lr,
weight_decay=args.weight_decay,
amsgrad=False)
elif args.optimizer == 'adamw':
optimizer = torch.optim.AdamW(model.parameters(),
lr=lr,
weight_decay=args.weight_decay)
elif args.optimizer == 'asgd':
optimizer = torch.optim.ASGD(model.parameters(),
lr=lr,
weight_decay=args.weight_decay)
elif args.optimizer == 'sgd':
optimizer = torch.optim.SGD(model.parameters(), lr=lr, momentum=0.9)
elif args.optimizer == 'rms':
optimizer = torch.optim.RMSprop(model.parameters(),
lr=lr,
alpha=0.99,
eps=1e-08,
weight_decay=args.weight_decay,
momentum=0.9,
centered=False)
if args.lr_schedule == 'cosinew':
scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(
optimizer,
T_0=args.lr_patience,
T_mult=2,
eta_min=1e-5,
last_epoch=-1)
elif args.lr_schedule == 'cosine':
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, T_max=args.lr_patience, eta_min=1e-5, last_epoch=-1)
elif args.lr_schedule == 'plateau':
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer,
mode='min',
factor=args.factor,
patience=args.lr_patience,
verbose=False,
threshold=0.000001,
threshold_mode='rel',
cooldown=0,
min_lr=0,
eps=1e-08)
elif args.lr_schedule == 'onecycle':
scheduler = torch.optim.lr_scheduler.OneCycleLR(
optimizer,
max_lr=lr,
total_steps=args.epochs * 26,
pct_start=0.3,
anneal_strategy='cos',
cycle_momentum=True,
base_momentum=0.85,
max_momentum=0.95,
div_factor=25.0,
final_div_factor=10000.0,
last_epoch=-1)
elif args.lr_schedule == 'step':
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=5,
gamma=0.1)
else:
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=1000,
gamma=1)
#optimizer = torch.optim.SGD(model.parameters(), lr=lr, weight_decay=args.weight_decay,momentum=0.9)
# print(model, file=sys.stdout)
# --
# Train
set_seeds(args.seed)
start_time = time()
val_metric = None
tolerance = 0
best_val_loss = 100000
best_val_acc = 0
best_result = None
best_model = None
for epoch in range(args.epochs):
# early stopping
if tolerance > args.tolerance:
break
train_loss = 0
# Train
_ = model.train()
id_count = 0
for ids, targets, epoch_progress in problem.iterate(
mode='train', shuffle=True, batch_size=args.batch_size):
loss, preds = train_step(
model=model,
optimizer=optimizer,
ids=ids,
targets=targets,
loss_fn=problem.loss_fn,
)
train_loss += loss.item() * ids.shape[0]
id_count += ids.shape[0]
# train_metric = problem.metric_fn(
# to_numpy(targets), to_numpy(preds))
# print(json.dumps({
# "epoch": epoch,
# "epoch_progress": epoch_progress,
# "train_metric": train_metric,
# "time": time() - start_time,
# }, double_precision=5))
# sys.stdout.flush()
if args.lr_schedule == 'onecycle':
scheduler.step()
if args.lr_schedule in ['cosine', 'cosinew']:
scheduler.step(epoch + epoch_progress)
print(
json.dumps(
{
"epoch": epoch,
'lr': [optimizer.param_groups[0]['lr']],
"time": time() - start_time,
"train_loss": train_loss / id_count,
},
double_precision=5))
sys.stdout.flush()
# Evaluate
if epoch >= -1:
_ = model.eval()
val_loss, val_metric = evaluate(
model,
problem,
batch_size=args.val_batch_size,
mode='val',
loss_fn=problem.loss_fn,
)
# _, test_metric = evaluate(
# model, problem, batch_size=8, mode='test', loss_fn=problem.loss_fn,)
if val_metric['accuracy'] > best_val_acc or (
val_metric['accuracy'] == best_val_acc
and val_loss < best_val_loss):
tolerance = 0
best_val_loss = val_loss
best_val_acc = val_metric['accuracy']
best_result = json.dumps(
{
"epoch": epoch,
"val_loss": val_loss,
"val_metric": val_metric,
# "test_metric": test_metric,
},
double_precision=5)
best_model = model
else:
tolerance += 1
print(
json.dumps(
{
"epoch": epoch,
"val_loss": val_loss,
"val_metric": val_metric,
# "test_metric": test_metric,
"tolerance:": tolerance,
},
double_precision=5))
sys.stdout.flush()
if args.lr_schedule == 'plateau':
scheduler.step(val_loss)
if args.lr_schedule in ['step']:
scheduler.step()
print('-- done --')
_, test_metric = evaluate(
best_model,
problem,
batch_size=args.val_batch_size,
mode='test',
loss_fn=problem.loss_fn,
)
print(
json.dumps(
{
# "epoch": epoch,
# "val_loss": loss,
# "val_metric": val_metric,
"test_metric": test_metric,
# "tolerance:": tolerance,
},
double_precision=5),
file=sys.stderr)
# print(best_result, file=sys.stderr)
sys.stdout.flush()
'yelp': ['BRKRB', 'BRURB'], #'BRURB',
'yago': ['MAM', 'MDM', 'MWM'],
'cora': ['PAP', 'PPP', 'PP']
}
m_index = {
'dblp': 4044,
'yelp': 2599,
'yago': 451,
}
schemes = mp_index[args.problem]
device = torch.device(
"cuda:0" if torch.cuda.is_available() and args.cuda else "cpu")
problem = NodeProblem(problem_path=args.problem_path,
problem=args.problem,
device=device,
schemes=schemes,
K=m_index[args.problem],
input_edge_dims=args.in_edge_len,
train_per=args.train_per)
print('args', args.train_per)
# --
# Define model
# n_train_samples = list(map(int, args.n_train_samples.split(',')))
# n_val_samples = list(map(int, args.n_val_samples.split(',')))
output_dims = list(map(int, args.output_dims.split(',')))
model = BipartiteGCNRandom(
**{
"problem":
problem,
"n_mp":
# --
# Load problem
mp_index = {
'dblp': ['APA', 'APAPA', 'APCPA'], #
'yelp': ['BRKRB', 'BRURB'], #'BRURB',
'yago': ['MAM', 'MDM', 'MWM'], #'MDM',
'cora': ['PAP', 'PPP', 'PP'],
'aminer': ['PAP', 'PCP'] #
}
schemes = mp_index[args.problem]
device = torch.device(
"cuda:0" if torch.cuda.is_available() and args.cuda else "cpu")
problem = NodeProblem(problem_path=args.problem_path,
problem=args.problem,
device=device,
schemes=schemes,
K=args.k,
input_edge_dims=args.in_edge_len,
train_per=args.train_per)
# cos_problem = ReadCosSim(problem_path=args.problem_path, problem=args.problem, device=device, schemes=schemes, K=args.k, input_edge_dims =args.in_edge_len,train_per=args.train_per)
# --
# Define model
# n_train_samples = list(map(int, args.n_train_samples.split(',')))
# n_val_samples = list(map(int, args.n_val_samples.split(',')))
output_dims = list(map(int, args.output_dims.split(',')))
model = conch_dgi_gs(
**{
"shuffle":
False,
"problem":
if __name__ == "__main__":
args = parse_args()
set_seeds(args.seed)
# --
# Load problem
mp_index = {'dblp': ['APA', 'APAPA', 'APCPA'],
'yelp': ['BRURB', 'BRKRB'],
'yago': ['MAM', 'MDM', 'MWM']
}
schemes = mp_index[args.problem]
device = torch.device(
"cuda:0" if torch.cuda.is_available() and args.cuda else "cpu")
problem = NodeProblem(problem_path=args.problem_path,
problem=args.problem, device=device, schemes=schemes)
# --
# Define model
n_train_samples = list(map(int, args.n_train_samples.split(',')))
n_val_samples = list(map(int, args.n_val_samples.split(',')))
output_dims = list(map(int, args.output_dims.split(',')))
model = HINGCN_GS(**{
"problem": problem,
"n_mp": len(schemes),
"sampler_class": sampler_lookup[args.sampler_class],
"prep_class": prep_lookup[args.prep_class],
"prep_len": args.prep_len,
"aggregator_class": aggregator_lookup[args.aggregator_class],