dataset[0].nodes.data['test_mask']].numpy()
accs = []
model_hp, decoder_hp = get_encoder_decoder_hp(args.model)
for seed in tqdm(range(args.repeat)):
solver = AutoNodeClassifier(
feature_module=None,
graph_models=(args.model, ),
ensemble_module=None,
max_evals=1,
hpo_module='random',
trainer_hp_space=fixed(
**{
"max_epoch": args.epoch,
"early_stopping_round": args.epoch + 1,
"lr": args.lr,
"weight_decay": args.weight_decay,
}),
model_hp_spaces=[{
"encoder": fixed(**model_hp),
"decoder": fixed(**decoder_hp)
}])
solver.fit(dataset, evaluation_method=['acc'], seed=seed)
output = solver.predict(dataset)
acc = (output == label).astype('float').mean()
accs.append(acc)
print('{:.4f} ~ {:.4f}'.format(np.mean(accs), np.std(accs)))
import os
os.environ["AUTOGL_BACKEND"] = "dgl"
from autogl.datasets import build_dataset_from_name
from autogl.solver import AutoNodeClassifier
from autogl.module.train import NodeClassificationFullTrainer
from autogl.backend import DependentBackend
key = "y" if DependentBackend.is_pyg() else "label"
cora = build_dataset_from_name("cora")
solver = AutoNodeClassifier(graph_models=("gin", ),
default_trainer=NodeClassificationFullTrainer(
decoder=None,
init=False,
max_epoch=200,
early_stopping_round=201,
lr=0.01,
weight_decay=0.0,
),
hpo_module=None,
device="auto")
solver.fit(cora, evaluation_method=["acc"])
result = solver.predict(cora)
print((result == cora[0].nodes.data[key][
cora[0].nodes.data["test_mask"]].cpu().numpy()).astype('float').mean())