import torch  # pytorch backend
from grb.dataset import Dataset
from grb.model.torch import GCN
from grb.utils.trainer import Trainer

# Load data
# name: ["grb-cora", "grb-citeseer", "grb-aminer", "grb-reddit", "grb-flickr"].
# mode: [["easy", "medium", "hard", "full"]
# mode: 

dataset = Dataset(name='grb-citeseer', mode='easy',feat_norm='None')

features = dataset.features      # 注意：不是 tensor，而是 scipy.sparse
adj = dataset.adj            # 是 numpy.ndarray 或 scipy.sparse
labels = dataset.labels 
index = dataset.idx_train
 
print(type(features))

print(type(adj))
print(type(labels))
print(type(index))
# model = GCN(in_features=dataset.num_features,
#             out_features=dataset.num_classes,
#             hidden_features=[64, 64])
# # Training
# adam = torch.optim.Adam(model.parameters(), lr=0.01)
# trainer = Trainer(dataset=dataset, optimizer=adam,
#                   loss=torch.nn.functional.nll_loss)
# trainer.train(model=model, n_epoch=200, dropout=0.1,
#               train_mode='inductive')