kindly need help to for several reproduction results #23

import time
from copy import deepcopy
import torch
import torch.optim as optim
import torch.nn.functional as F
from dhg import Hypergraph,Graph
from dhg.data import Cooking200, CoauthorshipCora,CocitationCora,CocitationCiteseer,CoauthorshipDBLP, CocitationPubmed,\
 Citeseer,Cora,Pubmed
from dhg.models import HGNN, HyperGCN, HGNNP
from dhg.random import set_seed
from dhg.metrics import HypergraphVertexClassificationEvaluator as Evaluator
from data import data
#from config import config
def train(net, X, A, lbls, train_idx, optimizer, epoch):
 net.train()
 st = time.time()
 optimizer.zero_grad()
 # import the data["features"] X and Graph structure G 
 outs = net(X, A)
 outs, lbls = outs[train_idx], lbls[train_idx]
 loss = F.cross_entropy(outs, lbls)
 #loss = F.nll_loss(outs, lbls) # decrease performance a lot
 loss.backward()
 optimizer.step()
 print(f"Epoch: {epoch}, Time: {time.time()-st:.5f}s, Loss: {loss.item():.5f}")
 return loss.item()
@torch.no_grad()
def infer(net, X, A, lbls, idx, test=False):
 net.eval()
 outs = net(X, A)
 outs, lbls = outs[idx], lbls[idx]
 if not test:
 res = evaluator.validate(lbls, outs)
 else:
 res = evaluator.test(lbls, outs)
 return res
if __name__ == "__main__":
 set_seed(2021)
 #args = config.parse()
 device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
 evaluator = Evaluator(["accuracy", "f1_score", {"f1_score": {"average": "micro"}}])
 
 # Load dataset of CocitationCiteseer, CocitationCora, CocitationPubmed
 #data = CocitationCora()
 data = CocitationCiteseer()
 # Build the hypergraph dataloader
 X, lbl = data["features"], data["labels"]
 HG = Hypergraph(data["num_vertices"], data["edge_list"])
 #net = HGNNP(data["dim_features"], 16, data["num_classes"], use_bn=False)
 net = HGNN(data["dim_features"], 16, data["num_classes"], use_bn=False)
 print("net is: ", net)
 optimizer = optim.Adam(net.parameters(), lr=0.01, weight_decay=0.0005)
 train_mask = data["train_mask"]
 val_mask = data["val_mask"]
 test_mask = data["test_mask"]
 print(f"length of train is : {sum(train_mask)}, length of val is: {sum(val_mask)},length of test is: {sum(test_mask)}")
 X, lbl = X.to(device), lbl.to(device)
 HG = HG.to(device)
 net = net.to(device)
 best_state = None
 best_epoch, best_val = 0, 0
 for epoch in range(200):
 # train
 train(net, X, HG, lbl, train_mask, optimizer, epoch)
 # validation
 if epoch % 10 == 0:
 with torch.no_grad():
 val_res = infer(net, X, HG, lbl, val_mask)
 print("val acc is: ",infer(net, X, HG, lbl, val_mask,test=True)["accuracy"])
 print("val_res is: ",val_res)
 if val_res > best_val:
 print(f"update best: {val_res:.5f}")
 best_epoch = epoch
 best_val = val_res
 best_state = deepcopy(net.state_dict())
 print("\ntrain finished!")
 print(f"best val: {best_val:.5f}")
 # test
 print("test...")
 net.load_state_dict(best_state)
 res = infer(net, X, HG, lbl, test_mask, test=True)
 print(f"final result: epoch: {best_epoch}")
 print(res)