kindly need help to for several reproduction results #24

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)