On Graph

Hint

• Author: Yifan Feng (丰一帆)

• Proof: Xinwei Zhang

In the following examples, four typical graph/hypergraph neural networks are used to perform vertex classification task on the graph structure.

Models

• GCN (dhg.models.GCN), Semi-Supervised Classification with Graph Convolutional Networks paper (ICLR 2017).

• GAT (dhg.models.GAT), Graph Attention Networks paper (ICLR 2018).

• HGNN (dhg.models.HGNN), Hypergraph Neural Networks paper (AAAI 2019).

• HGNN+ (dhg.models.HGNNP), HGNN+: General Hypergraph Neural Networks paper (IEEE T-PAMI 2022).

Dataset

The Cora dataset (dhg.data.Cora) is a citation network dataset for vertex classification task. More details can be found in this website.

Results

Model	Accuracy on Validation	Accuracy on Testing	F1 score on Testing
GCN	0.800	0.823	0.814
GAT	0.804	0.824	0.817
HGNN	0.804	0.820	0.811
HGNN+	0.802	0.827	0.820

GCN on Cora

Import Libraries

import time
from copy import deepcopy

import torch
import torch.optim as optim
import torch.nn.functional as F

from dhg import Graph
from dhg.data import Cora
from dhg.models import GCN
from dhg.random import set_seed
from dhg.metrics import GraphVertexClassificationEvaluator as Evaluator

Define Functions

def train(net, X, A, lbls, train_idx, optimizer, epoch):
    net.train()

    st = time.time()
    optimizer.zero_grad()
    outs = net(X, A)
    outs, lbls = outs[train_idx], lbls[train_idx]
    loss = F.cross_entropy(outs, lbls)
    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

Main

Note

More details about the metric Evaluator can be found in the Building Evaluator section.

if __name__ == "__main__":
    set_seed(2022)
    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
    evaluator = Evaluator(["accuracy", "f1_score", {"f1_score": {"average": "micro"}}])
    data = Cora()
    X, lbl = data["features"], data["labels"]
    G = Graph(data["num_vertices"], data["edge_list"])
    train_mask = data["train_mask"]
    val_mask = data["val_mask"]
    test_mask = data["test_mask"]

    net = GCN(data["dim_features"], 16, data["num_classes"])
    optimizer = optim.Adam(net.parameters(), lr=0.01, weight_decay=5e-4)

    X, lbl = X.to(device), lbl.to(device)
    G = G.to(device)
    net = net.to(device)

    best_state = None
    best_epoch, best_val = 0, 0
    for epoch in range(300):
        # train
        train(net, X, G, lbl, train_mask, optimizer, epoch)
        # validation
        if epoch % 1 == 0:
            with torch.no_grad():
                val_res = infer(net, X, G, lbl, val_mask)
            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, G, lbl, test_mask, test=True)
    print(f"final result: epoch: {best_epoch}")
    print(res)

Outputs

Epoch: 0, Time: 0.51907s, Loss: 1.95010
update best: 0.31600
Epoch: 1, Time: 0.00182s, Loss: 1.94601
Epoch: 2, Time: 0.00164s, Loss: 1.94383
Epoch: 3, Time: 0.00167s, Loss: 1.93907
Epoch: 4, Time: 0.00164s, Loss: 1.93350
update best: 0.38000
Epoch: 5, Time: 0.00166s, Loss: 1.92899
Epoch: 6, Time: 0.00323s, Loss: 1.92461
Epoch: 7, Time: 0.00164s, Loss: 1.91764
Epoch: 8, Time: 0.00341s, Loss: 1.91163
Epoch: 9, Time: 0.00167s, Loss: 1.90064
Epoch: 10, Time: 0.00154s, Loss: 1.89617
Epoch: 11, Time: 0.00159s, Loss: 1.88721
Epoch: 12, Time: 0.00223s, Loss: 1.87626
Epoch: 13, Time: 0.00178s, Loss: 1.86941
Epoch: 14, Time: 0.00167s, Loss: 1.86202
Epoch: 15, Time: 0.00316s, Loss: 1.85207
Epoch: 16, Time: 0.00215s, Loss: 1.84285
Epoch: 17, Time: 0.00289s, Loss: 1.83392
Epoch: 18, Time: 0.00203s, Loss: 1.82120
Epoch: 19, Time: 0.00202s, Loss: 1.80663
Epoch: 20, Time: 0.00246s, Loss: 1.79340
Epoch: 21, Time: 0.00201s, Loss: 1.77829
Epoch: 22, Time: 0.00203s, Loss: 1.76851
update best: 0.38800
Epoch: 23, Time: 0.00162s, Loss: 1.75592
update best: 0.40200
Epoch: 24, Time: 0.00159s, Loss: 1.74545
update best: 0.43000
Epoch: 25, Time: 0.00175s, Loss: 1.72373
update best: 0.45000
Epoch: 26, Time: 0.00157s, Loss: 1.71025
update best: 0.46000
Epoch: 27, Time: 0.00164s, Loss: 1.68904
update best: 0.46400
Epoch: 28, Time: 0.00211s, Loss: 1.67401
update best: 0.46600
Epoch: 29, Time: 0.00168s, Loss: 1.67025
update best: 0.48400
Epoch: 30, Time: 0.00176s, Loss: 1.65349
update best: 0.49200
Epoch: 31, Time: 0.00250s, Loss: 1.61911
update best: 0.49800
Epoch: 32, Time: 0.00177s, Loss: 1.61325
update best: 0.51400
Epoch: 33, Time: 0.00192s, Loss: 1.56832
update best: 0.52600
Epoch: 34, Time: 0.00173s, Loss: 1.55827
update best: 0.55000
Epoch: 35, Time: 0.00172s, Loss: 1.55186
update best: 0.56200
Epoch: 36, Time: 0.00183s, Loss: 1.53794
update best: 0.57400
Epoch: 37, Time: 0.00222s, Loss: 1.50345
update best: 0.58600
Epoch: 38, Time: 0.00169s, Loss: 1.49760
update best: 0.59600
Epoch: 39, Time: 0.00164s, Loss: 1.47143
update best: 0.60200
Epoch: 40, Time: 0.00171s, Loss: 1.43501
update best: 0.62800
Epoch: 41, Time: 0.00170s, Loss: 1.42085
update best: 0.64800
Epoch: 42, Time: 0.00360s, Loss: 1.38769
update best: 0.65400
Epoch: 43, Time: 0.00156s, Loss: 1.36689
update best: 0.66200
Epoch: 44, Time: 0.00152s, Loss: 1.36428
update best: 0.66800
Epoch: 45, Time: 0.00167s, Loss: 1.32395
Epoch: 46, Time: 0.00153s, Loss: 1.29274
update best: 0.67600
Epoch: 47, Time: 0.00164s, Loss: 1.30380
Epoch: 48, Time: 0.00439s, Loss: 1.26099
update best: 0.68800
Epoch: 49, Time: 0.00186s, Loss: 1.25379
Epoch: 50, Time: 0.00175s, Loss: 1.23854
update best: 0.69800
Epoch: 51, Time: 0.00171s, Loss: 1.20378
update best: 0.72200
Epoch: 52, Time: 0.00170s, Loss: 1.16979
update best: 0.73200
Epoch: 53, Time: 0.00326s, Loss: 1.15275
update best: 0.74800
Epoch: 54, Time: 0.00183s, Loss: 1.11128
update best: 0.75200
Epoch: 55, Time: 0.00183s, Loss: 1.12654
update best: 0.75600
Epoch: 56, Time: 0.00172s, Loss: 1.12641
update best: 0.76400
Epoch: 57, Time: 0.00171s, Loss: 1.08093
update best: 0.76600
Epoch: 58, Time: 0.00228s, Loss: 1.06145
Epoch: 59, Time: 0.00163s, Loss: 1.03330
Epoch: 60, Time: 0.00240s, Loss: 1.02479
Epoch: 61, Time: 0.00179s, Loss: 1.01496
Epoch: 62, Time: 0.00187s, Loss: 0.93007
Epoch: 63, Time: 0.00176s, Loss: 0.97366
Epoch: 64, Time: 0.00296s, Loss: 0.92534
Epoch: 65, Time: 0.00230s, Loss: 0.91500
update best: 0.77400
Epoch: 66, Time: 0.00169s, Loss: 0.93400
update best: 0.77800
Epoch: 67, Time: 0.00161s, Loss: 0.86869
update best: 0.78000
Epoch: 68, Time: 0.00162s, Loss: 0.89109
Epoch: 69, Time: 0.00177s, Loss: 0.89371
Epoch: 70, Time: 0.00259s, Loss: 0.87362
update best: 0.78200
Epoch: 71, Time: 0.00159s, Loss: 0.80287
Epoch: 72, Time: 0.00155s, Loss: 0.88049
Epoch: 73, Time: 0.00160s, Loss: 0.78692
Epoch: 74, Time: 0.00163s, Loss: 0.79204
Epoch: 75, Time: 0.00152s, Loss: 0.81149
update best: 0.78400
Epoch: 76, Time: 0.00288s, Loss: 0.79278
Epoch: 77, Time: 0.00183s, Loss: 0.75974
update best: 0.78600
Epoch: 78, Time: 0.00155s, Loss: 0.74237
Epoch: 79, Time: 0.00162s, Loss: 0.72129
update best: 0.78800
Epoch: 80, Time: 0.00154s, Loss: 0.72252
update best: 0.79000
Epoch: 81, Time: 0.00170s, Loss: 0.69306
update best: 0.79200
Epoch: 82, Time: 0.00274s, Loss: 0.64976
Epoch: 83, Time: 0.00157s, Loss: 0.66782
Epoch: 84, Time: 0.00155s, Loss: 0.68008
Epoch: 85, Time: 0.00160s, Loss: 0.70714
Epoch: 86, Time: 0.00164s, Loss: 0.64139
Epoch: 87, Time: 0.00159s, Loss: 0.66335
Epoch: 88, Time: 0.00223s, Loss: 0.65881
Epoch: 89, Time: 0.00248s, Loss: 0.65215
Epoch: 90, Time: 0.00151s, Loss: 0.57064
Epoch: 91, Time: 0.00155s, Loss: 0.64725
Epoch: 92, Time: 0.00157s, Loss: 0.58507
Epoch: 93, Time: 0.00174s, Loss: 0.62494
Epoch: 94, Time: 0.00158s, Loss: 0.58289
Epoch: 95, Time: 0.00157s, Loss: 0.56591
Epoch: 96, Time: 0.00289s, Loss: 0.59959
Epoch: 97, Time: 0.00157s, Loss: 0.62588
Epoch: 98, Time: 0.00154s, Loss: 0.58035
Epoch: 99, Time: 0.00156s, Loss: 0.58727
Epoch: 100, Time: 0.00158s, Loss: 0.56111
Epoch: 101, Time: 0.00152s, Loss: 0.54035
Epoch: 102, Time: 0.00151s, Loss: 0.56815
Epoch: 103, Time: 0.00233s, Loss: 0.50579
Epoch: 104, Time: 0.00150s, Loss: 0.53285
Epoch: 105, Time: 0.00147s, Loss: 0.56204
Epoch: 106, Time: 0.00153s, Loss: 0.51602
Epoch: 107, Time: 0.00160s, Loss: 0.52320
Epoch: 108, Time: 0.00150s, Loss: 0.53845
Epoch: 109, Time: 0.00151s, Loss: 0.55428
Epoch: 110, Time: 0.00307s, Loss: 0.52966
Epoch: 111, Time: 0.00150s, Loss: 0.56845
Epoch: 112, Time: 0.00148s, Loss: 0.52385
update best: 0.79400
Epoch: 113, Time: 0.00155s, Loss: 0.52051
Epoch: 114, Time: 0.00178s, Loss: 0.51860
Epoch: 115, Time: 0.00159s, Loss: 0.48878
Epoch: 116, Time: 0.00375s, Loss: 0.50367
Epoch: 117, Time: 0.00160s, Loss: 0.49782
Epoch: 118, Time: 0.00153s, Loss: 0.51155
Epoch: 119, Time: 0.00153s, Loss: 0.47739
Epoch: 120, Time: 0.00178s, Loss: 0.50645
Epoch: 121, Time: 0.00157s, Loss: 0.49175
Epoch: 122, Time: 0.00157s, Loss: 0.47638
Epoch: 123, Time: 0.00345s, Loss: 0.46064
Epoch: 124, Time: 0.00159s, Loss: 0.44845
Epoch: 125, Time: 0.00153s, Loss: 0.44286
Epoch: 126, Time: 0.00151s, Loss: 0.46044
Epoch: 127, Time: 0.00156s, Loss: 0.45707
Epoch: 128, Time: 0.00177s, Loss: 0.50700
Epoch: 129, Time: 0.00153s, Loss: 0.46442
Epoch: 130, Time: 0.00345s, Loss: 0.44911
Epoch: 131, Time: 0.00153s, Loss: 0.46168
Epoch: 132, Time: 0.00153s, Loss: 0.47634
Epoch: 133, Time: 0.00152s, Loss: 0.41177
Epoch: 134, Time: 0.00162s, Loss: 0.42612
Epoch: 135, Time: 0.00160s, Loss: 0.46436
Epoch: 136, Time: 0.00153s, Loss: 0.42374
Epoch: 137, Time: 0.00380s, Loss: 0.42290
Epoch: 138, Time: 0.00181s, Loss: 0.43096
Epoch: 139, Time: 0.00166s, Loss: 0.43386
Epoch: 140, Time: 0.00170s, Loss: 0.47472
Epoch: 141, Time: 0.00175s, Loss: 0.40687
Epoch: 142, Time: 0.00170s, Loss: 0.43927
Epoch: 143, Time: 0.00347s, Loss: 0.39323
Epoch: 144, Time: 0.00174s, Loss: 0.42356
Epoch: 145, Time: 0.00168s, Loss: 0.44625
Epoch: 146, Time: 0.00165s, Loss: 0.38619
Epoch: 147, Time: 0.00171s, Loss: 0.40754
Epoch: 148, Time: 0.00169s, Loss: 0.38543
Epoch: 149, Time: 0.00166s, Loss: 0.39466
Epoch: 150, Time: 0.00280s, Loss: 0.43009
Epoch: 151, Time: 0.00165s, Loss: 0.38695
Epoch: 152, Time: 0.00166s, Loss: 0.41950
Epoch: 153, Time: 0.00166s, Loss: 0.41095
Epoch: 154, Time: 0.00174s, Loss: 0.40313
Epoch: 155, Time: 0.00167s, Loss: 0.43876
Epoch: 156, Time: 0.00384s, Loss: 0.40152
Epoch: 157, Time: 0.00170s, Loss: 0.39797
update best: 0.80000
Epoch: 158, Time: 0.00165s, Loss: 0.35990
Epoch: 159, Time: 0.00168s, Loss: 0.40668
Epoch: 160, Time: 0.00161s, Loss: 0.39737
Epoch: 161, Time: 0.00153s, Loss: 0.42709
Epoch: 162, Time: 0.00174s, Loss: 0.40306
Epoch: 163, Time: 0.00262s, Loss: 0.44195
Epoch: 164, Time: 0.00150s, Loss: 0.35434
Epoch: 165, Time: 0.00154s, Loss: 0.39269
Epoch: 166, Time: 0.00159s, Loss: 0.32633
Epoch: 167, Time: 0.00154s, Loss: 0.38579
Epoch: 168, Time: 0.00155s, Loss: 0.38941
Epoch: 169, Time: 0.00150s, Loss: 0.38425
Epoch: 170, Time: 0.00250s, Loss: 0.39287
Epoch: 171, Time: 0.00153s, Loss: 0.36239
Epoch: 172, Time: 0.00153s, Loss: 0.37962
Epoch: 173, Time: 0.00154s, Loss: 0.35394
Epoch: 174, Time: 0.00159s, Loss: 0.34589
Epoch: 175, Time: 0.00161s, Loss: 0.38056
Epoch: 176, Time: 0.00156s, Loss: 0.37199
Epoch: 177, Time: 0.00309s, Loss: 0.36108
Epoch: 178, Time: 0.00181s, Loss: 0.37211
Epoch: 179, Time: 0.00153s, Loss: 0.35234
Epoch: 180, Time: 0.00155s, Loss: 0.33577
Epoch: 181, Time: 0.00153s, Loss: 0.37541
Epoch: 182, Time: 0.00156s, Loss: 0.30629
Epoch: 183, Time: 0.00149s, Loss: 0.36643
Epoch: 184, Time: 0.00346s, Loss: 0.34131
Epoch: 185, Time: 0.00153s, Loss: 0.35421
Epoch: 186, Time: 0.00146s, Loss: 0.33999
Epoch: 187, Time: 0.00149s, Loss: 0.36365
Epoch: 188, Time: 0.00152s, Loss: 0.36926
Epoch: 189, Time: 0.00152s, Loss: 0.31029
Epoch: 190, Time: 0.00155s, Loss: 0.32959
Epoch: 191, Time: 0.00247s, Loss: 0.35637
Epoch: 192, Time: 0.00208s, Loss: 0.30936
Epoch: 193, Time: 0.00154s, Loss: 0.32842
Epoch: 194, Time: 0.00154s, Loss: 0.31046
Epoch: 195, Time: 0.00156s, Loss: 0.34217
Epoch: 196, Time: 0.00169s, Loss: 0.35384
Epoch: 197, Time: 0.00157s, Loss: 0.31096
Epoch: 198, Time: 0.00307s, Loss: 0.31790
Epoch: 199, Time: 0.00160s, Loss: 0.29574

train finished!
best val: 0.80000
test...
final result: epoch: 157
{'accuracy': 0.8230000138282776, 'f1_score': 0.8135442845966843, 'f1_score -> average@micro': 0.823}

GAT on Cora

Import Libraries

import time
from copy import deepcopy

import torch
import torch.optim as optim
import torch.nn.functional as F

from dhg import Graph
from dhg.data import Cora
from dhg.models import GAT
from dhg.random import set_seed
from dhg.metrics import GraphVertexClassificationEvaluator as Evaluator

Define Functions

def train(net, X, A, lbls, train_idx, optimizer, epoch):
    net.train()

    st = time.time()
    optimizer.zero_grad()
    outs = net(X, A)
    outs, lbls = outs[train_idx], lbls[train_idx]
    loss = F.cross_entropy(outs, lbls)
    # loss = F.nll_loss(outs, lbls)
    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

Main

Note

More details about the metric Evaluator can be found in the Building Evaluator section.

if __name__ == "__main__":
    set_seed(2022)
    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
    evaluator = Evaluator(["accuracy", "f1_score", {"f1_score": {"average": "micro"}}])
    data = Cora()
    X, lbl = data["features"], data["labels"]
    G = Graph(data["num_vertices"], data["edge_list"])
    train_mask = data["train_mask"]
    val_mask = data["val_mask"]
    test_mask = data["test_mask"]

    net = GAT(data["dim_features"], 8, data["num_classes"], num_heads=8, drop_rate=0.6)
    optimizer = optim.Adam(net.parameters(), lr=0.005, weight_decay=5e-4)

    X, lbl = X.cuda(), lbl.cuda()
    G = G.to(device)
    net = net.cuda()

    best_state = None
    best_epoch, best_val = 0, 0
    for epoch in range(300):
        # train
        train(net, X, G, lbl, train_mask, optimizer, epoch)
        # validation
        if epoch % 1 == 0:
            with torch.no_grad():
                val_res = infer(net, X, G, lbl, val_mask)
            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, G, lbl, test_mask, test=True)
    print(f"final result: epoch: {best_epoch}")
    print(res)

Outputs

Epoch: 0, Time: 0.56263s, Loss: 1.94867
update best: 0.12200
Epoch: 1, Time: 0.03209s, Loss: 1.94319
Epoch: 2, Time: 0.03641s, Loss: 1.94076
Epoch: 3, Time: 0.03197s, Loss: 1.93497
Epoch: 4, Time: 0.03536s, Loss: 1.92976
Epoch: 5, Time: 0.03239s, Loss: 1.92725
update best: 0.18200
Epoch: 6, Time: 0.03332s, Loss: 1.91903
update best: 0.38200
Epoch: 7, Time: 0.03125s, Loss: 1.91507
update best: 0.49200
Epoch: 8, Time: 0.02778s, Loss: 1.91092
update best: 0.50400
Epoch: 9, Time: 0.03188s, Loss: 1.90407
update best: 0.51800
Epoch: 10, Time: 0.02726s, Loss: 1.89345
update best: 0.54000
Epoch: 11, Time: 0.03213s, Loss: 1.88819
update best: 0.56800
Epoch: 12, Time: 0.03265s, Loss: 1.88074
update best: 0.58800
Epoch: 13, Time: 0.03181s, Loss: 1.87387
update best: 0.61200
Epoch: 14, Time: 0.02740s, Loss: 1.86807
update best: 0.63600
Epoch: 15, Time: 0.02897s, Loss: 1.85900
update best: 0.68200
Epoch: 16, Time: 0.02701s, Loss: 1.84736
update best: 0.69800
Epoch: 17, Time: 0.02716s, Loss: 1.83908
update best: 0.72200
Epoch: 18, Time: 0.02782s, Loss: 1.83323
update best: 0.74800
Epoch: 19, Time: 0.02795s, Loss: 1.81363
update best: 0.77400
Epoch: 20, Time: 0.02841s, Loss: 1.80020
update best: 0.78200
Epoch: 21, Time: 0.02796s, Loss: 1.79673
update best: 0.79400
Epoch: 22, Time: 0.02665s, Loss: 1.77684
update best: 0.79600
Epoch: 23, Time: 0.02657s, Loss: 1.75575
Epoch: 24, Time: 0.02713s, Loss: 1.74837
Epoch: 25, Time: 0.02716s, Loss: 1.74580
Epoch: 26, Time: 0.02909s, Loss: 1.71996
Epoch: 27, Time: 0.02656s, Loss: 1.70845
Epoch: 28, Time: 0.02675s, Loss: 1.69779
Epoch: 29, Time: 0.02614s, Loss: 1.66469
Epoch: 30, Time: 0.02627s, Loss: 1.66196
Epoch: 31, Time: 0.02743s, Loss: 1.65097
Epoch: 32, Time: 0.02776s, Loss: 1.62630
Epoch: 33, Time: 0.02752s, Loss: 1.60284
Epoch: 34, Time: 0.02749s, Loss: 1.58056
Epoch: 35, Time: 0.02549s, Loss: 1.57601
Epoch: 36, Time: 0.02724s, Loss: 1.55081
Epoch: 37, Time: 0.02836s, Loss: 1.53101
Epoch: 38, Time: 0.02641s, Loss: 1.53054
Epoch: 39, Time: 0.02638s, Loss: 1.51172
Epoch: 40, Time: 0.02669s, Loss: 1.45463
Epoch: 41, Time: 0.02674s, Loss: 1.43432
Epoch: 42, Time: 0.02729s, Loss: 1.39888
Epoch: 43, Time: 0.02715s, Loss: 1.40660
Epoch: 44, Time: 0.02975s, Loss: 1.41301
Epoch: 45, Time: 0.02658s, Loss: 1.32990
Epoch: 46, Time: 0.02753s, Loss: 1.31327
Epoch: 47, Time: 0.02823s, Loss: 1.30501
Epoch: 48, Time: 0.02904s, Loss: 1.28125
Epoch: 49, Time: 0.02605s, Loss: 1.23469
Epoch: 50, Time: 0.02684s, Loss: 1.25209
Epoch: 51, Time: 0.02576s, Loss: 1.24679
Epoch: 52, Time: 0.02693s, Loss: 1.20283
Epoch: 53, Time: 0.02735s, Loss: 1.16539
Epoch: 54, Time: 0.02733s, Loss: 1.16182
Epoch: 55, Time: 0.02691s, Loss: 1.12086
Epoch: 56, Time: 0.02706s, Loss: 1.09962
Epoch: 57, Time: 0.02628s, Loss: 1.09911
Epoch: 58, Time: 0.02716s, Loss: 1.05156
Epoch: 59, Time: 0.02729s, Loss: 1.03817
Epoch: 60, Time: 0.03020s, Loss: 0.99580
Epoch: 61, Time: 0.02628s, Loss: 0.98298
Epoch: 62, Time: 0.02804s, Loss: 0.95318
Epoch: 63, Time: 0.02650s, Loss: 0.94846
Epoch: 64, Time: 0.02753s, Loss: 0.94741
Epoch: 65, Time: 0.02678s, Loss: 0.92977
Epoch: 66, Time: 0.02639s, Loss: 0.85785
Epoch: 67, Time: 0.02938s, Loss: 0.87859
Epoch: 68, Time: 0.02816s, Loss: 0.81501
Epoch: 69, Time: 0.02799s, Loss: 0.82868
Epoch: 70, Time: 0.02577s, Loss: 0.83454
Epoch: 71, Time: 0.03040s, Loss: 0.81279
Epoch: 72, Time: 0.02764s, Loss: 0.80267
Epoch: 73, Time: 0.02707s, Loss: 0.77012
Epoch: 74, Time: 0.02769s, Loss: 0.75785
Epoch: 75, Time: 0.02844s, Loss: 0.70275
Epoch: 76, Time: 0.02718s, Loss: 0.73779
Epoch: 77, Time: 0.02707s, Loss: 0.75283
Epoch: 78, Time: 0.02642s, Loss: 0.71528
Epoch: 79, Time: 0.02563s, Loss: 0.65665
Epoch: 80, Time: 0.02572s, Loss: 0.72648
Epoch: 81, Time: 0.02690s, Loss: 0.64160
Epoch: 82, Time: 0.02741s, Loss: 0.67890
Epoch: 83, Time: 0.03295s, Loss: 0.66671
Epoch: 84, Time: 0.02697s, Loss: 0.68267
Epoch: 85, Time: 0.02802s, Loss: 0.62096
Epoch: 86, Time: 0.02694s, Loss: 0.59566
Epoch: 87, Time: 0.02695s, Loss: 0.61715
Epoch: 88, Time: 0.02584s, Loss: 0.56823
Epoch: 89, Time: 0.02680s, Loss: 0.58922
Epoch: 90, Time: 0.02628s, Loss: 0.62176
Epoch: 91, Time: 0.02630s, Loss: 0.56168
Epoch: 92, Time: 0.02729s, Loss: 0.59730
Epoch: 93, Time: 0.03309s, Loss: 0.54350
Epoch: 94, Time: 0.02711s, Loss: 0.52554
Epoch: 95, Time: 0.03073s, Loss: 0.55863
Epoch: 96, Time: 0.03009s, Loss: 0.54187
Epoch: 97, Time: 0.02847s, Loss: 0.51606
Epoch: 98, Time: 0.02721s, Loss: 0.58703
Epoch: 99, Time: 0.02683s, Loss: 0.45709
Epoch: 100, Time: 0.02546s, Loss: 0.48065
Epoch: 101, Time: 0.02661s, Loss: 0.47521
Epoch: 102, Time: 0.02708s, Loss: 0.49044
Epoch: 103, Time: 0.02877s, Loss: 0.54857
Epoch: 104, Time: 0.02891s, Loss: 0.49147
Epoch: 105, Time: 0.02831s, Loss: 0.51098
Epoch: 106, Time: 0.02855s, Loss: 0.47384
Epoch: 107, Time: 0.02663s, Loss: 0.44903
Epoch: 108, Time: 0.02739s, Loss: 0.48902
Epoch: 109, Time: 0.02786s, Loss: 0.47107
Epoch: 110, Time: 0.02680s, Loss: 0.44998
Epoch: 111, Time: 0.02667s, Loss: 0.45758
Epoch: 112, Time: 0.02677s, Loss: 0.48968
Epoch: 113, Time: 0.03363s, Loss: 0.47052
Epoch: 114, Time: 0.02720s, Loss: 0.42302
Epoch: 115, Time: 0.02691s, Loss: 0.46022
Epoch: 116, Time: 0.02800s, Loss: 0.44152
Epoch: 117, Time: 0.02809s, Loss: 0.41619
Epoch: 118, Time: 0.02747s, Loss: 0.42209
Epoch: 119, Time: 0.02731s, Loss: 0.39555
Epoch: 120, Time: 0.02757s, Loss: 0.41737
Epoch: 121, Time: 0.02572s, Loss: 0.43961
Epoch: 122, Time: 0.02781s, Loss: 0.45638
Epoch: 123, Time: 0.03219s, Loss: 0.40218
Epoch: 124, Time: 0.02912s, Loss: 0.39478
Epoch: 125, Time: 0.02836s, Loss: 0.42770
Epoch: 126, Time: 0.02821s, Loss: 0.44723
Epoch: 127, Time: 0.02668s, Loss: 0.44981
Epoch: 128, Time: 0.02659s, Loss: 0.36467
Epoch: 129, Time: 0.02790s, Loss: 0.41371
Epoch: 130, Time: 0.02687s, Loss: 0.43008
Epoch: 131, Time: 0.02749s, Loss: 0.39013
Epoch: 132, Time: 0.02737s, Loss: 0.38068
Epoch: 133, Time: 0.02744s, Loss: 0.41307
Epoch: 134, Time: 0.02709s, Loss: 0.37499
Epoch: 135, Time: 0.03620s, Loss: 0.38330
Epoch: 136, Time: 0.03489s, Loss: 0.36262
Epoch: 137, Time: 0.03187s, Loss: 0.37654
Epoch: 138, Time: 0.03120s, Loss: 0.39200
Epoch: 139, Time: 0.03104s, Loss: 0.38622
Epoch: 140, Time: 0.03423s, Loss: 0.40245
Epoch: 141, Time: 0.02714s, Loss: 0.42246
Epoch: 142, Time: 0.02613s, Loss: 0.38597
Epoch: 143, Time: 0.02614s, Loss: 0.33846
Epoch: 144, Time: 0.02727s, Loss: 0.35218
Epoch: 145, Time: 0.02886s, Loss: 0.34761
Epoch: 146, Time: 0.02711s, Loss: 0.36396
Epoch: 147, Time: 0.02971s, Loss: 0.36457
Epoch: 148, Time: 0.02699s, Loss: 0.34745
Epoch: 149, Time: 0.02773s, Loss: 0.35060
Epoch: 150, Time: 0.02763s, Loss: 0.33626
Epoch: 151, Time: 0.02665s, Loss: 0.31920
Epoch: 152, Time: 0.02700s, Loss: 0.35494
Epoch: 153, Time: 0.02631s, Loss: 0.32023
Epoch: 154, Time: 0.02521s, Loss: 0.33341
Epoch: 155, Time: 0.02761s, Loss: 0.33163
Epoch: 156, Time: 0.03211s, Loss: 0.37067
Epoch: 157, Time: 0.02632s, Loss: 0.31185
Epoch: 158, Time: 0.02799s, Loss: 0.32024
Epoch: 159, Time: 0.02868s, Loss: 0.33890
Epoch: 160, Time: 0.02777s, Loss: 0.34390
Epoch: 161, Time: 0.02628s, Loss: 0.34751
Epoch: 162, Time: 0.02660s, Loss: 0.34165
Epoch: 163, Time: 0.02635s, Loss: 0.32915
Epoch: 164, Time: 0.02783s, Loss: 0.34125
Epoch: 165, Time: 0.02822s, Loss: 0.35261
Epoch: 166, Time: 0.02855s, Loss: 0.31803
Epoch: 167, Time: 0.02532s, Loss: 0.34157
Epoch: 168, Time: 0.02748s, Loss: 0.36173
Epoch: 169, Time: 0.02843s, Loss: 0.29295
Epoch: 170, Time: 0.02735s, Loss: 0.32935
Epoch: 171, Time: 0.02742s, Loss: 0.32463
Epoch: 172, Time: 0.02704s, Loss: 0.34419
Epoch: 173, Time: 0.02737s, Loss: 0.32393
Epoch: 174, Time: 0.02667s, Loss: 0.32464
Epoch: 175, Time: 0.02750s, Loss: 0.32668
Epoch: 176, Time: 0.02771s, Loss: 0.33835
Epoch: 177, Time: 0.02783s, Loss: 0.32610
Epoch: 178, Time: 0.03027s, Loss: 0.31611
Epoch: 179, Time: 0.02945s, Loss: 0.31614
Epoch: 180, Time: 0.02750s, Loss: 0.33912
Epoch: 181, Time: 0.02655s, Loss: 0.29072
Epoch: 182, Time: 0.02566s, Loss: 0.33455
Epoch: 183, Time: 0.02669s, Loss: 0.29251
Epoch: 184, Time: 0.02900s, Loss: 0.32722
Epoch: 185, Time: 0.02738s, Loss: 0.29612
Epoch: 186, Time: 0.02708s, Loss: 0.30084
Epoch: 187, Time: 0.02681s, Loss: 0.28315
Epoch: 188, Time: 0.02847s, Loss: 0.31396
Epoch: 189, Time: 0.02638s, Loss: 0.31683
Epoch: 190, Time: 0.02819s, Loss: 0.33803
Epoch: 191, Time: 0.02756s, Loss: 0.31791
Epoch: 192, Time: 0.02695s, Loss: 0.35256
Epoch: 193, Time: 0.02624s, Loss: 0.30407
Epoch: 194, Time: 0.02629s, Loss: 0.30797
Epoch: 195, Time: 0.02591s, Loss: 0.29365
Epoch: 196, Time: 0.02655s, Loss: 0.28897
Epoch: 197, Time: 0.02585s, Loss: 0.31783
Epoch: 198, Time: 0.02900s, Loss: 0.28889
Epoch: 199, Time: 0.02735s, Loss: 0.31066
Epoch: 200, Time: 0.02652s, Loss: 0.31168
Epoch: 201, Time: 0.02635s, Loss: 0.26849
Epoch: 202, Time: 0.02685s, Loss: 0.29419
Epoch: 203, Time: 0.02794s, Loss: 0.31236
update best: 0.79800
Epoch: 204, Time: 0.02748s, Loss: 0.29655
Epoch: 205, Time: 0.02772s, Loss: 0.32185
update best: 0.80000
Epoch: 206, Time: 0.03271s, Loss: 0.28461
Epoch: 207, Time: 0.02841s, Loss: 0.28718
Epoch: 208, Time: 0.02810s, Loss: 0.28859
Epoch: 209, Time: 0.02825s, Loss: 0.33484
Epoch: 210, Time: 0.02748s, Loss: 0.25476
Epoch: 211, Time: 0.02689s, Loss: 0.31217
Epoch: 212, Time: 0.02616s, Loss: 0.30048
Epoch: 213, Time: 0.02599s, Loss: 0.25396
Epoch: 214, Time: 0.02509s, Loss: 0.25659
Epoch: 215, Time: 0.02558s, Loss: 0.27736
Epoch: 216, Time: 0.02744s, Loss: 0.29813
Epoch: 217, Time: 0.02797s, Loss: 0.26633
Epoch: 218, Time: 0.02972s, Loss: 0.26556
Epoch: 219, Time: 0.02468s, Loss: 0.26812
Epoch: 220, Time: 0.02691s, Loss: 0.27502
Epoch: 221, Time: 0.02941s, Loss: 0.27201
Epoch: 222, Time: 0.03062s, Loss: 0.24750
Epoch: 223, Time: 0.02580s, Loss: 0.25536
Epoch: 224, Time: 0.02601s, Loss: 0.24400
Epoch: 225, Time: 0.02609s, Loss: 0.26673
Epoch: 226, Time: 0.02816s, Loss: 0.28496
Epoch: 227, Time: 0.02798s, Loss: 0.27348
Epoch: 228, Time: 0.02800s, Loss: 0.30068
Epoch: 229, Time: 0.02711s, Loss: 0.25621
Epoch: 230, Time: 0.02845s, Loss: 0.28133
Epoch: 231, Time: 0.02709s, Loss: 0.26263
Epoch: 232, Time: 0.02776s, Loss: 0.28019
Epoch: 233, Time: 0.02760s, Loss: 0.24621
Epoch: 234, Time: 0.02652s, Loss: 0.25726
Epoch: 235, Time: 0.02607s, Loss: 0.27996
Epoch: 236, Time: 0.02545s, Loss: 0.26172
Epoch: 237, Time: 0.02611s, Loss: 0.28643
update best: 0.80200
Epoch: 238, Time: 0.02843s, Loss: 0.27893
Epoch: 239, Time: 0.02436s, Loss: 0.23068
Epoch: 240, Time: 0.02698s, Loss: 0.26539
Epoch: 241, Time: 0.02526s, Loss: 0.26346
Epoch: 242, Time: 0.02636s, Loss: 0.25852
Epoch: 243, Time: 0.02681s, Loss: 0.24250
Epoch: 244, Time: 0.02879s, Loss: 0.26560
Epoch: 245, Time: 0.02841s, Loss: 0.24397
Epoch: 246, Time: 0.02649s, Loss: 0.22487
Epoch: 247, Time: 0.02529s, Loss: 0.28920
Epoch: 248, Time: 0.02598s, Loss: 0.25361
Epoch: 249, Time: 0.02651s, Loss: 0.23220
Epoch: 250, Time: 0.02981s, Loss: 0.24851
Epoch: 251, Time: 0.02647s, Loss: 0.26154
Epoch: 252, Time: 0.02915s, Loss: 0.28003
Epoch: 253, Time: 0.02627s, Loss: 0.27142
Epoch: 254, Time: 0.02771s, Loss: 0.24000
Epoch: 255, Time: 0.02807s, Loss: 0.22970
Epoch: 256, Time: 0.02778s, Loss: 0.25055
Epoch: 257, Time: 0.02756s, Loss: 0.25298
Epoch: 258, Time: 0.02604s, Loss: 0.25399
Epoch: 259, Time: 0.02515s, Loss: 0.23506
Epoch: 260, Time: 0.02584s, Loss: 0.27011
Epoch: 261, Time: 0.02733s, Loss: 0.27896
Epoch: 262, Time: 0.03368s, Loss: 0.27697
Epoch: 263, Time: 0.02622s, Loss: 0.25122
Epoch: 264, Time: 0.02557s, Loss: 0.22288
Epoch: 265, Time: 0.02677s, Loss: 0.24788
Epoch: 266, Time: 0.02789s, Loss: 0.25024
Epoch: 267, Time: 0.02766s, Loss: 0.24291
Epoch: 268, Time: 0.02734s, Loss: 0.23501
Epoch: 269, Time: 0.02628s, Loss: 0.22473
update best: 0.80400
Epoch: 270, Time: 0.02710s, Loss: 0.23869
Epoch: 271, Time: 0.02704s, Loss: 0.23497
Epoch: 272, Time: 0.02797s, Loss: 0.27661
Epoch: 273, Time: 0.02528s, Loss: 0.22743
Epoch: 274, Time: 0.02586s, Loss: 0.27344
Epoch: 275, Time: 0.02527s, Loss: 0.24526
Epoch: 276, Time: 0.02694s, Loss: 0.23004
Epoch: 277, Time: 0.02799s, Loss: 0.26727
Epoch: 278, Time: 0.02743s, Loss: 0.24816
Epoch: 279, Time: 0.02808s, Loss: 0.24808
Epoch: 280, Time: 0.02596s, Loss: 0.21776
Epoch: 281, Time: 0.02563s, Loss: 0.21926
Epoch: 282, Time: 0.02653s, Loss: 0.22270
Epoch: 283, Time: 0.02805s, Loss: 0.24317
Epoch: 284, Time: 0.02826s, Loss: 0.26508
Epoch: 285, Time: 0.02821s, Loss: 0.27642
Epoch: 286, Time: 0.02656s, Loss: 0.28210
Epoch: 287, Time: 0.02595s, Loss: 0.21376
Epoch: 288, Time: 0.02581s, Loss: 0.22294
Epoch: 289, Time: 0.02792s, Loss: 0.22761
Epoch: 290, Time: 0.02788s, Loss: 0.21223
Epoch: 291, Time: 0.02840s, Loss: 0.25497
Epoch: 292, Time: 0.02945s, Loss: 0.25667
Epoch: 293, Time: 0.02686s, Loss: 0.28930
Epoch: 294, Time: 0.02824s, Loss: 0.27815
Epoch: 295, Time: 0.02799s, Loss: 0.29124
Epoch: 296, Time: 0.02615s, Loss: 0.23398
Epoch: 297, Time: 0.02607s, Loss: 0.21476
Epoch: 298, Time: 0.02598s, Loss: 0.22739
Epoch: 299, Time: 0.02830s, Loss: 0.26215

train finished!
best val: 0.80400
test...
final result: epoch: 269
{'accuracy': 0.8240000009536743, 'f1_score': 0.8174891298012773, 'f1_score -> average@micro': 0.824}

HGNN on Cora

Import Libraries

import time
from copy import deepcopy

import torch
import torch.optim as optim
import torch.nn.functional as F

from dhg import Graph, Hypergraph
from dhg.data import Cora
from dhg.models import HGNN
from dhg.random import set_seed
from dhg.metrics import HypergraphVertexClassificationEvaluator as Evaluator

Define Functions

def train(net, X, G, lbls, train_idx, optimizer, epoch):
    net.train()

    st = time.time()
    optimizer.zero_grad()
    outs = net(X, G)
    outs, lbls = outs[train_idx], lbls[train_idx]
    loss = F.cross_entropy(outs, lbls)
    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, G, lbls, idx, test=False):
    net.eval()
    outs = net(X, G)
    outs, lbls = outs[idx], lbls[idx]
    if not test:
        res = evaluator.validate(lbls, outs)
    else:
        res = evaluator.test(lbls, outs)
    return res

Main

Note

More details about the metric Evaluator can be found in the Building Evaluator section.

if __name__ == "__main__":
    set_seed(2022)
    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
    evaluator = Evaluator(["accuracy", "f1_score", {"f1_score": {"average": "micro"}}])
    data = Cora()
    X, lbl = data["features"], data["labels"]
    G = Graph(data["num_vertices"], data["edge_list"])
    HG = Hypergraph.from_graph_kHop(G, k=1)
    train_mask = data["train_mask"]
    val_mask = data["val_mask"]
    test_mask = data["test_mask"]

    net = HGNN(data["dim_features"], 16, data["num_classes"])
    optimizer = optim.Adam(net.parameters(), lr=0.01, weight_decay=5e-4)

    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 % 1 == 0:
            with torch.no_grad():
                val_res = infer(net, X, HG, lbl, val_mask)
            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)

Outputs

Epoch: 0, Time: 0.50315s, Loss: 1.94993
update best: 0.31600
Epoch: 1, Time: 0.00196s, Loss: 1.94627
Epoch: 2, Time: 0.00175s, Loss: 1.94413
Epoch: 3, Time: 0.00200s, Loss: 1.93941
Epoch: 4, Time: 0.00180s, Loss: 1.93488
Epoch: 5, Time: 0.00174s, Loss: 1.92980
update best: 0.32000
Epoch: 6, Time: 0.00184s, Loss: 1.92559
update best: 0.36400
Epoch: 7, Time: 0.00256s, Loss: 1.91934
update best: 0.46400
Epoch: 8, Time: 0.00198s, Loss: 1.91385
Epoch: 9, Time: 0.00177s, Loss: 1.90319
Epoch: 10, Time: 0.00248s, Loss: 1.89834
Epoch: 11, Time: 0.00248s, Loss: 1.89127
Epoch: 12, Time: 0.00173s, Loss: 1.87880
Epoch: 13, Time: 0.00247s, Loss: 1.87467
Epoch: 14, Time: 0.00194s, Loss: 1.86688
Epoch: 15, Time: 0.00181s, Loss: 1.85891
Epoch: 16, Time: 0.00266s, Loss: 1.85094
Epoch: 17, Time: 0.00289s, Loss: 1.84161
Epoch: 18, Time: 0.00179s, Loss: 1.82744
Epoch: 19, Time: 0.00239s, Loss: 1.81666
Epoch: 20, Time: 0.00198s, Loss: 1.80902
Epoch: 21, Time: 0.00177s, Loss: 1.78956
Epoch: 22, Time: 0.00252s, Loss: 1.78221
update best: 0.49000
Epoch: 23, Time: 0.00191s, Loss: 1.76655
update best: 0.50200
Epoch: 24, Time: 0.00174s, Loss: 1.76185
update best: 0.51600
Epoch: 25, Time: 0.00253s, Loss: 1.74321
update best: 0.51800
Epoch: 26, Time: 0.00187s, Loss: 1.72027
update best: 0.52200
Epoch: 27, Time: 0.00369s, Loss: 1.70986
update best: 0.52600
Epoch: 28, Time: 0.00241s, Loss: 1.69354
update best: 0.53000
Epoch: 29, Time: 0.00309s, Loss: 1.69100
update best: 0.53800
Epoch: 30, Time: 0.00232s, Loss: 1.66968
update best: 0.54400
Epoch: 31, Time: 0.00313s, Loss: 1.65087
update best: 0.54600
Epoch: 32, Time: 0.00224s, Loss: 1.64182
update best: 0.56000
Epoch: 33, Time: 0.00277s, Loss: 1.60257
update best: 0.57800
Epoch: 34, Time: 0.00208s, Loss: 1.58798
update best: 0.59200
Epoch: 35, Time: 0.00176s, Loss: 1.58344
update best: 0.60000
Epoch: 36, Time: 0.00200s, Loss: 1.56942
update best: 0.63200
Epoch: 37, Time: 0.00206s, Loss: 1.53224
update best: 0.64800
Epoch: 38, Time: 0.00215s, Loss: 1.53036
update best: 0.67000
Epoch: 39, Time: 0.00200s, Loss: 1.50875
update best: 0.68000
Epoch: 40, Time: 0.00209s, Loss: 1.46828
update best: 0.69200
Epoch: 41, Time: 0.00243s, Loss: 1.45782
update best: 0.69400
Epoch: 42, Time: 0.00208s, Loss: 1.42179
Epoch: 43, Time: 0.00267s, Loss: 1.40893
Epoch: 44, Time: 0.00176s, Loss: 1.40358
update best: 0.69800
Epoch: 45, Time: 0.00175s, Loss: 1.37788
Epoch: 46, Time: 0.00274s, Loss: 1.34310
Epoch: 47, Time: 0.00173s, Loss: 1.32779
update best: 0.70200
Epoch: 48, Time: 0.00175s, Loss: 1.30572
update best: 0.71200
Epoch: 49, Time: 0.00221s, Loss: 1.28909
update best: 0.71800
Epoch: 50, Time: 0.00184s, Loss: 1.28903
update best: 0.72400
Epoch: 51, Time: 0.00345s, Loss: 1.25486
update best: 0.73200
Epoch: 52, Time: 0.00176s, Loss: 1.22994
update best: 0.74200
Epoch: 53, Time: 0.00173s, Loss: 1.20690
update best: 0.75000
Epoch: 54, Time: 0.00241s, Loss: 1.17115
Epoch: 55, Time: 0.00198s, Loss: 1.18836
update best: 0.75600
Epoch: 56, Time: 0.00279s, Loss: 1.17722
update best: 0.75800
Epoch: 57, Time: 0.00204s, Loss: 1.13414
Epoch: 58, Time: 0.00173s, Loss: 1.12058
update best: 0.76200
Epoch: 59, Time: 0.00228s, Loss: 1.09260
update best: 0.77400
Epoch: 60, Time: 0.00188s, Loss: 1.07260
Epoch: 61, Time: 0.00256s, Loss: 1.09610
Epoch: 62, Time: 0.00280s, Loss: 1.02422
Epoch: 63, Time: 0.00221s, Loss: 1.03871
update best: 0.77800
Epoch: 64, Time: 0.00311s, Loss: 1.00255
Epoch: 65, Time: 0.00226s, Loss: 0.99640
update best: 0.78000
Epoch: 66, Time: 0.00296s, Loss: 0.99191
update best: 0.78200
Epoch: 67, Time: 0.00235s, Loss: 0.95631
update best: 0.78600
Epoch: 68, Time: 0.00255s, Loss: 0.94336
Epoch: 69, Time: 0.00183s, Loss: 0.92673
update best: 0.79000
Epoch: 70, Time: 0.00165s, Loss: 0.92654
update best: 0.79600
Epoch: 71, Time: 0.00188s, Loss: 0.86986
update best: 0.80000
Epoch: 72, Time: 0.00170s, Loss: 0.90749
Epoch: 73, Time: 0.00164s, Loss: 0.86787
Epoch: 74, Time: 0.00218s, Loss: 0.86549
Epoch: 75, Time: 0.00182s, Loss: 0.86944
Epoch: 76, Time: 0.00189s, Loss: 0.83897
Epoch: 77, Time: 0.00167s, Loss: 0.82139
Epoch: 78, Time: 0.00168s, Loss: 0.81658
Epoch: 79, Time: 0.00198s, Loss: 0.78883
Epoch: 80, Time: 0.00207s, Loss: 0.78880
Epoch: 81, Time: 0.00209s, Loss: 0.77039
Epoch: 82, Time: 0.00170s, Loss: 0.74785
Epoch: 83, Time: 0.00185s, Loss: 0.74238
Epoch: 84, Time: 0.00293s, Loss: 0.73360
Epoch: 85, Time: 0.00164s, Loss: 0.76029
Epoch: 86, Time: 0.00163s, Loss: 0.71382
Epoch: 87, Time: 0.00162s, Loss: 0.72503
Epoch: 88, Time: 0.00202s, Loss: 0.70878
Epoch: 89, Time: 0.00172s, Loss: 0.71945
Epoch: 90, Time: 0.00180s, Loss: 0.65032
Epoch: 91, Time: 0.00302s, Loss: 0.71030
Epoch: 92, Time: 0.00157s, Loss: 0.67237
Epoch: 93, Time: 0.00161s, Loss: 0.68624
Epoch: 94, Time: 0.00161s, Loss: 0.65738
Epoch: 95, Time: 0.00203s, Loss: 0.65683
Epoch: 96, Time: 0.00171s, Loss: 0.63819
Epoch: 97, Time: 0.00177s, Loss: 0.66612
Epoch: 98, Time: 0.00231s, Loss: 0.64060
Epoch: 99, Time: 0.00161s, Loss: 0.63596
Epoch: 100, Time: 0.00161s, Loss: 0.62215
Epoch: 101, Time: 0.00195s, Loss: 0.59992
Epoch: 102, Time: 0.00184s, Loss: 0.63610
Epoch: 103, Time: 0.00168s, Loss: 0.60803
Epoch: 104, Time: 0.00174s, Loss: 0.60519
Epoch: 105, Time: 0.00203s, Loss: 0.61317
update best: 0.80200
Epoch: 106, Time: 0.00163s, Loss: 0.56701
Epoch: 107, Time: 0.00160s, Loss: 0.58649
Epoch: 108, Time: 0.00202s, Loss: 0.60864
Epoch: 109, Time: 0.00171s, Loss: 0.59734
Epoch: 110, Time: 0.00174s, Loss: 0.58395
Epoch: 111, Time: 0.00262s, Loss: 0.59959
Epoch: 112, Time: 0.00166s, Loss: 0.57178
Epoch: 113, Time: 0.00162s, Loss: 0.57493
Epoch: 114, Time: 0.00166s, Loss: 0.56720
Epoch: 115, Time: 0.00207s, Loss: 0.57864
Epoch: 116, Time: 0.00174s, Loss: 0.55171
Epoch: 117, Time: 0.00201s, Loss: 0.56022
Epoch: 118, Time: 0.00295s, Loss: 0.54393
Epoch: 119, Time: 0.00162s, Loss: 0.54266
Epoch: 120, Time: 0.00162s, Loss: 0.54640
Epoch: 121, Time: 0.00165s, Loss: 0.51695
Epoch: 122, Time: 0.00193s, Loss: 0.53059
Epoch: 123, Time: 0.00175s, Loss: 0.49817
Epoch: 124, Time: 0.00168s, Loss: 0.49963
Epoch: 125, Time: 0.00280s, Loss: 0.50499
Epoch: 126, Time: 0.00165s, Loss: 0.51792
Epoch: 127, Time: 0.00162s, Loss: 0.48759
Epoch: 128, Time: 0.00188s, Loss: 0.52524
Epoch: 129, Time: 0.00192s, Loss: 0.49752
Epoch: 130, Time: 0.00182s, Loss: 0.48539
Epoch: 131, Time: 0.00178s, Loss: 0.51904
Epoch: 132, Time: 0.00210s, Loss: 0.51619
Epoch: 133, Time: 0.00164s, Loss: 0.46799
Epoch: 134, Time: 0.00168s, Loss: 0.47253
Epoch: 135, Time: 0.00220s, Loss: 0.50235
Epoch: 136, Time: 0.00179s, Loss: 0.48068
Epoch: 137, Time: 0.00181s, Loss: 0.48230
Epoch: 138, Time: 0.00311s, Loss: 0.47752
Epoch: 139, Time: 0.00165s, Loss: 0.46344
Epoch: 140, Time: 0.00168s, Loss: 0.50513
Epoch: 141, Time: 0.00175s, Loss: 0.45315
Epoch: 142, Time: 0.00234s, Loss: 0.45984
Epoch: 143, Time: 0.00184s, Loss: 0.45598
Epoch: 144, Time: 0.00181s, Loss: 0.48745
Epoch: 145, Time: 0.00208s, Loss: 0.47391
Epoch: 146, Time: 0.00167s, Loss: 0.42658
Epoch: 147, Time: 0.00164s, Loss: 0.44139
Epoch: 148, Time: 0.00211s, Loss: 0.44337
Epoch: 149, Time: 0.00174s, Loss: 0.43854
Epoch: 150, Time: 0.00194s, Loss: 0.45141
Epoch: 151, Time: 0.00337s, Loss: 0.43659
Epoch: 152, Time: 0.00223s, Loss: 0.45104
Epoch: 153, Time: 0.00217s, Loss: 0.45788
Epoch: 154, Time: 0.00256s, Loss: 0.44208
Epoch: 155, Time: 0.00216s, Loss: 0.47642
Epoch: 156, Time: 0.00289s, Loss: 0.41826
Epoch: 157, Time: 0.00219s, Loss: 0.44075
Epoch: 158, Time: 0.00212s, Loss: 0.39873
Epoch: 159, Time: 0.00235s, Loss: 0.43970
Epoch: 160, Time: 0.00170s, Loss: 0.41875
Epoch: 161, Time: 0.00185s, Loss: 0.42697
Epoch: 162, Time: 0.00185s, Loss: 0.44240
Epoch: 163, Time: 0.00165s, Loss: 0.45397
Epoch: 164, Time: 0.00217s, Loss: 0.38061
Epoch: 165, Time: 0.00187s, Loss: 0.40102
Epoch: 166, Time: 0.00194s, Loss: 0.39496
Epoch: 167, Time: 0.00208s, Loss: 0.41661
Epoch: 168, Time: 0.00187s, Loss: 0.41864
Epoch: 169, Time: 0.00262s, Loss: 0.41757
Epoch: 170, Time: 0.00188s, Loss: 0.41356
Epoch: 171, Time: 0.00180s, Loss: 0.38835
Epoch: 172, Time: 0.00213s, Loss: 0.42775
Epoch: 173, Time: 0.00187s, Loss: 0.39169
Epoch: 174, Time: 0.00164s, Loss: 0.41415
Epoch: 175, Time: 0.00290s, Loss: 0.39668
update best: 0.80400
Epoch: 176, Time: 0.00161s, Loss: 0.42034
Epoch: 177, Time: 0.00164s, Loss: 0.40507
Epoch: 178, Time: 0.00206s, Loss: 0.39741
Epoch: 179, Time: 0.00181s, Loss: 0.40042
Epoch: 180, Time: 0.00163s, Loss: 0.37404
Epoch: 181, Time: 0.00167s, Loss: 0.40175
Epoch: 182, Time: 0.00217s, Loss: 0.35673
Epoch: 183, Time: 0.00162s, Loss: 0.39076
Epoch: 184, Time: 0.00157s, Loss: 0.39327
Epoch: 185, Time: 0.00208s, Loss: 0.38354
Epoch: 186, Time: 0.00172s, Loss: 0.36611
Epoch: 187, Time: 0.00174s, Loss: 0.38952
Epoch: 188, Time: 0.00276s, Loss: 0.39074
Epoch: 189, Time: 0.00160s, Loss: 0.36561
Epoch: 190, Time: 0.00164s, Loss: 0.37361
Epoch: 191, Time: 0.00162s, Loss: 0.37590
Epoch: 192, Time: 0.00188s, Loss: 0.36160
Epoch: 193, Time: 0.00173s, Loss: 0.37451
Epoch: 194, Time: 0.00170s, Loss: 0.36310
Epoch: 195, Time: 0.00285s, Loss: 0.39782
Epoch: 196, Time: 0.00160s, Loss: 0.36185
Epoch: 197, Time: 0.00161s, Loss: 0.35991
Epoch: 198, Time: 0.00191s, Loss: 0.37487
Epoch: 199, Time: 0.00219s, Loss: 0.36310

train finished!
best val: 0.80400
test...
final result: epoch: 175
{'accuracy': 0.8209999799728394, 'f1_score': 0.8113491851888245, 'f1_score -> average@micro': 0.821}

HGNN+ on Cora

Import Libraries

import time
from copy import deepcopy

import torch
import torch.optim as optim
import torch.nn.functional as F

from dhg import Graph, Hypergraph
from dhg.data import Cora
from dhg.models import HGNNP
from dhg.random import set_seed
from dhg.metrics import HypergraphVertexClassificationEvaluator as Evaluator

Define Functions

def train(net, X, G, lbls, train_idx, optimizer, epoch):
    net.train()

    st = time.time()
    optimizer.zero_grad()
    outs = net(X, G)
    outs, lbls = outs[train_idx], lbls[train_idx]
    loss = F.cross_entropy(outs, lbls)
    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, G, lbls, idx, test=False):
    net.eval()
    outs = net(X, G)
    outs, lbls = outs[idx], lbls[idx]
    if not test:
        res = evaluator.validate(lbls, outs)
    else:
        res = evaluator.test(lbls, outs)
    return res

Main

Note

More details about the metric Evaluator can be found in the Building Evaluator section.

if __name__ == "__main__":
    set_seed(2022)
    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
    evaluator = Evaluator(["accuracy", "f1_score", {"f1_score": {"average": "micro"}}])
    data = Cora()
    X, lbl = data["features"], data["labels"]
    G = Graph(data["num_vertices"], data["edge_list"])
    HG = Hypergraph.from_graph(G)
    HG.add_hyperedges_from_graph_kHop(G, k=1)
    train_mask = data["train_mask"]
    val_mask = data["val_mask"]
    test_mask = data["test_mask"]

    net = HGNNP(data["dim_features"], 16, data["num_classes"])
    optimizer = optim.Adam(net.parameters(), lr=0.01, weight_decay=5e-4)

    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 % 1 == 0:
            with torch.no_grad():
                val_res = infer(net, X, HG, lbl, val_mask)
            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)

Outputs

Epoch: 0, Time: 0.50397s, Loss: 1.95489
update best: 0.31600
Epoch: 1, Time: 0.00688s, Loss: 1.95044
Epoch: 2, Time: 0.00594s, Loss: 1.94790
Epoch: 3, Time: 0.00777s, Loss: 1.94277
Epoch: 4, Time: 0.00543s, Loss: 1.93662
Epoch: 5, Time: 0.00805s, Loss: 1.93121
Epoch: 6, Time: 0.00549s, Loss: 1.92640
update best: 0.31800
Epoch: 7, Time: 0.00687s, Loss: 1.91871
update best: 0.37600
Epoch: 8, Time: 0.00722s, Loss: 1.91161
update best: 0.41000
Epoch: 9, Time: 0.00553s, Loss: 1.90009
update best: 0.50400
Epoch: 10, Time: 0.00770s, Loss: 1.89464
update best: 0.57000
Epoch: 11, Time: 0.00566s, Loss: 1.88557
Epoch: 12, Time: 0.00769s, Loss: 1.87337
Epoch: 13, Time: 0.00549s, Loss: 1.86598
Epoch: 14, Time: 0.00767s, Loss: 1.85734
Epoch: 15, Time: 0.00546s, Loss: 1.84511
Epoch: 16, Time: 0.00752s, Loss: 1.83575
Epoch: 17, Time: 0.00545s, Loss: 1.82488
Epoch: 18, Time: 0.00840s, Loss: 1.80935
Epoch: 19, Time: 0.00536s, Loss: 1.79647
Epoch: 20, Time: 0.00756s, Loss: 1.78831
Epoch: 21, Time: 0.00538s, Loss: 1.76364
Epoch: 22, Time: 0.00797s, Loss: 1.75609
Epoch: 23, Time: 0.00601s, Loss: 1.74039
Epoch: 24, Time: 0.00737s, Loss: 1.73402
update best: 0.57200
Epoch: 25, Time: 0.00510s, Loss: 1.70649
Epoch: 26, Time: 0.00626s, Loss: 1.68333
update best: 0.57600
Epoch: 27, Time: 0.00489s, Loss: 1.67384
Epoch: 28, Time: 0.00637s, Loss: 1.64703
Epoch: 29, Time: 0.00569s, Loss: 1.65015
Epoch: 30, Time: 0.00616s, Loss: 1.61904
Epoch: 31, Time: 0.00482s, Loss: 1.60483
Epoch: 32, Time: 0.00657s, Loss: 1.58717
update best: 0.57800
Epoch: 33, Time: 0.00671s, Loss: 1.54870
update best: 0.58400
Epoch: 34, Time: 0.00547s, Loss: 1.53594
update best: 0.59800
Epoch: 35, Time: 0.00591s, Loss: 1.52464
update best: 0.61000
Epoch: 36, Time: 0.00569s, Loss: 1.50577
update best: 0.62800
Epoch: 37, Time: 0.00447s, Loss: 1.47224
update best: 0.64400
Epoch: 38, Time: 0.00566s, Loss: 1.46083
update best: 0.65800
Epoch: 39, Time: 0.00448s, Loss: 1.44008
update best: 0.67400
Epoch: 40, Time: 0.00560s, Loss: 1.39763
update best: 0.68800
Epoch: 41, Time: 0.00452s, Loss: 1.38902
update best: 0.69600
Epoch: 42, Time: 0.00592s, Loss: 1.34805
update best: 0.70600
Epoch: 43, Time: 0.00460s, Loss: 1.32505
update best: 0.71200
Epoch: 44, Time: 0.00575s, Loss: 1.32579
update best: 0.71600
Epoch: 45, Time: 0.00456s, Loss: 1.29263
update best: 0.72200
Epoch: 46, Time: 0.00590s, Loss: 1.25758
update best: 0.72800
Epoch: 47, Time: 0.00457s, Loss: 1.25460
update best: 0.73000
Epoch: 48, Time: 0.00577s, Loss: 1.21283
update best: 0.73200
Epoch: 49, Time: 0.00555s, Loss: 1.22506
update best: 0.73800
Epoch: 50, Time: 0.00590s, Loss: 1.20866
update best: 0.74200
Epoch: 51, Time: 0.00607s, Loss: 1.17283
update best: 0.75800
Epoch: 52, Time: 0.00558s, Loss: 1.14841
update best: 0.78000
Epoch: 53, Time: 0.00534s, Loss: 1.12203
update best: 0.78800
Epoch: 54, Time: 0.00525s, Loss: 1.07957
update best: 0.79000
Epoch: 55, Time: 0.00598s, Loss: 1.09576
update best: 0.79200
Epoch: 56, Time: 0.00518s, Loss: 1.08737
update best: 0.79400
Epoch: 57, Time: 0.00666s, Loss: 1.03506
Epoch: 58, Time: 0.00471s, Loss: 1.02326
Epoch: 59, Time: 0.00623s, Loss: 1.01210
Epoch: 60, Time: 0.00557s, Loss: 0.99087
Epoch: 61, Time: 0.00454s, Loss: 0.99048
Epoch: 62, Time: 0.00614s, Loss: 0.92911
Epoch: 63, Time: 0.00461s, Loss: 0.96758
Epoch: 64, Time: 0.00739s, Loss: 0.90397
Epoch: 65, Time: 0.00469s, Loss: 0.89135
Epoch: 66, Time: 0.00745s, Loss: 0.90936
Epoch: 67, Time: 0.00459s, Loss: 0.85870
Epoch: 68, Time: 0.00657s, Loss: 0.86560
Epoch: 69, Time: 0.00534s, Loss: 0.84675
Epoch: 70, Time: 0.00564s, Loss: 0.85727
Epoch: 71, Time: 0.00590s, Loss: 0.79680
Epoch: 72, Time: 0.00453s, Loss: 0.82477
Epoch: 73, Time: 0.00614s, Loss: 0.79762
Epoch: 74, Time: 0.00452s, Loss: 0.78480
Epoch: 75, Time: 0.00735s, Loss: 0.81077
Epoch: 76, Time: 0.00463s, Loss: 0.77174
Epoch: 77, Time: 0.00706s, Loss: 0.74386
Epoch: 78, Time: 0.00569s, Loss: 0.73486
Epoch: 79, Time: 0.00738s, Loss: 0.70369
update best: 0.79600
Epoch: 80, Time: 0.00563s, Loss: 0.70949
Epoch: 81, Time: 0.00649s, Loss: 0.68134
Epoch: 82, Time: 0.00542s, Loss: 0.65184
update best: 0.79800
Epoch: 83, Time: 0.00635s, Loss: 0.66273
Epoch: 84, Time: 0.00545s, Loss: 0.65232
Epoch: 85, Time: 0.00696s, Loss: 0.69817
Epoch: 86, Time: 0.00574s, Loss: 0.64078
Epoch: 87, Time: 0.00686s, Loss: 0.65521
Epoch: 88, Time: 0.00470s, Loss: 0.63180
Epoch: 89, Time: 0.00449s, Loss: 0.65444
Epoch: 90, Time: 0.00605s, Loss: 0.56861
Epoch: 91, Time: 0.00456s, Loss: 0.64074
Epoch: 92, Time: 0.00659s, Loss: 0.59132
update best: 0.80200
Epoch: 93, Time: 0.00465s, Loss: 0.62925
Epoch: 94, Time: 0.00662s, Loss: 0.60163
Epoch: 95, Time: 0.00453s, Loss: 0.58727
Epoch: 96, Time: 0.00693s, Loss: 0.57620
Epoch: 97, Time: 0.00481s, Loss: 0.60987
Epoch: 98, Time: 0.00702s, Loss: 0.57996
Epoch: 99, Time: 0.00462s, Loss: 0.56781
Epoch: 100, Time: 0.00570s, Loss: 0.54706
Epoch: 101, Time: 0.00507s, Loss: 0.54080
Epoch: 102, Time: 0.00444s, Loss: 0.57735
Epoch: 103, Time: 0.00613s, Loss: 0.52275
Epoch: 104, Time: 0.00452s, Loss: 0.53871
Epoch: 105, Time: 0.00667s, Loss: 0.54541
Epoch: 106, Time: 0.00565s, Loss: 0.51127
Epoch: 107, Time: 0.00738s, Loss: 0.52514
Epoch: 108, Time: 0.00540s, Loss: 0.54392
Epoch: 109, Time: 0.00604s, Loss: 0.54753
Epoch: 110, Time: 0.00465s, Loss: 0.53154
Epoch: 111, Time: 0.00629s, Loss: 0.53460
Epoch: 112, Time: 0.00568s, Loss: 0.52337
Epoch: 113, Time: 0.00587s, Loss: 0.52842
Epoch: 114, Time: 0.00562s, Loss: 0.50907
Epoch: 115, Time: 0.00454s, Loss: 0.51616
Epoch: 116, Time: 0.00561s, Loss: 0.50364
Epoch: 117, Time: 0.00459s, Loss: 0.49458
Epoch: 118, Time: 0.00545s, Loss: 0.49913
Epoch: 119, Time: 0.00529s, Loss: 0.48824
Epoch: 120, Time: 0.00519s, Loss: 0.52106
Epoch: 121, Time: 0.00555s, Loss: 0.46541
Epoch: 122, Time: 0.00459s, Loss: 0.47356
Epoch: 123, Time: 0.00539s, Loss: 0.44043
Epoch: 124, Time: 0.00468s, Loss: 0.44389
Epoch: 125, Time: 0.00569s, Loss: 0.45298
Epoch: 126, Time: 0.00500s, Loss: 0.46986
Epoch: 127, Time: 0.00551s, Loss: 0.45141
Epoch: 128, Time: 0.00533s, Loss: 0.48571
Epoch: 129, Time: 0.00460s, Loss: 0.43895
Epoch: 130, Time: 0.00600s, Loss: 0.44426
Epoch: 131, Time: 0.00457s, Loss: 0.47401
Epoch: 132, Time: 0.00579s, Loss: 0.46865
Epoch: 133, Time: 0.00464s, Loss: 0.41215
Epoch: 134, Time: 0.00528s, Loss: 0.42941
Epoch: 135, Time: 0.00642s, Loss: 0.46532
Epoch: 136, Time: 0.00538s, Loss: 0.42108
Epoch: 137, Time: 0.00690s, Loss: 0.41919
Epoch: 138, Time: 0.00617s, Loss: 0.44285
Epoch: 139, Time: 0.00577s, Loss: 0.42653
Epoch: 140, Time: 0.00548s, Loss: 0.45898
Epoch: 141, Time: 0.00539s, Loss: 0.41800
Epoch: 142, Time: 0.00467s, Loss: 0.40399
Epoch: 143, Time: 0.00487s, Loss: 0.38347
Epoch: 144, Time: 0.00509s, Loss: 0.42234
Epoch: 145, Time: 0.00721s, Loss: 0.42908
Epoch: 146, Time: 0.00489s, Loss: 0.37335
Epoch: 147, Time: 0.00664s, Loss: 0.40119
Epoch: 148, Time: 0.00465s, Loss: 0.38477
Epoch: 149, Time: 0.00451s, Loss: 0.40037
Epoch: 150, Time: 0.00553s, Loss: 0.40168
Epoch: 151, Time: 0.00454s, Loss: 0.38555
Epoch: 152, Time: 0.00729s, Loss: 0.40183
Epoch: 153, Time: 0.00465s, Loss: 0.40610
Epoch: 154, Time: 0.00669s, Loss: 0.39806
Epoch: 155, Time: 0.00463s, Loss: 0.43478
Epoch: 156, Time: 0.00641s, Loss: 0.37409
Epoch: 157, Time: 0.00509s, Loss: 0.39802
Epoch: 158, Time: 0.00453s, Loss: 0.34516
Epoch: 159, Time: 0.00563s, Loss: 0.39663
Epoch: 160, Time: 0.00456s, Loss: 0.37089
Epoch: 161, Time: 0.00711s, Loss: 0.39547
Epoch: 162, Time: 0.00455s, Loss: 0.41472
Epoch: 163, Time: 0.00645s, Loss: 0.40523
Epoch: 164, Time: 0.00465s, Loss: 0.33511
Epoch: 165, Time: 0.00565s, Loss: 0.35864
Epoch: 166, Time: 0.00575s, Loss: 0.33017
Epoch: 167, Time: 0.00785s, Loss: 0.36668
Epoch: 168, Time: 0.00604s, Loss: 0.36207
Epoch: 169, Time: 0.00650s, Loss: 0.37902
Epoch: 170, Time: 0.00473s, Loss: 0.38248
Epoch: 171, Time: 0.00664s, Loss: 0.34953
Epoch: 172, Time: 0.00556s, Loss: 0.38132
Epoch: 173, Time: 0.00686s, Loss: 0.34698
Epoch: 174, Time: 0.00619s, Loss: 0.36063
Epoch: 175, Time: 0.00468s, Loss: 0.34594
Epoch: 176, Time: 0.00545s, Loss: 0.37555
Epoch: 177, Time: 0.00457s, Loss: 0.35946
Epoch: 178, Time: 0.00718s, Loss: 0.35694
Epoch: 179, Time: 0.00458s, Loss: 0.34922
Epoch: 180, Time: 0.00693s, Loss: 0.30437
Epoch: 181, Time: 0.00461s, Loss: 0.34730
Epoch: 182, Time: 0.00632s, Loss: 0.31228
Epoch: 183, Time: 0.00509s, Loss: 0.36002
Epoch: 184, Time: 0.00454s, Loss: 0.36114
Epoch: 185, Time: 0.00546s, Loss: 0.34812
Epoch: 186, Time: 0.00456s, Loss: 0.33244
Epoch: 187, Time: 0.00696s, Loss: 0.34411
Epoch: 188, Time: 0.00459s, Loss: 0.35262
Epoch: 189, Time: 0.00628s, Loss: 0.32643
Epoch: 190, Time: 0.00472s, Loss: 0.32591
Epoch: 191, Time: 0.00451s, Loss: 0.33036
Epoch: 192, Time: 0.00594s, Loss: 0.31552
Epoch: 193, Time: 0.00559s, Loss: 0.32376
Epoch: 194, Time: 0.00627s, Loss: 0.31232
Epoch: 195, Time: 0.00550s, Loss: 0.33725
Epoch: 196, Time: 0.00570s, Loss: 0.34083
Epoch: 197, Time: 0.00508s, Loss: 0.30638
Epoch: 198, Time: 0.00559s, Loss: 0.33905
Epoch: 199, Time: 0.00603s, Loss: 0.30302

train finished!
best val: 0.80200
test...
final result: epoch: 92
{'accuracy': 0.8270000219345093, 'f1_score': 0.8198394539104813, 'f1_score -> average@micro': 0.827}