nohup: ignoring input
<frozen importlib._bootstrap>:219: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject
INFO:root:Using: cuda:2
INFO:root:Using seed 1234.
/home/ntu/Documents/zk/GeoGNN_ICLR/utils/data_utils.py:53: RuntimeWarning: divide by zero encountered in power
  r_inv = np.power(rowsum, -1).flatten()
INFO:root:LPModel(
  (encoder): GeoGCN(
    (linearlayer1): Linear(in_features=3703, out_features=64, bias=True)
    (layers): Sequential(
      (0): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
      (1): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
      (2): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
    )
  )
  (dc): FermiDiracDecoder()
  (pred): MLPPredictor(
    (W1): Linear(in_features=128, out_features=64, bias=True)
    (W2): Linear(in_features=64, out_features=1, bias=True)
  )
  (w_e): Linear(in_features=64, out_features=1, bias=False)
  (w_h): Linear(in_features=64, out_features=1, bias=False)
)
INFO:root:Total number of parameters: 6562884
INFO:root:Epoch: 0001 test_loss: 1.5737 test_roc: 0.7976 test_ap: 0.8648
INFO:root:Epoch: 0050 lr: 0.001 train_loss: 1.3829 train_roc: 1.0000 train_ap: 1.0000 time: 0.3862s
INFO:root:Epoch: 0050 val_loss: 1.5558 val_roc: 0.2843 val_ap: 0.3734
INFO:root:Epoch: 0083 test_loss: 1.2082 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0098 test_loss: 1.1957 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0099 test_loss: 1.1947 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0100 lr: 0.001 train_loss: 1.0780 train_roc: 1.0000 train_ap: 1.0000 time: 0.4116s
INFO:root:Epoch: 0100 val_loss: 1.3878 val_roc: 0.5828 val_ap: 0.5017
INFO:root:Epoch: 0100 test_loss: 1.1932 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0101 test_loss: 1.1918 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0102 test_loss: 1.1914 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0103 test_loss: 1.1906 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0104 test_loss: 1.1897 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0108 test_loss: 1.1873 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0109 test_loss: 1.1871 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0110 test_loss: 1.1867 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0126 test_loss: 1.1807 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0127 test_loss: 1.1796 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0150 lr: 0.001 train_loss: 1.3156 train_roc: 0.9987 train_ap: 0.9991 time: 0.4207s
INFO:root:Epoch: 0150 val_loss: 1.1690 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Epoch: 0200 lr: 0.001 train_loss: 1.1132 train_roc: 1.0000 train_ap: 1.0000 time: 0.4138s
INFO:root:Epoch: 0200 val_loss: 1.3990 val_roc: 0.5208 val_ap: 0.4670
INFO:root:Epoch: 0250 lr: 0.001 train_loss: 1.7164 train_roc: 0.9014 train_ap: 0.9210 time: 0.4193s
INFO:root:Epoch: 0250 val_loss: 1.1379 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Epoch: 0300 lr: 0.001 train_loss: 1.3007 train_roc: 1.0000 train_ap: 1.0000 time: 0.4134s
INFO:root:Epoch: 0300 val_loss: 1.2827 val_roc: 0.9804 val_ap: 0.9853
INFO:root:Early stopping
INFO:root:Optimization Finished!
INFO:root:Total time elapsed: 183.2241s
INFO:root:Val set results: val_loss: 1.2864 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Test set results: test_loss: 1.1796 test_roc: 1.0000 test_ap: 1.0000
INFO:root:test_acc_clean: 
INFO:root:[1.0]
INFO:root:Using: cuda:2
INFO:root:Using seed 1334.
/home/ntu/Documents/zk/GeoGNN_ICLR/utils/data_utils.py:53: RuntimeWarning: divide by zero encountered in power
  r_inv = np.power(rowsum, -1).flatten()
INFO:root:LPModel(
  (encoder): GeoGCN(
    (linearlayer1): Linear(in_features=3703, out_features=64, bias=True)
    (layers): Sequential(
      (0): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
      (1): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
      (2): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
    )
  )
  (dc): FermiDiracDecoder()
  (pred): MLPPredictor(
    (W1): Linear(in_features=128, out_features=64, bias=True)
    (W2): Linear(in_features=64, out_features=1, bias=True)
  )
  (w_e): Linear(in_features=64, out_features=1, bias=False)
  (w_h): Linear(in_features=64, out_features=1, bias=False)
)
INFO:root:Total number of parameters: 6562884
INFO:root:Epoch: 0001 test_loss: 1.5738 test_roc: 0.6981 test_ap: 0.7864
INFO:root:Epoch: 0050 lr: 0.001 train_loss: 1.4316 train_roc: 1.0000 train_ap: 1.0000 time: 0.4119s
INFO:root:Epoch: 0050 val_loss: 1.5813 val_roc: 0.1836 val_ap: 0.3465
INFO:root:Epoch: 0100 lr: 0.001 train_loss: 1.3322 train_roc: 0.9991 train_ap: 0.9993 time: 0.4161s
INFO:root:Epoch: 0100 val_loss: 1.4822 val_roc: 0.2997 val_ap: 0.3805
INFO:root:Epoch: 0150 lr: 0.001 train_loss: 1.3176 train_roc: 0.9994 train_ap: 0.9995 time: 0.4155s
INFO:root:Epoch: 0150 val_loss: 1.5862 val_roc: 0.2401 val_ap: 0.3628
INFO:root:Epoch: 0200 lr: 0.001 train_loss: 1.3147 train_roc: 0.9993 train_ap: 0.9995 time: 0.4198s
INFO:root:Epoch: 0200 val_loss: 1.4507 val_roc: 0.2545 val_ap: 0.3673
INFO:root:Epoch: 0203 test_loss: 1.1589 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0204 test_loss: 1.1583 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0205 test_loss: 1.1593 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0250 lr: 0.001 train_loss: 1.6843 train_roc: 0.9370 train_ap: 0.9507 time: 0.4235s
INFO:root:Epoch: 0250 val_loss: 1.1654 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Epoch: 0300 lr: 0.001 train_loss: 0.9778 train_roc: 1.0000 train_ap: 1.0000 time: 0.4121s
INFO:root:Epoch: 0300 val_loss: 1.5488 val_roc: 0.6241 val_ap: 0.6827
INFO:root:Epoch: 0350 lr: 0.001 train_loss: 0.9527 train_roc: 1.0000 train_ap: 1.0000 time: 0.4417s
INFO:root:Epoch: 0350 val_loss: 1.5392 val_roc: 0.6592 val_ap: 0.7127
INFO:root:Epoch: 0400 lr: 0.001 train_loss: 1.4113 train_roc: 0.9970 train_ap: 0.9979 time: 0.4177s
INFO:root:Epoch: 0400 val_loss: 1.1314 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Early stopping
INFO:root:Optimization Finished!
INFO:root:Total time elapsed: 234.8018s
INFO:root:Val set results: val_loss: 1.2501 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Test set results: test_loss: 1.1593 test_roc: 1.0000 test_ap: 1.0000
INFO:root:test_acc_clean: 
INFO:root:[1.0, 1.0]
INFO:root:Using: cuda:2
INFO:root:Using seed 1434.
/home/ntu/Documents/zk/GeoGNN_ICLR/utils/data_utils.py:53: RuntimeWarning: divide by zero encountered in power
  r_inv = np.power(rowsum, -1).flatten()
INFO:root:LPModel(
  (encoder): GeoGCN(
    (linearlayer1): Linear(in_features=3703, out_features=64, bias=True)
    (layers): Sequential(
      (0): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
      (1): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
      (2): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
    )
  )
  (dc): FermiDiracDecoder()
  (pred): MLPPredictor(
    (W1): Linear(in_features=128, out_features=64, bias=True)
    (W2): Linear(in_features=64, out_features=1, bias=True)
  )
  (w_e): Linear(in_features=64, out_features=1, bias=False)
  (w_h): Linear(in_features=64, out_features=1, bias=False)
)
INFO:root:Total number of parameters: 6562884
INFO:root:Epoch: 0001 test_loss: 1.5739 test_roc: 0.6346 test_ap: 0.7170
INFO:root:Epoch: 0050 lr: 0.001 train_loss: 1.4212 train_roc: 1.0000 train_ap: 1.0000 time: 0.4299s
INFO:root:Epoch: 0050 val_loss: 1.5762 val_roc: 0.2397 val_ap: 0.3612
INFO:root:Epoch: 0085 test_loss: 1.5171 test_roc: 0.5024 test_ap: 0.4546
INFO:root:Epoch: 0086 test_loss: 1.5204 test_roc: 0.4926 test_ap: 0.4499
INFO:root:Epoch: 0087 test_loss: 1.5307 test_roc: 0.4676 test_ap: 0.4388
INFO:root:Epoch: 0088 test_loss: 1.5366 test_roc: 0.4534 test_ap: 0.4327
INFO:root:Epoch: 0089 test_loss: 1.5394 test_roc: 0.4450 test_ap: 0.4292
INFO:root:Epoch: 0090 test_loss: 1.5394 test_roc: 0.4409 test_ap: 0.4274
INFO:root:Epoch: 0091 test_loss: 1.5451 test_roc: 0.4316 test_ap: 0.4238
INFO:root:Epoch: 0092 test_loss: 1.5558 test_roc: 0.4152 test_ap: 0.4177
INFO:root:Epoch: 0093 test_loss: 1.5624 test_roc: 0.4032 test_ap: 0.4134
INFO:root:Epoch: 0094 test_loss: 1.5681 test_roc: 0.3966 test_ap: 0.4102
INFO:root:Epoch: 0095 test_loss: 1.5691 test_roc: 0.3911 test_ap: 0.4079
INFO:root:Epoch: 0096 test_loss: 1.5699 test_roc: 0.3896 test_ap: 0.4073
INFO:root:Epoch: 0097 test_loss: 1.5705 test_roc: 0.3817 test_ap: 0.4044
INFO:root:Epoch: 0098 test_loss: 1.5709 test_roc: 0.3784 test_ap: 0.4031
INFO:root:Epoch: 0099 test_loss: 1.5711 test_roc: 0.3764 test_ap: 0.4024
INFO:root:Epoch: 0100 lr: 0.001 train_loss: 1.5722 train_roc: 0.8497 train_ap: 0.8834 time: 0.4101s
INFO:root:Epoch: 0100 val_loss: 1.5722 val_roc: 0.7188 val_ap: 0.7719
INFO:root:Epoch: 0101 test_loss: 1.5712 test_roc: 0.3709 test_ap: 0.4002
INFO:root:Epoch: 0102 test_loss: 1.5711 test_roc: 0.3684 test_ap: 0.3993
INFO:root:Epoch: 0103 test_loss: 1.5710 test_roc: 0.3680 test_ap: 0.3993
INFO:root:Epoch: 0104 test_loss: 1.5707 test_roc: 0.3687 test_ap: 0.3994
INFO:root:Epoch: 0105 test_loss: 1.5705 test_roc: 0.3683 test_ap: 0.3994
INFO:root:Epoch: 0110 test_loss: 1.5680 test_roc: 0.3645 test_ap: 0.3981
INFO:root:Epoch: 0139 test_loss: 1.3311 test_roc: 0.9853 test_ap: 0.9901
INFO:root:Epoch: 0140 test_loss: 1.2853 test_roc: 0.9950 test_ap: 0.9961
INFO:root:Epoch: 0141 test_loss: 1.2436 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0150 lr: 0.001 train_loss: 1.5207 train_roc: 0.9857 train_ap: 0.9901 time: 0.4253s
INFO:root:Epoch: 0150 val_loss: 1.5844 val_roc: 0.1191 val_ap: 0.3319
INFO:root:Epoch: 0200 lr: 0.001 train_loss: 1.2516 train_roc: 1.0000 train_ap: 1.0000 time: 0.4413s
INFO:root:Epoch: 0200 val_loss: 1.5286 val_roc: 0.3966 val_ap: 0.4125
INFO:root:Epoch: 0250 lr: 0.001 train_loss: 1.6915 train_roc: 0.9152 train_ap: 0.9339 time: 0.4253s
INFO:root:Epoch: 0250 val_loss: 1.4847 val_roc: 0.7541 val_ap: 0.7838
INFO:root:Epoch: 0300 lr: 0.001 train_loss: 1.3813 train_roc: 0.9995 train_ap: 0.9997 time: 0.4251s
INFO:root:Epoch: 0300 val_loss: 1.6730 val_roc: 0.0000 val_ap: 0.3095
INFO:root:Early stopping
INFO:root:Optimization Finished!
INFO:root:Total time elapsed: 198.6806s
INFO:root:Val set results: val_loss: 1.3703 val_roc: 0.9754 val_ap: 0.9831
INFO:root:Test set results: test_loss: 1.2436 test_roc: 1.0000 test_ap: 1.0000
INFO:root:test_acc_clean: 
INFO:root:[1.0, 1.0, 0.9999999999999999]
INFO:root:Using: cuda:2
INFO:root:Using seed 1534.
/home/ntu/Documents/zk/GeoGNN_ICLR/utils/data_utils.py:53: RuntimeWarning: divide by zero encountered in power
  r_inv = np.power(rowsum, -1).flatten()
INFO:root:LPModel(
  (encoder): GeoGCN(
    (linearlayer1): Linear(in_features=3703, out_features=64, bias=True)
    (layers): Sequential(
      (0): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
      (1): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
      (2): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
    )
  )
  (dc): FermiDiracDecoder()
  (pred): MLPPredictor(
    (W1): Linear(in_features=128, out_features=64, bias=True)
    (W2): Linear(in_features=64, out_features=1, bias=True)
  )
  (w_e): Linear(in_features=64, out_features=1, bias=False)
  (w_h): Linear(in_features=64, out_features=1, bias=False)
)
INFO:root:Total number of parameters: 6562884
INFO:root:Epoch: 0001 test_loss: 1.5739 test_roc: 0.6416 test_ap: 0.7285
INFO:root:Epoch: 0050 lr: 0.001 train_loss: 1.4149 train_roc: 1.0000 train_ap: 1.0000 time: 0.4385s
INFO:root:Epoch: 0050 val_loss: 1.5864 val_roc: 0.1209 val_ap: 0.3317
INFO:root:Epoch: 0100 lr: 0.001 train_loss: 1.0730 train_roc: 1.0000 train_ap: 1.0000 time: 0.4245s
INFO:root:Epoch: 0100 val_loss: 1.4688 val_roc: 0.2946 val_ap: 0.3786
INFO:root:Epoch: 0102 test_loss: 1.1350 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0103 test_loss: 1.1405 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0104 test_loss: 1.1423 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0105 test_loss: 1.1413 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0106 test_loss: 1.1371 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0107 test_loss: 1.1343 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0108 test_loss: 1.1513 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0109 test_loss: 1.1684 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0111 test_loss: 1.1794 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0120 test_loss: 1.1771 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0121 test_loss: 1.1744 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0122 test_loss: 1.1533 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0150 lr: 0.001 train_loss: 1.3117 train_roc: 0.9995 train_ap: 0.9997 time: 0.4343s
INFO:root:Epoch: 0150 val_loss: 1.2083 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Epoch: 0200 lr: 0.001 train_loss: 1.3068 train_roc: 0.9994 train_ap: 0.9996 time: 0.4186s
INFO:root:Epoch: 0200 val_loss: 1.3469 val_roc: 0.9389 val_ap: 0.9598
INFO:root:Epoch: 0250 lr: 0.001 train_loss: 1.7146 train_roc: 0.6265 train_ap: 0.5265 time: 0.4156s
INFO:root:Epoch: 0250 val_loss: 1.1573 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Epoch: 0300 lr: 0.001 train_loss: 1.2906 train_roc: 1.0000 train_ap: 1.0000 time: 0.4133s
INFO:root:Epoch: 0300 val_loss: 1.1180 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Early stopping
INFO:root:Optimization Finished!
INFO:root:Total time elapsed: 191.3703s
INFO:root:Val set results: val_loss: 1.2805 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Test set results: test_loss: 1.1533 test_roc: 1.0000 test_ap: 1.0000
INFO:root:test_acc_clean: 
INFO:root:[1.0, 1.0, 0.9999999999999999, 1.0]
INFO:root:Using: cuda:2
INFO:root:Using seed 1634.
/home/ntu/Documents/zk/GeoGNN_ICLR/utils/data_utils.py:53: RuntimeWarning: divide by zero encountered in power
  r_inv = np.power(rowsum, -1).flatten()
INFO:root:LPModel(
  (encoder): GeoGCN(
    (linearlayer1): Linear(in_features=3703, out_features=64, bias=True)
    (layers): Sequential(
      (0): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
      (1): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
      (2): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
    )
  )
  (dc): FermiDiracDecoder()
  (pred): MLPPredictor(
    (W1): Linear(in_features=128, out_features=64, bias=True)
    (W2): Linear(in_features=64, out_features=1, bias=True)
  )
  (w_e): Linear(in_features=64, out_features=1, bias=False)
  (w_h): Linear(in_features=64, out_features=1, bias=False)
)
INFO:root:Total number of parameters: 6562884
INFO:root:Epoch: 0001 test_loss: 1.5737 test_roc: 0.7419 test_ap: 0.8211
INFO:root:Epoch: 0050 lr: 0.001 train_loss: 1.4362 train_roc: 1.0000 train_ap: 1.0000 time: 0.4121s
INFO:root:Epoch: 0050 val_loss: 1.5792 val_roc: 0.1329 val_ap: 0.3346
INFO:root:Epoch: 0100 lr: 0.001 train_loss: 1.3228 train_roc: 0.9996 train_ap: 0.9997 time: 0.3968s
INFO:root:Epoch: 0100 val_loss: 1.4023 val_roc: 0.5323 val_ap: 0.4703
INFO:root:Epoch: 0100 test_loss: 1.1607 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0101 test_loss: 1.1672 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0102 test_loss: 1.1827 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0103 test_loss: 1.1821 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0104 test_loss: 1.1812 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0105 test_loss: 1.1806 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0106 test_loss: 1.1806 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0107 test_loss: 1.1815 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0108 test_loss: 1.1813 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0109 test_loss: 1.1947 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0141 test_loss: 1.1617 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0142 test_loss: 1.1603 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0143 test_loss: 1.1319 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0150 lr: 0.001 train_loss: 1.3322 train_roc: 0.9994 train_ap: 0.9996 time: 0.4153s
INFO:root:Epoch: 0150 val_loss: 1.3558 val_roc: 0.9399 val_ap: 0.9604
INFO:root:Epoch: 0154 test_loss: 1.1532 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0155 test_loss: 1.1534 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0200 lr: 0.001 train_loss: 1.3061 train_roc: 0.9994 train_ap: 0.9995 time: 0.4238s
INFO:root:Epoch: 0200 val_loss: 1.2795 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Epoch: 0250 lr: 0.001 train_loss: 1.7114 train_roc: 0.9223 train_ap: 0.9359 time: 0.4203s
INFO:root:Epoch: 0250 val_loss: 1.1284 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Epoch: 0300 lr: 0.001 train_loss: 0.9424 train_roc: 1.0000 train_ap: 1.0000 time: 0.4245s
INFO:root:Epoch: 0300 val_loss: 1.1194 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Epoch: 0350 lr: 0.001 train_loss: 1.3031 train_roc: 1.0000 train_ap: 1.0000 time: 0.4256s
INFO:root:Epoch: 0350 val_loss: 1.1155 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Early stopping
INFO:root:Optimization Finished!
INFO:root:Total time elapsed: 209.3383s
INFO:root:Val set results: val_loss: 1.2779 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Test set results: test_loss: 1.1534 test_roc: 1.0000 test_ap: 1.0000
INFO:root:test_acc_clean: 
INFO:root:[1.0, 1.0, 0.9999999999999999, 1.0, 1.0]
INFO:root:Using: cuda:2
INFO:root:Using seed 1734.
/home/ntu/Documents/zk/GeoGNN_ICLR/utils/data_utils.py:53: RuntimeWarning: divide by zero encountered in power
  r_inv = np.power(rowsum, -1).flatten()
INFO:root:LPModel(
  (encoder): GeoGCN(
    (linearlayer1): Linear(in_features=3703, out_features=64, bias=True)
    (layers): Sequential(
      (0): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
      (1): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
      (2): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
    )
  )
  (dc): FermiDiracDecoder()
  (pred): MLPPredictor(
    (W1): Linear(in_features=128, out_features=64, bias=True)
    (W2): Linear(in_features=64, out_features=1, bias=True)
  )
  (w_e): Linear(in_features=64, out_features=1, bias=False)
  (w_h): Linear(in_features=64, out_features=1, bias=False)
)
INFO:root:Total number of parameters: 6562884
INFO:root:Epoch: 0001 test_loss: 1.5736 test_roc: 0.8125 test_ap: 0.8733
INFO:root:Epoch: 0050 lr: 0.001 train_loss: 1.3891 train_roc: 1.0000 train_ap: 1.0000 time: 0.3907s
INFO:root:Epoch: 0050 val_loss: 1.5355 val_roc: 0.3466 val_ap: 0.3929
INFO:root:Epoch: 0080 test_loss: 1.1847 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0093 test_loss: 1.1185 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0096 test_loss: 1.1615 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0097 test_loss: 1.1759 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0098 test_loss: 1.1909 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0099 test_loss: 1.2043 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0100 lr: 0.001 train_loss: 1.0674 train_roc: 1.0000 train_ap: 1.0000 time: 0.4052s
INFO:root:Epoch: 0100 val_loss: 1.3545 val_roc: 0.9395 val_ap: 0.9596
INFO:root:Epoch: 0101 test_loss: 1.2026 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0102 test_loss: 1.2026 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0103 test_loss: 1.2010 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0104 test_loss: 1.1989 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0105 test_loss: 1.1969 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0126 test_loss: 1.1586 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0127 test_loss: 1.1834 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0150 lr: 0.001 train_loss: 1.3166 train_roc: 0.9995 train_ap: 0.9996 time: 0.4204s
INFO:root:Epoch: 0150 val_loss: 1.2572 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Epoch: 0200 lr: 0.001 train_loss: 1.0469 train_roc: 1.0000 train_ap: 1.0000 time: 0.4248s
INFO:root:Epoch: 0200 val_loss: 1.2963 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Epoch: 0250 lr: 0.001 train_loss: 1.7231 train_roc: 0.8914 train_ap: 0.9150 time: 0.2443s
INFO:root:Epoch: 0250 val_loss: 1.1445 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Epoch: 0300 lr: 0.001 train_loss: 0.9493 train_roc: 1.0000 train_ap: 1.0000 time: 0.4167s
INFO:root:Epoch: 0300 val_loss: 1.1258 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Early stopping
INFO:root:Optimization Finished!
INFO:root:Total time elapsed: 187.8349s
INFO:root:Val set results: val_loss: 1.2748 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Test set results: test_loss: 1.1834 test_roc: 1.0000 test_ap: 1.0000
INFO:root:test_acc_clean: 
INFO:root:[1.0, 1.0, 0.9999999999999999, 1.0, 1.0, 0.9999999999999999]
INFO:root:Using: cuda:2
INFO:root:Using seed 1834.
/home/ntu/Documents/zk/GeoGNN_ICLR/utils/data_utils.py:53: RuntimeWarning: divide by zero encountered in power
  r_inv = np.power(rowsum, -1).flatten()
INFO:root:LPModel(
  (encoder): GeoGCN(
    (linearlayer1): Linear(in_features=3703, out_features=64, bias=True)
    (layers): Sequential(
      (0): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
      (1): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
      (2): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
    )
  )
  (dc): FermiDiracDecoder()
  (pred): MLPPredictor(
    (W1): Linear(in_features=128, out_features=64, bias=True)
    (W2): Linear(in_features=64, out_features=1, bias=True)
  )
  (w_e): Linear(in_features=64, out_features=1, bias=False)
  (w_h): Linear(in_features=64, out_features=1, bias=False)
)
INFO:root:Total number of parameters: 6562884
INFO:root:Epoch: 0001 test_loss: 1.5736 test_roc: 0.8701 test_ap: 0.9103
INFO:root:Epoch: 0050 lr: 0.001 train_loss: 1.4568 train_roc: 1.0000 train_ap: 1.0000 time: 0.4118s
INFO:root:Epoch: 0050 val_loss: 1.5925 val_roc: 0.0956 val_ap: 0.3265
INFO:root:Epoch: 0100 lr: 0.001 train_loss: 1.0488 train_roc: 1.0000 train_ap: 1.0000 time: 0.3971s
INFO:root:Epoch: 0100 val_loss: 1.5129 val_roc: 0.0000 val_ap: 0.3085
INFO:root:Epoch: 0133 test_loss: 1.2995 test_roc: 0.9881 test_ap: 0.9918
INFO:root:Epoch: 0134 test_loss: 1.3162 test_roc: 0.9816 test_ap: 0.9877
INFO:root:Epoch: 0135 test_loss: 1.2804 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0136 test_loss: 1.2794 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0137 test_loss: 1.2764 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0138 test_loss: 1.2617 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0139 test_loss: 1.1906 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0150 lr: 0.001 train_loss: 1.3092 train_roc: 0.9988 train_ap: 0.9991 time: 0.4244s
INFO:root:Epoch: 0150 val_loss: 1.5901 val_roc: 0.0000 val_ap: 0.3084
INFO:root:Epoch: 0200 lr: 0.001 train_loss: 0.9848 train_roc: 1.0000 train_ap: 1.0000 time: 0.4154s
INFO:root:Epoch: 0200 val_loss: 1.7764 val_roc: 0.0000 val_ap: 0.3085
INFO:root:Epoch: 0250 lr: 0.001 train_loss: 1.7040 train_roc: 0.9302 train_ap: 0.9441 time: 0.4142s
INFO:root:Epoch: 0250 val_loss: 1.7371 val_roc: 0.3306 val_ap: 0.3910
INFO:root:Epoch: 0300 lr: 0.001 train_loss: 1.2894 train_roc: 1.0000 train_ap: 1.0000 time: 0.4183s
INFO:root:Epoch: 0300 val_loss: 1.7499 val_roc: 0.2871 val_ap: 0.3767
INFO:root:Epoch: 0310 test_loss: 0.9741 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0311 test_loss: 0.9740 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0312 test_loss: 0.9746 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0313 test_loss: 0.9782 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0314 test_loss: 0.9816 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0315 test_loss: 0.9847 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0316 test_loss: 0.9947 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0317 test_loss: 1.0068 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0318 test_loss: 1.0118 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0337 test_loss: 1.0947 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0338 test_loss: 1.1054 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0339 test_loss: 1.1138 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0344 test_loss: 1.1053 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0350 lr: 0.001 train_loss: 0.9410 train_roc: 1.0000 train_ap: 1.0000 time: 0.4141s
INFO:root:Epoch: 0350 val_loss: 1.4852 val_roc: 0.7932 val_ap: 0.8216
INFO:root:Epoch: 0400 lr: 0.001 train_loss: 1.2913 train_roc: 1.0000 train_ap: 1.0000 time: 0.4190s
INFO:root:Epoch: 0400 val_loss: 1.4852 val_roc: 0.7859 val_ap: 0.8191
INFO:root:Epoch: 0450 lr: 0.001 train_loss: 1.2844 train_roc: 1.0000 train_ap: 1.0000 time: 0.4253s
INFO:root:Epoch: 0450 val_loss: 1.4840 val_roc: 0.7675 val_ap: 0.7974
INFO:root:Epoch: 0483 test_loss: 0.9262 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0484 test_loss: 0.9324 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0485 test_loss: 0.9361 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0486 test_loss: 0.9375 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0487 test_loss: 0.9363 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0488 test_loss: 0.9326 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0489 test_loss: 0.9332 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0490 test_loss: 0.9331 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0491 test_loss: 0.9428 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0500 lr: 0.001 train_loss: 1.7015 train_roc: 0.9658 train_ap: 0.9767 time: 0.4118s
INFO:root:Epoch: 0500 val_loss: 1.3482 val_roc: 0.9668 val_ap: 0.9774
INFO:root:Epoch: 0550 lr: 0.001 train_loss: 0.9982 train_roc: 1.0000 train_ap: 1.0000 time: 0.5917s
INFO:root:Epoch: 0550 val_loss: 1.3928 val_roc: 0.9583 val_ap: 0.9724
INFO:root:Epoch: 0600 lr: 0.001 train_loss: 1.2769 train_roc: 1.0000 train_ap: 1.0000 time: 0.5847s
INFO:root:Epoch: 0600 val_loss: 1.4785 val_roc: 0.8072 val_ap: 0.8373
INFO:root:Epoch: 0650 lr: 0.001 train_loss: 0.8970 train_roc: 1.0000 train_ap: 1.0000 time: 0.5940s
INFO:root:Epoch: 0650 val_loss: 1.3347 val_roc: 0.9697 val_ap: 0.9789
INFO:root:Early stopping
INFO:root:Optimization Finished!
INFO:root:Total time elapsed: 436.8710s
INFO:root:Val set results: val_loss: 1.3310 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Test set results: test_loss: 0.9428 test_roc: 1.0000 test_ap: 1.0000
INFO:root:test_acc_clean: 
INFO:root:[1.0, 1.0, 0.9999999999999999, 1.0, 1.0, 0.9999999999999999, 1.0]
INFO:root:Using: cuda:2
INFO:root:Using seed 1934.
/home/ntu/Documents/zk/GeoGNN_ICLR/utils/data_utils.py:53: RuntimeWarning: divide by zero encountered in power
  r_inv = np.power(rowsum, -1).flatten()
INFO:root:LPModel(
  (encoder): GeoGCN(
    (linearlayer1): Linear(in_features=3703, out_features=64, bias=True)
    (layers): Sequential(
      (0): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
      (1): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
      (2): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
    )
  )
  (dc): FermiDiracDecoder()
  (pred): MLPPredictor(
    (W1): Linear(in_features=128, out_features=64, bias=True)
    (W2): Linear(in_features=64, out_features=1, bias=True)
  )
  (w_e): Linear(in_features=64, out_features=1, bias=False)
  (w_h): Linear(in_features=64, out_features=1, bias=False)
)
INFO:root:Total number of parameters: 6562884
INFO:root:Epoch: 0001 test_loss: 1.5736 test_roc: 0.8429 test_ap: 0.8945
INFO:root:Epoch: 0050 lr: 0.001 train_loss: 1.4070 train_roc: 1.0000 train_ap: 1.0000 time: 0.4195s
INFO:root:Epoch: 0050 val_loss: 1.5847 val_roc: 0.1395 val_ap: 0.3359
INFO:root:Epoch: 0100 lr: 0.001 train_loss: 1.3265 train_roc: 0.9992 train_ap: 0.9994 time: 0.4200s
INFO:root:Epoch: 0100 val_loss: 1.4226 val_roc: 0.3874 val_ap: 0.4096
INFO:root:Epoch: 0104 test_loss: 1.1196 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0105 test_loss: 1.1113 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0106 test_loss: 1.1172 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0107 test_loss: 1.1253 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0117 test_loss: 1.1212 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0118 test_loss: 1.1034 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0120 test_loss: 1.0808 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0122 test_loss: 1.0605 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0123 test_loss: 1.0599 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0124 test_loss: 1.0655 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0125 test_loss: 1.0613 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0126 test_loss: 1.0563 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0127 test_loss: 1.0539 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0128 test_loss: 1.0669 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0150 lr: 0.001 train_loss: 1.0041 train_roc: 1.0000 train_ap: 1.0000 time: 0.4238s
INFO:root:Epoch: 0150 val_loss: 1.2410 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Epoch: 0200 lr: 0.001 train_loss: 1.3086 train_roc: 0.9994 train_ap: 0.9996 time: 0.1936s
INFO:root:Epoch: 0200 val_loss: 1.3030 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Epoch: 0250 lr: 0.001 train_loss: 1.7107 train_roc: 0.8955 train_ap: 0.9099 time: 0.4264s
INFO:root:Epoch: 0250 val_loss: 1.3627 val_roc: 0.9363 val_ap: 0.9566
INFO:root:Epoch: 0300 lr: 0.001 train_loss: 1.0225 train_roc: 1.0000 train_ap: 1.0000 time: 0.4312s
INFO:root:Epoch: 0300 val_loss: 1.3804 val_roc: 0.8011 val_ap: 0.8383
INFO:root:Early stopping
INFO:root:Optimization Finished!
INFO:root:Total time elapsed: 189.0010s
INFO:root:Val set results: val_loss: 1.2754 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Test set results: test_loss: 1.0669 test_roc: 1.0000 test_ap: 1.0000
INFO:root:test_acc_clean: 
INFO:root:[1.0, 1.0, 0.9999999999999999, 1.0, 1.0, 0.9999999999999999, 1.0, 1.0]
INFO:root:Using: cuda:2
INFO:root:Using seed 2034.
/home/ntu/Documents/zk/GeoGNN_ICLR/utils/data_utils.py:53: RuntimeWarning: divide by zero encountered in power
  r_inv = np.power(rowsum, -1).flatten()
INFO:root:LPModel(
  (encoder): GeoGCN(
    (linearlayer1): Linear(in_features=3703, out_features=64, bias=True)
    (layers): Sequential(
      (0): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
      (1): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
      (2): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
    )
  )
  (dc): FermiDiracDecoder()
  (pred): MLPPredictor(
    (W1): Linear(in_features=128, out_features=64, bias=True)
    (W2): Linear(in_features=64, out_features=1, bias=True)
  )
  (w_e): Linear(in_features=64, out_features=1, bias=False)
  (w_h): Linear(in_features=64, out_features=1, bias=False)
)
INFO:root:Total number of parameters: 6562884
INFO:root:Epoch: 0001 test_loss: 1.5739 test_roc: 0.5141 test_ap: 0.5656
INFO:root:Epoch: 0050 lr: 0.001 train_loss: 1.4518 train_roc: 1.0000 train_ap: 1.0000 time: 0.4238s
INFO:root:Epoch: 0050 val_loss: 1.5900 val_roc: 0.0909 val_ap: 0.3257
INFO:root:Epoch: 0078 test_loss: 1.2172 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0097 test_loss: 1.2027 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0098 test_loss: 1.2019 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0099 test_loss: 1.2017 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0100 lr: 0.001 train_loss: 1.3288 train_roc: 0.9985 train_ap: 0.9990 time: 0.4276s
INFO:root:Epoch: 0100 val_loss: 1.4125 val_roc: 0.5508 val_ap: 0.4792
INFO:root:Epoch: 0100 test_loss: 1.2014 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0101 test_loss: 1.2009 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0102 test_loss: 1.2004 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0103 test_loss: 1.1994 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0104 test_loss: 1.1983 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0105 test_loss: 1.1976 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0106 test_loss: 1.1975 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0107 test_loss: 1.1987 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0150 lr: 0.001 train_loss: 1.3322 train_roc: 0.9986 train_ap: 0.9990 time: 0.4139s
INFO:root:Epoch: 0150 val_loss: 1.4977 val_roc: 0.6686 val_ap: 0.6042
INFO:root:Epoch: 0192 test_loss: 1.1515 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0193 test_loss: 1.1514 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0194 test_loss: 1.1510 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0195 test_loss: 1.1510 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0196 test_loss: 1.1510 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0197 test_loss: 1.1514 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0198 test_loss: 1.1526 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0200 lr: 0.001 train_loss: 1.3118 train_roc: 0.9991 train_ap: 0.9994 time: 0.5838s
INFO:root:Epoch: 0200 val_loss: 1.3622 val_roc: 0.9418 val_ap: 0.9619
INFO:root:Epoch: 0205 test_loss: 1.1533 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0250 lr: 0.001 train_loss: 1.7614 train_roc: 0.5022 train_ap: 0.4617 time: 0.5739s
INFO:root:Epoch: 0250 val_loss: 1.5016 val_roc: 0.7284 val_ap: 0.7677
INFO:root:Epoch: 0300 lr: 0.001 train_loss: 0.9473 train_roc: 1.0000 train_ap: 1.0000 time: 0.4211s
INFO:root:Epoch: 0300 val_loss: 1.1228 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Epoch: 0350 lr: 0.001 train_loss: 0.9337 train_roc: 1.0000 train_ap: 1.0000 time: 0.4238s
INFO:root:Epoch: 0350 val_loss: 1.2349 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Epoch: 0400 lr: 0.001 train_loss: 1.3041 train_roc: 1.0000 train_ap: 1.0000 time: 0.4330s
INFO:root:Epoch: 0400 val_loss: 1.2664 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Early stopping
INFO:root:Optimization Finished!
INFO:root:Total time elapsed: 262.7697s
INFO:root:Val set results: val_loss: 1.3030 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Test set results: test_loss: 1.1533 test_roc: 1.0000 test_ap: 1.0000
INFO:root:test_acc_clean: 
INFO:root:[1.0, 1.0, 0.9999999999999999, 1.0, 1.0, 0.9999999999999999, 1.0, 1.0, 1.0]
INFO:root:Using: cuda:2
INFO:root:Using seed 2134.
/home/ntu/Documents/zk/GeoGNN_ICLR/utils/data_utils.py:53: RuntimeWarning: divide by zero encountered in power
  r_inv = np.power(rowsum, -1).flatten()
INFO:root:LPModel(
  (encoder): GeoGCN(
    (linearlayer1): Linear(in_features=3703, out_features=64, bias=True)
    (layers): Sequential(
      (0): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
      (1): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
      (2): GeoGraphConvolution(
        (agg): GeoAgg(
          (linearlayer): Linear(in_features=64, out_features=64, bias=True)
          (odefunc): Hamilton_V2(
            (H): myLinear(
              (w0_y): Linear(in_features=128, out_features=128, bias=True)
            )
          )
          (odeblock_exp): ODEBlock(
            (odefunc): Hamilton_V2(
              (H): myLinear(
                (w0_y): Linear(in_features=128, out_features=128, bias=True)
              )
            )
          )
        )
        (hyp_act): GeoAct()
      )
    )
  )
  (dc): FermiDiracDecoder()
  (pred): MLPPredictor(
    (W1): Linear(in_features=128, out_features=64, bias=True)
    (W2): Linear(in_features=64, out_features=1, bias=True)
  )
  (w_e): Linear(in_features=64, out_features=1, bias=False)
  (w_h): Linear(in_features=64, out_features=1, bias=False)
)
INFO:root:Total number of parameters: 6562884
INFO:root:Epoch: 0001 test_loss: 1.5736 test_roc: 0.8467 test_ap: 0.8946
INFO:root:Epoch: 0050 lr: 0.001 train_loss: 1.4284 train_roc: 1.0000 train_ap: 1.0000 time: 0.4582s
INFO:root:Epoch: 0050 val_loss: 1.5731 val_roc: 0.1864 val_ap: 0.3466
INFO:root:Epoch: 0076 test_loss: 1.1606 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0077 test_loss: 1.1594 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0082 test_loss: 1.2393 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0083 test_loss: 1.2596 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0084 test_loss: 1.2526 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0085 test_loss: 1.2512 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0086 test_loss: 1.2424 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0087 test_loss: 1.2295 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0088 test_loss: 1.2162 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0089 test_loss: 1.2055 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0090 test_loss: 1.2044 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0091 test_loss: 1.2040 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0092 test_loss: 1.1863 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0093 test_loss: 1.1677 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0097 test_loss: 1.1608 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0098 test_loss: 1.1706 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0100 lr: 0.001 train_loss: 1.3357 train_roc: 0.9998 train_ap: 0.9999 time: 0.3180s
INFO:root:Epoch: 0100 val_loss: 1.3595 val_roc: 0.9486 val_ap: 0.9655
INFO:root:Epoch: 0104 test_loss: 1.2165 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0105 test_loss: 1.2298 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0106 test_loss: 1.2359 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0107 test_loss: 1.2448 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0108 test_loss: 1.2351 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0109 test_loss: 1.2087 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0112 test_loss: 1.1949 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0135 test_loss: 1.1774 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0136 test_loss: 1.1772 test_roc: 1.0000 test_ap: 1.0000
INFO:root:Epoch: 0150 lr: 0.001 train_loss: 1.2760 train_roc: 1.0000 train_ap: 1.0000 time: 0.4224s
INFO:root:Epoch: 0150 val_loss: 1.3274 val_roc: 0.9873 val_ap: 0.9911
INFO:root:Epoch: 0200 lr: 0.001 train_loss: 1.3156 train_roc: 0.9990 train_ap: 0.9993 time: 0.4162s
INFO:root:Epoch: 0200 val_loss: 1.2939 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Epoch: 0250 lr: 0.001 train_loss: 1.7160 train_roc: 0.8854 train_ap: 0.9061 time: 0.4048s
INFO:root:Epoch: 0250 val_loss: 1.1439 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Epoch: 0300 lr: 0.001 train_loss: 1.1093 train_roc: 1.0000 train_ap: 1.0000 time: 0.4314s
INFO:root:Epoch: 0300 val_loss: 1.1535 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Early stopping
INFO:root:Optimization Finished!
INFO:root:Total time elapsed: 198.7515s
INFO:root:Val set results: val_loss: 1.2974 val_roc: 1.0000 val_ap: 1.0000
INFO:root:Test set results: test_loss: 1.1772 test_roc: 1.0000 test_ap: 1.0000
INFO:root:test_acc_clean: 
INFO:root:[1.0, 1.0, 0.9999999999999999, 1.0, 1.0, 0.9999999999999999, 1.0, 1.0, 1.0, 1.0]
INFO:root:********************************************************************************
INFO:root:test_acc_roc: 
INFO:root:[1.0, 1.0, 0.9999999999999999, 1.0, 1.0, 0.9999999999999999, 1.0, 1.0, 1.0, 1.0]
INFO:root:test_acc_ap
INFO:root:[1.0, 1.0, 1.0, 1.0, 1.0000000000000002, 1.0, 1.0, 1.0, 1.0, 1.0]
INFO:root:Mean of test_acc_roc: 
INFO:root:1.0
INFO:root:Std of test_acc_roc: 
INFO:root:4.681111291435601e-17
INFO:root:Mean of test_acc_ap: 
INFO:root:1.0
INFO:root:Std of test_acc_ap: 
INFO:root:7.021666937153402e-17
test_acc_roc:  [1.0, 1.0, 0.9999999999999999, 1.0, 1.0, 0.9999999999999999, 1.0, 1.0, 1.0, 1.0]
test_acc_roc mean:  1.0
test_acc_roc var:  4.681111291435601e-17
Namespace(act='relu', alpha=0.2, bias=0, c=1.0, cuda=2, dataset='citeseer', device='cuda:2', dim=64, double_precision='0', dropout=0.2, epochs=1000, eval_freq=1, feat_dim=3703, gamma=0.5, grad_clip=None, kdim=8, local_agg=0, log_freq=50, logmethods='ode', lr=0.001, lr_reduce_freq=1000, manifold='Freemanifold', min_epochs=300, model='GeoGCN', momentum=0.999, n_heads=4, n_nodes=3327, nb_edges=3976, nb_false_edges=5532152, normalize_adj=1, normalize_feats=1, num_layers=3, odemap='h2extend', optimizer='Adam', patience=200, pos_weight=0, pretrained_embeddings=None, print_epoch=True, r=2.0, save=0, save_dir='./experiment3', seed=2234, split_seed=1234, sweep_c=0, t=1.0, task='lp', test_prop=0.1, use_att=0, use_feats=1, val_prop=0.05, weight_decay=0.001)
