donghande / autodebias Goto Github PK

If I want to change the data set, how should I operate it?

Hi. Thank you for sharing the code.

I download your code and directly run it. But it shows:
ModuleNotFoundError: No module named 'utils.ex'

If I delete import utils.ex as ex and add exp= cppimport.imp_from_filepath('utils/ex.cpp'), it still shows the following error:
#include <forward_list>
^~~~~~~~~~~~~~
1 error generated.
error: command 'gcc' failed with exit status 1

Can you help me how to run your code if possible. Thank you very much!

Hello, I am very happy to see the release of Autodebias.

When I seek to reproduce the MF_biased and MF_combine approaches, they exhibits much better performance than the IPS/DR/CausE approaches. The MF_combine approach, in particular, reaches an AUC of 0.735-0.737, which is competitive to AutoDebias.

I'm very curious as to why the IPS/DR/CausE approach would fail in this implementation, and why the biased/combine approaches would perform better than debiased approaches. This issue is important to me because this strange result makes me question the validity of the fundamental causal approaches (IPS/DR) in practice.

Thanks!

hello，
def auc(vector_predict, vector_true, device = 'cuda'): pos_indexes = torch.where(vector_true == 1)[0].to(device) pos_whe=(vector_true == 1).to(device) sort_indexes = torch.argsort(vector_predict).to(device) rank=torch.zeros((len(vector_predict))).to(device) rank[sort_indexes] = torch.FloatTensor(list(range(len(vector_predict)))).to(device) rank = rank * pos_whe auc = (torch.sum(rank) - len(pos_indexes) * (len(pos_indexes) - 1) / 2) / (len(pos_indexes) * (len(vector_predict) - len(pos_indexes))) return auc.item()
vector_predict是所有用户对物品的预测，但是计算auc时应该要分用户进行计算，这样混着计算没什么用吧？

生成了so文件依然显示无法找到utils.ex 请大佬指点一下,谢谢!

IN the paper the threshold said to be 3 but in fact it's 4 when run python train_implicit.py --dataset yahooR3

see threshold value assignment in the code

For threshold = 4, the performance is:
(pytorch) C:\Users\Administrator\Desktop\auto\AutoDebias>python train_implicit.py --dataset yahooR3
Epoch: 0 / 500, Validation: MSE:0.441 NLL:-0.527 AUC:0.550
Epoch: 1 / 500, Validation: MSE:0.305 NLL:-0.452 AUC:0.617
Epoch: 2 / 500, Validation: MSE:0.292 NLL:-0.429 AUC:0.654
Epoch: 3 / 500, Validation: MSE:0.292 NLL:-0.420 AUC:0.673
Epoch: 4 / 500, Validation: MSE:0.293 NLL:-0.417 AUC:0.685
Epoch: 5 / 500, Validation: MSE:0.293 NLL:-0.415 AUC:0.692
Epoch: 6 / 500, Validation: MSE:0.293 NLL:-0.415 AUC:0.693
Epoch: 7 / 500, Validation: MSE:0.293 NLL:-0.415 AUC:0.697
Epoch: 8 / 500, Validation: MSE:0.292 NLL:-0.416 AUC:0.697
Epoch: 9 / 500, Validation: MSE:0.291 NLL:-0.417 AUC:0.697
Epoch: 10 / 500, Validation: MSE:0.291 NLL:-0.419 AUC:0.700
Epoch: 11 / 500, Validation: MSE:0.290 NLL:-0.420 AUC:0.698
Epoch: 12 / 500, Validation: MSE:0.289 NLL:-0.422 AUC:0.699
Epoch: 13 / 500, Validation: MSE:0.289 NLL:-0.423 AUC:0.697
Epoch: 14 / 500, Validation: MSE:0.289 NLL:-0.424 AUC:0.698
Epoch: 15 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.698
Epoch: 16 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.701
Epoch: 17 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.699
Epoch: 18 / 500, Validation: MSE:0.289 NLL:-0.426 AUC:0.698
Epoch: 19 / 500, Validation: MSE:0.289 NLL:-0.427 AUC:0.699
Epoch: 20 / 500, Validation: MSE:0.288 NLL:-0.427 AUC:0.704
Epoch: 21 / 500, Validation: MSE:0.288 NLL:-0.427 AUC:0.700
Epoch: 22 / 500, Validation: MSE:0.288 NLL:-0.427 AUC:0.701
Epoch: 23 / 500, Validation: MSE:0.288 NLL:-0.427 AUC:0.702
Epoch: 24 / 500, Validation: MSE:0.288 NLL:-0.427 AUC:0.701
Epoch: 25 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.701
Epoch: 26 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.701
Epoch: 27 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.700
Epoch: 28 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.700
Epoch: 29 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.700
Epoch: 30 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.700
Epoch: 31 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.700
Epoch: 32 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.699
Epoch: 33 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.700
Epoch: 34 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.701
Epoch: 35 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.701
Epoch: 36 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.703
Epoch: 37 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.701
Epoch: 38 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.701
Epoch: 39 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.701
Epoch: 40 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.701
Epoch: 41 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.702
Epoch: 42 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.702
Epoch: 43 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.702
Epoch: 44 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.703
Epoch: 45 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.702
Epoch: 46 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.701
Epoch: 47 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.700
Epoch: 48 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.699
Epoch: 49 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.700
Epoch: 50 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.700
Epoch: 51 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.703
Epoch: 52 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.702
Epoch: 53 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.703
Epoch: 54 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.702
Epoch: 55 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.701
Epoch: 56 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.701
Epoch: 57 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.702
Epoch: 58 / 500, Validation: MSE:0.288 NLL:-0.426 AUC:0.701
Epoch: 59 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.701
Epoch: 60 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.699
Epoch: 61 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.701
Epoch: 62 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.704
Epoch: 63 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.701
Epoch: 64 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.702
Epoch: 65 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.704
Epoch: 66 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.703
Epoch: 67 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.701
Epoch: 68 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.701
Epoch: 69 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.701
Epoch: 70 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.700
Epoch: 71 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.700
Epoch: 72 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.702
Epoch: 73 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.701
Epoch: 74 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.698
Epoch: 75 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.701
Epoch: 76 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.701
Epoch: 77 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.702
Epoch: 78 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.702
Epoch: 79 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.702
Epoch: 80 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.702
Epoch: 81 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.701
Epoch: 82 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.701
Epoch: 83 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.703
Epoch: 84 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.702
Epoch: 85 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.699
Epoch: 86 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.701
Epoch: 87 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.701
Epoch: 88 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.703
Epoch: 89 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.703
Epoch: 90 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.704
Epoch: 91 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.701
Epoch: 92 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.704
Epoch: 93 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.704
Epoch: 94 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.704
Epoch: 95 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.703
Epoch: 96 / 500, Validation: MSE:0.287 NLL:-0.425 AUC:0.701
Epoch: 97 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.704
Epoch: 98 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.703
Epoch: 99 / 500, Validation: MSE:0.287 NLL:-0.425 AUC:0.703
Epoch: 100 / 500, Validation: MSE:0.287 NLL:-0.425 AUC:0.704
Epoch: 101 / 500, Validation: MSE:0.288 NLL:-0.425 AUC:0.703
Epoch: 102 / 500, Validation: MSE:0.287 NLL:-0.425 AUC:0.703
Epoch: 103 / 500, Validation: MSE:0.287 NLL:-0.425 AUC:0.704
Epoch: 104 / 500, Validation: MSE:0.287 NLL:-0.425 AUC:0.705
Epoch: 105 / 500, Validation: MSE:0.287 NLL:-0.425 AUC:0.706
Epoch: 106 / 500, Validation: MSE:0.287 NLL:-0.425 AUC:0.704
Epoch: 107 / 500, Validation: MSE:0.287 NLL:-0.424 AUC:0.706
Epoch: 108 / 500, Validation: MSE:0.287 NLL:-0.424 AUC:0.706
Epoch: 109 / 500, Validation: MSE:0.287 NLL:-0.424 AUC:0.707
Epoch: 110 / 500, Validation: MSE:0.287 NLL:-0.424 AUC:0.709
Epoch: 111 / 500, Validation: MSE:0.287 NLL:-0.424 AUC:0.708
Epoch: 112 / 500, Validation: MSE:0.287 NLL:-0.424 AUC:0.706
Epoch: 113 / 500, Validation: MSE:0.287 NLL:-0.424 AUC:0.708
Epoch: 114 / 500, Validation: MSE:0.287 NLL:-0.424 AUC:0.708
Epoch: 115 / 500, Validation: MSE:0.287 NLL:-0.424 AUC:0.711
Epoch: 116 / 500, Validation: MSE:0.287 NLL:-0.424 AUC:0.709
Epoch: 117 / 500, Validation: MSE:0.287 NLL:-0.424 AUC:0.710
Epoch: 118 / 500, Validation: MSE:0.287 NLL:-0.424 AUC:0.713
Epoch: 119 / 500, Validation: MSE:0.287 NLL:-0.424 AUC:0.710
Epoch: 120 / 500, Validation: MSE:0.287 NLL:-0.424 AUC:0.713
Epoch: 121 / 500, Validation: MSE:0.287 NLL:-0.424 AUC:0.713
Epoch: 122 / 500, Validation: MSE:0.287 NLL:-0.424 AUC:0.714
Epoch: 123 / 500, Validation: MSE:0.287 NLL:-0.424 AUC:0.712
Epoch: 124 / 500, Validation: MSE:0.287 NLL:-0.424 AUC:0.714
Epoch: 125 / 500, Validation: MSE:0.287 NLL:-0.424 AUC:0.714
Epoch: 126 / 500, Validation: MSE:0.287 NLL:-0.424 AUC:0.715
Epoch: 127 / 500, Validation: MSE:0.287 NLL:-0.424 AUC:0.717
Epoch: 128 / 500, Validation: MSE:0.287 NLL:-0.424 AUC:0.718
Epoch: 129 / 500, Validation: MSE:0.286 NLL:-0.424 AUC:0.717
Epoch: 130 / 500, Validation: MSE:0.287 NLL:-0.424 AUC:0.715
Epoch: 131 / 500, Validation: MSE:0.286 NLL:-0.424 AUC:0.717
Epoch: 132 / 500, Validation: MSE:0.286 NLL:-0.424 AUC:0.716
Epoch: 133 / 500, Validation: MSE:0.286 NLL:-0.424 AUC:0.716
Epoch: 134 / 500, Validation: MSE:0.287 NLL:-0.424 AUC:0.712
Epoch: 135 / 500, Validation: MSE:0.286 NLL:-0.424 AUC:0.714
Epoch: 136 / 500, Validation: MSE:0.286 NLL:-0.424 AUC:0.714
Epoch: 137 / 500, Validation: MSE:0.286 NLL:-0.424 AUC:0.715
Epoch: 138 / 500, Validation: MSE:0.286 NLL:-0.423 AUC:0.715
Epoch: 139 / 500, Validation: MSE:0.286 NLL:-0.423 AUC:0.713
Epoch: 140 / 500, Validation: MSE:0.286 NLL:-0.423 AUC:0.712
Epoch: 141 / 500, Validation: MSE:0.286 NLL:-0.423 AUC:0.715
Epoch: 142 / 500, Validation: MSE:0.286 NLL:-0.423 AUC:0.713
Epoch: 143 / 500, Validation: MSE:0.286 NLL:-0.423 AUC:0.713
Epoch: 144 / 500, Validation: MSE:0.286 NLL:-0.423 AUC:0.713
Epoch: 145 / 500, Validation: MSE:0.286 NLL:-0.423 AUC:0.712
Epoch: 146 / 500, Validation: MSE:0.286 NLL:-0.423 AUC:0.711
Epoch: 147 / 500, Validation: MSE:0.286 NLL:-0.423 AUC:0.710
Epoch: 148 / 500, Validation: MSE:0.286 NLL:-0.423 AUC:0.711
Epoch: 149 / 500, Validation: MSE:0.286 NLL:-0.423 AUC:0.712
Epoch: 150 / 500, Validation: MSE:0.286 NLL:-0.423 AUC:0.714
Epoch: 151 / 500, Validation: MSE:0.286 NLL:-0.423 AUC:0.712
Epoch: 152 / 500, Validation: MSE:0.286 NLL:-0.423 AUC:0.711
Epoch: 153 / 500, Validation: MSE:0.286 NLL:-0.423 AUC:0.714
Epoch: 154 / 500, Validation: MSE:0.286 NLL:-0.423 AUC:0.714
Epoch: 155 / 500, Validation: MSE:0.286 NLL:-0.423 AUC:0.713
Epoch: 156 / 500, Validation: MSE:0.286 NLL:-0.423 AUC:0.712
Epoch: 157 / 500, Validation: MSE:0.286 NLL:-0.423 AUC:0.714
Epoch: 158 / 500, Validation: MSE:0.286 NLL:-0.423 AUC:0.714
Epoch: 159 / 500, Validation: MSE:0.286 NLL:-0.423 AUC:0.711
Epoch: 160 / 500, Validation: MSE:0.286 NLL:-0.423 AUC:0.713
Epoch: 161 / 500, Validation: MSE:0.286 NLL:-0.423 AUC:0.711
Epoch: 162 / 500, Validation: MSE:0.285 NLL:-0.423 AUC:0.712
Epoch: 163 / 500, Validation: MSE:0.285 NLL:-0.423 AUC:0.712
Epoch: 164 / 500, Validation: MSE:0.285 NLL:-0.423 AUC:0.708
Epoch: 165 / 500, Validation: MSE:0.285 NLL:-0.423 AUC:0.711
Epoch: 166 / 500, Validation: MSE:0.285 NLL:-0.423 AUC:0.711
Epoch: 167 / 500, Validation: MSE:0.285 NLL:-0.423 AUC:0.710
Epoch: 168 / 500, Validation: MSE:0.285 NLL:-0.423 AUC:0.710
Epoch: 169 / 500, Validation: MSE:0.285 NLL:-0.423 AUC:0.714
Epoch: 170 / 500, Validation: MSE:0.285 NLL:-0.423 AUC:0.712
Epoch: 171 / 500, Validation: MSE:0.285 NLL:-0.423 AUC:0.711
Epoch: 172 / 500, Validation: MSE:0.285 NLL:-0.422 AUC:0.711
Epoch: 173 / 500, Validation: MSE:0.285 NLL:-0.422 AUC:0.712
Epoch: 174 / 500, Validation: MSE:0.285 NLL:-0.422 AUC:0.710
Epoch: 175 / 500, Validation: MSE:0.285 NLL:-0.422 AUC:0.712
Epoch: 176 / 500, Validation: MSE:0.285 NLL:-0.422 AUC:0.711
Epoch: 177 / 500, Validation: MSE:0.285 NLL:-0.422 AUC:0.712
Epoch: 178 / 500, Validation: MSE:0.285 NLL:-0.422 AUC:0.710
Epoch: 179 / 500, Validation: MSE:0.284 NLL:-0.422 AUC:0.714
Epoch: 180 / 500, Validation: MSE:0.284 NLL:-0.422 AUC:0.711
Epoch: 181 / 500, Validation: MSE:0.284 NLL:-0.422 AUC:0.712
Epoch: 182 / 500, Validation: MSE:0.284 NLL:-0.422 AUC:0.711
Epoch: 183 / 500, Validation: MSE:0.284 NLL:-0.422 AUC:0.711
Epoch: 184 / 500, Validation: MSE:0.284 NLL:-0.422 AUC:0.709
Epoch: 185 / 500, Validation: MSE:0.284 NLL:-0.422 AUC:0.710
Epoch: 186 / 500, Validation: MSE:0.284 NLL:-0.422 AUC:0.714
Epoch: 187 / 500, Validation: MSE:0.284 NLL:-0.422 AUC:0.711
Epoch: 188 / 500, Validation: MSE:0.284 NLL:-0.422 AUC:0.716
Loading 128th epoch

The performance of validation set: MSE:0.287 NLL:-0.424 AUC:0.718
The performance of testing set: MSE:0.307 NLL:-0.429 AUC:0.722 Precision:0.263 Recall:0.743 NDCG:0.587

So the performance is:
NLL -0.429 AUC 0.722 NDCG@5 0.587 (for threshold of 3) , roughly the same with that in the paper: -0.419 0.741 0.645
but thus less competitive compared with other methods.

But when I change this line and set threshold to 3, the performance is:

(pytorch) C:\Users\Administrator\Desktop\auto\AutoDebias>python train_implicit.py --dataset yahooR3
Epoch: 0 / 500, Validation: MSE:0.711 NLL:-0.596 AUC:0.529
Epoch: 1 / 500, Validation: MSE:0.712 NLL:-0.565 AUC:0.569
Epoch: 2 / 500, Validation: MSE:0.725 NLL:-0.559 AUC:0.584
Epoch: 3 / 500, Validation: MSE:0.717 NLL:-0.561 AUC:0.604
Epoch: 4 / 500, Validation: MSE:0.702 NLL:-0.568 AUC:0.613
Epoch: 5 / 500, Validation: MSE:0.693 NLL:-0.577 AUC:0.622
Epoch: 6 / 500, Validation: MSE:0.693 NLL:-0.583 AUC:0.627
Epoch: 7 / 500, Validation: MSE:0.692 NLL:-0.580 AUC:0.635
Epoch: 8 / 500, Validation: MSE:0.691 NLL:-0.575 AUC:0.639
Epoch: 9 / 500, Validation: MSE:0.692 NLL:-0.573 AUC:0.639
Epoch: 10 / 500, Validation: MSE:0.691 NLL:-0.574 AUC:0.643
Epoch: 11 / 500, Validation: MSE:0.690 NLL:-0.575 AUC:0.644
Epoch: 12 / 500, Validation: MSE:0.689 NLL:-0.576 AUC:0.648
Epoch: 13 / 500, Validation: MSE:0.690 NLL:-0.576 AUC:0.645
Epoch: 14 / 500, Validation: MSE:0.689 NLL:-0.575 AUC:0.648
Epoch: 15 / 500, Validation: MSE:0.689 NLL:-0.575 AUC:0.649
Epoch: 16 / 500, Validation: MSE:0.689 NLL:-0.575 AUC:0.649
Epoch: 17 / 500, Validation: MSE:0.689 NLL:-0.575 AUC:0.647
Epoch: 18 / 500, Validation: MSE:0.689 NLL:-0.575 AUC:0.651
Epoch: 19 / 500, Validation: MSE:0.689 NLL:-0.575 AUC:0.651
Epoch: 20 / 500, Validation: MSE:0.689 NLL:-0.575 AUC:0.649
Epoch: 21 / 500, Validation: MSE:0.689 NLL:-0.575 AUC:0.647
Epoch: 22 / 500, Validation: MSE:0.689 NLL:-0.575 AUC:0.651
Epoch: 23 / 500, Validation: MSE:0.689 NLL:-0.575 AUC:0.649
Epoch: 24 / 500, Validation: MSE:0.688 NLL:-0.575 AUC:0.650
Epoch: 25 / 500, Validation: MSE:0.689 NLL:-0.574 AUC:0.650
Epoch: 26 / 500, Validation: MSE:0.688 NLL:-0.575 AUC:0.649
Epoch: 27 / 500, Validation: MSE:0.688 NLL:-0.574 AUC:0.651
Epoch: 28 / 500, Validation: MSE:0.688 NLL:-0.574 AUC:0.650
Epoch: 29 / 500, Validation: MSE:0.688 NLL:-0.575 AUC:0.649
Epoch: 30 / 500, Validation: MSE:0.688 NLL:-0.574 AUC:0.651
Epoch: 31 / 500, Validation: MSE:0.689 NLL:-0.574 AUC:0.649
Epoch: 32 / 500, Validation: MSE:0.688 NLL:-0.574 AUC:0.651
Epoch: 33 / 500, Validation: MSE:0.688 NLL:-0.574 AUC:0.651
Epoch: 34 / 500, Validation: MSE:0.688 NLL:-0.574 AUC:0.649
Epoch: 35 / 500, Validation: MSE:0.688 NLL:-0.574 AUC:0.651
Epoch: 36 / 500, Validation: MSE:0.688 NLL:-0.574 AUC:0.649
Epoch: 37 / 500, Validation: MSE:0.688 NLL:-0.574 AUC:0.651
Epoch: 38 / 500, Validation: MSE:0.688 NLL:-0.574 AUC:0.650
Epoch: 39 / 500, Validation: MSE:0.688 NLL:-0.574 AUC:0.650
Epoch: 40 / 500, Validation: MSE:0.688 NLL:-0.574 AUC:0.652
Epoch: 41 / 500, Validation: MSE:0.688 NLL:-0.574 AUC:0.651
Epoch: 42 / 500, Validation: MSE:0.688 NLL:-0.574 AUC:0.650
Epoch: 43 / 500, Validation: MSE:0.688 NLL:-0.574 AUC:0.651
Epoch: 44 / 500, Validation: MSE:0.688 NLL:-0.574 AUC:0.652
Epoch: 45 / 500, Validation: MSE:0.688 NLL:-0.574 AUC:0.650
Epoch: 46 / 500, Validation: MSE:0.688 NLL:-0.574 AUC:0.652
Epoch: 47 / 500, Validation: MSE:0.688 NLL:-0.574 AUC:0.650
Epoch: 48 / 500, Validation: MSE:0.688 NLL:-0.574 AUC:0.650
Epoch: 49 / 500, Validation: MSE:0.688 NLL:-0.573 AUC:0.651
Epoch: 50 / 500, Validation: MSE:0.688 NLL:-0.574 AUC:0.650
Epoch: 51 / 500, Validation: MSE:0.688 NLL:-0.574 AUC:0.652
Epoch: 52 / 500, Validation: MSE:0.688 NLL:-0.574 AUC:0.651
Epoch: 53 / 500, Validation: MSE:0.688 NLL:-0.574 AUC:0.652
Epoch: 54 / 500, Validation: MSE:0.688 NLL:-0.573 AUC:0.650
Epoch: 55 / 500, Validation: MSE:0.688 NLL:-0.574 AUC:0.649
Epoch: 56 / 500, Validation: MSE:0.688 NLL:-0.573 AUC:0.649
Epoch: 57 / 500, Validation: MSE:0.688 NLL:-0.573 AUC:0.650
Epoch: 58 / 500, Validation: MSE:0.688 NLL:-0.573 AUC:0.649
Epoch: 59 / 500, Validation: MSE:0.688 NLL:-0.573 AUC:0.649
Epoch: 60 / 500, Validation: MSE:0.688 NLL:-0.573 AUC:0.649
Epoch: 61 / 500, Validation: MSE:0.688 NLL:-0.574 AUC:0.649
Epoch: 62 / 500, Validation: MSE:0.688 NLL:-0.573 AUC:0.650
Epoch: 63 / 500, Validation: MSE:0.688 NLL:-0.573 AUC:0.651
Epoch: 64 / 500, Validation: MSE:0.688 NLL:-0.573 AUC:0.649
Epoch: 65 / 500, Validation: MSE:0.688 NLL:-0.573 AUC:0.652
Epoch: 66 / 500, Validation: MSE:0.688 NLL:-0.573 AUC:0.653
Epoch: 67 / 500, Validation: MSE:0.688 NLL:-0.573 AUC:0.650
Epoch: 68 / 500, Validation: MSE:0.688 NLL:-0.573 AUC:0.652
Epoch: 69 / 500, Validation: MSE:0.688 NLL:-0.573 AUC:0.651
Epoch: 70 / 500, Validation: MSE:0.688 NLL:-0.573 AUC:0.651
Epoch: 71 / 500, Validation: MSE:0.688 NLL:-0.573 AUC:0.652
Epoch: 72 / 500, Validation: MSE:0.688 NLL:-0.573 AUC:0.651
Epoch: 73 / 500, Validation: MSE:0.688 NLL:-0.573 AUC:0.651
Epoch: 74 / 500, Validation: MSE:0.688 NLL:-0.573 AUC:0.649
Epoch: 75 / 500, Validation: MSE:0.688 NLL:-0.573 AUC:0.650
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Epoch: 77 / 500, Validation: MSE:0.687 NLL:-0.573 AUC:0.650
Epoch: 78 / 500, Validation: MSE:0.688 NLL:-0.573 AUC:0.651
Epoch: 79 / 500, Validation: MSE:0.688 NLL:-0.573 AUC:0.649
Epoch: 80 / 500, Validation: MSE:0.688 NLL:-0.573 AUC:0.651
Epoch: 81 / 500, Validation: MSE:0.687 NLL:-0.573 AUC:0.651
Epoch: 82 / 500, Validation: MSE:0.687 NLL:-0.573 AUC:0.649
Epoch: 83 / 500, Validation: MSE:0.687 NLL:-0.573 AUC:0.651
Epoch: 84 / 500, Validation: MSE:0.687 NLL:-0.572 AUC:0.652
Epoch: 85 / 500, Validation: MSE:0.687 NLL:-0.573 AUC:0.649
Epoch: 86 / 500, Validation: MSE:0.687 NLL:-0.573 AUC:0.650
Epoch: 87 / 500, Validation: MSE:0.687 NLL:-0.573 AUC:0.649
Epoch: 88 / 500, Validation: MSE:0.687 NLL:-0.573 AUC:0.650
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Epoch: 90 / 500, Validation: MSE:0.687 NLL:-0.573 AUC:0.652
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Epoch: 96 / 500, Validation: MSE:0.687 NLL:-0.572 AUC:0.652
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Epoch: 98 / 500, Validation: MSE:0.687 NLL:-0.572 AUC:0.653
Epoch: 99 / 500, Validation: MSE:0.686 NLL:-0.572 AUC:0.653
Epoch: 100 / 500, Validation: MSE:0.686 NLL:-0.572 AUC:0.652
Epoch: 101 / 500, Validation: MSE:0.687 NLL:-0.572 AUC:0.652
Epoch: 102 / 500, Validation: MSE:0.686 NLL:-0.572 AUC:0.653
Epoch: 103 / 500, Validation: MSE:0.686 NLL:-0.572 AUC:0.652
Epoch: 104 / 500, Validation: MSE:0.686 NLL:-0.572 AUC:0.656
Epoch: 105 / 500, Validation: MSE:0.686 NLL:-0.572 AUC:0.654
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Epoch: 107 / 500, Validation: MSE:0.685 NLL:-0.572 AUC:0.655
Epoch: 108 / 500, Validation: MSE:0.686 NLL:-0.571 AUC:0.654
Epoch: 109 / 500, Validation: MSE:0.685 NLL:-0.572 AUC:0.655
Epoch: 110 / 500, Validation: MSE:0.685 NLL:-0.572 AUC:0.655
Epoch: 111 / 500, Validation: MSE:0.685 NLL:-0.572 AUC:0.655
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Epoch: 113 / 500, Validation: MSE:0.685 NLL:-0.572 AUC:0.656
Epoch: 114 / 500, Validation: MSE:0.685 NLL:-0.572 AUC:0.656
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Epoch: 117 / 500, Validation: MSE:0.685 NLL:-0.571 AUC:0.658
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Epoch: 120 / 500, Validation: MSE:0.684 NLL:-0.571 AUC:0.658
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Epoch: 122 / 500, Validation: MSE:0.684 NLL:-0.571 AUC:0.660
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Epoch: 124 / 500, Validation: MSE:0.683 NLL:-0.571 AUC:0.662
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Epoch: 126 / 500, Validation: MSE:0.684 NLL:-0.569 AUC:0.663
Epoch: 127 / 500, Validation: MSE:0.682 NLL:-0.571 AUC:0.664
Epoch: 128 / 500, Validation: MSE:0.683 NLL:-0.570 AUC:0.665
Epoch: 129 / 500, Validation: MSE:0.683 NLL:-0.570 AUC:0.666
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Epoch: 135 / 500, Validation: MSE:0.682 NLL:-0.569 AUC:0.669
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Epoch: 138 / 500, Validation: MSE:0.681 NLL:-0.570 AUC:0.669
Epoch: 139 / 500, Validation: MSE:0.681 NLL:-0.569 AUC:0.670
Epoch: 140 / 500, Validation: MSE:0.681 NLL:-0.570 AUC:0.669
Epoch: 141 / 500, Validation: MSE:0.681 NLL:-0.569 AUC:0.671
Epoch: 142 / 500, Validation: MSE:0.681 NLL:-0.569 AUC:0.670
Epoch: 143 / 500, Validation: MSE:0.680 NLL:-0.570 AUC:0.671
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Epoch: 145 / 500, Validation: MSE:0.680 NLL:-0.570 AUC:0.670
Epoch: 146 / 500, Validation: MSE:0.681 NLL:-0.569 AUC:0.669
Epoch: 147 / 500, Validation: MSE:0.681 NLL:-0.569 AUC:0.670
Epoch: 148 / 500, Validation: MSE:0.681 NLL:-0.569 AUC:0.669
Epoch: 149 / 500, Validation: MSE:0.680 NLL:-0.570 AUC:0.671
Epoch: 150 / 500, Validation: MSE:0.681 NLL:-0.569 AUC:0.672
Epoch: 151 / 500, Validation: MSE:0.680 NLL:-0.569 AUC:0.670
Epoch: 152 / 500, Validation: MSE:0.680 NLL:-0.570 AUC:0.671
Epoch: 153 / 500, Validation: MSE:0.680 NLL:-0.569 AUC:0.672
Epoch: 154 / 500, Validation: MSE:0.680 NLL:-0.569 AUC:0.674
Epoch: 155 / 500, Validation: MSE:0.680 NLL:-0.569 AUC:0.673
Epoch: 156 / 500, Validation: MSE:0.680 NLL:-0.569 AUC:0.674
Epoch: 157 / 500, Validation: MSE:0.680 NLL:-0.568 AUC:0.675
Epoch: 158 / 500, Validation: MSE:0.679 NLL:-0.569 AUC:0.674
Epoch: 159 / 500, Validation: MSE:0.680 NLL:-0.569 AUC:0.673
Epoch: 160 / 500, Validation: MSE:0.679 NLL:-0.569 AUC:0.673
Epoch: 161 / 500, Validation: MSE:0.679 NLL:-0.569 AUC:0.675
Epoch: 162 / 500, Validation: MSE:0.679 NLL:-0.568 AUC:0.674
Epoch: 163 / 500, Validation: MSE:0.679 NLL:-0.569 AUC:0.676
Epoch: 164 / 500, Validation: MSE:0.679 NLL:-0.569 AUC:0.675
Epoch: 165 / 500, Validation: MSE:0.679 NLL:-0.569 AUC:0.675
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Epoch: 168 / 500, Validation: MSE:0.678 NLL:-0.568 AUC:0.675
Epoch: 169 / 500, Validation: MSE:0.679 NLL:-0.568 AUC:0.676
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Epoch: 183 / 500, Validation: MSE:0.677 NLL:-0.568 AUC:0.679
Epoch: 184 / 500, Validation: MSE:0.677 NLL:-0.568 AUC:0.679
Epoch: 185 / 500, Validation: MSE:0.677 NLL:-0.568 AUC:0.678
Epoch: 186 / 500, Validation: MSE:0.677 NLL:-0.568 AUC:0.680
Epoch: 187 / 500, Validation: MSE:0.677 NLL:-0.568 AUC:0.680
Epoch: 188 / 500, Validation: MSE:0.677 NLL:-0.567 AUC:0.681
Epoch: 189 / 500, Validation: MSE:0.676 NLL:-0.567 AUC:0.681
Epoch: 190 / 500, Validation: MSE:0.676 NLL:-0.568 AUC:0.680
Epoch: 191 / 500, Validation: MSE:0.676 NLL:-0.568 AUC:0.679
Epoch: 192 / 500, Validation: MSE:0.676 NLL:-0.567 AUC:0.682
Epoch: 193 / 500, Validation: MSE:0.676 NLL:-0.568 AUC:0.682
Epoch: 194 / 500, Validation: MSE:0.676 NLL:-0.567 AUC:0.684
Epoch: 195 / 500, Validation: MSE:0.676 NLL:-0.567 AUC:0.685
Epoch: 196 / 500, Validation: MSE:0.676 NLL:-0.568 AUC:0.684
Epoch: 197 / 500, Validation: MSE:0.676 NLL:-0.567 AUC:0.684
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Epoch: 199 / 500, Validation: MSE:0.676 NLL:-0.567 AUC:0.684
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Epoch: 205 / 500, Validation: MSE:0.675 NLL:-0.567 AUC:0.686
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Epoch: 208 / 500, Validation: MSE:0.675 NLL:-0.567 AUC:0.686
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Epoch: 261 / 500, Validation: MSE:0.671 NLL:-0.566 AUC:0.689
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Epoch: 268 / 500, Validation: MSE:0.671 NLL:-0.566 AUC:0.686
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Epoch: 279 / 500, Validation: MSE:0.672 NLL:-0.564 AUC:0.686
Epoch: 280 / 500, Validation: MSE:0.671 NLL:-0.566 AUC:0.686
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Epoch: 287 / 500, Validation: MSE:0.671 NLL:-0.566 AUC:0.688
Epoch: 288 / 500, Validation: MSE:0.671 NLL:-0.565 AUC:0.688
Epoch: 289 / 500, Validation: MSE:0.671 NLL:-0.565 AUC:0.689
Epoch: 290 / 500, Validation: MSE:0.671 NLL:-0.565 AUC:0.687
Loading 230th epoch

The performance of validation set: MSE:0.673 NLL:-0.567 AUC:0.692
The performance of testing set: MSE:0.678 NLL:-0.568 AUC:0.684 Precision:0.414 Recall:0.748 NDCG:0.676

So the performance is NLL:-0.568 AUC:0.684 NDCG:0.676
And so this is much different with that in the paper: -0.419 0.741 0.645.

Hi,

Could you please provide the code of baseline methods? That would help a lot.

Thanks!