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Hybrid Models for Learning to Branch (NeurIPS 2020)

Home Page: https://arxiv.org/abs/2006.15212

License: MIT License

Python 100.00%

deep-learning discrete-optimization graph-neural-networks neurips-2020

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wangshgeo sahilm1992 aoe-khkhan xijunlee davidkillerhahaha chenzh12 theamrzaki jokwon0426 cpeng-pz linboqiao hesamshaelaie

hybrid-learn2branch's Issues

Train and valid accuracy not increase

Like the title, the accuracy is pretty low for combinatorial auction, which is the same as ecole/learn2branch. Is this situation normal?

[2021-10-21 20:12:46.586854] EPOCH 253...
[2021-10-21 20:13:00.615919] TRAIN LOSS: 2.607  acc@1: 0.474 acc@3: 0.708 acc@5: 0.816 acc@10: 0.934
[2021-10-21 20:13:16.086814] VALID LOSS: 2.611  acc@1: 0.474 acc@3: 0.703 acc@5: 0.812 acc@10: 0.931
[2021-10-21 20:13:16.087240] EPOCH 254...
[2021-10-21 20:13:29.891938] TRAIN LOSS: 2.604  acc@1: 0.474 acc@3: 0.711 acc@5: 0.818 acc@10: 0.933
[2021-10-21 20:13:45.182254] VALID LOSS: 2.611  acc@1: 0.474 acc@3: 0.703 acc@5: 0.812 acc@10: 0.930
[2021-10-21 20:13:45.182633] EPOCH 255...
[2021-10-21 20:13:58.630930] TRAIN LOSS: 2.593  acc@1: 0.473 acc@3: 0.703 acc@5: 0.815 acc@10: 0.930
[2021-10-21 20:14:13.781607] VALID LOSS: 2.611  acc@1: 0.473 acc@3: 0.703 acc@5: 0.812 acc@10: 0.931
[2021-10-21 20:14:13.782002] EPOCH 256...
[2021-10-21 20:14:27.094316] TRAIN LOSS: 2.591  acc@1: 0.478 acc@3: 0.706 acc@5: 0.816 acc@10: 0.934
[2021-10-21 20:14:42.387882] VALID LOSS: 2.611  acc@1: 0.473 acc@3: 0.703 acc@5: 0.812 acc@10: 0.930
[2021-10-21 20:14:42.388286] EPOCH 257...
[2021-10-21 20:14:55.826369] TRAIN LOSS: 2.579  acc@1: 0.480 acc@3: 0.708 acc@5: 0.817 acc@10: 0.935
[2021-10-21 20:15:10.879072] VALID LOSS: 2.611  acc@1: 0.474 acc@3: 0.703 acc@5: 0.812 acc@10: 0.930
[2021-10-21 20:15:10.879466] EPOCH 258...
[2021-10-21 20:15:24.284918] TRAIN LOSS: 2.575  acc@1: 0.473 acc@3: 0.716 acc@5: 0.820 acc@10: 0.939
[2021-10-21 20:15:39.623349] VALID LOSS: 2.612  acc@1: 0.475 acc@3: 0.703 acc@5: 0.812 acc@10: 0.930
[2021-10-21 20:15:39.623798] EPOCH 259...
[2021-10-21 20:15:53.617875] TRAIN LOSS: 2.597  acc@1: 0.475 acc@3: 0.707 acc@5: 0.812 acc@10: 0.935
[2021-10-21 20:16:09.005999] VALID LOSS: 2.611  acc@1: 0.474 acc@3: 0.703 acc@5: 0.812 acc@10: 0.930
[2021-10-21 20:16:09.006399] EPOCH 260...
[2021-10-21 20:16:22.500397] TRAIN LOSS: 2.576  acc@1: 0.461 acc@3: 0.705 acc@5: 0.810 acc@10: 0.935
[2021-10-21 20:16:38.666407] VALID LOSS: 2.612  acc@1: 0.475 acc@3: 0.704 acc@5: 0.812 acc@10: 0.930
[2021-10-21 20:16:38.666809] EPOCH 261...
[2021-10-21 20:16:52.327668] TRAIN LOSS: 2.598  acc@1: 0.469 acc@3: 0.703 acc@5: 0.818 acc@10: 0.932
[2021-10-21 20:17:06.524168] VALID LOSS: 2.611  acc@1: 0.475 acc@3: 0.704 acc@5: 0.812 acc@10: 0.930
[2021-10-21 20:17:06.524482] EPOCH 262...
[2021-10-21 20:17:19.644947] TRAIN LOSS: 2.595  acc@1: 0.485 acc@3: 0.711 acc@5: 0.818 acc@10: 0.934
[2021-10-21 20:17:35.656261] VALID LOSS: 2.611  acc@1: 0.474 acc@3: 0.703 acc@5: 0.812 acc@10: 0.930
[2021-10-21 20:17:35.656523]   30 epochs without improvement, early stopping
[2021-10-21 20:17:50.041247] BEST VALID LOSS: 2.611  acc@1: 0.474 acc@3: 0.704 acc@5: 0.812 acc@10: 0.931

AttributeError: 'pyscipopt.scip.Column' object has no attribute 'getIndex'

I tried to generate some samples, get this error at "self.map = sorted([x.getCol().getIndex() for x in self.model.getVars(transformed=True)])".

The performance of the evaluation is poor

Hi,

It's a good job and so kind of you to share the code on the Github.

I have run and trained the model following the instruction, and choose the best performing model to evaluate on Setcover problem.

However, I found that the performance is so poor that it can hardly solve one of the big instance(all exceed the limit of time).

I wonder to know whether the situation is normal, or maybe something is wrong with the progress of my training?

The result can be attached as the following

learn2branch_CPU_20221023-214346.csv

hybrid_CPU_20221025-232556.csv

Thank you and have a good day.

Availability of trained weights

Hi,

I really liked your paper and thank you for publicly releasing your codebase.

I wondered if there is any way to have access to the final trained weights used to produce the tables in the paper else than redoing the training from scratch ? This would help me very much help in accurately comparing my technique to this one.

If ever the trained weights are available somewhere that I missed, please excuse me for missing it and just point me towards where to get them.

Thank you and have a good day.

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