syr-cn / simsgt Goto Github PK

Did you do hyper-parameter tuning for each of the 10 random seeds?
Or did you hyper-parameter tune using a particular seed and train the model using another 10 different random seeds to get the test set predictions?

Hello,

Can you share more details about how you prepared DAVIS and KIBA datasets?

I downloaded these datasets from here https://github.com/chao1224/GraphMVP/tree/main/datasets. Then preprocessed as instructed there. I then combined the resulting train.csv and test.csv files to create the full dataset. Then I used scaffold splitting to split this full dataset to train, valid and test. For DAVIS I used the transformed affinities (-np.log10(y / 1e9) to train the model. Is this the approach you used as well?

It would be great if you could add your preprocessed train, valid and test folds to the repository.

Dear author

Thank you very much for your great work!

I want to ask where I can find you paper or your preprint?

My email is [email protected]. Could you please send the paper via my email? I will only read it only for academic study.

`class NonParaGINConv(MessagePassing):
## non-parametric gin
def init(self, eps, aggr = "add", **kwargs):
kwargs.setdefault('aggr', aggr)
super().init(**kwargs)
self.aggr = aggr
self.eps = eps

def forward(self, x, edge_index):
    return self.propagate(edge_index, x=x) + x * self.eps

def message(self, x_j):
    return x_j`

In the definition of GIN, the forward function should be self.propagate(edge_index, x=x) + x * (1 + self.eps)

Hi,

Do you have the tuning_dta.py file used in the parallel_tuning_dta.py? If so, could you please add it to the repository?

Thank you.

[2024-05-18 23:51:48] Start Tuning
kiba

0%| | 0/3 [00:00<?, ?it/s]
0%| | 0/3 [00:00<?, ?it/s]
Training For Running Seed 0
Loading model from checkpoints/GEOM.pth.
Traceback (most recent call last):
File "./tuning_dta.py", line 510, in
main()
File "./tuning_dta.py", line 502, in main
tuning_dta(args, train_dataset, valid_dataset,
File "./tuning_dta.py", line 351, in tuning_dta
gnn = load_chem_gnn_model(args)
File "./tuning_dta.py", line 231, in load_chem_gnn_model
gnn.load_state_dict(model_state_dict, strict=False)
File "/home/penghuan/miniconda3/envs/calm/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1497, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for TokenMAEClf:
size mismatch for tokenizer.x_embedding.atom_embedding_list.1.weight: copying a param with shape torch.Size([4, 300]) from checkpoint, the shape in current model is torch.Size([5, 300]).
微调dta过程中报错，是因为没有跑上一步的pretrain GEOM吗？