LARS-research / SNAG

Source code for CIKM-CSSA 2020 paper SNAG "Simplified Neural Architecture search for Graph Neural Networks".
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Question about Reproducibility #2

Open wjwangppt opened 3 years ago

wjwangppt commented 3 years ago

Hello, I have read your paper on CIKM2020 and the code recently, it is an interesting work. Congratulations! But I have some problems about the reproducibility. I tried to reproduce the results using the command you provide "python -m rlctr.main --dataset Cora --layers_of_child_model 3 --shared_initial_step 10 --shared_params True", but the result seems that far below ones you provide in the paper. The pic below is the final architecture using best hyper params and retrained five times, the average test accuracy of these five result is 84.624, but the paper provided is 88.95. image

wei-ln commented 3 years ago

Hi,we provide an example to use this code in the introduction. The settings and reproduce results are shown in the following:

# SNAG-WS on Cora dataset
python -u -m rlctr.main  --dataset Cora    --shared_params True  --hyper_eval_inters 15  --layers_of_child_model 3  --shared_initial_step 10   --random_seed 333 --train_epochs 500 --epochs 800 --early_stop_epoch 800  --gnn_hidden 64 --weight_decay 0.001  --in_drop 0 --cos_lr True

1624846459(1)

wjwangppt commented 3 years ago

Hi,we provide an example to use this code in the introduction. The settings and reproduce results are shown in the following:

# SNAG-WS on Cora dataset
python -u -m rlctr.main  --dataset Cora    --shared_params True  --hyper_eval_inters 15  --layers_of_child_model 3  --shared_initial_step 10   --random_seed 333 --train_epochs 500 --epochs 800 --early_stop_epoch 800  --gnn_hidden 64 --weight_decay 0.001  --in_drop 0 --cos_lr True

1624846459(1)

Thanks for your response ! I also wonder two questions. 1). whether the other two datasets "Citeseer" and "Pubmed" share the same params setting? If not, could you give the other two settings? 2). I noticed that the random_seed you set is "333", which impress data_split of "60% 20% 20%". I wonder whether datasets "Citeseer" and "Pubmed" also shared with the same random_seed?

wei-ln commented 3 years ago

A1:

# citeseer dataset
python -u -m rlctr.main  --dataset Citeseer --shared_params True   --hyper_eval_inters 15  --layers_of_child_model 3  --shared_initial_step 10   --random_seed 333 --train_epochs 500 --epochs 600 --early_stop_epoch 600  --gnn_hidden 64 --in_drop 0 

# pubmed dataset    
python -u -m rlctr.main  --dataset Pubmed --shared_params True   --hyper_eval_inters 15  --layers_of_child_model 3  --shared_initial_step 10   --random_seed 333 --train_epochs 500 --epochs 600 --early_stop_epoch 600  --gnn_hidden 64  --weight_decay 0 --in_drop 0 --cos_lr True 

A2: All the methods have the same seed '333'.

wjwangppt commented 3 years ago

A1:

# citeseer dataset
python -u -m rlctr.main  --dataset Citeseer --shared_params True   --hyper_eval_inters 15  --layers_of_child_model 3  --shared_initial_step 10   --random_seed 333 --train_epochs 500 --epochs 600 --early_stop_epoch 600  --gnn_hidden 64 --in_drop 0 

# pubmed dataset    
python -u -m rlctr.main  --dataset Pubmed --shared_params True   --hyper_eval_inters 15  --layers_of_child_model 3  --shared_initial_step 10   --random_seed 333 --train_epochs 500 --epochs 600 --early_stop_epoch 600  --gnn_hidden 64  --weight_decay 0 --in_drop 0 --cos_lr True 

A2: All the methods have the same seed '333'.

Thanks for your reply!!

PestyVesty commented 2 years ago

Hello, I was wondering if the parameter setting for the PPI dataset could also be shared?