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Update README.md
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@Turningl
Turningl authored Dec 3, 2024
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Expand Up @@ -55,17 +55,17 @@ All parameters of dataloader:
usage: dataloader.py [--root] [--dataset] [--split] [--seed] [--device]
optional arguments:
--root root directory for differernt molecular discovery databases:
--root --root directory for differernt molecular discovery databases:
MoleculeNet, DrugBank, BIOSNAP, LITPCBA, CoCrystal
--dataset In different root directory, choose dataset of different databases
--dataset --In different root directory, choose dataset of different databases
--split split type for different root and dataset:
--split --split type for different root and dataset:
smi, smi1, smi2
--seed fixed data initialization and training seeds
--seed --fixed data initialization and training seeds
--device cuda or cpu
--device --cuda or cpu
```

## Usage
Expand All @@ -78,76 +78,76 @@ usage: main.py [--alg] [--root] [--dataset] [--node_size] [--bond_size] [--hidde
[--weight_decay] [--eps] [constant] [--delta] [--dp] [--batch_size] [--device] [--save_dir] [--beta1] [--beta2] [--local_round] [--proj_dims] [--lanczos_iter] [--global_round] [--comm_optimization] [--lr] [--clip]
optional arguments:
--alg federated learning algorithm:
--alg --federated learning algorithm:
fedavg, fedprox, fedsgd, fedlg, fedadam, fedchem
--root root directory for differernt molecular discovery databases:
--root --root directory for differernt molecular discovery databases:
MoleculeNet, DrugBank, BIOSNAP, LITPCBA, CoCrystal
--dataset In different root directory, choose dataset of different databases
--dataset --In different root directory, choose dataset of different databases
--node_size molecular node size
--node_size --molecular node size
--bond_size molecular bond size
--bond_size --molecular bond size
--hidden_size hidden size
--extend_dim extend dim for neural network
--extend_dim --extend dim for neural network
--output_size output size
--output_size --output size
--model graph neural network:
--model --graph neural network:
MPNN, GCN, GAT
--split split type for different root and dataset:
--split --split type for different root and dataset:
smi, smi1, smi2
--drooput dropout rate
--drooput --dropout rate
--message steps message step for graph neural network
--message steps --message step for graph neural network
--num_clients clients number, here we set the max clients number is up to 4
--num_clients --clients number, here we set the max clients number is up to 4
--alpha alpha for molecule dirichlet distribution
--alpha --alpha for molecule dirichlet distribution
--null_value null value
--null_value --null value
--seed fixed data initialization and training seed
--seed --fixed data initialization and training seed
--weight_decay weight decay for optimizer
--weight_decay --weight decay for optimizer
--eps epsilons distribution
--eps --epsilons distribution
--constant constant for local differently privacy
--constant --constant for local differently privacy
--delta differential privacy parameter
--delta --differential privacy parameter
--dp if True, use differential privacy
--dp --if True, use differential privacy
--batch_size batch size of the model training:
--batch_size --batch size of the model training:
32, 64 or 128
--device cuda or cpu
--device --cuda or cpu
--save_dir results save directory, the model test results is saved to ./results/
--save_dir --results save directory, the model test results is saved to ./results/
--beta1 beta1 for Adam optimizer
--beta1 --beta1 for Adam optimizer
--beta2 beta2 for Adam optimizer
--beta2 --beta2 for Adam optimizer
--local_round local model training round
--local_round --local model training round
--proj_dims project dim of lanczos algorithm
--proj_dims --project dim of lanczos algorithm
--lanczos_iter the iterations of lanczos
--lanczos_iter --the iterations of lanczos
--global_round global model training round
--global_round --global model training round
--comm_optimization using Bayesian Optimization or not
--comm_optimization --using Bayesian Optimization or not
--lr the learning rate of graph model
--lr --the learning rate of graph model
--clip clip value for local differently privacy
--clip --clip value for local differently privacy
```

## Bayesian optimization
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