# params
# 10/6/2021, newest packages. 
ENV=gnn_ak
CUDA=11.1
TORCH=1.9.1
PYG=2.0.1

# create env 
conda create --name $ENV python=3.9 -y
conda activate $ENV

# install pytorch 
conda install pytorch=$TORCH torchvision torchaudio cudatoolkit=$cuda -c pytorch -c nvidia -y

# install pyg2.0
conda install pyg=$PYG -c pyg -c conda-forge -y

# install ogb 
pip install ogb

# install rdkit
conda install -c conda-forge rdkit -y

# update yacs and tensorboard
pip install yacs==0.1.8 --force  # PyG currently use 0.1.6 which doesn't support None argument. 
pip install tensorboard
pip install matplotlib

# Run different normal GNNs 
python -m train.zinc model.mini_layers 0 model.gnn_type GINEConv
python -m train.zinc model.mini_layers 0 model.gnn_type SimplifiedPNAConv
python -m train.zinc model.mini_layers 0 model.gnn_type GCNConv
python -m train.zinc model.mini_layers 0 model.gnn_type GATConv
python -m train.zinc model.mini_layers 0 model.gnn_type ...

python -m train.zinc model.num_layers 6 model.mini_layers 0 model.gnn_type GCNConv # 6-layer GCN

# Run different datasets
python -m train.zinc 
python -m train.cifar10 
python -m train.counting 
python -m train.graph_property 
python -m ...

python -m train.zinc model.mini_layers 1 model.gnn_type GINEConv model.embs "(0,1)" model.hops_dim 0  

# Run GNN-AK+ with different normal GNNs
python -m train.zinc model.mini_layers 1 model.gnn_type GINEConv            # 1-layer base model
python -m train.zinc model.mini_layers 1 model.gnn_type SimplifiedPNAConv   # 1-layer base model
python -m train.zinc model.mini_layers 2 model.gnn_type GINEConv            # 2-layer base model
python -m train.zinc model.mini_layers 2 model.gnn_type SimplifiedPNAConv   # 2-layer base model

python -m train.zinc model.num_layers 4 
python -m train.zinc model.num_layers 6 
python -m train.zinc model.num_layers 8 

python -m train.zinc subgraph.hops 2      # 2-hop egonet
python -m train.zinc subgraph.hops 3      # 3-hop egonet

# Run with random-walk subgraphs based on node2vec 
python -m train.zinc subgraph.hops 0 subgraph.walk_length 10 subgraph.walk_p 1.0 subgraph.walk_q 1.0  

python -m train.zinc sampling.mode shortest_path sampling.redundancy 1 sampling.stride 5 sampling.batch_factor 4
python -m train.zinc sampling.mode shortest_path sampling.redundancy 3 sampling.stride 5 sampling.batch_factor 4
python -m train.zinc sampling.mode shortest_path sampling.redundancy 5 sampling.stride 5 sampling.batch_factor 4


python -m train.cifar10 sampling.mode random sampling.redundancy 1 sampling.random_rate 0.07 sampling.batch_factor 8 
python -m train.cifar10 sampling.mode random sampling.redundancy 3 sampling.random_rate 0.21 sampling.batch_factor 8 
python -m train.cifar10 sampling.mode random sampling.redundancy 5 sampling.random_rate 0.35 sampling.batch_factor 8 
## Note: sampling.random_rate = 0.07*sampling.redundancy. 0.07 is set based on dataset. 

@inproceedings{
anonymous2022from,
title={From Stars to Subgraphs: Uplifting Any {GNN} with Local Structure Awareness},
author={Anonymous},
booktitle={Submitted to The Tenth International Conference on Learning Representations },
year={2022},
url={https://openreview.net/forum?id=Mspk_WYKoEH},
note={under review}
}