graphcode2vec/preContext_training.sh at main · MajeedAskari/graphcode2vec · GitHub

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#!/bin/bash

device=$1
i=context
for gnn_type in gat gin gcn graphsage
do
#gnn_type=gat
gp=attention
output=${i}_${gp}/${gnn_type}
sw=lstm
jk=sum
lstm_emb_dim=150
mkdir -p $output
python context_pretrain.py --batch_size 300 --num_workers 5  --epochs 10 --num_layer 5 \
--subword_embedding  $sw \
--lstm_emb_dim $lstm_emb_dim \
--graph_pooling $gp \
--JK $jk \
--saved_model_path ${output} \
--log_file ${output}/log.txt \
--gnn_type $gnn_type \
--sub_token_path ./tokens/jars \
--emb_file emb_100.txt \
--dataset DV_PDG \
--task node_class \
--device $device
done


# i=context
# for gnn_type in gat gin gcn graphsage
# do
# #gnn_type=gat
# sw=lstm
# gp=mean
# output=${i}_${gp}/${gnn_type}
# jk=sum
# lstm_emb_dim=150
# mkdir -p $output
# python context_pretrain.py --batch_size 300 --num_workers 5  --epochs 10 --num_layer 5 \
# --subword_embedding  $sw \
# --lstm_emb_dim $lstm_emb_dim \
# --graph_pooling $gp \
# --JK $jk \
# --saved_model_path ${output} \
# --log_file ${output}/log.txt \
# --gnn_type $gnn_type \
# --sub_token_path ./tokens/jars \
# --emb_file emb_100.txt \
# --dataset DV_PDG \
# --task node_class \
# --device $device
# done


# i=context_all
# for gnn_type in gat gin gcn graphsage
# do
# #gnn_type=gat
# gp=attention
# output=${i}_${gp}/${gnn_type}
# sw=lstm
# jk=sum
# lstm_emb_dim=150
# mkdir -p $output
# python context_pretrain.py --batch_size 300 --num_workers 5  --epochs 10 --num_layer 5 \
# --subword_embedding  $sw \
# --lstm_emb_dim $lstm_emb_dim \
# --graph_pooling $gp \
# --JK $jk \
# --saved_model_path ${output} \
# --log_file ${output}/log.txt \
# --gnn_type $gnn_type \
# --sub_token_path ./tokens/jars \
# --emb_file emb_100.txt \
# --dataset DV_PDG \
# --task node_class \
# --device $device \
# --check_all
# done


# i=context_all
# for gnn_type in gat gin gcn graphsage
# do
# #gnn_type=gat
# sw=lstm
# gp=mean
# output=${i}_${gp}/${gnn_type}
# jk=sum
# lstm_emb_dim=150
# mkdir -p $output
# python context_pretrain.py --batch_size 300 --num_workers 5  --epochs 10 --num_layer 5 \
# --subword_embedding  $sw \
# --lstm_emb_dim $lstm_emb_dim \
# --graph_pooling $gp \
# --JK $jk \
# --saved_model_path ${output} \
# --log_file ${output}/log.txt \
# --gnn_type $gnn_type \
# --sub_token_path ./tokens/jars \
# --emb_file emb_100.txt \
# --dataset DV_PDG \
# --task node_class \
# --device $device \
# --check_all
# done