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run_baselines.sh
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model_name=DySAT
dataset=reddit_hyperlinks #radoslaw #auton-sys
task=link_prediction
method=degree
num_graphs=20
context=19
target_ts=19
ntargets=1
nsamples=100
khop=2
constraint=noise
online=false
tga_disturbance_ratio=0
tga_thresh_prob=0.5
declare -a epsilons_arr=(0.02 0.06 0.1 0.3 0.5 0.7 0.9)
epsilon1=100
# 2 3 5 7 4, 6
if [ "$constraint" == noise_feat ]; then
file=baselines/baseline_main_feat.py
else
file=baselines/baseline_main.py
fi
seed=123 #123 483, 665, 811
saved_model=models/${model_name}/${dataset}
resparent_dir="baselines/${method}/results_${model_name,,}"
if [ ! -d "${resparent_dir}/${dataset}/multi_targets/${constraint}" ]; then
mkdir -p ${resparent_dir}/${dataset}/multi_targets/${constraint}
fi
results_dir="baselines/${method}/results"
if [ ! -d "${results_dir}/${dataset}/${constraint}" ]; then
mkdir -p ${results_dir}/${dataset}/${constraint}
fi
for epsilon in "${epsilons_arr[@]}"
do
python3 run.py \
-file $file \
-method ${method} \
-model_name ${model_name} \
-constraint ${constraint} \
-budget ${budget} \
-epsilon ${epsilon} \
-epsilon1 ${epsilon1} \
-saved_model ${saved_model} \
-dataset ${dataset} \
-task ${task} \
-num_graphs ${num_graphs} \
-ntargets ${ntargets} \
-khop ${khop} \
-context ${context} \
-target_ts ${target_ts} \
-featureless \
-num_samples $nsamples \
-device cpu \
-neg_sampling \
-tga_thresh_prob $tga_thresh_prob \
-tga_disturbance_ratio $tga_disturbance_ratio \
-tga_iter_ub 500 \
-seed ${seed} \
-sampling rd \
-analyze_only > ${resparent_dir}/${dataset}/multi_targets/${constraint}/results_td_tg${ntargets}_n${num_graphs}_c${context}t${target_ts}_e${epsilon}_eb${epsilon1}_seed${seed}.txt
# -dyn_feats \
# _seed${seed}.txt
# -large_graph \
# -debug \
#
# b${budget}_l${lambda1}.txt
# -seq_tg_attk \
done