examples/model_selection/TRAILS-Database-Native-Model-Selection/internal/ml/model_selection/scripts/baseline_system_tab.sh

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export PYTHONPATH=$PYTHONPATH:./internal/ml/model_selection
conda activate trails



# run both training-based MS
############## frappe dataset ##############
python internal/ml/model_selection/exps/baseline/train_with_ea.py \
  --search_space mlp_sp \
  --num_layers 4 \
  --hidden_choice_len 20 \
  --epoch 19 \
  --batch_size=512 \
  --lr=0.001 \
  --iter_per_epoch=200 \
  --nfeat=5500 \
  --nfield=10 \
  --nemb=10 \
  --base_dir ../exp_data/ \
  --dataset frappe \
  --num_labels 2 \
  --device=cpu \
  --log_folder baseline_frappe \
  --result_dir ./internal/ml/model_selection/exp_result/


############## uci dataset ##############
python internal/ml/model_selection/exps/baseline/train_with_ea.py \
  --search_space mlp_sp \
  --num_layers 4 \
  --hidden_choice_len 20 \
  --epoch 0 \
  --batch_size=1024 \
  --lr=0.001 \
  --iter_per_epoch=200 \
  --nfeat=369 \
  --nfield=43 \
  --nemb=10 \
  --base_dir ../exp_data/ \
  --dataset uci_diabetes \
  --num_labels 2 \
  --device=cpu \
  --log_folder baseline_uci_diabetes \
  --result_dir ./internal/ml/model_selection/exp_result/


############## criteo dataset ##############
python internal/ml/model_selection/exps/baseline/train_with_ea.py \
  --search_space mlp_sp \
  --num_layers 4 \
  --hidden_choice_len 10 \
  --epoch 9 \
  --batch_size=1024 \
  --lr=0.001 \
  --iter_per_epoch=2000 \
  --nfeat=2100000 \
  --nfield=39 \
  --nemb=10 \
  --base_dir ../exp_data/ \
  --dataset criteo \
  --num_labels 2 \
  --device=cpu \
  --log_folder baseline_criteo \
  --result_dir ./internal/ml/model_selection/exp_result/