runAll_maxwell_area_normed.sh 2.0 KB

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  1. #! bin/bash
  2. # erase "results/models_comparisons.csv" file and write new header
  3. file_path='results/models_comparisons.csv'
  4. erased=$1
  5. if [ "${erased}" == "Y" ]; then
  6. echo "Previous data file erased..."
  7. rm ${file_path}
  8. mkdir -p models_info
  9. touch ${file_path}
  10. # add of header
  11. echo 'model_name; vector_size; start_index; end; nb_zones; feature; mode; tran_size; val_size; test_size; train_pct_size; val_pct_size; test_pct_size; train_acc; val_acc; test_acc; all_acc; F1_train; recall_train; roc_auc_train; F1_val; recall_val; roc_auc_val; F1_test; recall_test; roc_auc_test; F1_all; recall_all; roc_auc_all;' >> ${file_path}
  12. fi
  13. feature="sub_blocks_area_normed"
  14. start_index=0
  15. end_index=16
  16. number=16
  17. # selection of four scenes (only maxwell)
  18. scenes="A, D, G, H"
  19. for nb_zones in {4,6,8,10,12}; do
  20. for mode in {"svd","svdn","svdne"}; do
  21. for model in {"svm_model","ensemble_model","ensemble_model_v2"}; do
  22. FILENAME="data/${model}_N${number}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${feature}_${mode}"
  23. MODEL_NAME="${model}_N${number}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${feature}_${mode}"
  24. echo $FILENAME
  25. # only compute if necessary (perhaps server will fall.. Just in case)
  26. if grep -q "${MODEL_NAME}" "${file_path}"; then
  27. echo "${MODEL_NAME} results already generated..."
  28. else
  29. python generate/generate_data_model_random.py --output ${FILENAME} --interval "${start_index},${end_index}" --kind ${mode} --feature ${feature} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 10 --random 1
  30. python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
  31. python others/save_model_result_in_md_maxwell.py --interval "${start_index},${end_index}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature}
  32. fi
  33. done
  34. done
  35. done