Merge branch 'release/v0.3.6'

README.md

@@ -111,7 +111,6 @@ ln -s /path/to/your/data dataset
 - **models_info/\***: all markdown files generated to get quick information about model performance and prediction obtained after running `run/runAll_*.sh` script.
 - **results/**:  This folder contains `model_comparisons.csv` file used for store models performance.
 
-
 ## How to use ?
 
 **Remark**: Note here that all python script have *--help* command.
@@ -183,4 +182,4 @@ Once you have simulation done. Checkout your **threshold_map/%MODEL_NAME%/simula
 
 ## License
 
-[The MIT license](https://github.com/prise-3d/Thesis-NoiseDetection-attributes/blob/master/LICENSE)
+[The MIT license](LICENSE)

others/save_model_result_in_md_maxwell.py

@@ -10,7 +10,7 @@ import json
 # models imports
 from sklearn.utils import shuffle
 from sklearn.externals import joblib
-from sklearn.features import accuracy_score, f1_score, recall_score, roc_auc_score
+from sklearn.metrics import accuracy_score, f1_score, recall_score, roc_auc_score
 from sklearn.model_selection import cross_val_score
 from sklearn.model_selection import StratifiedKFold
 from sklearn.model_selection import train_test_split

run/runAll_maxwell_custom_filters.sh

@@ -18,7 +18,8 @@ fi
 
 for size in {"4","8","16","26","32","40","60","80"}; do
 
-    for metric in {"highest_sv_std_filters","lowest_sv_std_filters","highest_wave_sv_std_filters","lowest_sv_std_filters","highest_sv_std_filters_full","lowest_sv_std_filters_full","highest_sv_entropy_std_filters","lowest_sv_entropy_std_filters"}; do
+#    for metric in {"highest_sv_std_filters","lowest_sv_std_filters","highest_wave_sv_std_filters","lowest_sv_std_filters","highest_sv_std_filters_full","lowest_sv_std_filters_full","highest_sv_entropy_std_filters","lowest_sv_entropy_std_filters"}; do
+    for metric in {"highest_sv_entropy_std_filters","lowest_sv_entropy_std_filters"}; do
         bash data_processing/generateAndTrain_maxwell_custom_filters.sh ${size} ${metric} &
     done
 done

run/runAll_maxwell_custom_filters_center.sh

@@ -18,7 +18,8 @@ fi
 
 for size in {"4","8","16","26","32","40","60","80"}; do
 
-    for metric in {"highest_sv_std_filters","lowest_sv_std_filters","highest_wave_sv_std_filters","lowest_sv_std_filters","highest_sv_std_filters_full","lowest_sv_std_filters_full","highest_sv_entropy_std_filters","lowest_sv_entropy_std_filters"}; do
+#     for metric in {"highest_sv_std_filters","lowest_sv_std_filters","highest_wave_sv_std_filters","lowest_sv_std_filters","highest_sv_std_filters_full","lowest_sv_std_filters_full","highest_sv_entropy_std_filters","lowest_sv_entropy_std_filters"}; do
+     for metric in {"highest_sv_entropy_std_filters","lowest_sv_entropy_std_filters"}; do
         bash data_processing/generateAndTrain_maxwell_custom_filters_center.sh ${size} ${metric} &
     done
 done

run/runAll_maxwell_custom_filters_split.sh

@@ -18,7 +18,8 @@ fi
 
 for size in {"4","8","16","26","32","40","60","80"}; do
 
-    for metric in {"highest_sv_std_filters","lowest_sv_std_filters","highest_wave_sv_std_filters","lowest_sv_std_filters","highest_sv_std_filters_full","lowest_sv_std_filters_full","highest_sv_entropy_std_filters","lowest_sv_entropy_std_filters"}; do
+#    for metric in {"highest_sv_std_filters","lowest_sv_std_filters","highest_wave_sv_std_filters","lowest_sv_std_filters","highest_sv_std_filters_full","lowest_sv_std_filters_full","highest_sv_entropy_std_filters","lowest_sv_entropy_std_filters"}; do
+    for metric in {"highest_sv_entropy_std_filters","lowest_sv_entropy_std_filters"}; do
         bash data_processing/generateAndTrain_maxwell_custom_filters_split.sh ${size} ${metric} &
     done
 done

simulation/run_maxwell_simulation.sh

@@ -8,7 +8,7 @@ scenes="A, D, G, H"
 VECTOR_SIZE=200
 
 for size in {"4","8","16","26","32","40"}; do
-    for metric in {"lab","mscn","mscn_revisited","low_bits_2","low_bits_3","low_bits_4","low_bits_5","low_bits_6","low_bits_4_shifted_2"}; do
+    for feature in {"lab","mscn","mscn_revisited","low_bits_2","low_bits_3","low_bits_4","low_bits_5","low_bits_6","low_bits_4_shifted_2"}; do
 
         half=$(($size/2))
         start=-$half
@@ -31,20 +31,20 @@ for size in {"4","8","16","26","32","40"}; do
                  for mode in {"svd","svdn","svdne"}; do
                      for model in {"svm_model","ensemble_model","ensemble_model_v2"}; do
 
-                        FILENAME="data/${model}_N${size}_B${start}_E${end}_nb_zones_${nb_zones}_${metric}_${mode}"
-                        MODEL_NAME="${model}_N${size}_B${start}_E${end}_nb_zones_${nb_zones}_${metric}_${mode}"
+                        FILENAME="data/${model}_N${size}_B${start}_E${end}_nb_zones_${nb_zones}_${feature}_${mode}"
+                        MODEL_NAME="${model}_N${size}_B${start}_E${end}_nb_zones_${nb_zones}_${feature}_${mode}"
 
                         if grep -xq "${MODEL_NAME}" "${simulate_models}"; then
                             echo "Run simulation for model ${MODEL_NAME}"
 
                             # by default regenerate model
-                            python generate/generate_data_model_random.py --output ${FILENAME} --interval "${start},${end}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 40 --random 1
+                            python generate/generate_data_model_random.py --output ${FILENAME} --interval "${start},${end}" --kind ${mode} --feature ${feature} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 40 --random 1
 
                             python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
-                            python prediction/predict_seuil_expe_maxwell_curve.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2'
+                            python prediction/predict_seuil_expe_maxwell_curve.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature} --limit_detection '2'
 
-                            python others/save_model_result_in_md_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric}
+                            python others/save_model_result_in_md_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature}
 
                         fi
                     done