Change metric to feature key words in some scripts

README.md

@@ -6,13 +6,13 @@
 pip install -r requirements.txt
 ```
 
-Generate all needed data for each metrics (which requires the the whole dataset. In order to get it, you need to contact us).
+Generate all needed data for each features (which requires the the whole dataset. In order to get it, you need to contact us).
 
 ```bash
-python generate/generate_all_data.py --metric all
+python generate/generate_all_data.py --feature all
 ```
 
-For noise detection, many metrics are available:
+For noise detection, many features are available:
 - lab
 - mscn
 - mscn_revisited
@@ -22,9 +22,9 @@ For noise detection, many metrics are available:
 - low_bits_6
 - low_bits_4_shifted_2
 
-You can also specify metric you want to compute and image step to avoid some images:
+You can also specify feature you want to compute and image step to avoid some images:
 ```bash
-python generate/generate_all_data.py --metric mscn --step 50
+python generate/generate_all_data.py --feature mscn --step 50
 ```
 
 - **step**: keep only image if image id % 50 == 0 (assumption is that keeping spaced data will let model better fit).
@@ -84,19 +84,19 @@ Expected values for the **choice** parameter are ['svm_model', 'ensemble_model',
 Now we have a model trained, we can use it with an image as input:
 
 ```bash
-python prediction/predict_noisy_image_svd.py --image path/to/image.png --interval "x,x" --model saved_models/xxxxxx.joblib --metric 'lab' --mode 'svdn' --custom 'min_max_filename'
+python prediction/predict_noisy_image_svd.py --image path/to/image.png --interval "x,x" --model saved_models/xxxxxx.joblib --feature 'lab' --mode 'svdn' --custom 'min_max_filename'
 ```
 
-- **metric**: metric choice need to be one of the listed above.
+- **feature**: feature choice need to be one of the listed above.
 - **custom**: specify filename with custom min and max from your data interval. This file was generated using **custom** parameter of one of the **generate_data_model\*.py** script (optional parameter).
 
 The model will return only 0 or 1:
 - 1 means noisy image is detected.
 - 0 means image seem to be not noisy.
 
-All SVD metrics developed need:
-- Name added into *metric_choices_labels* global array variable of **modules/utils/config.py** file.
-- A specification of how you compute the metric into *get_svd_data* method of **modules/utils/data_type.py** file.
+All SVD features developed need:
+- Name added into *feature_choices_labels* global array variable of **modules/utils/config.py** file.
+- A specification of how you compute the feature into *get_svd_data* method of **modules/utils/data_type.py** file.
 
 ### Predict scene using model
 
@@ -134,7 +134,7 @@ Parameters list:
 - 2: End of interval of data from SVD to use
 - 3: Model we want to test
 - 4: Kind of data input used by trained model
-- 5: Metric used by model
+- 5: feature used by model
 
 
 ### Get treshold map
@@ -142,7 +142,7 @@ Parameters list:
 Main objective of this project is to predict as well as a human the noise perception on a photo realistic image. Human threshold is available from training data. So a script was developed to give the predicted treshold from model and compare predicted treshold from the expected one.
 
 ```bash
-python prediction/predict_seuil_expe.py --interval "x,x" --model 'saved_models/xxxx.joblib' --mode ["svd", "svdn", "svdne"] --metric ['lab', 'mscn', ...] --limit_detection xx --custom 'custom_min_max_filename'
+python prediction/predict_seuil_expe.py --interval "x,x" --model 'saved_models/xxxx.joblib' --mode ["svd", "svdn", "svdne"] --feature ['lab', 'mscn', ...] --limit_detection xx --custom 'custom_min_max_filename'
 ```
 
 Parameters list:
@@ -163,7 +163,7 @@ The content will be divised into two parts:
 The previous script need to already have ran to obtain and display treshold maps on this markdown file.
 
 ```bash
-python others/save_model_result_in_md.py --interval "xx,xx" --model saved_models/xxxx.joblib --mode ["svd", "svdn", "svdne"] --metric ['lab', 'mscn']
+python others/save_model_result_in_md.py --interval "xx,xx" --model saved_models/xxxx.joblib --mode ["svd", "svdn", "svdne"] --feature ['lab', 'mscn']
 ```
 
 Parameters list:

data_processing/generateAndTrain_maxwell.sh

@@ -10,14 +10,14 @@ fi
 if [ -z "$2" ]
   then
     echo "No argument supplied"
-    echo "Need of metric information"
+    echo "Need of feature information"
     exit 1
 fi
 
 result_filename="results/models_comparisons.csv"
 VECTOR_SIZE=200
 size=$1
-metric=$2
+feature=$2
 
 # selection of four scenes (only maxwell)
 scenes="A, D, G, H"
@@ -44,8 +44,8 @@ for counter in {0..4}; 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}"
 
                 echo $FILENAME
 
@@ -54,11 +54,11 @@ for counter in {0..4}; do
 
                     echo "${MODEL_NAME} results already generated..."
                 else
-                    python generate/generate_data_model_random.py --output ${FILENAME} --interval "${start},${end}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --renderer "maxwell" --step 40 --random 1 --percent 1
+                    python generate/generate_data_model_random.py --output ${FILENAME} --interval "${start},${end}" --kind ${mode} --feature ${feature} --scenes "${scenes}" --nb_zones "${nb_zones}" --renderer "maxwell" --step 40 --random 1 --percent 1
                     python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
-                    #python prediction/predict_seuil_expe_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2'
-                    python others/save_model_result_in_md_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric}
+                    #python prediction/predict_seuil_expe_maxwell.py --interval "${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}" --feature ${feature}
                 fi
             done
         done

data_processing/generateAndTrain_maxwell_custom.sh

@@ -10,14 +10,14 @@ fi
 if [ -z "$2" ]
   then
     echo "No argument supplied"
-    echo "Need of metric information"
+    echo "Need of feature information"
     exit 1
 fi
 
 result_filename="results/models_comparisons.csv"
 VECTOR_SIZE=200
 size=$1
-metric=$2
+feature=$2
 
 # selection of four scenes (only maxwell)
 scenes="A, D, G, H"
@@ -44,9 +44,9 @@ for counter in {0..4}; 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}"
-                CUSTOM_MIN_MAX_FILENAME="N${size}_B${start}_E${end}_nb_zones_${nb_zones}_${metric}_${mode}_min_max"
+                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}"
+                CUSTOM_MIN_MAX_FILENAME="N${size}_B${start}_E${end}_nb_zones_${nb_zones}_${feature}_${mode}_min_max"
 
                 echo $FILENAME
 
@@ -55,11 +55,11 @@ for counter in {0..4}; do
 
                     echo "${MODEL_NAME} results already generated..."
                 else
-                    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 --custom ${CUSTOM_MIN_MAX_FILENAME}
+                    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 --custom ${CUSTOM_MIN_MAX_FILENAME}
                     python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
-                    #python prediction/predict_seuil_expe_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
-                    python others/save_model_result_in_md_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric}
+                    #python prediction/predict_seuil_expe_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
+                    python others/save_model_result_in_md_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature}
                 fi
             done
         done

data_processing/generateAndTrain_maxwell_custom_center.sh

@@ -10,14 +10,14 @@ fi
 if [ -z "$2" ]
   then
     echo "No argument supplied"
-    echo "Need of metric information"
+    echo "Need of feature information"
     exit 1
 fi
 
 result_filename="results/models_comparisons.csv"
 VECTOR_SIZE=200
 size=$1
-metric=$2
+feature=$2
 
 # selection of four scenes (only maxwell)
 scenes="A, D, G, H"
@@ -44,9 +44,9 @@ for counter in {0..4}; 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}"
-                CUSTOM_MIN_MAX_FILENAME="N${size}_B${start}_E${end}_nb_zones_${nb_zones}_${metric}_${mode}_min_max"
+                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}"
+                CUSTOM_MIN_MAX_FILENAME="N${size}_B${start}_E${end}_nb_zones_${nb_zones}_${feature}_${mode}_min_max"
 
                 echo $FILENAME
 
@@ -55,11 +55,11 @@ for counter in {0..4}; do
 
                     echo "${MODEL_NAME} results already generated..."
                 else
-                    python generate/generate_data_model_random_center.py --output ${FILENAME} --interval "${start},${end}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 10 --random 1 --custom ${CUSTOM_MIN_MAX_FILENAME}
+                    python generate/generate_data_model_random_center.py --output ${FILENAME} --interval "${start},${end}" --kind ${mode} --feature ${feature} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 10 --random 1 --custom ${CUSTOM_MIN_MAX_FILENAME}
                     python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
-                    #python prediction/predict_seuil_expe_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
-                    python others/save_model_result_in_md_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric}
+                    #python prediction/predict_seuil_expe_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
+                    python others/save_model_result_in_md_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature}
                 fi
             done
         done

data_processing/generateAndTrain_maxwell_custom_filters.sh

@@ -10,14 +10,14 @@ fi
 if [ -z "$2" ]
   then
     echo "No argument supplied"
-    echo "Need of metric information"
+    echo "Need of feature information"
     exit 1
 fi
 
 result_filename="results/models_comparisons.csv"
 VECTOR_SIZE=200
 size=$1
-metric=$2
+feature=$2
 
 # selection of four scenes (only maxwell)
 scenes="A, D, G, H"
@@ -26,9 +26,9 @@ for nb_zones in {4,6,8,10,12}; do
     for mode in {"svd","svdn","svdne"}; do
         for model in {"svm_model","ensemble_model","ensemble_model_v2"}; do
 
-            FILENAME="data/${model}_N${size}_B0_E${size}_nb_zones_${nb_zones}_${metric}_${mode}"
-            MODEL_NAME="${model}_N${size}_B0_E${size}_nb_zones_${nb_zones}_${metric}_${mode}"
-            CUSTOM_MIN_MAX_FILENAME="N${size}_B0_E${size}_nb_zones_${nb_zones}_${metric}_${mode}_min_max"
+            FILENAME="data/${model}_N${size}_B0_E${size}_nb_zones_${nb_zones}_${feature}_${mode}"
+            MODEL_NAME="${model}_N${size}_B0_E${size}_nb_zones_${nb_zones}_${feature}_${mode}"
+            CUSTOM_MIN_MAX_FILENAME="N${size}_B0_E${size}_nb_zones_${nb_zones}_${feature}_${mode}_min_max"
 
             echo $FILENAME
 
@@ -37,10 +37,10 @@ for nb_zones in {4,6,8,10,12}; do
 
                 echo "${MODEL_NAME} results already generated..."
             else
-                python generate/generate_data_model_random.py --output ${FILENAME} --interval "0,${size}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 40 --random 1 --custom ${CUSTOM_MIN_MAX_FILENAME}
+                python generate/generate_data_model_random.py --output ${FILENAME} --interval "0,${size}" --kind ${mode} --feature ${feature} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 40 --random 1 --custom ${CUSTOM_MIN_MAX_FILENAME}
                 python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
-                python others/save_model_result_in_md_maxwell.py --interval "0,${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric}
+                python others/save_model_result_in_md_maxwell.py --interval "0,${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature}
             fi
         done
     done

data_processing/generateAndTrain_maxwell_custom_filters_center.sh

@@ -10,14 +10,14 @@ fi
 if [ -z "$2" ]
   then
     echo "No argument supplied"
-    echo "Need of metric information"
+    echo "Need of feature information"
     exit 1
 fi
 
 result_filename="results/models_comparisons.csv"
 VECTOR_SIZE=200
 size=$1
-metric=$2
+feature=$2
 
 # selection of four scenes (only maxwell)
 scenes="A, D, G, H"
@@ -26,9 +26,9 @@ for nb_zones in {4,6,8,10,12}; do
     for mode in {"svd","svdn","svdne"}; do
         for model in {"svm_model","ensemble_model","ensemble_model_v2"}; do
 
-            FILENAME="data/${model}_N${size}_B0_E${size}_nb_zones_${nb_zones}_${metric}_${mode}"
-            MODEL_NAME="${model}_N${size}_B0_E${size}_nb_zones_${nb_zones}_${metric}_${mode}"
-            CUSTOM_MIN_MAX_FILENAME="N${size}_B0_E${size}_nb_zones_${nb_zones}_${metric}_${mode}_min_max"
+            FILENAME="data/${model}_N${size}_B0_E${size}_nb_zones_${nb_zones}_${feature}_${mode}"
+            MODEL_NAME="${model}_N${size}_B0_E${size}_nb_zones_${nb_zones}_${feature}_${mode}"
+            CUSTOM_MIN_MAX_FILENAME="N${size}_B0_E${size}_nb_zones_${nb_zones}_${feature}_${mode}_min_max"
 
             echo $FILENAME
 
@@ -37,10 +37,10 @@ for nb_zones in {4,6,8,10,12}; do
 
                 echo "${MODEL_NAME} results already generated..."
             else
-                python generate/generate_data_model_random_center.py --output ${FILENAME} --interval "0,${size}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 40 --random 1 --custom ${CUSTOM_MIN_MAX_FILENAME}
+                python generate/generate_data_model_random_center.py --output ${FILENAME} --interval "0,${size}" --kind ${mode} --feature ${feature} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 40 --random 1 --custom ${CUSTOM_MIN_MAX_FILENAME}
                 python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
-                python others/save_model_result_in_md_maxwell.py --interval "0,${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric}
+                python others/save_model_result_in_md_maxwell.py --interval "0,${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature}
             fi
         done
     done

data_processing/generateAndTrain_maxwell_custom_filters_split.sh

@@ -10,14 +10,14 @@ fi
 if [ -z "$2" ]
   then
     echo "No argument supplied"
-    echo "Need of metric information"
+    echo "Need of feature information"
     exit 1
 fi
 
 result_filename="results/models_comparisons.csv"
 VECTOR_SIZE=200
 size=$1
-metric=$2
+feature=$2
 
 # selection of four scenes (only maxwell)
 scenes="A, D, G, H"
@@ -26,9 +26,9 @@ for nb_zones in {4,6,8,10,12}; do
     for mode in {"svd","svdn","svdne"}; do
         for model in {"svm_model","ensemble_model","ensemble_model_v2"}; do
 
-            FILENAME="data/${model}_N${size}_B0_E${size}_nb_zones_${nb_zones}_${metric}_${mode}"
-            MODEL_NAME="${model}_N${size}_B0_E${size}_nb_zones_${nb_zones}_${metric}_${mode}"
-            CUSTOM_MIN_MAX_FILENAME="N${size}_B0_E${size}_nb_zones_${nb_zones}_${metric}_${mode}_min_max"
+            FILENAME="data/${model}_N${size}_B0_E${size}_nb_zones_${nb_zones}_${feature}_${mode}"
+            MODEL_NAME="${model}_N${size}_B0_E${size}_nb_zones_${nb_zones}_${feature}_${mode}"
+            CUSTOM_MIN_MAX_FILENAME="N${size}_B0_E${size}_nb_zones_${nb_zones}_${feature}_${mode}_min_max"
 
             echo $FILENAME
 
@@ -37,10 +37,10 @@ for nb_zones in {4,6,8,10,12}; do
 
                 echo "${MODEL_NAME} results already generated..."
             else
-                python generate/generate_data_model_random_split.py --output ${FILENAME} --interval "0,${size}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 40 --random 1 --custom ${CUSTOM_MIN_MAX_FILENAME}
+                python generate/generate_data_model_random_split.py --output ${FILENAME} --interval "0,${size}" --kind ${mode} --feature ${feature} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 40 --random 1 --custom ${CUSTOM_MIN_MAX_FILENAME}
                 python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
-                python others/save_model_result_in_md_maxwell.py --interval "0,${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric}
+                python others/save_model_result_in_md_maxwell.py --interval "0,${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature}
             fi
         done
     done

data_processing/generateAndTrain_maxwell_custom_split.sh

@@ -10,14 +10,14 @@ fi
 if [ -z "$2" ]
   then
     echo "No argument supplied"
-    echo "Need of metric information"
+    echo "Need of feature information"
     exit 1
 fi
 
 result_filename="results/models_comparisons.csv"
 VECTOR_SIZE=200
 size=$1
-metric=$2
+feature=$2
 
 # selection of four scenes (only maxwell)
 scenes="A, D, G, H"
@@ -44,9 +44,9 @@ for counter in {0..4}; 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}"
-                CUSTOM_MIN_MAX_FILENAME="N${size}_B${start}_E${end}_nb_zones_${nb_zones}_${metric}_${mode}_min_max"
+                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}"
+                CUSTOM_MIN_MAX_FILENAME="N${size}_B${start}_E${end}_nb_zones_${nb_zones}_${feature}_${mode}_min_max"
 
                 echo $FILENAME
 
@@ -55,11 +55,11 @@ for counter in {0..4}; do
 
                     echo "${MODEL_NAME} results already generated..."
                 else
-                    python generate/generate_data_model_random_split.py --output ${FILENAME} --interval "${start},${end}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 10 --random 1 --custom ${CUSTOM_MIN_MAX_FILENAME}
+                    python generate/generate_data_model_random_split.py --output ${FILENAME} --interval "${start},${end}" --kind ${mode} --feature ${feature} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 10 --random 1 --custom ${CUSTOM_MIN_MAX_FILENAME}
                     python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
-                    #python prediction/predict_seuil_expe_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
-                    python others/save_model_result_in_md_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric}
+                    #python prediction/predict_seuil_expe_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
+                    python others/save_model_result_in_md_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature}
                 fi
             done
         done

generate/generate_data_model_corr_random.py

@@ -364,10 +364,8 @@ def main():
         if not os.path.exists(custom_min_max_folder):
             os.makedirs(custom_min_max_folder)
 
-        min_max_folder_path = os.path.join(os.path.dirname(__file__), custom_min_max_folder)
-
         min_max_current_filename = p_filename.replace(cfg.output_data_folder + '/', '').replace('deep_keras_', '') + min_max_filename
-        min_max_filename_path = os.path.join(min_max_folder_path, min_max_current_filename)
+        min_max_filename_path = os.path.join(custom_min_max_folder, min_max_current_filename)
 
         print(min_max_filename_path)
         with open(min_max_filename_path, 'w') as f:

generate/generate_data_model_random.py

@@ -286,8 +286,7 @@ def main():
         if not os.path.exists(custom_min_max_folder):
             os.makedirs(custom_min_max_folder)
 
-        min_max_folder_path = os.path.join(os.path.dirname(__file__), custom_min_max_folder)
-        min_max_filename_path = os.path.join(min_max_folder_path, p_custom)
+        min_max_filename_path = os.path.join(custom_min_max_folder, p_custom)
 
         with open(min_max_filename_path, 'w') as f:
             f.write(str(min_value_interval) + '\n')

generate/generate_data_model_random_center.py

@@ -297,8 +297,7 @@ def main():
         if not os.path.exists(custom_min_max_folder):
             os.makedirs(custom_min_max_folder)
 
-        min_max_folder_path = os.path.join(os.path.dirname(__file__), custom_min_max_folder)
-        min_max_filename_path = os.path.join(min_max_folder_path, p_custom)
+        min_max_filename_path = os.path.join(custom_min_max_folder, p_custom)
 
         with open(min_max_filename_path, 'w') as f:
             f.write(str(min_value_interval) + '\n')

generate/generate_data_model_random_split.py

@@ -296,8 +296,7 @@ def main():
         if not os.path.exists(custom_min_max_folder):
             os.makedirs(custom_min_max_folder)
 
-        min_max_folder_path = os.path.join(os.path.dirname(__file__), custom_min_max_folder)
-        min_max_filename_path = os.path.join(min_max_folder_path, p_custom)
+        min_max_filename_path = os.path.join(custom_min_max_folder, p_custom)
 
         with open(min_max_filename_path, 'w') as f:
             f.write(str(min_value_interval) + '\n')

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.metrics import accuracy_score, f1_score, recall_score, roc_auc_score
+from sklearn.features 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
@@ -53,21 +53,21 @@ def main():
 
     parser.add_argument('--interval', type=str, help='Interval value to keep from svd', default='"0, 200"')
     parser.add_argument('--model', type=str, help='.joblib or .json file (sklearn or keras model)')
-    parser.add_argument('--metric', type=str, help='Metric data choice', choices=cfg.metric_choices_labels)
+    parser.add_argument('--feature', type=str, help='Metric data choice', choices=cfg.feature_choices_labels)
     parser.add_argument('--mode', type=str, help='Kind of normalization level wished', choices=cfg.normalization_choices)
 
     args = parser.parse_args()
 
     p_interval   = list(map(int, args.interval.split(',')))
     p_model_file = args.model
-    p_metric     = args.metric
+    p_feature    = args.feature
     p_mode       = args.mode
 
 
     # call model and get global result in scenes
     begin, end = p_interval
 
-    bash_cmd = "bash others/testModelByScene_maxwell.sh '" + str(begin) + "' '" + str(end) + "' '" + p_model_file + "' '" + p_mode + "' '" + p_metric + "'"
+    bash_cmd = "bash others/testModelByScene_maxwell.sh '" + str(begin) + "' '" + str(end) + "' '" + p_model_file + "' '" + p_mode + "' '" + p_feature + "'"
 
     print(bash_cmd)
 
@@ -187,7 +187,7 @@ def main():
 
             model.compile(loss='binary_crossentropy',
                         optimizer='adam',
-                        metrics=['accuracy'])
+                        features=['accuracy'])
 
         # reshape all input data
         x_dataset_train = np.array(x_dataset_train).reshape(len(x_dataset_train), end, 1)
@@ -308,7 +308,7 @@ def main():
     # check if it's always the case...
     nb_zones = current_data_file_path.split('_')[7]
 
-    final_file_line = current_model_name + '; ' + str(end - begin) + '; ' + str(begin) + '; ' + str(end) + '; ' + str(nb_zones) + '; ' + p_metric + '; ' + p_mode
+    final_file_line = current_model_name + '; ' + str(end - begin) + '; ' + str(begin) + '; ' + str(end) + '; ' + str(nb_zones) + '; ' + p_feature + '; ' + p_mode
 
     for s in model_scores:
         final_file_line += '; ' + str(s)

others/testModelByScene.sh

@@ -31,7 +31,7 @@ fi
 if [ -z "$5" ]
   then
     echo "No fifth argument supplied"
-    echo "Need of metric : 'lab', 'mscn'"
+    echo "Need of feature : 'lab', 'mscn'"
     exit 1
 fi
 
@@ -55,8 +55,8 @@ for scene in {"A","B","C","D","E","F","G","H","I"}; do
 
   FILENAME="data/data_${INPUT_MODE}_${INPUT_METRIC}_B${INPUT_BEGIN}_E${INPUT_END}_scene${scene}"
 
-  python generate/generate_data_model.py --output ${FILENAME} --interval "${INPUT_BEGIN},${INPUT_END}" --kind ${INPUT_MODE} --metric ${INPUT_METRIC} --scenes "${scene}" --zones "${zones}" --percent 1 --sep ";" --rowindex "0"
+  python generate/generate_data_model.py --output ${FILENAME} --interval "${INPUT_BEGIN},${INPUT_END}" --kind ${INPUT_MODE} --feature ${INPUT_METRIC} --scenes "${scene}" --zones "${zones}" --percent 1 --sep ";" --rowindex "0"
 
-  python prediction/prediction_scene.py --data "$FILENAME.train" --model ${INPUT_MODEL} --output "${INPUT_MODEL}_Scene${scene}_mode_${INPUT_MODE}_metric_${INPUT_METRIC}.prediction" --scene ${scene}
+  python prediction/prediction_scene.py --data "$FILENAME.train" --model ${INPUT_MODEL} --output "${INPUT_MODEL}_Scene${scene}_mode_${INPUT_MODE}_feature_${INPUT_METRIC}.prediction" --scene ${scene}
 
 done

others/testModelByScene_maxwell.sh

@@ -31,7 +31,7 @@ fi
 if [ -z "$5" ]
   then
     echo "No fifth argument supplied"
-    echo "Need of metric : 'lab', 'mscn'"
+    echo "Need of feature : 'lab', 'mscn'"
     exit 1
 fi
 
@@ -63,8 +63,8 @@ for scene in {"A","D","G","H"}; do
 
   FILENAME="data/data_${INPUT_MODE}_${INPUT_METRIC}_B${INPUT_BEGIN}_E${INPUT_END}_scene${scene}"
 
-  python generate/generate_data_model.py --output ${FILENAME} --interval "${INPUT_BEGIN},${INPUT_END}" --kind ${INPUT_MODE} --metric ${INPUT_METRIC} --scenes "${scene}" --zones "${zones}" --percent 1
+  python generate/generate_data_model.py --output ${FILENAME} --interval "${INPUT_BEGIN},${INPUT_END}" --kind ${INPUT_MODE} --feature ${INPUT_METRIC} --scenes "${scene}" --zones "${zones}" --percent 1
 
-  python prediction/prediction_scene.py --data "$FILENAME.train" --model ${INPUT_MODEL} --output "${INPUT_MODEL}_Scene${scene}_mode_${INPUT_MODE}_metric_${INPUT_METRIC}.prediction" --scene ${scene}
+  python prediction/prediction_scene.py --data "$FILENAME.train" --model ${INPUT_MODEL} --output "${INPUT_MODEL}_Scene${scene}_mode_${INPUT_MODE}_feature_${INPUT_METRIC}.prediction" --scene ${scene}
 
 done

run/runAll_display_data_scene.sh

@@ -1,7 +1,7 @@
 #! bin/bash
 
-for metric in {"lab","mscn","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","low_bits_2","low_bits_3","low_bits_4","low_bits_5","low_bits_6","low_bits_4_shifted_2"}; do
     for scene in {"A","D","G","H"}; do
-        python display/display_svd_data_scene.py --scene ${scene} --interval "0,800" --indices "0, 2000" --metric ${metric} --mode svdne --step 100 --norm 1 --ylim "0, 0.01"
+        python display/display_svd_data_scene.py --scene ${scene} --interval "0,800" --indices "0, 2000" --feature ${feature} --mode svdne --step 100 --norm 1 --ylim "0, 0.01"
     done
 done

run/runAll_maxwell_area.sh

@@ -12,11 +12,11 @@ if [ "${erased}" == "Y" ]; then
     touch ${file_path}
 
     # add of header
-    echo 'model_name; vector_size; start_index; end; nb_zones; metric; 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}
+    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}
 
 fi
 
-metric="sub_blocks_area"
+feature="sub_blocks_area"
 start_index=0
 end_index=16
 number=16
@@ -30,8 +30,8 @@ for nb_zones in {4,6,8,10,12}; do
     for mode in {"svd","svdn","svdne"}; do
         for model in {"svm_model","ensemble_model","ensemble_model_v2"}; do
 
-            FILENAME="data/${model}_N${number}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${metric}_${mode}"
-            MODEL_NAME="${model}_N${number}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${metric}_${mode}"
+            FILENAME="data/${model}_N${number}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${feature}_${mode}"
+            MODEL_NAME="${model}_N${number}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${feature}_${mode}"
 
             echo $FILENAME
 
@@ -40,10 +40,10 @@ for nb_zones in {4,6,8,10,12}; do
 
                 echo "${MODEL_NAME} results already generated..."
             else
-                python generate/generate_data_model_random.py --output ${FILENAME} --interval "${start_index},${end_index}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 10 --random 1
+                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
                 python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
-                python others/save_model_result_in_md_maxwell.py --interval "${start_index},${end_index}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric}
+                python others/save_model_result_in_md_maxwell.py --interval "${start_index},${end_index}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature}
             fi
         done
     done

run/runAll_maxwell_area_normed.sh

@@ -12,11 +12,11 @@ if [ "${erased}" == "Y" ]; then
     touch ${file_path}
 
     # add of header
-    echo 'model_name; vector_size; start_index; end; nb_zones; metric; 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}
+    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}
 
 fi
 
-metric="sub_blocks_area_normed"
+feature="sub_blocks_area_normed"
 start_index=0
 end_index=16
 number=16
@@ -30,8 +30,8 @@ for nb_zones in {4,6,8,10,12}; do
     for mode in {"svd","svdn","svdne"}; do
         for model in {"svm_model","ensemble_model","ensemble_model_v2"}; do
 
-            FILENAME="data/${model}_N${number}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${metric}_${mode}"
-            MODEL_NAME="${model}_N${number}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${metric}_${mode}"
+            FILENAME="data/${model}_N${number}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${feature}_${mode}"
+            MODEL_NAME="${model}_N${number}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${feature}_${mode}"
 
             echo $FILENAME
 
@@ -40,10 +40,10 @@ for nb_zones in {4,6,8,10,12}; do
 
                 echo "${MODEL_NAME} results already generated..."
             else
-                python generate/generate_data_model_random.py --output ${FILENAME} --interval "${start_index},${end_index}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 10 --random 1
+                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
                 python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
-                python others/save_model_result_in_md_maxwell.py --interval "${start_index},${end_index}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric}
+                python others/save_model_result_in_md_maxwell.py --interval "${start_index},${end_index}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature}
             fi
         done
     done

run/runAll_maxwell_corr_custom.sh

@@ -12,7 +12,7 @@ if [ "${erased}" == "Y" ]; then
     touch ${file_path}
 
     # add of header
-    echo 'model_name; vector_size; start; end; nb_zones; metric; 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}
+    echo 'model_name; vector_size; start; 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}
 
 
 fi
@@ -22,7 +22,7 @@ end_index=24
 
 # selection of four scenes (only maxwell)
 scenes="A, D, G, H"
-metric="lab"
+feature="lab"
 
 for label in {"0","1"}; do
     for highest in {"0","1"}; do
@@ -31,8 +31,8 @@ for label in {"0","1"}; 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${start_index}_E${size}_nb_zones_${nb_zones}_${metric}_${mode}_corr_L${label}_H${highest}"
-                        MODEL_NAME="${model}_N${size}_B${start_index}_E${size}_nb_zones_${nb_zones}_${metric}_${mode}_corr_L${label}_H${highest}"
+                        FILENAME="data/${model}_N${size}_B${start_index}_E${size}_nb_zones_${nb_zones}_${feature}_${mode}_corr_L${label}_H${highest}"
+                        MODEL_NAME="${model}_N${size}_B${start_index}_E${size}_nb_zones_${nb_zones}_${feature}_${mode}_corr_L${label}_H${highest}"
 
                         echo $FILENAME
 
@@ -41,11 +41,11 @@ for label in {"0","1"}; do
 
                             echo "${MODEL_NAME} results already generated..."
                         else
-                            python generate/generate_data_model_corr_random.py --output ${FILENAME} --n ${size} --highest ${highest} --label ${label} --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 10 --random 1 --custom 1
+                            python generate/generate_data_model_corr_random.py --output ${FILENAME} --n ${size} --highest ${highest} --label ${label} --kind ${mode} --feature ${feature} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 10 --random 1 --custom 1
                             python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
                             # use of interval but it is not really an interval..
-                            python others/save_model_result_in_md_maxwell.py --interval "${start_index},${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric}
+                            python others/save_model_result_in_md_maxwell.py --interval "${start_index},${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature}
                         fi
                     done
                 done

run/runAll_maxwell_keras.sh

@@ -12,7 +12,7 @@ if [ "${erased}" == "Y" ]; then
     touch ${file_path}
 
     # add of header
-    echo 'model_name; vector_size; start; end; nb_zones; metric; 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}
+    echo 'model_name; vector_size; start; 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}
 
 
 fi
@@ -23,18 +23,18 @@ end_index=24
 # selection of four scenes (only maxwell)
 scenes="A, D, G, H"
 
-declare -A metrics_size
-metrics_size=( ["sub_blocks_stats"]="24" ["sub_blocks_stats_reduced"]="20" ["sub_blocks_area"]="16" ["sub_blocks_area_normed"]="20")
+declare -A features_size
+features_size=( ["sub_blocks_stats"]="24" ["sub_blocks_stats_reduced"]="20" ["sub_blocks_area"]="16" ["sub_blocks_area_normed"]="20")
 
-for metric in {"sub_blocks_stats","sub_blocks_stats_reduced","sub_blocks_area","sub_blocks_area_normed"}; do
+for feature in {"sub_blocks_stats","sub_blocks_stats_reduced","sub_blocks_area","sub_blocks_area_normed"}; do
     for nb_zones in {4,6,8,10,12}; do
 
         for mode in {"svd","svdn","svdne"}; do
 
-            end_index=${metrics_size[${metric}]}
+            end_index=${features_size[${feature}]}
 
-            FILENAME="data/deep_keras_N${end_index}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${metric}_${mode}"
-            MODEL_NAME="deep_keras_N${end_index}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${metric}_${mode}"
+            FILENAME="data/deep_keras_N${end_index}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${feature}_${mode}"
+            MODEL_NAME="deep_keras_N${end_index}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${feature}_${mode}"
 
             echo $FILENAME
 
@@ -44,10 +44,10 @@ for metric in {"sub_blocks_stats","sub_blocks_stats_reduced","sub_blocks_area","
                 echo "${MODEL_NAME} results already generated..."
             else
                 echo "test"
-                #python generate/generate_data_model_random.py --output ${FILENAME} --interval "${start_index},${end_index}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 10 --random 1
+                #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
                 #python deep_network_keras_svd.py --data ${FILENAME} --output ${MODEL_NAME} --size ${end_index}
 
-                #python others/save_model_result_in_md_maxwell.py --interval "${start_index},${end_index}" --model "saved_models/${MODEL_NAME}.json" --mode "${mode}" --metric ${metric}
+                #python others/save_model_result_in_md_maxwell.py --interval "${start_index},${end_index}" --model "saved_models/${MODEL_NAME}.json" --mode "${mode}" --feature ${feature}
             fi
         done
     done

run/runAll_maxwell_keras_corr.sh

@@ -12,7 +12,7 @@ if [ "${erased}" == "Y" ]; then
     touch ${file_path}
 
     # add of header
-    echo 'model_name; vector_size; start; end; nb_zones; metric; 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}
+    echo 'model_name; vector_size; start; 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}
 
 
 fi
@@ -22,7 +22,7 @@ end_index=24
 
 # selection of four scenes (only maxwell)
 scenes="A, D, G, H"
-metric="lab"
+feature="lab"
 
 for label in {"0","1"}; do
     for highest in {"0","1"}; do
@@ -30,8 +30,8 @@ for label in {"0","1"}; do
             for size in {5,10,15,20,25,30,35,40}; do
                 for mode in {"svd","svdn","svdne"}; do
 
-                    FILENAME="data/deep_keras_N${size}_B${start_index}_E${size}_nb_zones_${nb_zones}_${metric}_${mode}_corr_L${label}_H${highest}"
-                    MODEL_NAME="deep_keras_N${size}_B${start_index}_E${size}_nb_zones_${nb_zones}_${metric}_${mode}_corr_L${label}_H${highest}"
+                    FILENAME="data/deep_keras_N${size}_B${start_index}_E${size}_nb_zones_${nb_zones}_${feature}_${mode}_corr_L${label}_H${highest}"
+                    MODEL_NAME="deep_keras_N${size}_B${start_index}_E${size}_nb_zones_${nb_zones}_${feature}_${mode}_corr_L${label}_H${highest}"
 
                     echo $FILENAME
 
@@ -40,11 +40,11 @@ for label in {"0","1"}; do
 
                         echo "${MODEL_NAME} results already generated..."
                     else
-                        python generate/generate_data_model_corr_random.py --output ${FILENAME} --n ${size} --highest ${highest} --label ${label} --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 10 --random 1
+                        python generate/generate_data_model_corr_random.py --output ${FILENAME} --n ${size} --highest ${highest} --label ${label} --kind ${mode} --feature ${feature} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 10 --random 1
                         python deep_network_keras_svd.py --data ${FILENAME} --output ${MODEL_NAME} --size ${size}
 
                         # use of interval but it is not really an interval..
-                        python others/save_model_result_in_md_maxwell.py --interval "${start_index},${size}" --model "saved_models/${MODEL_NAME}.json" --mode "${mode}" --metric ${metric}
+                        python others/save_model_result_in_md_maxwell.py --interval "${start_index},${size}" --model "saved_models/${MODEL_NAME}.json" --mode "${mode}" --feature ${feature}
                     fi
                 done
             done

run/runAll_maxwell_keras_corr_custom.sh

@@ -12,7 +12,7 @@ if [ "${erased}" == "Y" ]; then
     touch ${file_path}
 
     # add of header
-    echo 'model_name; vector_size; start; end; nb_zones; metric; 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}
+    echo 'model_name; vector_size; start; 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}
 
 
 fi
@@ -22,7 +22,7 @@ end_index=24
 
 # selection of four scenes (only maxwell)
 scenes="A, D, G, H"
-metric="lab"
+feature="lab"
 
 for label in {"0","1"}; do
     for highest in {"0","1"}; do
@@ -30,8 +30,8 @@ for label in {"0","1"}; do
             for size in {5,10,15,20,25,30,35,40}; do
                 for mode in {"svd","svdn","svdne"}; do
 
-                    FILENAME="data/deep_keras_N${size}_B${start_index}_E${size}_nb_zones_${nb_zones}_${metric}_${mode}_corr_L${label}_H${highest}"
-                    MODEL_NAME="deep_keras_N${size}_B${start_index}_E${size}_nb_zones_${nb_zones}_${metric}_${mode}_corr_L${label}_H${highest}"
+                    FILENAME="data/deep_keras_N${size}_B${start_index}_E${size}_nb_zones_${nb_zones}_${feature}_${mode}_corr_L${label}_H${highest}"
+                    MODEL_NAME="deep_keras_N${size}_B${start_index}_E${size}_nb_zones_${nb_zones}_${feature}_${mode}_corr_L${label}_H${highest}"
 
                     echo $FILENAME
 
@@ -40,11 +40,11 @@ for label in {"0","1"}; do
 
                         echo "${MODEL_NAME} results already generated..."
                     else
-                        python generate/generate_data_model_corr_random.py --output ${FILENAME} --n ${size} --highest ${highest} --label ${label} --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 10 --random 1 --custom 1
+                        python generate/generate_data_model_corr_random.py --output ${FILENAME} --n ${size} --highest ${highest} --label ${label} --kind ${mode} --feature ${feature} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 10 --random 1 --custom 1
                         python deep_network_keras_svd.py --data ${FILENAME} --output ${MODEL_NAME} --size ${size}
 
                         # use of interval but it is not really an interval..
-                        python others/save_model_result_in_md_maxwell.py --interval "${start_index},${size}" --model "saved_models/${MODEL_NAME}.json" --mode "${mode}" --metric ${metric}
+                        python others/save_model_result_in_md_maxwell.py --interval "${start_index},${size}" --model "saved_models/${MODEL_NAME}.json" --mode "${mode}" --feature ${feature}
                     fi
                 done
             done

run/runAll_maxwell_mscn_var.sh

@@ -12,7 +12,7 @@ if [ "${erased}" == "Y" ]; then
     touch ${file_path}
 
     # add of header
-    echo 'model_name; vector_size; start_index; end; nb_zones; metric; 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}
+    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}
 
 fi
 
@@ -22,19 +22,19 @@ end_index=4
 # selection of four scenes (only maxwell)
 scenes="A, D, G, H"
 
-declare -A metrics_size
-metrics_size=( ["mscn_var_4"]=4 ["mscn_var_16"]=16 ["mscn_var_64"]=64 ["mscn_var_16_max"]=4 ["mscn_var_64_max"]=16)
+declare -A features_size
+features_size=( ["mscn_var_4"]=4 ["mscn_var_16"]=16 ["mscn_var_64"]=64 ["mscn_var_16_max"]=4 ["mscn_var_64_max"]=16)
 
 for nb_zones in {4,6,8,10,12}; do
 
     for mode in {"svd","svdn","svdne"}; do
-        for metric in {"mscn_var_4","mscn_var_16","mscn_var_64","mscn_var_16_max","mscn_var_64_max"}; do
+        for feature in {"mscn_var_4","mscn_var_16","mscn_var_64","mscn_var_16_max","mscn_var_64_max"}; do
             for model in {"svm_model","ensemble_model","ensemble_model_v2"}; do
 
-                end_index=${metrics_size[${metric}]}
+                end_index=${features_size[${feature}]}
 
-                FILENAME="data/${model}_N${end_index}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${metric}_${mode}"
-                MODEL_NAME="${model}_N${end_index}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${metric}_${mode}"
+                FILENAME="data/${model}_N${end_index}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${feature}_${mode}"
+                MODEL_NAME="${model}_N${end_index}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${feature}_${mode}"
 
                 echo $FILENAME
 
@@ -43,10 +43,10 @@ for nb_zones in {4,6,8,10,12}; do
 
                     echo "${MODEL_NAME} results already generated..."
                 else
-                    python generate/generate_data_model_random.py --output ${FILENAME} --interval "${start_index},${end_index}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 10 --random 1
+                    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
                     python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
-                    python others/save_model_result_in_md_maxwell.py --interval "${start_index},${end_index}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric}
+                    python others/save_model_result_in_md_maxwell.py --interval "${start_index},${end_index}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature}
                 fi
             done
         done

run/runAll_maxwell_sub_blocks_stats.sh

@@ -12,11 +12,11 @@ if [ "${erased}" == "Y" ]; then
     touch ${file_path}
 
     # add of header
-    echo 'model_name; vector_size; start_index; end; nb_zones; metric; 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}
+    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}
 
 fi
 
-metric="sub_blocks_stats"
+feature="sub_blocks_stats"
 start_index=0
 end_index=24
 number=24
@@ -30,8 +30,8 @@ for nb_zones in {4,6,8,10,12}; do
     for mode in {"svd","svdn","svdne"}; do
         for model in {"svm_model","ensemble_model","ensemble_model_v2"}; do
 
-            FILENAME="data/${model}_N${number}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${metric}_${mode}"
-            MODEL_NAME="${model}_N${number}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${metric}_${mode}"
+            FILENAME="data/${model}_N${number}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${feature}_${mode}"
+            MODEL_NAME="${model}_N${number}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${feature}_${mode}"
 
             echo $FILENAME
 
@@ -40,10 +40,10 @@ for nb_zones in {4,6,8,10,12}; do
 
                 echo "${MODEL_NAME} results already generated..."
             else
-                python generate/generate_data_model_random.py --output ${FILENAME} --interval "${start_index},${end_index}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 10 --random 1
+                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
                 python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
-                python others/save_model_result_in_md_maxwell.py --interval "${start_index},${end_index}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric}
+                python others/save_model_result_in_md_maxwell.py --interval "${start_index},${end_index}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature}
             fi
         done
     done

run/runAll_maxwell_sub_blocks_stats_reduced.sh

@@ -12,11 +12,11 @@ if [ "${erased}" == "Y" ]; then
     touch ${file_path}
 
     # add of header
-    echo 'model_name; vector_size; start_index; end; nb_zones; metric; 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}
+    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}
 
 fi
 
-metric="sub_blocks_stats_reduced"
+feature="sub_blocks_stats_reduced"
 start_index=0
 end_index=24
 number=24
@@ -30,8 +30,8 @@ for nb_zones in {4,6,8,10,12}; do
     for mode in {"svd","svdn","svdne"}; do
         for model in {"svm_model","ensemble_model","ensemble_model_v2"}; do
 
-            FILENAME="data/${model}_N${number}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${metric}_${mode}"
-            MODEL_NAME="${model}_N${number}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${metric}_${mode}"
+            FILENAME="data/${model}_N${number}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${feature}_${mode}"
+            MODEL_NAME="${model}_N${number}_B${start_index}_E${end_index}_nb_zones_${nb_zones}_${feature}_${mode}"
 
             echo $FILENAME
 
@@ -40,10 +40,10 @@ for nb_zones in {4,6,8,10,12}; do
 
                 echo "${MODEL_NAME} results already generated..."
             else
-                python generate/generate_data_model_random.py --output ${FILENAME} --interval "${start_index},${end_index}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 10 --random 1
+                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
                 python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
-                python others/save_model_result_in_md_maxwell.py --interval "${start_index},${end_index}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric}
+                python others/save_model_result_in_md_maxwell.py --interval "${start_index},${end_index}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature}
             fi
         done
     done

simulation/run_maxwell_simulation_corr_custom.sh

@@ -8,7 +8,7 @@ size=24
 
 # selection of four scenes (only maxwell)
 scenes="A, D, G, H"
-metric="lab"
+feature="lab"
 
 for label in {"0","1"}; do
     for highest in {"0","1"}; do
@@ -17,22 +17,22 @@ for label in {"0","1"}; 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${start_index}_E${size}_nb_zones_${nb_zones}_${metric}_${mode}_corr_L${label}_H${highest}"
-                            MODEL_NAME="${model}_N${size}_B${start_index}_E${size}_nb_zones_${nb_zones}_${metric}_${mode}_corr_L${label}_H${highest}"
-                            CUSTOM_MIN_MAX_FILENAME="${model}_N${size}_B${start_index}_E${size}_nb_zones_${nb_zones}_${metric}_${mode}_corr_L${label}_H${highest}_min_max_values"
+                            FILENAME="data/${model}_N${size}_B${start_index}_E${size}_nb_zones_${nb_zones}_${feature}_${mode}_corr_L${label}_H${highest}"
+                            MODEL_NAME="${model}_N${size}_B${start_index}_E${size}_nb_zones_${nb_zones}_${feature}_${mode}_corr_L${label}_H${highest}"
+                            CUSTOM_MIN_MAX_FILENAME="${model}_N${size}_B${start_index}_E${size}_nb_zones_${nb_zones}_${feature}_${mode}_corr_L${label}_H${highest}_min_max_values"
 
                             echo ${MODEL_NAME}
 
                         if grep -xq "${MODEL_NAME}" "${simulate_models}"; then
                             echo "Run simulation for model ${MODEL_NAME}"
 
-                            python generate/generate/generate_data_model_corr_random.py --output ${FILENAME} --n ${size} --highest ${highest} --label ${label} --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 10 --random 1 --custom 1
+                            python generate/generate/generate_data_model_corr_random.py --output ${FILENAME} --n ${size} --highest ${highest} --label ${label} --kind ${mode} --feature ${feature} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 10 --random 1 --custom 1
 
                             python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
-                            python prediction/prediction/predict_seuil_expe_maxwell_curve.py --interval "${start_index},${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
+                            python prediction/prediction/predict_seuil_expe_maxwell_curve.py --interval "${start_index},${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
 
-                            python others/save_model_result_in_md_maxwell.py --interval "${start_index},${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric}
+                            python others/save_model_result_in_md_maxwell.py --interval "${start_index},${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature}
 
                         fi
                     done

simulation/run_maxwell_simulation_custom.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","ica_diff","ipca_diff","svd_trunc_diff","svd_reconstruct"}; 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","ica_diff","ipca_diff","svd_trunc_diff","svd_reconstruct"}; do
 
         half=$(($size/2))
         start=-$half
@@ -31,21 +31,21 @@ 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}"
-                        CUSTOM_MIN_MAX_FILENAME="N${size}_B${start}_E${end}_nb_zones_${nb_zones}_${metric}_${mode}_min_max"
+                        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}"
+                        CUSTOM_MIN_MAX_FILENAME="N${size}_B${start}_E${end}_nb_zones_${nb_zones}_${feature}_${mode}_min_max"
 
                         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 --custom ${CUSTOM_MIN_MAX_FILENAME}
+                            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 --custom ${CUSTOM_MIN_MAX_FILENAME}
 
                             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' --custom ${CUSTOM_MIN_MAX_FILENAME}
+                            python prediction/predict_seuil_expe_maxwell_curve.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
 
-                            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

simulation/run_maxwell_simulation_custom_filters.sh

@@ -12,16 +12,16 @@ scenes="A, D, G, H"
 
 for size in {"4","8","16","26","32","40"}; do
 
-    # for metric in {"lab","mscn","low_bits_2","low_bits_3","low_bits_4","low_bits_5","low_bits_6","low_bits_4_shifted_2","ica_diff","svd_trunc_diff","ipca_diff","svd_reconstruct"}; do
-    for metric in {"highest_sv_std_filters_full","lowest_sv_std_filters_full"}; do
+    # for feature in {"lab","mscn","low_bits_2","low_bits_3","low_bits_4","low_bits_5","low_bits_6","low_bits_4_shifted_2","ica_diff","svd_trunc_diff","ipca_diff","svd_reconstruct"}; do
+    for feature in {"highest_sv_std_filters_full","lowest_sv_std_filters_full"}; do
 
         for nb_zones in {4,6,8,10,12}; do
             for mode in {"svd","svdn","svdne"}; do
                 for model in {"svm_model","ensemble_model","ensemble_model_v2"}; do
 
-                    FILENAME="data/${model}_N${size}_B0_E${size}_nb_zones_${nb_zones}_${metric}_${mode}"
-                    MODEL_NAME="${model}_N${size}_B0_E${size}_nb_zones_${nb_zones}_${metric}_${mode}"
-                    CUSTOM_MIN_MAX_FILENAME="N${size}_B0_E${size}_nb_zones_${nb_zones}_${metric}_${mode}_min_max"
+                    FILENAME="data/${model}_N${size}_B0_E${size}_nb_zones_${nb_zones}_${feature}_${mode}"
+                    MODEL_NAME="${model}_N${size}_B0_E${size}_nb_zones_${nb_zones}_${feature}_${mode}"
+                    CUSTOM_MIN_MAX_FILENAME="N${size}_B0_E${size}_nb_zones_${nb_zones}_${feature}_${mode}_min_max"
 
                     echo $MODEL_NAME
 
@@ -31,12 +31,12 @@ for size in {"4","8","16","26","32","40"}; do
                         echo "${MODEL_NAME} results already generated..."
                     else
                         # Use of already generated model
-                        # python generate/generate_data_model_random.py --output ${FILENAME} --interval "0,${size}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 40 --random 1 --custom ${CUSTOM_MIN_MAX_FILENAME}
+                        # python generate/generate_data_model_random.py --output ${FILENAME} --interval "0,${size}" --kind ${mode} --feature ${feature} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 40 --random 1 --custom ${CUSTOM_MIN_MAX_FILENAME}
                         # python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
-                        python prediction/predict_seuil_expe_maxwell_curve.py --interval "0,${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
+                        python prediction/predict_seuil_expe_maxwell_curve.py --interval "0,${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
 
-                        python others/save_model_result_in_md_maxwell.py --interval "0,${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric}
+                        python others/save_model_result_in_md_maxwell.py --interval "0,${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature}
                     fi
                 done
             done

simulation/run_maxwell_simulation_filters_statistics.sh

@@ -8,16 +8,16 @@ scenes="A, D, G, H"
 
 size="26"
 
-# for metric in {"lab","mscn","low_bits_2","low_bits_3","low_bits_4","low_bits_5","low_bits_6","low_bits_4_shifted_2","ica_diff","svd_trunc_diff","ipca_diff","svd_reconstruct"}; do
-metric="filters_statistics"
+# for feature in {"lab","mscn","low_bits_2","low_bits_3","low_bits_4","low_bits_5","low_bits_6","low_bits_4_shifted_2","ica_diff","svd_trunc_diff","ipca_diff","svd_reconstruct"}; do
+feature="filters_statistics"
 
 for nb_zones in {4,6,8,10,12}; do
     for mode in {"svd","svdn","svdne"}; do
         for model in {"svm_model","ensemble_model","ensemble_model_v2"}; do
 
-            FILENAME="data/${model}_N${size}_B0_E${size}_nb_zones_${nb_zones}_${metric}_${mode}"
-            MODEL_NAME="${model}_N${size}_B0_E${size}_nb_zones_${nb_zones}_${metric}_${mode}"
-            CUSTOM_MIN_MAX_FILENAME="N${size}_B0_E${size}_nb_zones_${nb_zones}_${metric}_${mode}_min_max"
+            FILENAME="data/${model}_N${size}_B0_E${size}_nb_zones_${nb_zones}_${feature}_${mode}"
+            MODEL_NAME="${model}_N${size}_B0_E${size}_nb_zones_${nb_zones}_${feature}_${mode}"
+            CUSTOM_MIN_MAX_FILENAME="N${size}_B0_E${size}_nb_zones_${nb_zones}_${feature}_${mode}_min_max"
 
             echo $MODEL_NAME
 
@@ -27,12 +27,12 @@ for nb_zones in {4,6,8,10,12}; do
                 echo "${MODEL_NAME} results already generated..."
             else
                 # Use of already generated model
-                # python generate/generate_data_model_random.py --output ${FILENAME} --interval "0,${size}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 40 --random 1 --custom ${CUSTOM_MIN_MAX_FILENAME}
+                # python generate/generate_data_model_random.py --output ${FILENAME} --interval "0,${size}" --kind ${mode} --feature ${feature} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 40 --random 1 --custom ${CUSTOM_MIN_MAX_FILENAME}
                 # python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
 
-                python prediction/predict_seuil_expe_maxwell_curve.py --interval "0,${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --custom ${CUSTOM_MIN_MAX_FILENAME}
+                python prediction/predict_seuil_expe_maxwell_curve.py --interval "0,${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature} --custom ${CUSTOM_MIN_MAX_FILENAME}
 
-                python others/save_model_result_in_md_maxwell.py --interval "0,${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric}
+                python others/save_model_result_in_md_maxwell.py --interval "0,${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature}
             fi
         done
     done

simulation/run_maxwell_simulation_keras_corr_custom.sh

@@ -8,7 +8,7 @@ size=24
 
 # selection of four scenes (only maxwell)
 scenes="A, D, G, H"
-metric="lab"
+feature="lab"
 
 for label in {"0","1"}; do
     for highest in {"0","1"}; do
@@ -16,24 +16,24 @@ for label in {"0","1"}; do
             for size in {5,10,15,20,25,30,35,40}; do
                 for mode in {"svd","svdn","svdne"}; do
 
-                    FILENAME="data/deep_keras_N${size}_B${start_index}_E${size}_nb_zones_${nb_zones}_${metric}_${mode}_corr_L${label}_H${highest}"
-                    MODEL_NAME="deep_keras_N${size}_B${start_index}_E${size}_nb_zones_${nb_zones}_${metric}_${mode}_corr_L${label}_H${highest}"
+                    FILENAME="data/deep_keras_N${size}_B${start_index}_E${size}_nb_zones_${nb_zones}_${feature}_${mode}_corr_L${label}_H${highest}"
+                    MODEL_NAME="deep_keras_N${size}_B${start_index}_E${size}_nb_zones_${nb_zones}_${feature}_${mode}_corr_L${label}_H${highest}"
 
 
-                    CUSTOM_MIN_MAX_FILENAME="N${size}_B${start_index}_E${size}_nb_zones_${nb_zones}_${metric}_${mode}_corr_L${label}_H${highest}_min_max_values"
+                    CUSTOM_MIN_MAX_FILENAME="N${size}_B${start_index}_E${size}_nb_zones_${nb_zones}_${feature}_${mode}_corr_L${label}_H${highest}_min_max_values"
 
                     echo ${MODEL_NAME}
 
                     if grep -xq "${MODEL_NAME}" "${simulate_models}"; then
                         echo "Run simulation for model ${MODEL_NAME}"
 
-                        python generate/generate_data_model_corr_random.py --output ${FILENAME} --n ${size} --highest ${highest} --label ${label} --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 10 --random 1 --custom 1
+                        python generate/generate_data_model_corr_random.py --output ${FILENAME} --n ${size} --highest ${highest} --label ${label} --kind ${mode} --feature ${feature} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 10 --random 1 --custom 1
 
                         python deep_network_keras_svd.py --data ${FILENAME} --output ${MODEL_NAME} --size ${size}
 
-                        python prediction/predict_seuil_expe_maxwell_curve.py --interval "${start_index},${size}" --model "saved_models/${MODEL_NAME}.json" --mode "${mode}" --metric ${metric} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
+                        python prediction/predict_seuil_expe_maxwell_curve.py --interval "${start_index},${size}" --model "saved_models/${MODEL_NAME}.json" --mode "${mode}" --feature ${feature} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
 
-                        python others/save_model_result_in_md_maxwell.py --interval "${start_index},${size}" --model "saved_models/${MODEL_NAME}.json" --mode "${mode}" --metric ${metric}
+                        python others/save_model_result_in_md_maxwell.py --interval "${start_index},${size}" --model "saved_models/${MODEL_NAME}.json" --mode "${mode}" --feature ${feature}
 
                     fi
                 done