Dependencies and documentation updates

.gitignore

@@ -6,6 +6,7 @@ models_info
 custom_norm
 learned_zones
 corr_indices
+metric_curves
 .ipynb_checkpoints
 
 # simulate_models.csv

README.md

@@ -17,10 +17,10 @@ Filters list:
 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_all_data.py --metric all
+python generate/generate_all_data.py --feature all
 ```
 
 ## How to use
@@ -68,19 +68,19 @@ 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 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
 
@@ -93,13 +93,13 @@ python prediction_scene.py --data path/to/xxxx.csv --model saved_model/xxxx.jobl
 
 ### Visualize data
 
-All scripts with names **display_\*.py** are used to display data information or results.
+All scripts with names **display/display_\*.py** are used to display data information or results.
 
 Just use --help option to get more information.
 
 ### Simulate model on scene
 
-All scripts named **predict_seuil_expe\*.py** are used to simulate model prediction during rendering process. Do not forget the **custom** parameter filename if necessary.
+All scripts named **prediction/predict_seuil_expe\*.py** are used to simulate model prediction during rendering process. Do not forget the **custom** parameter filename if necessary.
 
 Once you have simulation done. Checkout your **threshold_map/%MODEL_NAME%/simulation\_curves\_zones\_\*/** folder and use it with help of **display_simulation_curves.py** script.
 

requirements.txt

@@ -8,4 +8,5 @@ Pillow
 pydot
 matplotlib
 path.py
-pandas
+pandas
+opencv-python

run/runAll_maxwell.sh

@@ -1,7 +1,7 @@
 #! bin/bash
 
-# erase "models_info/models_comparisons.csv" file and write new header
-file_path='models_info/models_comparisons.csv'
+# erase "results/models_comparisons.csv" file and write new header
+file_path='results/models_comparisons.csv'
 
 erased=$1
 
@@ -19,6 +19,6 @@ fi
 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"}; do
-        bash generateAndTrain_maxwell.sh ${size} ${metric}
+        bash data_processing/generateAndTrain_maxwell.sh ${size} ${metric}
     done
 done

run/runAll_maxwell_custom.sh

@@ -1,7 +1,7 @@
 #! bin/bash
 
-# erase "models_info/models_comparisons.csv" file and write new header
-file_path='models_info/models_comparisons.csv'
+# erase "results/models_comparisons.csv" file and write new header
+file_path='results/models_comparisons.csv'
 
 erased=$1
 
@@ -19,6 +19,6 @@ fi
 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
-        bash generateAndTrain_maxwell_custom.sh ${size} ${metric}
+        bash data_processing/generateAndTrain_maxwell_custom.sh ${size} ${metric}
     done
 done

run/runAll_maxwell_custom_center.sh

@@ -1,7 +1,7 @@
 #! bin/bash
 
-# erase "models_info/models_comparisons.csv" file and write new header
-file_path='models_info/models_comparisons.csv'
+# erase "results/models_comparisons.csv" file and write new header
+file_path='results/models_comparisons.csv'
 
 erased=$1
 
@@ -19,6 +19,6 @@ fi
 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
-        bash generateAndTrain_maxwell_custom_center.sh ${size} ${metric}
+        bash data_processing/generateAndTrain_maxwell_custom_center.sh ${size} ${metric}
     done
 done

run/runAll_maxwell_custom_split.sh

@@ -1,7 +1,7 @@
 #! bin/bash
 
-# erase "models_info/models_comparisons.csv" file and write new header
-file_path='models_info/models_comparisons.csv'
+# erase "results/models_comparisons.csv" file and write new header
+file_path='results/models_comparisons.csv'
 
 erased=$1
 
@@ -19,6 +19,6 @@ fi
 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
-        bash generateAndTrain_maxwell_custom_split.sh ${size} ${metric}
+        bash data_processing/generateAndTrain_maxwell_custom_split.sh ${size} ${metric}
     done
 done

simulation/generate_all_simulate_curves.sh

@@ -2,5 +2,5 @@ for file in "threshold_map"/*; do
 
     echo ${file}
 
-    python display_simulation_curves.py --folder ${file}
+    python display/display_simulation_curves.py --folder ${file}
 done

simulation/run_maxwell_simulation.sh

@@ -38,13 +38,13 @@ for size in {"4","8","16","26","32","40"}; do
                             echo "Run simulation for model ${MODEL_NAME}"
 
                             # by default regenerate model
-                            python 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} --metric ${metric} --scenes "${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 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}" --metric ${metric} --limit_detection '2'
 
-                            python 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}" --metric ${metric}
 
                         fi
                     done

simulation/run_maxwell_simulation_custom.sh

@@ -39,13 +39,13 @@ for size in {"4","8","16","26","32","40"}; do
                             echo "Run simulation for model ${MODEL_NAME}"
 
                             # by default regenerate model
-                            python 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} --metric ${metric} --scenes "${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 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}" --metric ${metric} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME}
 
-                            python 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}" --metric ${metric}
 
                         fi
                     done

train_model.py

@@ -1,3 +1,9 @@
+# main imports
+import numpy as np
+import pandas as pd
+import sys, os, argparse
+
+# models imports
 from sklearn.model_selection import train_test_split
 from sklearn.model_selection import GridSearchCV
 from sklearn.linear_model import LogisticRegression
@@ -9,17 +15,17 @@ from sklearn.externals import joblib
 from sklearn.metrics import accuracy_score, f1_score
 from sklearn.model_selection import cross_val_score
 
-import numpy as np
-import pandas as pd
-import sys, os, argparse
+# modules and config imports
+sys.path.insert(0, '') # trick to enable import of main folder module
 
-from modules.utils import config as cfg
-from modules import models as mdl
+import custom_config as cfg
+import models as mdl
 
+# variables and parameters
 saved_models_folder = cfg.saved_models_folder
 models_list         = cfg.models_names_list
 
-current_dirpath = os.getcwd()
+current_dirpath     = os.getcwd()
 output_model_folder = os.path.join(current_dirpath, saved_models_folder)