Add of new metrics

generateAndTrain_maxwell.sh

@@ -37,7 +37,7 @@ for counter in {0..4}; do
         end=$(($size))
     fi
 
-    for nb_zones in {4,6,8,10,12,14}; do
+    for nb_zones in {4,6,8,10,12}; do
 
         echo $start $end
 

generateAndTrain_maxwell_custom.sh

@@ -37,7 +37,7 @@ for counter in {0..4}; do
         end=$(($size))
     fi
 
-    for nb_zones in {4,6,8,10,12,14}; do
+    for nb_zones in {4,6,8,10,12}; do
 
         echo $start $end
 

runAll_maxwell.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; F1_val; F1_test; F1_all' >> ${file_path}
+    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}
 
 fi
 

runAll_maxwell_custom.sh

@@ -0,0 +1,24 @@
+#! bin/bash
+
+# erase "models_info/models_comparisons.csv" file and write new header
+file_path='models_info/models_comparisons.csv'
+
+erased=$1
+
+if [ "${erased}" == "Y" ]; then
+    echo "Previous data file erased..."
+    rm ${file_path}
+    mkdir -p models_info
+    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}
+
+fi
+
+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
+        bash generateAndTrain_maxwell_custom.sh ${size} ${metric}
+    done
+done

save_model_result_in_md_maxwell.py

@@ -1,6 +1,6 @@
 from sklearn.utils import shuffle
 from sklearn.externals import joblib
-from sklearn.metrics import accuracy_score, f1_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 train_test_split
 
@@ -194,9 +194,18 @@ def main():
     test_accuracy = accuracy_score(y_test, y_test_model)
 
     y_train_model = model.predict(x_dataset_train)
+
     train_f1 = f1_score(y_dataset_train, y_train_model)
+    train_recall = recall_score(y_dataset_train, y_train_model)
+    train_roc_auc = roc_auc_score(y_dataset_train, y_train_model)
+
     val_f1 = f1_score(y_val, y_val_model)
+    val_recall = recall_score(y_val, y_val_model)
+    val_roc_auc = roc_auc_score(y_val, y_val_model)
+
     test_f1 = f1_score(y_test, y_test_model)
+    test_recall = recall_score(y_test, y_test_model)
+    test_roc_auc = roc_auc_score(y_test, y_test_model)
 
     # stats of all dataset
     all_x_data = pd.concat([x_dataset_train, X_test, X_val])
@@ -205,6 +214,8 @@ def main():
     all_y_model = model.predict(all_x_data)
     all_accuracy = accuracy_score(all_y_data, all_y_model)
     all_f1_score = f1_score(all_y_data, all_y_model)
+    all_recall_score = recall_score(all_y_data, all_y_model)
+    all_roc_auc_score = roc_auc_score(all_y_data, all_y_model)
 
     # stats of dataset sizes
     total_samples = final_df_train_size + val_set_size + test_set_size
@@ -224,9 +235,20 @@ def main():
     model_scores.append(all_accuracy)
 
     model_scores.append(train_f1)
+    model_scores.append(train_recall)
+    model_scores.append(train_roc_auc)
+
     model_scores.append(val_f1)
+    model_scores.append(val_recall)
+    model_scores.append(val_roc_auc)
+
     model_scores.append(test_f1)
+    model_scores.append(test_recall)
+    model_scores.append(test_roc_auc)
+
     model_scores.append(all_f1_score)
+    model_scores.append(all_recall_score)
+    model_scores.append(all_roc_auc_score)
 
     # TODO : improve...
     # check if it's always the case...