Keras runs script added

modules/utils/config.py

@@ -35,5 +35,5 @@ zones_indices                   = np.arange(16)
 
 metric_choices_labels           = ['lab', 'mscn_revisited', 'low_bits_2', 'low_bits_3', 'low_bits_4', 'low_bits_5', 'low_bits_6','low_bits_4_shifted_2', 'sub_blocks_stats', 'sub_blocks_area', 'sub_blocks_stats_reduced', 'sub_blocks_area_normed']
 
-keras_epochs                    = 10
+keras_epochs                    = 1000
 keras_batch                     = 32

prediction_scene.py

@@ -101,11 +101,6 @@ def main():
         y_noisy_pred = model.predict(x_noisy_dataset)
         y_not_noisy_pred = model.predict(x_not_noisy_dataset)
 
-    print("Prediction done")
-
-    with open('test_result.txt', 'w') as f:
-        f.write(str(y_pred))
-
     accuracy_global = accuracy_score(y_dataset, y_pred)
     accuracy_noisy = accuracy_score(y_noisy_dataset, y_noisy_pred)
     accuracy_not_noisy = accuracy_score(y_not_noisy_dataset, y_not_noisy_pred)

runAll_maxwell_keras.sh

@@ -12,7 +12,8 @@ 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_test; recall_test; roc_auc_test; F1_all; recall_all; roc_auc_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
 

save_model_result_in_md_maxwell.py

@@ -259,14 +259,10 @@ def main():
     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])
-    all_y_data = pd.concat([y_dataset_train, y_test, y_val])
-
     if kind_model == 'keras':
         # stats of all dataset
-        all_x_data = pd.concat([pd.DataFrame.from_records(x_dataset_train), X_test, X_val])
-        all_y_data = pd.concat([y_dataset_train, y_test, y_val])
+        all_x_data = np.concatenate([x_dataset_train, X_test, X_val])
+        all_y_data = np.concatenate([y_dataset_train, y_test, y_val])
         all_y_model = model.predict_classes(all_x_data)
 
     if kind_model == 'sklearn':