Update src/bin/function_predictor.py

src/bin/function_predictor.py

@@ -83,8 +83,16 @@ def MultiLabelSVC_cross_val_predict(representation_name, dataset, X, y, classifi
         rc_we_cv.append(np.round(recall_score(y.iloc[fold_test_index, :], y_pred[fold_test_index], average="weighted"), decimals=5))
         hamm_cv.append(np.round(hamming_loss(y.iloc[fold_test_index, :], y_pred[fold_test_index]), decimals=5))
 
+    means = list(np.mean([acc_cv, f1_mi_cv, f1_ma_cv, f1_we_cv, pr_mi_cv, pr_ma_cv, pr_we_cv, rc_mi_cv, rc_ma_cv, rc_we_cv, hamm_cv], axis=1))
+    means = [np.round(i, decimals=5) for i in means]
+
+    stds = list(np.std([acc_cv, f1_mi_cv, f1_ma_cv, f1_we_cv, pr_mi_cv, pr_ma_cv, pr_we_cv, rc_mi_cv, rc_ma_cv, rc_we_cv, hamm_cv], axis=1))
+    stds = [np.round(i, decimals=5) for i in stds]
+
     return {
         "cv_results": [representation_name + "_" + dataset, acc_cv, f1_mi_cv, f1_ma_cv, f1_we_cv, pr_mi_cv, pr_ma_cv, pr_we_cv, rc_mi_cv, rc_ma_cv, rc_we_cv, hamm_cv],
+        "means": [representation_name + "_" + dataset] + means,
+        "stds": [representation_name + "_" + dataset] + stds,
         "predictions": y_pred
     }
 
@@ -101,6 +109,8 @@ def ProtDescModel():
         filtered_datasets = [dataset for dataset in datasets if aspect_type in dataset and dataset_type in dataset]
 
     cv_results = []
+    cv_mean_results = []
+    cv_std_results = []
 
     for dt in tqdm(filtered_datasets, total=len(filtered_datasets)):
         print(f"Protein function prediction is started for the dataset: {dt.split('.')[0]}")
@@ -119,17 +129,26 @@ def ProtDescModel():
 
         if model is not None:
             cv_results.append(model["cv_results"])
+            cv_mean_results.append(model["means"])
+            cv_std_results.append(model["stds"])
 
     return {
-        "cv_results": cv_results
+        "cv_results": cv_results,
+        "cv_mean_results": cv_mean_results,
+        "cv_std_results": cv_std_results
     }
 
 def pred_output():
     model = ProtDescModel()
     cv_result = model["cv_results"]
 
+    cv_mean_result = model["cv_mean_results"]
+    cv_std_result = model["cv_std_results"]
+
     return {
-        "cv_result": cv_result
+        "cv_result": cv_result,
+        "cv_mean_result": cv_mean_result,
+        "cv_std_result": cv_std_result
     }
 
 # Example call to the function