Update src/bin/function_predictor.py

src/bin/function_predictor.py

@@ -86,14 +86,11 @@ def MultiLabelSVC_cross_val_predict(representation_name, dataset, X, y, classifi
     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]
+    #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
     }
 
 def ProtDescModel():
@@ -108,9 +105,9 @@ def ProtDescModel():
     else:
         filtered_datasets = [dataset for dataset in datasets if aspect_type in dataset and dataset_type in dataset]
 
-    cv_results = []
+    #cv_results = []
     cv_mean_results = []
-    cv_std_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]}")
@@ -128,28 +125,16 @@ def ProtDescModel():
                                                 classifier=BinaryRelevance(SGDClassifier(n_jobs=cpu_number, random_state=42)))
 
         if model is not None:
-            cv_results.append(model["cv_results"])
+            #cv_results.append(model["cv_results"])
             cv_mean_results.append(model["means"])
-            cv_std_results.append(model["stds"])
+            #cv_std_results.append(model["stds"])
 
-    return {
-        "cv_results": cv_results,
-        "cv_mean_results": cv_mean_results,
-        "cv_std_results": cv_std_results
-    }
+    return cv_mean_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"]
+    result = ProtDescModel()
 
-    return {
-        "cv_result": cv_result,
-        "cv_mean_result": cv_mean_result,
-        "cv_std_result": cv_std_result
-    }
+    return result
 
 # Example call to the function
 # result = pred_output()