remove unused code in metrics

deepod/metrics/_anomaly_detection.py

@@ -3,6 +3,7 @@
 from deepod.metrics.affiliation.generics import convert_vector_to_events
 from deepod.metrics.vus.metrics import get_range_vus_roc
 from deepod.metrics.affiliation.metrics import pr_from_events
+from deepod.metrics._tsad_adjustment import point_adjustment
 
 
 def auc_roc(y_true, y_score):
@@ -169,19 +170,19 @@ def ts_metrics_enhanced(y_true, y_score, y_test):
         tuple: A tuple containing:
         
         - auroc (float):
-            The score of the area under the ROC curve.
+            The score of the area under the ROC curve after point adjustment.
             
         - aupr (float):
-            The score of the area under the precision-recall curve.
+            The score of the area under the precision-recall curve after point adjustment.
             
         - best_f1 (float): 
-            The best score of F1-score.
+            The best score of F1-score after point adjustment.
             
         - best_p (float): 
-            The best score of precision.
+            The best score of precision after point adjustment.
             
         - best_r (float): 
-            The best score of recall.
+            The best score of recall after point adjustment.
             
         - affiliation_precision (float):
             The score of affiliation precision.
@@ -202,16 +203,15 @@ def ts_metrics_enhanced(y_true, y_score, y_test):
             The score of VUS PR.
     """
 
-    best_f1, best_p, best_r = get_best_f1(y_true, y_score)
+    auroc = auc_roc(y_true, point_adjustment(y_true, y_score))
+    aupr = auc_pr(y_true, point_adjustment(y_true, y_score))
+    best_f1, best_p, best_r = get_best_f1(y_true, point_adjustment(y_true, y_score))
 
     events_pred = convert_vector_to_events(y_test)
     events_gt = convert_vector_to_events(y_true)
     Trange = (0, len(y_test))
     affiliation = pr_from_events(events_pred, events_gt, Trange)
-    vus_results = get_range_vus_roc(y_score, y_true, 100)  # default slidingWindow = 100
-
-    auroc = auc_roc(y_true, y_score)
-    aupr = auc_pr(y_true, y_score)
+    vus_results = get_range_vus_roc(y_score, y_true, slidingWindow=100)  # default slidingWindow = 100
 
     affiliation_precision = affiliation['Affiliation_Precision']
     affiliation_recall = affiliation['Affiliation_Recall']

deepod/metrics/vus/metrics.py

@@ -1,11 +1,13 @@
-from .utils.metrics import metricor
-from .analysis.robustness_eval import generate_curve
+from deepod.metrics.vus.utils.metrics import metricor
 
 
 def get_range_vus_roc(score, labels, slidingWindow):
-    grader = metricor()
-    R_AUC_ROC, R_AUC_PR, _, _, _ = grader.RangeAUC(labels=labels, score=score, window=slidingWindow, plot_ROC=True)
-    _, _, _, _, _, _,VUS_ROC, VUS_PR = generate_curve(labels, score, 2*slidingWindow)
+    R_AUC_ROC, R_AUC_PR, _, _, _ = metricor().RangeAUC(labels=labels, score=score,
+                                                       window=slidingWindow, plot_ROC=True)
+    VUS_ROC, VUS_PR = metricor().RangeAUC_volume(labels_original=labels,
+                                                 score=score,
+                                                 windowSize=2*slidingWindow)
+
     metrics = {'R_AUC_ROC': R_AUC_ROC, 'R_AUC_PR': R_AUC_PR, 'VUS_ROC': VUS_ROC, 'VUS_PR': VUS_PR}
 
     return metrics