Fix macro average calculation in geometric_mean_score for binary classification

imblearn/metrics/_classification.py

@@ -662,25 +662,36 @@ class is unrecognized by the classifier, G-mean resolves to zero. To
     >>> geometric_mean_score(y_true, y_pred, correction=0.001)
     0.010...
     >>> geometric_mean_score(y_true, y_pred, average='macro')
-    0.471...
+    0.288...
     >>> geometric_mean_score(y_true, y_pred, average='micro')
     0.471...
     >>> geometric_mean_score(y_true, y_pred, average='weighted')
-    0.471...
+    0.288...
     >>> geometric_mean_score(y_true, y_pred, average=None)
     array([0.866...,  0.       ,  0.       ])
     """
     if average is None or average != "multiclass":
-        sen, spe, _ = sensitivity_specificity_support(
+        # LOGIC:
+        # For macro and weighted, calculate per-class first, then average
+        calc_average = None if average in ["macro", "weighted"] else average
+
+        sen, spe, sup = sensitivity_specificity_support(
             y_true,
             y_pred,
             labels=labels,
             pos_label=pos_label,
-            average=average,
+            average=calc_average,
             warn_for=("specificity", "specificity"),
             sample_weight=sample_weight,
         )
 
+        if average in ["macro", "weighted"]:
+            gmean_per_class = np.sqrt(sen * spe)
+            if average == "macro":
+                return np.mean(gmean_per_class)
+            else: # weighted
+                return np.average(gmean_per_class, weights=sup)
+
         return np.sqrt(sen * spe)
     else:
         present_labels = unique_labels(y_true, y_pred)