Refactor hierarchical_precision_recall_fmeasure function***

ham.py

@@ -30,20 +30,3 @@ def hierarchical_precision_recall_fmeasure(
     hF = ((beta**2 + 1) * hP * hR) / (beta**2 * hP + hR) if (hP + hR) else 0
 
     return hP, hR, hF
-
-
-# Example usage:
-true_labels = [{"G"}]  # The true class for the instance
-predicted_labels = [{"F"}]  # The predicted class for the instance
-ancestors = {  # The ancestors for each class, excluding the root
-    "G": {"B", "C", "E"},
-    "F": {"C"},
-}
-
-# Calculate hierarchical measures
-hP, hR, hF = hierarchical_precision_recall_fmeasure(
-    true_labels, predicted_labels, ancestors
-)
-print(f"Hierarchical Precision (hP): {hP}")
-print(f"Hierarchical Recall (hR): {hR}")
-print(f"Hierarchical F-measure (hF): {hF}")