Add hierarchical precision, recall, and F-measure calculation function

ham.py

@@ -0,0 +1,49 @@
+# After review with respect to equations
+
+
+def hierarchical_precision_recall_fmeasure(
+    true_labels, predicted_labels, ancestors, beta=1.0
+):
+    # Initialize counters for true positives, predicted, and true conditions
+    true_positive_sum = predicted_sum = true_sum = 0
+
+    # Process each instance
+    for true, predicted in zip(true_labels, predicted_labels):
+        # Extend the sets with ancestors
+        extended_true = true.union(
+            *[ancestors[label] for label in true if label in ancestors]
+        )
+        extended_predicted = predicted.union(
+            *[ancestors[label] for label in predicted if label in ancestors]
+        )
+
+        # Update counters
+        true_positive_sum += len(extended_true.intersection(extended_predicted))
+        predicted_sum += len(extended_predicted)
+        true_sum += len(extended_true)
+
+    # Calculate hierarchical precision and recall
+    hP = true_positive_sum / predicted_sum if predicted_sum else 0
+    hR = true_positive_sum / true_sum if true_sum else 0
+
+    # Calculate hierarchical F-measure
+    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}")