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# 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}")
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