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def find_ancestors(tree, code):
"""
Recursively finds ancestors of a given class (e.g., an ISCO-08 code) in a hierarchical JSON structure.
Args:
- tree: A dictionary representing the hierarchical structure.
- code: A string representing the label of the class.
Returns:
- A list of strings, each representing an ancestor of the input class.
"""
ancestors = []
current = code
while current:
parent = tree[current]["parent"]
if parent:
ancestors.append(parent)
current = parent
return ancestors
def calculate_hierarchical_measures(true_labels, predicted_labels, tree):
"""
Calculates hierarchical precision, recall, and F-measure in a hierarchical structure.
Args:
- true_labels: A list of strings representing true class labels.
- predicted_labels: A list of strings representing predicted class labels.
- tree: A dictionary representing the hierarchical structure.
Returns:
- hP: A floating point number representing hierarchical precision.
- hR: A floating point number representing hierarchical recall.
- hF: A floating point number representing hierarchical F-measure.
"""
extended_true = [set(find_ancestors(tree, code) | {code}) for code in true_labels]
extended_pred = [
set(find_ancestors(tree, code) | {code}) for code in predicted_labels
]
true_positive = sum(len(t & p) for t, p in zip(extended_true, extended_pred))
predicted = sum(len(p) for p in extended_pred)
actual = sum(len(t) for t in extended_true)
hP = true_positive / predicted if predicted else 0
hR = true_positive / actual if actual else 0
hF = (2 * hP * hR) / (hP + hR) if (hP + hR) else 0
return hP, hR, hF
def hierarchical_f_measure(hP, hR, beta=1.0):
"""Calculate the hierarchical F-measure."""
if hP + hR == 0:
return 0
return (beta**2 + 1) * hP * hR / (beta**2 * hP + hR)
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