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| """This module provides functions for calculating hierarchical precicion, recall and f1.""" | |
| from typing import List, Set, Dict, Tuple | |
| def find_ancestors(node: str, hierarchy: dict) -> set: | |
| """ | |
| Find the ancestors of a given node in a hierarchy. | |
| Args: | |
| node (str): The node for which to find ancestors. | |
| hierarchy (dict): A dictionary representing the hierarchy, where the keys are nodes and the values are their parents. | |
| Returns: | |
| set: A set of ancestors of the given node. | |
| """ | |
| ancestors = set() | |
| nodes_to_visit = [node] | |
| while nodes_to_visit: | |
| current_node = nodes_to_visit.pop() | |
| if current_node in hierarchy: | |
| parents = hierarchy[current_node] | |
| ancestors.update(parents) | |
| nodes_to_visit.extend(parents) | |
| return ancestors | |
| def extend_with_ancestors(classes: set, hierarchy: dict) -> set: | |
| """ | |
| Extend the given set of classes with their ancestors from the hierarchy. | |
| Args: | |
| classes (set): The set of classes to extend. | |
| hierarchy (dict): The hierarchy of classes. | |
| Returns: | |
| set: The extended set of classes including their ancestors. | |
| """ | |
| extended_classes = set(classes) | |
| for cls in classes: | |
| ancestors = find_ancestors(cls, hierarchy) | |
| extended_classes.update(ancestors) | |
| return extended_classes | |
| def calculate_hierarchical_precision_recall( | |
| reference_codes: List[str], | |
| predicted_codes: List[str], | |
| hierarchy: Dict[str, Set[str]], | |
| ) -> Tuple[float, float]: | |
| """ | |
| Calculates the hierarchical precision and recall given the reference codes, predicted codes, and hierarchy definition. | |
| Args: | |
| real_codes (List[str]): The list of reference codes. | |
| predicted_codes (List[str]): The list of predicted codes. | |
| hierarchy (Dict[str, Set[str]]): The hierarchy definition where keys are nodes and values are sets of parent nodes. | |
| Returns: | |
| Tuple[float, float]: A tuple containing the hierarchical precision and recall floating point values. | |
| """ | |
| # Extend the sets of real and predicted codes with their ancestors | |
| extended_real = set() | |
| for code in reference_codes: | |
| extended_real.add(code) | |
| extended_real.update(hierarchy.get(code, set())) | |
| extended_predicted = set() | |
| for code in predicted_codes: | |
| extended_predicted.add(code) | |
| extended_predicted.update(hierarchy.get(code, set())) | |
| # Calculate the intersection | |
| correct_predictions = extended_real.intersection(extended_predicted) | |
| # Calculate hierarchical precision and recall | |
| hP = len(correct_predictions) / len(extended_predicted) if extended_predicted else 0 | |
| hR = len(correct_predictions) / len(extended_real) if extended_real else 0 | |
| return hP, hR | |
| def hierarchical_f_measure(hP, hR, beta=1.0): | |
| """ | |
| Calculate the hierarchical F-measure. | |
| Parameters: | |
| hP (float): The hierarchical precision. | |
| hR (float): The hierarchical recall. | |
| beta (float, optional): The beta value for F-measure calculation. Default is 1.0. | |
| Returns: | |
| float: The hierarchical F-measure. | |
| """ | |
| if hP + hR == 0: | |
| return 0 | |
| return (beta**2 + 1) * hP * hR / (beta**2 * hP + hR) | |