| # 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 | |