We present CAFA-evaluator, a powerful Python program designed to evaluate the performance of prediction methods on targets with hierarchical concept dependencies. It generalizes multi-label evaluation to modern ontologies where the prediction targets are drawn from a directed acyclic graph and achieves high efficiency by leveraging matrix computation and topological sorting. The program requirements include a small number of standard Python libraries, making CAFA-evaluator easy to maintain. The code replicates the Critical Assessment of protein Function Annotation (CAFA) benchmarking, which evaluates predictions of the consistent subgraphs in Gene Ontology. Owing to its reliability and accuracy, the organizers have selected CAFA-evaluator as the official CAFA evaluation software.

翻译：我们提出CAFA-evaluator，这是一个强大的Python程序，旨在评估具有层次概念依赖关系的目标上预测方法的性能。它将多标签评估推广到现代本体中（其中预测目标来自有向无环图），并通过利用矩阵计算和拓扑排序实现高效率。该程序所需的依赖项包括少量标准Python库，使CAFA-evaluator易于维护。该代码复现了蛋白质功能注释关键评估（CAFA）的基准测试，该测试评估基因本体中一致子图的预测。由于其可靠性和准确性，组织者已选择CAFA-evaluator作为官方CAFA评估软件。