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：我们提出了CAFA-evaluator，这是一款强大的Python程序，旨在评估具有层次概念依赖性的目标预测方法的性能。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运行。该程序仅需少量标准Python库，便于维护。该代码复现了蛋白质功能注释关键评估（CAFA）基准测试，用于评估基因本体中一致子图的预测。凭借其可靠性和准确性，组织方已选定CAFA-evaluator作为官方CAFA评估软件。