In this paper we present SurvLIMEpy, an open-source Python package that implements the SurvLIME algorithm. This method allows to compute local feature importance for machine learning algorithms designed for modelling Survival Analysis data. Our implementation takes advantage of the parallelisation paradigm as all computations are performed in a matrix-wise fashion which speeds up execution time. Additionally, SurvLIMEpy assists the user with visualization tools to better understand the result of the algorithm. The package supports a wide variety of survival models, from the Cox Proportional Hazards Model to deep learning models such as DeepHit or DeepSurv. Two types of experiments are presented in this paper. First, by means of simulated data, we study the ability of the algorithm to capture the importance of the features. Second, we use three open source survival datasets together with a set of survival algorithms in order to demonstrate how SurvLIMEpy behaves when applied to different models.

本文介绍了一款名为SurvLIMEpy的Python工具包，实现了SurvLIME算法，可用于计算模型预测的生存分析数据的局部特征重要性，并提供了可视化工具来更好地理解算法结果，支持多种生存模型。通过模拟数据和三个公开生存数据集的实验，研究了算法捕捉特征重要性和应用不同模型时的行为。

SurvLIMEpy: 一个实现SurvLIME的Python包