Worked on the pytorch/ignite repository to improve the robustness of clustering metrics, specifically focusing on the SilhouetteScore implementation. Addressed a critical issue where invalid cluster configurations previously caused runtime crashes by updating the metric to return NaN for edge cases, such as when the number of unique labels was insufficient. Enhanced the reliability and performance of label counting by replacing set-based logic with NumPy’s np.unique function. Added comprehensive, parametrized tests to cover edge scenarios, increasing regression safety. Utilized Python and data science skills throughout, with an emphasis on machine learning workflows and rigorous testing to ensure stable clustering evaluations.