MuLaConf: Multi-label Conformal Prediction

This paper presents MuLaConf, a Scikit-Learn-compatible Python package that implements Inductive (or Split) Conformal Prediction for multi-label classification tasks. Conformal Prediction is a rigorous machine learning framework for constructing prediction regions with coverage guarantees under the minimal assumption of data exchangeability. In multi-label classification, the Powerset Scoring approach explicitly computes p-values for all possible label combinations. However, it often yields un informatively large prediction sets. MuLaConf addresses this by defining nonconformity measures in the error vector space. This allows users to utilize either the Mahalanobis distance to capture label correlations or the standard Euclidean Norm. Furthermore, the package incorporates structural penalties, based on Hamming distance and label-set cardinality, to significantly reduce prediction set sizes while strictly maintaining theoretical coverage. To overcome the exponential computational complexity of the powerset space, MuLaConf leverages PyTorch-accelerated tensor operations and a memory management mechanism. Finally, its modular architecture features lazy evaluation, enabling the on-the-fly updating of distance measures and penalty weights without the need to retrain the underlying machine learning models. Together, these features make MuLaConf a practical tool for reliable uncertainty quantification in multi-label settings and a foundation for future research in Conformal Prediction.