MuLaConf Package

A flexible Python package for Conformal Prediction (CP) in Multi-label classification settings. It implements the Powerset Scoring approach [3] using both the Mahalanobis distance [1] and the standard Euclidean Norm [4] as nonconformity measures, and applies Structural Penalties to provide more informative prediction sets, based on Hamming distance and label-set cardinality [2]. Designed for efficiency, it handles model training, calibration, and the on-the-fly update of structural penalty weights or distance measures without the need for model retraining. This package bridges Scikit-Learn (for the underlying classifiers) and PyTorch (for efficient tensor computations and GPU acceleration).

Key Features

• Multi-label Conformal Prediction: Provides sets of label-sets with guaranteed coverage under the assumption of data exchangeability.

• Powerset Scoring: Explicitly assigns p-values to all possible label-sets.

• Distance Measures: Supports both the Mahalanobis distance and the standard Euclidean Norm in the error vector space.

• Structural Penalties: Incorporates Hamming and Cardinality penalties to produce more informative prediction sets.

• Post-training Penalty Updates: Modify penalty weights after fitting, with no need to retrain the model or recalculate the covariance matrix.

• Automatic Classifier Switching: Replace the underlying classifier (e.g., from RandomForestClassifier to KNeighborsClassifier) and let the wrapper handles retraining automatically.

• Compatible with any model: Provides a wrapper (ICPWrapper) for any sklearn multi-label classifier (e.g., MultiOutputClassifier, ClassifierChain) plus a model agnostic InductiveConformalPredictor.

• GPU Support: Offloads heavy matrix computations to CUDA devices.

Contents:

• Getting Started
  • Installation
  • Tutorial
  • Alternative usage
  • Performance & Memory Management
• The MuLaConf Package
  • Package Wrapper
  • Inductive Conformal Predictor
  • Prediction Regions
• Citing package
• References