Changelog

1.0.2 (2025-02-17)

  • Refactored examples codebase for better organization and clarity

  • Enhanced classification and Graph trainers with improved architecture

  • Added new loss functions and trainer for Uncertainty-aware classifiers

  • Changed default quantile value to infinity for better handling of edge cases

  • Fixed handling of large calibration sets (>2^24 elements) in quantile computation (#45)

1.0.1 (2024-12-16)

  • Fixed the bugs of the RAPS score function and covgap in classification task

  • Refactored the classification.loss, graph.score.snaps and regression.predictor.aci

  • Fixed the bug where logo was not displayed in PyPi

  • Updated the requirements.txt and examples for classification

  • Added the trainer for Temperature Scaling and ConfTS in classification.trainer

  • Added the Changelog page in the ReadtheDocs documentation

1.0.0 (2024-12-06)

  • Added new score functions and training methods for classification, including KNN, TOPK, C-Adapter, and ConfTS.

  • Introduced CP algorithms for graph node classification, such as DAPS, SNAPS, and NAPS.

  • Added new conformal algorithms for regression, including CQRFM, CQRR, CQRM, and Ensemble CP.

  • Introduced CP algorithms for LLMs.

  • Added unit-test and examples.

  • Optimized the form of prediction sets to improve the computational efficiency.

  • Refactored the module design of Regression to improve the scalability.

0.1.3 (2024-02-22)

  • Introduced R2CCP in regression task.

0.1.2 (2023-12-24)

  • Introduced the ReadtheDocs documentation for TorchCP.

0.1.1 (2023-12-24)

  • Introduced Margin score in classification task.

0.1.0 (2023-12-23)

  • Introduced CP algorithms for classification, including ConfTr, THR, APS, RAPS, SAPS, Classwise CP, Clustered CP and Weighted CP.

  • Introduced CP algorithms for regression, including ACI, ABS and CQR.