Changelog ========= 1.2.1 (2025-10-13) ------------------ * Updated the automatic dependency installation of TorchCP. 1.2.0 (2025-08-26) ------------------ * Added Conformal Predictive Distribution in regression task. * Added p-value computation in classification predictors and regression predictors. * Added more tyeps of difficulty estimation in the NORABS score function. 1.1.0 (2025-07-15) ------------------ * Refactored `__init__` of predictors to include `alpha` and `device` parameters for greater flexibility. * Added EntmaxScore, SCPO and RC3P methods for classification tasks. * Added the normalized residual score in regression task. * Added p-value computation and efficiency metrics from the paper "Criteria of Efficiency for Conformal Prediction". 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, LAC, APS, RAPS, SAPS, ClassConditional CP, Clustered CP and Weighted CP. * Introduced CP algorithms for regression, including ACI, ABS and CQR.