1.2.1

New features

• Allow to optimize specific ranking loss functions with YetiRank and YetiRankPairwise by specifying mode parameter. See Which Tricks are Important for Learning to Rank? paper for details (this family of losses is called YetiLoss there). CPU-only for now.
• Add Kernel Gradient Boosting support (use catboost.sample_gaussian_process function). #2408, thanks to @TakeOver. See Gradient Boosting Performs Gaussian Process Inference paper for details.
• LambdaMart loss: support new target metrics MRR, ERR and MAP.
• StochasticRank loss: support new target metrics ERR and MRR.
• Support MultiRMSE on GPU. #2264, #2390
• Load JSON model format in Java Client. #1627, thanks to @timotta
• Implement exporting of Multiclass models to C++ and Python. #2283, thanks to @antoninkriz

Improvements

• Speedup BM25 feature calcers 3x
• Use int instead of deprecated numpy.int. #2378
• Add ModelCalcerWrapper::CalcFlatTransposed, #2413 thanks to @faucct
• Update dependencies to avoid known vulnerabilities

Bugfixes

• Fix __shfl_up_sync mask. #2339
• TFocalMetric negative values fix. #2386, thanks to @diditforlulz273
• Focal loss: Use user-defined alpha and gamma
• Fix exception propagation: Rethrow exceptions caused by user's python code as C++ exceptions
• CatBoost trained with user defined objective was incompatible with ShapValues calculation
• Avoid nan's in Newton step calculation for RMSEWithUncertainty
• Fix score method for y with shape (N, 1). #2405
• Fix scalePosWeight support for Spark. #2470