catboost/catboost v0.21 on GitHub

New features:

The main feature of this release is the Stochastic Gradient Langevin Boosting (SGLB) mode that can improve quality of your models with non-convex loss functions. To use it specify langevin option and tune diffusion_temperature and model_shrink_rate. See the corresponding paper for details.

Automatic learning rate is applied by default not only for Logloss objective, but also for RMSE (on CPU and GPU) and MultiClass (on GPU).
Class labels type information is stored in the model. Now estimators in python package return values of proper type in classes_ attribute and for prediction functions with prediction_type=Class. #305, #999, #1017.
Note: Class labels loaded from datasets in CatBoost dsv format always have string type now.

Fixed huge memory consumption for text features. #1107
Fixed crash on GPU on big datasets with groups (hundred million+ groups).
Fixed class labels consistency check and merging in model sums (now class names in binary classification are properly checked and added to the result as well)
Fix for confusion matrix (PR #1152), thanks to @dmsivkov.
Fixed shap values calculation when boost_from_average=True. #1125
Fixed use-after-free in fstr PredictionValuesChange with specified dataset
Target border and class weights are now taken from model when necessary for feature strength, metrics evaluation, roc_curve, object importances and calc_feature_statistics calculations.
Fixed that L2 regularization was not applied for non symmetric trees for binary classification on GPU.
[R-package] Fixed the bug that catboost.get_feature_importance did not work after model is loaded #1064
[R-package] Fixed the bug that catboost.train did not work when called with the single dataset parameter. #1162
Fixed L2 score calculation on CPU

Starting from this release Java applier is released simultaneously with other components and has the same version.

Models trained with this release require applier from this release or later to work correctly.