Catboost

New features
* Multiquantile regression

Now it's possible to train models with shared tree structure and multiple predicted quantile values in each leaf. Currently this approach doesn't give a strong guarantee for predicted quantile values consistency, but it still provides more consistency than training multiple independent models for each quantile. You can read [short description in the documentation](https://catboost.ai/en/docs/concepts/loss-functions-regression#MultiQuantile). Short example for Python: `loss_function='MultiQuantile:alpha=0.2,0.4'`. Supported only on CPU for now.
* Support text and embedding features for regression and ranking.
* Spark: Read/write Spark's Dataset-like API for Pool. 2030
* Support HashedCateg column type. This allows to use externally prehashed categorical features both in training and prediction.
* New option `plot_file` in Python functions with `plot` parameter allows to save plots to file. 758
* Add eval_fraction parameter. 1500
* Non-symmetric trees model summation.
* `init_model` parameter now works with non-symmetric trees.
* Partial support for Apache Spark 3.3 (only for Scala 2.12 and without PySpark).

Speedups
* 2x speedup DCG, nDCG and FilteredDCG metrics calculation for groups with >= 50 objects and with top=-1 (all objects from each group, default value)
* Fixed 2x slowdown of PairLogit and other ranking losses on CPU introduced in release 0.23

Bugfixes
* Fix for pandas integer array. 2096
* Save feature names to json format. 2102
* Fix feature weights on CPU
* Use feature weights on GPU
* Fix gradient calculation for QueryRMSE on GPU
* Fix ranking metrics with group weights in calc_metrics
* Fix JVM applier on data with text features. 2132

New features

* Fixed splits for binary features on gpu for non-symmetric trees -- specify the set of splits to start each tree in the model with `--fixed-binary-splits` or `fixed_binary_splits` in Python package (by default, there are no fixed splits)


Documentation

* New sections on [MultiRMSEWithMissingValues](https://catboost.ai/en/docs/concepts/loss-functions-multiregression#MultiRMSEWithMissingValues)
and [LogCosh](https://catboost.ai/en/docs/concepts/loss-functions-regression#LogCosh)
* New section on [get_embedding_feature_indices](https://catboost.ai/en/docs/concepts/python-reference_pool_get_embedding_feature_indices)
* Add info on gpu support for metrics


Bug-fixes

* Fix warning about resetting logger when logging to sys.stdout & sys.stderr from different threads 1855
* Fix model summation in CatBoost for Apache Spark
* Fix performance and scalability of query auc for ranking (1m samples, query size 2, 8 cpu cores 0.55s -> 0.04s)
* Fix support for text features and embeddings in Java applier 2043
* Fix nan/inf split scores with yeti rank pairwise loss
* Fix nan/inf feature strengths in pair logit on cpu

New features

* Support Apple Darwin arm64 architecture. 1526.
* Support feature tags in feature selection.
* Support for Apache Spark 3.2.
* Model sum in Apache Spark.

Python package

* Accommodate multiple target-platform arguments used to build universal binaries.
* Add grid creation function to utils.py
* Custom multilabel eval metrics by ELitvinova
* Metrics plotter by evgenabramov
* Fbeta score by ELitvinova

Bugfixes

* Fix group weights in metrics calculation.
* Fix `fit` for PySpark estimators. 1976.
* Fix predict on GPU. 1901, 1923.
* Disable exact leafs calculation for `MAE`, `MAPE`, `Quantile` on GPU.
* Fix counter description for plotting. 1973.
* Allow weights in `BrierScore`. 1967.
* Disable AUC calculation for learn by default on GPU as well.
* Fix `plot_tree` example in documentation.
* Fix plots in `cv`.
* Fix ui32 overflows in pairwise losses on GPU.
* Fix for multiclass in nodejs evaluator. 1903.
* Fix CatBoost R package installation on Monterey. 1912.
* Fix CUDA error 700 caused by data race in mimalloc and CUDA driver.
* Fix slow compilation with CUDA 11.2+.
* Fix 2nd derivative in RMSEWithUncertainty.

New features
* Add `sort` param to `FilteredDCG` metric.
* Add `StochasticRank` for `FilteredDCG`.

Python package
* add is_max/minimizable methods. 1915
* Support custom metric in select_features 1920

R package
* Register functions from libcatboostr natively in R, removing one of CRAN notes.

Bugfixes
* Fix apply for models without main `loss_function`.
* Fix text calcer options specification. 1916
* Fix `calc_feature_statistics`
* Fix Multi-approx support in CLI `calc_metrics` mode.
* Fix processing for text options. 1930
* Fix snapshot saving in feature selection.
* Fix CatBoost models serialization inside pipeline models in PySpark. 1936

CatBoost for Apache Spark
* Fix Linux `so` in deployed Maven artifacts (no code changes)

CatBoost for Apache Spark
* PySpark: Fix python -> JVM `datetime.timedelta` conversion.
* Fix: proper handling of constant categorical features. 1867
* Fix SIGSEGV for for Multiclassification with Ctrs. 1886

New features.
* Add `is_min_optimal`, `is_max_optimal` for `BuiltinMetrics`. 1890

R package
* Use `libcatboostr-darwin.dylib` instead of `libcatboostr-darwin.so` on macOS. 1834

Bugfixes
* Fix `CatBoostError: (No such file or directory) bad new file name` when using `grid_search`. 1893