Catboost

Changes:
* Pure GPU implementation of NDCG metric
* Enabled LQ loss function
* Fixed NDCG metric on CPU
* Added `model_sum` mode to command line interface
* Added SHAP values benchmark (566)
* Fixed `random_strength` for `Plain` boosting (448)
* Enabled passing a test pool to caret training (544)
* Fixed a bug in exporting the model as python code (556)
* Fixed label mapper for multiclassification custom labels (523)
* Fixed hash type of categorical features (558)
* Fixed handling of cross-validation fold count options in python package (568)

Changes:
* Accelerated formula evaluation by ~15%
* Improved model application interface
* Improved compilation time for building GPU version
* Better handling of stray commas in list arguments
* Added a benchmark that employs Rossman Store Sales dataset to compare quality of GBDT packages
* Added references to Catboost papers in R-package CITATION file
* Fixed a build issue in compilation for GPU
* Fixed a bug in model applicator
* Fixed model conversion, 533
* Returned pre 0.11 behaviour for `best_score_` and `evals_result_` (issue 539)
* Make valid RECORD in wheel (issue 534)

Changes:
* Changed default border count for float feature binarization to 254 on CPU to achieve better quality
* Fixed random seed to `0` by default
* Support model with more than 254 feature borders or one hot values when doing predictions
* Added model summation support in python: use `catboost.sum_models()` to sum models with provided weights.
* Added json model tutorial [json_model_tutorial.ipynb](https://github.com/catboost/catboost/blob/master/catboost/tutorials/apply_model/json_model_tutorial.ipynb)

Changes:
- Bugfix for 518

Breaking changes:
In python 3 some functions returned dictionaries with keys of type `bytes` - particularly eval_metrics and get_best_score. These are fixed to have keys of type `str`.
Changes:
- New metric NumErrors:greater_than=value
- New metric and objective L_q:q=value
- model.score(X, y) - can now work with Pool and labels from Pool

Changes:
* Added EvalResult output after GPU catboost training
* Supported prediction type option on GPU
* Added `get_evals_result()` method and `evals_result_` property to model in python wrapper to allow user access metric values
* Supported string labels for GPU training in cmdline mode
* Many improvements in JNI wrapper
* Updated NDCG metric: speeded up and added NDCG with exponentiation in numerator as a new NDCG mode
* CatBoost doesn't drop unused features from model after training
* Write training finish time and catboost build info to model metadata
* Fix automatic pairs generation for GPU PairLogitPairwise target