GitLab

* use json instead of repr/eval for pandas_categorical

* fix json dumps with numpy data

* add more test cases

* expose save model to string to c_api

* add 'save-model-to-string' to python; use it to copy model

* remove boosting_type

* add plot importance

* add plot example

* Attempt to fix categorical issue in R package

* Add small fix for categorical IDs

* Add error for unknown numbers/columns

* fix bug for categorical_feature

* add test on load model with categorical feature

* add unseen category in test dataset

* save/load pandas_categorical to model

* fix logic

* cast pandas columns to string

* add load pandas_categorical from file to _InnerPredictor init

* Added possibility to compile without openmp (useful for clang on Mac)

* Added CMake option to create dylib on MacOS

sklearn compatibility update- renames .feature_importance_ to .featur…

minor typo fixes

* suppprt pandas categorical

* refine logic

* make default=auto

* fix train/valid categorical codes

* add test

* unify set _predictor

* fix tests

* fix warning

* support feature_name=int