GitLab

* add start_iteration in model saving

* fix test

* shuffle models ability

* fix bug

* update document

* refine

* Update engine.py

* Update basic.py

* fix comments

* fix comment

* fix include

* reduce dependency on header file

* fix build

* decode error description

* added break line char in log massages

* added code to expose c_api_pred_contrib in the R package

* removed Rprintf

* reverted to previous version of install.libs.R

* use int64 for string

* [R] Fatal when CSC exceed int32.max

* fix r random crash problem.

* fix R error msg.

* add R's library file to vs project and cmake.

* support using dll built by vs.

* better search for the library file.

* remove mingw related doc .

* update document.

* Let R handle the library compile.

* try fix build from github.

* Update README.md

* cleaner build.

* fix the install problem in linux.

* Update README.md

* fix multi-threading.

* fix name style.

* support in CLI version.

* remove warnings.

* Not default parameters.

* fix if...else... .

* fix bug.

* fix warning.

* refine c_api.

* fix R-package.

* fix R's warning.

* fix tests.

* fix pep8 .

* Update lgb.Booster.R

Added method to call LGBM_BoosterLoadModelFromString_R if model_str is provided for initialization, added option to load from model_str in lgb.load

* Update lightgbm_R.cpp

Adding LGBM_BoosterLoadModelFromString_R

* Update lightgbm_R.cpp

Added LGBM_BoosterSaveModelToString_R

* Update lightgbm_R.cpp

* Update lgb.Booster.R

Added save_model_to_string method

* Update lgb.Booster.R

Implemented @Laurae2 comments

* Update lgb.Booster.R

* Update lightgbm_R.h

Added load and save model from/to string exports

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

* Added CMake option to create dylib on MacOS

* finish R's c_api

* clean code

* fix sizeof pointer in 32bit system.

* add predictor class

* add Dataset class

* format code

* add booster

* add type check for expose function

* add a simple callback

* add all callbacks

* finish the basic training logic

* update docs

* add an simple training interface

* add basic test

* adapt the changes in c_api

* add test for Dataset

* add test for custom obj/eval functions

* fix python test

* fix bug in metadata init

* fix R CMD check