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* fix bug for GOSS with customized objective (fixes #3243)

* Apply suggestions from code review
Co-authored-by: Guolin Ke <guolin.ke@outlook.com>

* Update multiclass_objective.hpp

* Apply suggestions from code review

* Update src/objective/multiclass_objective.hpp

* Apply suggestions from code review

* Update test_basic.R

* Update test_basic.R

* Update src/objective/multiclass_objective.hpp
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Apply suggestions from code review
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Fix bug introduced in PR #2992 for Fast predict

* Faster Fast predict API

* Add const to SingleRow Fast methods

* ~ Modified name of method DatasetLoader::CostructFromSampleData to DatasetLoader::ConstructFromSampleData.
& Build passes for Debug, Debug_DLL, DLL and Release (not tested Debug_mpi and Release_mpi).

* ~ Refactored indentations.
Co-authored-by: Guolin Ke <guolin.ke@outlook.com>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* fix

* Apply suggestions from code review

* Add capability to get possible max and min values for a model

* Change implementation to have return value in tree.cpp, change naming to upper and lower bound, move implementation to gdbt.cpp

* Update include/LightGBM/c_api.h
Co-Authored-By: Nikita Titov <nekit94-08@mail.ru>

* Change iteration to avoid potential overflow, add bindings to R and Python and a basic test

* Adjust test values

* Consider const correctness and multithreading protection

* Put everything possible as const

* Include shared_mutex, for now as unique_lock

* Update test values

* Put everything possible as const

* Include shared_mutex, for now as unique_lock

* Make PredictSingleRow const and share the lock with other reading threads

* Update test values

* Add test to check that model is exactly the same in all platforms

* Try to parse the model to get the expected values

* Try to parse the model to get the expected values

* Fix implementation, num_leaves can be lower than the leaf_value_ size

* Do not check for num_leaves to be smaller than actual size and get back to test with hardcoded value

* Change test order

* Add gpu_use_dp option in test

* Remove helper test method

* Remove TODO

* Add preprocessing option to compile with c++17

* Update python-package/setup.py
Co-Authored-By: Nikita Titov <nekit94-08@mail.ru>

* Remove unwanted changes

* Move option

* Fix problems introduced by conflict fix

* Avoid switching to c++17 and use yamc mutex library to access shared lock functionality

* Add extra yamc include

* Change header order

* some lint fix

* change include order and remove some extra blank lines

* Further fix lint issues

* Update c_api.cpp

* Further fix lint issues

* Move yamc include files to a new yamc folder

* Use standard unique_lock

* Update windows/LightGBM.vcxproj
Co-authored-by: Guolin Ke <guolin.ke@outlook.com>

* Update windows/LightGBM.vcxproj.filters
Co-authored-by: Guolin Ke <guolin.ke@outlook.com>

* Update windows/LightGBM.vcxproj.filters
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update windows/LightGBM.vcxproj.filters
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update windows/LightGBM.vcxproj.filters
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Fix problems coming from merge conflict resolution
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>
Co-authored-by: joanfontanals <jfontanals@ntent.com>
Co-authored-by: Guolin Ke <guolin.ke@outlook.com>

In the current implementation, the index is an int32, which will segfault with large data sets and a large number of estimators.

* [performance] Add Fast methods to C API for SingleRow Predictions

 * Add methods to C API to make single-row predictions faster:

   - LGBM_BoosterPredictForMatSingleRowFastInit (setup)
   - LGBM_BoosterPredictForMatSingleRowFast (predict)
   - LGBM_FastConfigFree (cleanup setup outputs)

* Code syle cleanup

* Fix lint errors

* [performance] Revert FastConfig improvement to pass data at init

This reduces optimization by 5% / 30% with this branch but makes it so it can be used for higher level wrappers in MMLSpark.
And outside it as well.

* [performance] Introduce Fast variants for SingleRow predictors.

Although this already provides performance gains by itself for any
callers, two new functions were added to Java's SWIG interfaces to
exploit that AND the GetPrimitiveArrayCritical data fetches.

* [tests/profiling] Profile Fast predict methods

Build with -DBUILD_PROFILING_TESTS=ON and copy the default
model trained on the Higgs dataset from the benchmarks repo

 https://github.com/guolinke/boosting_tree_benchmarks.git



to LightGBM repo root and run the lightgbm_profile_* binaries.

The single instance used is the first row from that dataset.

* Update comment on CMakeLists.

* Fix doxygen-introduced issue (#threads)

* Fix conflicts due to new RowFunctionFromCSR signature in master

* Change FastConfig ncol to int32_t.

* Removed profiling folder

* fix doxygen typo include/LightGBM/c_api.h
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* fix doxygen typo include/LightGBM/c_api.h
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* fix doxygen typo include/LightGBM/c_api.h
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Doxygen: change new docstrings to double back-quote
Co-authored-by: alberto.ferreira <alberto.ferreira@feedzai.com>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* feature importance type in saved model file

* fix nullptr

* fixed formatting

* fix python/R

* Update src/c_api.cpp

* Apply suggestions from code review
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* fix c_api test

* fix swig

* minor docs improvements and added defines for importance types
Co-authored-by: StrikerRUS <nekit94-12@hotmail.com>
Co-authored-by: James Lamb <jaylamb20@gmail.com>

Co-authored-by: StrikerRUS <nekit94-12@hotmail.com>

* adding sparse support to TreeSHAP in lightgbm

* updating based on comments

* updated based on comments, used fromiter instead of frombuffer

* updated based on comments

* fixed limits import order

* fix sparse feature contribs to work with more than int32 max rows

* really fixed int64 max error and build warnings

* added sparse test with >int32 max rows

* fixed python side reshape check on sparse data

* updated based on latest comments

* fixed comments

* added CSC INT32_MAX validation to test, fixed comments

* Fix bug: crashes when interaction_constraints is nonempty and not all features are used.

* Fix python lint error.

* Add interaction constraints functionality.

* Minor fixes.

* Minor fixes.

* Change lambda to function.

* Fix gpu bug, remove extra blank lines.

* Fix gpu bug.

* Fix style issues.

* Try to fix segfault on MACOS.

* Fix bug.

* Fix bug.

* Fix bugs.

* Change parameter format for R.

* Fix R style issues.

* Change string formatting code.

* Change docs to say R package not supported.

* Remove R functionality, moving to separate PR.

* Keep track of branch features in tree object.

* Only track branch features when feature interactions are enabled.

* Fix lint error.

* Update docs and simplify tests.

This reverts commit 656d2676.

* memory corruption fix for distributed data parallel version before SyncUpGlobalBestSplit

* updated based on comments

* updated voting and feature parallel based on comments

* fixing macos failure

* rename variable

* [R-package] move R source files into R-package

* fix linting warning

* stuff

* [R-package] fix MSVC warning about control paths (fixes #3067)

* linting

* simplify

* Fix loss computation

* fix test

* Optimize ranking loss computation.

* redir log to python console

* fix pylint

* Apply suggestions from code review

* Update basic.py

* Apply suggestions from code review
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update c_api.h

* Apply suggestions from code review

* Apply suggestions from code review

* super-minor: better wording
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>
Co-authored-by: StrikerRUS <nekit94-12@hotmail.com>

* Path smoothing

* Try to fix issue with gpu version.

* Fix failing CI for R package.

* Minor fixes.

* Minor refactor.

* Restore old code to get CI working.

* Fix style issues.

* Fix ci for R package.

* Minor fixes for docs and code style.

* Update docs.

* Fix loss computation

* fix test

* fix

* Apply suggestions from code review
Co-authored-by: StrikerRUS <nekit94-12@hotmail.com>

This reverts commit 6b68967d (#2891) and fixes #2979.