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* check the shape for mat, csr and csc

* guess from csr

* support file checking

* better error msg

* grammar

* clean code

* code clean

* check range for CSR

* Update test_.py

* Update test_.py

* added tests

* fix many cpp lint errors

* indent

* fix bug

* fix more

* fix gpu

* more fixes

* added all necessary includes - fixed build/include_what_you_use error

* fixed the order of includes (build/include_order)

* fix optimization rule

* preallocate vector in RowFunctionFromCSR

* 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

* remove unnecessary  std::move

* remove unused-lambda-capture

* remove unused-lambda-capture

* fix unused parameter

* minor fix

*  invalid capture of lambda function

* [WIP] refine config

* [wip] ready for the auto code generate

* auto generate config codes

* use with to open file

* fix bug

* fix pylint

* fix bug

* fix pylint

* fix bugs.

* tmp for failed test.

* fix tests.

* added nthreads alias

* added new aliases from new config.h

* fixed duplicated alias

* refactored parameter_generator.py

* added new aliases from config.h and removed remaining old names

* fix bugs & some miss alias

* added aliases

* add more descriptions.

* add comment.

* decode error description

* added break line char in log massages

* Read and write datsets from hdfs.
* Only enabled when cmake is run with -DUSE_HDFS:BOOL=TRUE
* Introduces VirtualFile(Reader|Writer) to asbtract VFS differences

* add code for refit tree

* add implementation.

* update documents.

* clean code

* fix a type

* remove protobuf

* add version number

* remove pmml script

* use float for split gain

* fix warnings

* refine the read model logic of gbdt

* fix compile error

* improve decode speed

* fix some bugs

* fix double accuracy problem

* fix bug

* multi-thread save model

* speed up save model to string

* parallel save/load model

* fix some warnings.

* fix warnings.

* fix a bug

* remove debug output

* fix doc

* fix max_bin warning in tests.

* fix max_bin warning

* fix pylint

* clean code for stringToArray

* clean code for TToString

* remove max_bin

* replace "class" with typename

* first commit

* fix bug

* fix by commits

* fix by commit

* add funcs to IfElse

* fix bug

* fix bug

* fix bug

* change tab to space

* [optional] support protobuf

* fix windows/LightGBM.vcxproj

* add doc

* fix doc

* fix vs support (#2)

* fix vs support

* fix cmake

This reverts commit 53b99854.

* add feature

* add label idx move func to .cpp file

* fix

* move func to predictor

* restore files

* fix by commits

* move the non-used features to the end of the feature vector

* fix by commits

* fix bug by commits

* add get params

clean codes

* check duplicate params

* Revert "add get params"

This reverts commit ec1d8dd17aa2c83dd0d5f7716c59b6a6fb94102c.

* set priority by length & check duplicate

* rename function

* Explain individual predictions using SHAP value feature attributions

* Address code review

* 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 .

* Add early stopping for prediction

* Fix GBDT if-else prediction with early stopping

* Small C++ embelishments to early stopping API and functions

* Fix early stopping efficiency issue by creating a singleton for no early stopping

* Python improvements to early stopping API

* Add assertion check for binary and multiclass prediction score length

* Update vcxproj and vcxproj.filters with new early stopping files

* Remove inline from PredictRaw(), the linker was not able to find it otherwise

* translate model to if-else

* support multiclass and predictleaf

* remove java option for now

* support multi-thread

* add task:convert_model

* refine prediction logic.

* fix test.

* fix out_len in training score of Dart.

* improve predict speed for high dimension data.

* try use unordered_map for sparse prediction.

* avoid using unordered_map.

* clean code.

* fix test.

* move predict buffer to Predictor.