GitLab

* added all necessary includes - fixed build/include_what_you_use error

* fixed the order of includes (build/include_order)

* average predictions for constant features

* fix possible numerical issues in std::log.

* fix pylint

* fix bugs in c_api

* fix styles

* clean code for multi class

* rewrite test

* fix pylint

* skip test_constant_features

* refine test

* fix tests

* fix tests

* update FAQ

* fix test

* Update FAQ.rst

* [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

* 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 .

* Revert "python-package: support valid_names in scikit-learn API (#420)"

This reverts commit de39dbcf.

* Revert "faster histogram sum up (#418)"

This reverts commit 98c7c2a3.

* some refactor.

* two stage sum up to reduce sum up error.

* add more two-stage sumup.

* some refactor.

* add alignment.

* change name to aligned_allocator.

* remove some useless sumup.

* fix a warning.

* add -march=native .

* remove the padding of gradients.

* no alignment.

* fix test.

* change KNumSumupGroup to 32768.

* change gcc flags.

* refine prediction logic.

* fix test.

* fix out_len in training score of Dart.

* improve predict speed for high dimension data.

* many refactors.

* remove multi_loglossova.

* fix tests.

* avoid using lambda function.

* fix some format.

* reduce branching.

* add un_balance to multi class

* change the calculation of label weights

* RAII for utils, application and c_api(partical)

* raii for class in include folder

* raii for application and boosting

* raii for dataset and dataset loader

* raii for dense bin and parser

* RAII refactor for almost all classes

* RAII for c_api

* clean code

* refine repeated code

* Decouple the "sigmoid" between objective and boosting.

* change std::vector<bool> back to std::vector<char> due to concurrence problem

* slight reduce some memory cost

Packages that parse LightGBM’s logs will require minor changes to
parsing logic to work correctly.

Support multiclass classification (#53)