GitLab

* Remove CMAKE_CXX_COMPILER and CMAKE_C_COMPILER for OSX

* update .travis/test.sh

* update again

* add make command to the python package.

* Update README.rst

* Update README.rst

* Update README.rst

* fix tests.

* fix unix build

* update readme

* fix setup.py

* update travis

* Update .travis.yml

* Update test.py

* some fixes.

* check the 64-bit python

* fix build.

* refine MANIFEST.in

* update Manifest.in

* add more build options.

* Add fatal in cmake

* fix a endif.

* fix bugs.

* fix pep8

* add test for the pip package build

* add test pip install in travis.

* fix version with pre-compile dll

* fix readme.rst

* update readme

* 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 .

Homebrew updated gcc to 7.1.
https://github.com/Homebrew/homebrew-core/commit/0387fe46e9ce9fe4a14dbce9d31d70f5824ff830
https://github.com/Homebrew/homebrew-core/commit/0ab90d39e3ca786c9f0b2fb44c2fd29880336cd2

* Fix warnings when compiled with -pedantic

* add -DBOOST_ALL_NO_LIB for windows build

* fix some more MSVC warnings

* Break OpenCL string literal to smaller pieces to avoid error C2026 of MSVC

The string was longer than the limit of 16380 single-byte characters.

This affects Visual Studio 2005 - 2015. Untested on VS 2017.

* 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.

* fix the size of vector cnt_per_class

* put Boost.Compute includes at the beginning

We want to put the submodule Boost.Compute at the beginning of compiler search
paths, because the submodule usually contains upstream bug fixes that have not
been included in the stable Boost releases installed on the host OS.

* add dummy gpu solver code

* initial GPU code

* fix crash bug

* first working version

* use asynchronous copy

* use a better kernel for root

* parallel read histogram

* sparse features now works, but no acceleration, compute on CPU

* compute sparse feature on CPU simultaneously

* fix big bug; add gpu selection; add kernel selection

* better debugging

* clean up

* add feature scatter

* Add sparse_threshold control

* fix a bug in feature scatter

* clean up debug

* temporarily add OpenCL kernels for k=64,256

* fix up CMakeList and definition USE_GPU

* add OpenCL kernels as string literals

* Add boost.compute as a submodule

* add boost dependency into CMakeList

* fix opencl pragma

* use pinned memory for histogram

* use pinned buffer for gradients and hessians

* better debugging message

* add double precision support on GPU

* fix boost version in CMakeList

* Add a README

* reconstruct GPU initialization code for ResetTrainingData

* move data to GPU in parallel

* fix a bug during feature copy

* update gpu kernels

* update gpu code

* initial port to LightGBM v2

* speedup GPU data loading process

* Add 4-bit bin support to GPU

* re-add sparse_threshold parameter

* remove kMaxNumWorkgroups and allows an unlimited number of features

* add feature mask support for skipping unused features

* enable kernel cache

* use GPU kernels withoug feature masks when all features are used

* REAdme.

* REAdme.

* update README

* fix typos (#349)

* change compile to gcc on Apple as default

* clean vscode related file

* refine api of constructing from sampling data.

* fix bug in the last commit.

* more efficient algorithm to sample k from n.

* fix bug in filter bin

* change to boost from average output.

* fix tests.

* only stop training when all classes are finshed in multi-class.

* limit the max tree output. change hessian in multi-class objective.

* robust tree model loading.

* fix test.

* convert the probabilities to raw score in boost_from_average of classification.

* fix the average label for binary classification.

* Add boost_from_average to docs (#354)

* don't use "ConvertToRawScore" for self-defined objective function.

* boost_from_average seems doesn't work well in binary classification. remove it.

* For a better jump link (#355)

* Update Python-API.md

* for a better jump in page

A space is needed between `#` and the headers content according to Github's markdown format [guideline](https://guides.github.com/features/mastering-markdown/)

After adding the spaces, we can jump to the exact position in page by click the link.

* fixed something mentioned by @wxchan

* Update Python-API.md

* add FitByExistingTree.

* adapt GPU tree learner for FitByExistingTree

* avoid NaN output.

* update boost.compute

* fix typos (#361)

* fix broken links (#359)

* update README

* disable GPU acceleration by default

* fix image url

* cleanup debug macro

* remove old README

* do not save sparse_threshold_ in FeatureGroup

* add details for new GPU settings

* ignore submodule when doing pep8 check

* allocate workspace for at least one thread during builing Feature4

* move sparse_threshold to class Dataset

* remove duplicated code in GPUTreeLearner::Split

* Remove duplicated code in FindBestThresholds and BeforeFindBestSplit

* do not rebuild ordered gradients and hessians for sparse features

* support feature groups in GPUTreeLearner

* Initial parallel learners with GPU support

* add option device, cleanup code

* clean up FindBestThresholds; add some omp parallel

* constant hessian optimization for GPU

* Fix GPUTreeLearner crash when there is zero feature

* use np.testing.assert_almost_equal() to compare lists of floats in tests

* travis for GPU

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

* Added CMake option to create dylib on MacOS

Provide a high level Dataset class for easy use.