* fixed docs
* reworker predict method of sklearn wrapper
* fixed encapsulation
* added test
* fixed consistency between docstring and params docs
* fixed verbose
* replaced predict_proba with predict in test
* fixed verbose again
* fixed fraction params descriptions
* added description of skip_drop and drop_rate constraints
* fixed subsample_freq consistency with C++ default value
* fixed nice look of params list
* made force splits json file example clickable
* fixed nice look of metrics list and added comma
* reduced warning in test about same param specified twice
* replaced pred_parameter with **kwargs in predict method
* added test for **kwargs in predict method
* fixed warnings
* fixed pylint
* updated pep8 to pycodestyle
* fixed E722 do not use bare 'except'
* fixed W605 invalid escape sequence '\*'
* fixed W504 line break after binary operator
* ignore W605 invalid escape sequence '\*' in nuget builder
* made pycodestyle happy
* 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
* 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
commit c9e123f24fcbb159c04e6694c7f830530bb2f27e
Author: Guolin Ke <i@yumumu.me>
Date: Wed Oct 18 10:00:19 2017 +0800
change default max_cat_to_onehot
commit 805a5c3125b9979d634922e1708877fa0fec80c6
Author: Guolin Ke <i@yumumu.me>
Date: Tue Oct 17 22:57:18 2017 +0800
use one hot coding for the small cats
* disabled logs from compilers; fixed#874
* fixed safe clear_fplder
* added windows folder to manifest.in
* added windows folder to build
* added library path
* added compilation with MSBuild from .sln-file
* fixed unknown PlatformToolset returns exitcode 0
* hotfix
* updated Readme
* removed return
* added installation with mingw test to appveyor
* let's test appveyor with both VS 2015 and VS 2017; but MinGW isn't installed on VS 2017 image
* fixed built-in name 'file'
* simplified appveyor
* removed excess data_files
* fixed unreadable paths
* separated exceptions for cmake and mingw
* refactored silent_call
* don't create artifacts with VS 2015 and mingw
* be more precise with python versioning in Travis
* removed unnecessary if statement
* added classifiers for PyPI and python versions badge
* changed python version in travis
* added support of scikit-learn 0.18.x
* added more python versions to Travis
* added more python versions to Appveyor
* reduced number of tests in Travis
* Travis trick is not needed anymore
* attempt to fix according to https://github.com/Microsoft/LightGBM/pull/880#discussion_r137438856
* improved sklearn interface; added sklearns' tests
* moved best_score into the if statement
* improved docstrings; simplified LGBMCheckConsistentLength
* fixed typo
* pylint
* updated example
* fixed Ranker interface
* added missed boosting_type
* fixed more comfortable autocomplete without unused objects
* removed check for None of eval_at
* fixed according to review
* fixed typo
* added description of fit return type
* dictionary->dict for short
* markdown cleanup
* expose feature importance to c_api
* support type=gain
* remove dump model from examples and tests temporarily because it's unstable
* use double instead of float
* added test for training when both train and valid are subsets of a single lgb.Dataset object
* pep8 changes
* more pep8
* added test involving subsets of subsets of lgb.Dataset objects
* minor fix to contruction of X matrix
* even more pep8
* simplified test further
* 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
* 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
Nikita Titov
Guolin Ke
ebernhardson
Guolin Ke
wxchan
Nikita Titov
ChenZhiyong
Scott Lundberg
Mikhail Korobov
j-mark-hou
cbecker
Tsukasa OMOTO
Laurae
Huan Zhang