GitLab

* 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

* add quantile metric.

* first draft.

* add "sqrt" transform in regression objective function.

* fix a bug.

* update parameter doc

* fix doc

* [optional] support protobuf

* fix windows/LightGBM.vcxproj

* add doc

* fix doc

* fix vs support (#2)

* fix vs support

* fix cmake

This reverts commit 53b99854.

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

* many fixes for categorical feature

* add l2 to categorcial split.

* remove useless file

* update version

* add cat_l2

* update appveyor verison

* remove file

* fix tests.

* change default cat_l2 value

* fix a bug in bin finder

* change default cat_smooth_ratio

* add network apis.

* support parallel loading dataset in c api.

* fix bug

* fix bug

* add get params

clean codes

* check duplicate params

* Revert "add get params"

This reverts commit ec1d8dd17aa2c83dd0d5f7716c59b6a6fb94102c.

* set priority by length & check duplicate

* rename function

* refine categorical split

* add test

* better log for "Check max_depth and num_leaves"

The default log said "[LightGBM] [Warning] Accuarcy may be bad since you didn't set num_leaves." but the case max_depth²>num_leaves could also trigger this log.
Correction of a typo too : Accuarcy > Accuracy

* Update config.cpp

* output warning when only set `max_depth`

* Remove check of max_depth

max_depth can take both positive and negative value

* Explain individual predictions using SHAP value feature attributions

* Address code review

* finish the data loading part

* allow prediction.

* fix bug for decision type.

* finish split finding part

* fix bugs.

* bug fixed. add a test .

* fix pep8 .

* update documents.

* fix test bugs.

* fix a format

* fix import error in python test.

* disable missing handle in categorial features.

* fix a bug.

* add more tests.

* fix pep8

* fix bugs.

* remove the missing handle code for categorical feature.

* add draft of RF.

* fix score bugs.

* fix scores.

* fix tests.

* update document

* fix GetPredictAt

* 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 .

* translate model to if-else

* support multiclass and predictleaf

* remove java option for now

* support multi-thread

* add task:convert_model

* fixed a bug in BinMapper: division by zero

* Revert "fixed a bug in BinMapper: division by zero"

This reverts commit 228380284f6a9ba174982e210e14d0aecde8e837.

* added CHECK for min_data_in_bin and fixed typo

* 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

* many refactors.

* remove multi_loglossova.

* fix tests.

* avoid using lambda function.

* fix some format.

* reduce branching.