GitLab

* fix model feature importance

* fix appveryor test

* Revert "fix appveryor test"

This reverts commit 3a10a1723df5b8579e345d0da07638186257ec64.

* fix warning & boost_from_average_

* fix bug

* alter num_used_model

* 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.

* Created objectives and metrics xentropy and xentropy1

* Some coment and code cleanup.

* Added Kullback-Leibler version of metric. Changed some warning messages.

* Fixed sign error in KL-divergence calc.

* Removed __PRETTY_FUNCTION__.

* Fixed better name for alternative xentropy parameterization.
Documented details on the objectives / metrics in code comments.

* Common code for label interval checks. Cleanups.

* Use common utility for various weight property checks.

* Added code for customizable initial average to boost from.

* Fixed spelling error in aliases.

* Fixed nullptr check error in custom average.

* Added network code for customized init score.

* Restructured so that "standard" and "custom" init auto scores share aux.
function.

* Updated name for aux. automatic initial score fetcher.

* Created objectives and metrics xentropy and xentropy1

* Some coment and code cleanup.

* Added Kullback-Leibler version of metric. Changed some warning messages.

* Fixed sign error in KL-divergence calc.

* Removed __PRETTY_FUNCTION__.

* Fixed better name for alternative xentropy parameterization.
Documented details on the objectives / metrics in code comments.

* Common code for label interval checks. Cleanups.

* Use common utility for various weight property checks.

* Added code for customizable initial average to boost from.

* Fixed spelling error in aliases.

* 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 .

* 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

* auto replace gbdt::prediction

* add test if_else_prediction

* not override gbdt_prediction.cpp

* close ifstream

* re-order .travis.yml

* translate model to if-else

* support multiclass and predictleaf

* remove java option for now

* support multi-thread

* add task:convert_model

* 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.

* try use unordered_map for sparse prediction.

* avoid using unordered_map.

* clean code.

* fix test.

* move predict buffer to Predictor.

* refine prediction logic.

* fix test.

* fix out_len in training score of Dart.

* improve predict speed for high dimension data.

* 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.

* reduce the sumup cost of constant hessians.

* fix test.

* fix bug when have weights.

* fix a comment.

* reduce branching.