GitLab

* [optional] support protobuf

* fix windows/LightGBM.vcxproj

* add doc

* fix doc

* fix vs support (#2)

* fix vs support

* fix cmake

This reverts commit 53b99854.

* 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

* Explain individual predictions using SHAP value feature attributions

* Address code review

* 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.