* 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
* fix bug for categorical_feature
* add test on load model with categorical feature
* add unseen category in test dataset
* save/load pandas_categorical to model
* fix logic
* cast pandas columns to string
* add load pandas_categorical from file to _InnerPredictor init
* add pmml to test
* refine pmml.py
* use ~n instead of -n-1
* change map to list comprehension
* fix check
* fix 'use ~n instead of -n-1'
* fix exception
* finish R's c_api
* clean code
* fix sizeof pointer in 32bit system.
* add predictor class
* add Dataset class
* format code
* add booster
* add type check for expose function
* add a simple callback
* add all callbacks
* finish the basic training logic
* update docs
* add an simple training interface
* add basic test
* adapt the changes in c_api
* add test for Dataset
* add test for custom obj/eval functions
* fix python test
* fix bug in metadata init
* fix R CMD check
* format python code with pep8
* **DO NOT MERGE** deliberately break rules to see what will happen during check
* Revert "**DO NOT MERGE** deliberately break rules to see what will happen during check"
This reverts commit 0db93cd7a4.
* fix format in test.py
* add docs for pep-8
* support pickle
* add pickle/joblib test; change test_basic to unittest
* remove file for deepcopy
* fix tests
* test basic predict from file
* Revert "test basic predict from file"
This reverts commit 60d2c31585.
* test predict from file
* use tempfile for copy & pickle
* use tempfile w/o binary mode
* clean test
Guolin Ke
wxchan
j-mark-hou
cbecker
Tsukasa OMOTO
Laurae
Huan Zhang
ClimbsRocks