GitLab

* new cuda framework

* add histogram construction kernel

* before removing multi-gpu

* new cuda framework

* tree learner cuda kernels

* single tree framework ready

* single tree training framework

* remove comments

* boosting with cuda

* optimize for best split find

* data split

* move boosting into cuda

* parallel synchronize best split point

* merge split data kernels

* before code refactor

* use tasks instead of features as units for split finding

* refactor cuda best split finder

* fix configuration error with small leaves in data split

* skip histogram construction of too small leaf

* skip split finding of invalid leaves

stop when no leaf to split

* support row wise with CUDA

* copy data for split by column

* copy data from host to CPU by column for data partition

* add synchronize best splits for one leaf from multiple blocks

* partition dense row data

* fix sync best split from task blocks

* add support for sparse row wise for CUDA

* remove useless code

* add l2 regression objective

* sparse multi value bin enabled for CUDA

* fix cuda ranking objective

* support for number of items <= 2048 per query

* speedup histogram construction by interleaving global memory access

* split optimization

* add cuda tree predictor

* remove comma

* refactor objective and score updater

* before use struct

* use structure for split information

* use structure for leaf splits

* return CUDASplitInfo directly after finding best split

* split with CUDATree directly

* use cuda row data in cuda histogram constructor

* clean src/treelearner/cuda

* gather shared cuda device functions

* put shared CUDA functions into header file

* change smaller leaf from <= back to < for consistent result with CPU

* add tree predictor

* remove useless cuda_tree_predictor

* predict on CUDA with pipeline

* add global sort algorithms

* add global argsort for queries with many items in ranking tasks

* remove limitation of maximum number of items per query in ranking

* add cuda metrics

* fix CUDA AUC

* remove debug code

* add regression metrics

* remove useless file

* don't use mask in shuffle reduce

* add more regression objectives

* fix cuda mape loss

add cuda xentropy loss

* use template for different versions of BitonicArgSortDevice

* add multiclass metrics

* add ndcg metric

* fix cross entropy objectives and metrics

* fix cross entropy and ndcg metrics

* add support for customized objective in CUDA

* complete multiclass ova for CUDA

* separate cuda tree learner

* use shuffle based prefix sum

* clean up cuda_algorithms.hpp

* add copy subset on CUDA

* add bagging for CUDA

* clean up code

* copy gradients from host to device

* support bagging without using subset

* add support of bagging with subset for CUDAColumnData

* add support of bagging with subset for dense CUDARowData

* refactor copy sparse subrow

* use copy subset for column subset

* add reset train data and reset config for CUDA tree learner

add deconstructors for cuda tree learner

* add USE_CUDA ifdef to cuda tree learner files

* check that dataset doesn't contain CUDA tree learner

* remove printf debug information

* use full new cuda tree learner only when using single GPU

* disable all CUDA code when using CPU version

* recover main.cpp

* add cpp files for multi value bins

* update LightGBM.vcxproj

* update LightGBM.vcxproj

fix lint errors

* fix lint errors

* fix lint errors

* update Makevars

fix lint errors

* fix the case with 0 feature and 0 bin

fix split finding for invalid leaves

create cuda column data when loaded from bin file

* fix lint errors

hide GetRowWiseData when cuda is not used

* recover default device type to cpu

* fix na_as_missing case

fix cuda feature meta information

* fix UpdateDataIndexToLeafIndexKernel

* create CUDA trees when needed in CUDADataPartition::UpdateTrainScore

* add refit by tree for cuda tree learner

* fix test_refit in test_engine.py

* create set of large bin partitions in CUDARowData

* add histogram construction for columns with a large number of bins

* add find best split for categorical features on CUDA

* add bitvectors for categorical split

* cuda data partition split for categorical features

* fix split tree with categorical feature

* fix categorical feature splits

* refactor cuda_data_partition.cu with multi-level templates

* refactor CUDABestSplitFinder by grouping task information into struct

* pre-allocate space for vector split_find_tasks_ in CUDABestSplitFinder

* fix misuse of reference

* remove useless changes

* add support for path smoothing

* virtual destructor for LightGBM::Tree

* fix overlapped cat threshold in best split infos

* reset histogram pointers in data partition and spllit finder in ResetConfig

* comment useless parameter

* fix reverse case when na is missing and default bin is zero

* fix mfb_is_na and mfb_is_zero and is_single_feature_column

* remove debug log

* fix cat_l2 when one-hot

fix gradient copy when data subset is used

* switch shared histogram size according to CUDA version

* gpu_use_dp=true when cuda test

* revert modification in config.h

* fix setting of gpu_use_dp=true in .ci/test.sh

* fix linter errors

* fix linter error

remove useless change

* recover main.cpp

* separate cuda_exp and cuda

* fix ci bash scripts

add description for cuda_exp

* add USE_CUDA_EXP flag

* switch off USE_CUDA_EXP

* revert changes in python-packages

* more careful separation for USE_CUDA_EXP

* fix CUDARowData::DivideCUDAFeatureGroups

fix set fields for cuda metadata

* revert config.h

* fix test settings for cuda experimental version

* skip some tests due to unsupported features or differences in implementation details for CUDA Experimental version

* fix lint issue by adding a blank line

* fix lint errors by resorting imports

* fix lint errors by resorting imports

* fix lint errors by resorting imports

* merge cuda.yml and cuda_exp.yml

* update python version in cuda.yml

* remove cuda_exp.yml

* remove unrelated changes

* fix compilation warnings

fix cuda exp ci task name

* recover task

* use multi-level template in histogram construction

check split only in debug mode

* ignore NVCC related lines in parameter_generator.py

* update job name for CUDA tests

* apply review suggestions

* Update .github/workflows/cuda.yml
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update .github/workflows/cuda.yml
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* update header

* remove useless TODOs

* remove [TODO(shiyu1994): constrain the split with min_data_in_group] and record in #5062

* #include <LightGBM/utils/log.h> for USE_CUDA_EXP only

* fix include order

* fix include order

* remove extra space

* address review comments

* add warning when cuda_exp is used together with deterministic

* add comment about gpu_use_dp in .ci/test.sh

* revert changing order of included headers
Co-authored-by: Yu Shi <shiyu1994@qq.com>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* clarify no-meaningful-features warning in Dataset construction (fixes #5081)

* update tests

* Add text file with GitHub commit hash to nightly builds

* Update .vsts-ci.yml

* Revert "Update .vsts-ci.yml"

This reverts commit 75c41893982ab34ef9d7044d8dbf434be184c175.

* Turn `early_stopping` into a Callable class

* Fix

* Lint

* Remove print

* Fix order

* Revert "Lint"

This reverts commit 7ca8b557572446888cf793c0082d9a7efd1e29a7.

* Apply suggestion from code review

* Nit

* Lint

* Move callable class outside the func for pickling

* Move _pickle and _unpickle to tests utils

* Add early stopping callback picklability test

* Nit

* Fix

* Lint

* Improve type hint

* Lint

* Lint

* Add cloudpickle to test_windows

* Update tests/python_package_test/test_engine.py

* Fix

* Apply suggestions from code review

* Update r_package.yml

* replace R 4.1 with R 3.6 for VS 2019 build

* expose FeatureNumBin in C api

* parametrize min_data_in_bin and add test with max_bin_by_feature

* include feature_num_bin in R package

* add suggestion from review
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* update error message and lint

* lint

* add call method

* minor improvements in tests

* add suggestions from review

* lint

* rename argument to feature in python and r packages
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* better logging of column datatypes

* update to checking function

* fix typo

* Update python-package/lightgbm/basic.py
Co-authored-by: José Morales <jmoralz92@gmail.com>

* Update python-package/lightgbm/basic.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>
Co-authored-by: José Morales <jmoralz92@gmail.com>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* [ci] update CODEOWNERS

* remove chivee from CODEOWNERS

[python-package] [R-package] propagate the best iteration of cvbooster into the individual boosters (#5066)

* Update test_windows.ps1

* Update .appveyor.yml

* Update test_windows.ps1

* Update .appveyor.yml

* fix duplicate added initial scores for single-leaf trees

* add test case

* Fix import in Python test

* commit python suggestions
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* docs: booster eval methods accept list of callables

* docs: remove extra space

[tests][python] move tests that use `train()` function defined in `engine.py` from `test_basic.py` to `test_engine.py` (#5034)

* Update test_basic.py

* Update test_engine.py

* Update test_engine.py

[python] fixes for supporting 2d numpy arrays for predictions, grads and hess in multiclass custom objective and eval (#5030)

* fixes for supporting 2d numpy arrays for predictions, grads and hess in multiclass custom objective

* Apply suggestions from code review
Co-authored-by: José Morales <jmoralz92@gmail.com>
Co-authored-by: José Morales <jmoralz92@gmail.com>

* Correct documentation for sparse predictions

The documentation says that the parameter `nindptr` for `LGBM_BoosterPredictSparseOutput` should be the number of rows plus one, but this is incorrect when the input type is CSC. This PR fixes it.

* Update c_api.h

* Update c_api.h

* Update c_api.h

* map nullable dtypes to regular float dtypes

* cast x3 to float after introducing missing values

* add test for regular dtypes

* use .astype and then values. update nullable_dtypes test and include test for regular numpy dtypes

* more specific allowed dtypes. test no copy when single float dtype df

* use np.find_common_type. set np.float128 to None when it isn't supported

* set default as type(None)

* move tests that use lgb.train to test_engine

* include np.float32 when finding common dtype

* Apply suggestions from code review
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* add linebreak
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update lightgbm.R

* Update lightgbm.Rd

* promote objective and init_score to top-level arguments

* follow comments

* Update R-package/R/lightgbm.R
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* update docs

* linter

* comments

* comments

* comments

* extend test for default objective

* Update R-package/tests/testthat/test_basic.R
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* docs: weight parameter non-negative

* docs: weights non negative only for train data

* docs: weights should be non negative for validation data

* typo in html render

* docs: brief weights non-negative description

[python-package] use 2d collections for predictions, grads and hess in multiclass custom objective (#4925)

* reshape predictions, grad and hess in multiclass custom objective

* add sklearn test. move custom obj to utils. docs for numpy

* use num_model_per_iteration to get num_classes

* update docs and dask multiclass custom objective test

* move reshaping to __inner_predict. add test for feval

* add missing note. remove extra line

* improve docs for sklearn wrapper

* empty commit

* install add scikit-learn to conda environment for building docs

* factor out lgb.check.obj()

* remove lgb.check.obj()

* add test on lgb.cv()

* clarify that categoricals will be converted to ints and not that they should be ints in the input data

* update remaining sections

* update config.h

* add suggestions

* CUDATreeLearner: free GPU memory in destruuctor if any allocated

* Minor changes: checking for num_gpu_feature_groups is not needed

* Trigger CI again

* [ci] [docs] use mamba for readthedocs builds (fixes #4954)

* update docs

* simplify build script and add docs flag to gitignore

* exit with non-0 if build fails

* update CI job

* add doxygen

* remove outdated requirement_base.txt reference

* use conda create instead of conda env create

* fix conda create flags

* add nodefaults to env.yml

* Update docs/README.rst
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* try to fix check-docs CI job

* additional changes

* switch from mamba to miniforge

* simplify docker command and fix issues in local build script

* Apply suggestions from code review
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* update docs and conda

* Apply suggestions from code review
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update .appveyor.yml

* Update .vsts-ci.yml

* [R-package] prefer params to keyword argument in lgb.train()

* make test stricter

* Apply suggestions from code review
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update setup.py

* Update cuda.yml

* Update static_analysis.yml

* Update linkchecker.yml

* Update .appveyor.yml

* Update .vsts-ci.yml

* Update python_package.yml

* Update .vsts-ci.yml

* Update python_package.yml

* Update cuda.yml

* Update static_analysis.yml

* Update linkchecker.yml

* Update .vsts-ci.yml

* Update .appveyor.yml

* re-arange Python version

* do not produce model files on disk by default

* remove 'save_name' argument from 'lightgbm()'