GitLab

* update lgb.convert_with_rules.R to use keyword arguments

* update lgb.cv.R to use keyword arguments

* update lgb.importance.R to use keyword arguments

* update lgb.interprete.R to use keyword arguments

* update lgb.plot.interpretation.R to use keyword arguments

* update more internal function calls to use keyword arguments

* update more internal function calls to use keyword arguments

* construct dataset earlier in lgb.train and lgb.cv

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

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

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

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

* fixing lint issues

* styling updates

* fix failing test
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* [R-package] {lightgbm} is now available on CRAN (fixes #629)

* more cleanup

* remove precompiled binaries section

* Update README.md

* Update README.md

* Update README.rst

* update lgb.Dataset.R to use keyword arguments

* minor adjustment

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

* fix linting error

* Update R-package/R/lgb.Dataset.R
Co-authored-by: James Lamb <jaylamb20@gmail.com>
Co-authored-by: Nick Zeng <361304605@qq.com>
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* [R-package] Use command-line args to customize CMake builds (fixes #2441)

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

* [R-package] use as.factor() instead of factor() in lgb.cv()

* comment out CIs

* Revert "comment out CIs"

This reverts commit fabaf9a92b7c3df4ed0fdd8d67cbc85a2fb18f47.

* empty commit

* use keyword arguments

* minor style adjustment

* Update R-package/R/lgb.model.dt.tree.R
Co-authored-by: James Lamb <jaylamb20@gmail.com>
Co-authored-by: zenggyu <zenggyu@foxmail.com>
Co-authored-by: James Lamb <jaylamb20@gmail.com>

Co-authored-by: Мартынов Максим Сергеевич <msmarty5@mts.ru>

* Refactor c_api.cpp with template code

* Further cleanup

* Fix whitespace for linter

* [refactor] SWIG - Split pointer manipulation to individual .i file

* Update module docstring

* space

Fix num_total_bin_ and bin_offsets_ of FeatureGroup
if a dense multi val feature group with non zero most freq bin
is the first feature group of the dataset.

* [ci] test 32-bit R in CI

* add R 3.6 Windows CRAN job

* add tests

* fix deterministic, part2

* Apply suggestions from code review

* remove max_block_size_ in train states (fix #3570)

* avoid zero elements per row

* add min constraint for min_block_size_

* [R-package] Remove CLI-only objects

* more guards

* more guards

* variable not string

* simplify fix

* revert build_r.R changes

* move define of global_timer
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* bump version for development

* Update .appveyor.yml

* fix typos in CRAN comments

* update configure
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* [ci] always use latest conda version on GitHub Actions

* try new use-conda version

* just remove conda

* skip conda even more

* Update VERSION.txt

* update configure

* Update .appveyor.yml

* update cran-comments
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* [ci] Use different PATH based on Rtools version

* better paths

* comment out CIs

* empty commit

* empty commit

* empty commit

* empty commit

* empty commit

* empty commit

* uncomment CIs

* reorganize PATH components

* store without offset in multi_val_dense_bin

* fix offset bug

* add comment for offset

* add comment for bin type selection

* faster operations for offset

* keep most freq bin in histogram for multi val dense

* use original feature iterators

* consider 9 cases (3 x 3) for multi val bin construction

* fix dense bin setting

* fix bin data in multi val group

* fix offset of the first feature histogram

* use float hist buf

* avx in histogram construction

* use avx for hist construction without prefetch

* vectorize bin extraction

* use only 128 vec

* use avx2

* use vectorization for sparse row wise

* add bit size for multi val dense bin

* float with no vectorization

* change multithreading strategy to dynamic

* remove intrinsic header

* fix dense multi val col copy

* remove bit size

* use large enough block size when the bin number is large

* calc min block size by sparsity

* rescale gradients

* rollback gradients scaling

* single precision histogram buffer as an option

* add float hist buffer with thread buffer

* fix setting zero in hist data

* fix hist begin pointer in tree learners

* remove debug logs

* remove omp simd

* update Makevars of R-package

* fix feature group binary storing

* two row wise for double hist buffer

* add subfeature for two row wise

* remove useless code and fix two row wise

* refactor code

* grouping the dense feature groups can get sparse multi val bin

* clean format problems

* one thread for two blocks in sep row wise

* use ordered gradients for sep row wise

* fix grad ptr

* ordered grad with combined block for sep row wise

* fix block threading

* use the same min block size

* rollback share min block size

* remove logs

* Update src/io/dataset.cpp
Co-authored-by: Guolin Ke <guolin.ke@outlook.com>

* fix parameter description

* remove sep_row_wise

* remove check codes

* add check for empty multi val bin

* fix lint error

* rollback changes in config.h

* Apply suggestions from code review
Co-authored-by: Ubuntu <shiyu@gbdt-04.ren3kv4wanvufliwrpy4k03lsf.xx.internal.cloudapp.net>
Co-authored-by: Guolin Ke <guolin.ke@outlook.com>

* Update requirements_base.txt

* Update requirements_rtd.txt

* Add note on LightGBM/OpenMP hanging on AWS Batch

* Update docs/FAQ.rst
Co-authored-by: James Lamb <jaylamb20@gmail.com>
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* TST make sklearn integration test compatible with 0.24

* remove useless import

* remove outdated comment

* order import

* use parametrize_with_checks

* change the reason

* skip constructible if != 0.23

* make tests behave the same across sklearn version

* linter

* address suggestions