GitLab

* fix leaf splits update after split in quantized training

* fix preparation ordered gradients for quantized training

* remove force_row_wise in distributed test for quantized training

* Update src/treelearner/leaf_splits.hpp

---------
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* add quantized training (first stage)

* add histogram construction functions for integer gradients

* add stochastic rounding

* update docs

* fix compilation errors by adding template instantiations

* update files for compilation

* fix compilation of gpu version

* initialize gradient discretizer before share states

* add a test case for quantized training

* add quantized training for data distributed training

* Delete origin.pred

* Delete ifelse.pred

* Delete LightGBM_model.txt

* remove useless changes

* fix lint error

* remove debug loggings

* fix mismatch of vector and allocator types

* remove changes in main.cpp

* fix bugs with uninitialized gradient discretizer

* initialize ordered gradients in gradient discretizer

* disable quantized training with gpu and cuda

fix msvc compilation errors and warnings

* fix bug in data parallel tree learner

* make quantized training test deterministic

* make quantized training in test case more accurate

* refactor test_quantized_training

* fix leaf splits initialization with quantized training

* check distributed quantized training result

* [ci] speed up if-else, swig, and lint conda setup

* add 'source activate'

* python constraint

* start removing cuda v1

* comment out CI

* remove more references

* revert some unnecessaary changes

* revert a few more mistakes

* revert another change that ignored params

* sigh

* remove CUDATreeLearner

* fix tests, docs

* fix quoting in setup.py

* restore all CI

* Apply suggestions from code review
Co-authored-by: shiyu1994 <shiyu_k1994@qq.com>

* Apply suggestions from code review

* completely remove cuda_exp, update docs

---------
Co-authored-by: shiyu1994 <shiyu_k1994@qq.com>

[ci] prefer CPython in Windows test environment and use safer approach for cleaning up network (fixes #5509) (#5510)

* 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>

* 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

[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

* Update test_dask.py

* Update test_engine.py

* Update test_sklearn.py

* Update test_sklearn.py

* Update test_sklearn.py

* Update test_sklearn.py

* Update test_sklearn.py

* Update test_sklearn.py

* Update test_engine.py

* Update test_sklearn.py

* Update test_sklearn.py

* Update test_sklearn.py

* add test for custom objective with regressor

* add test for custom binary classification objective with classifier

* isort

* got tests working for multiclass

* update docs

* train deeper model for classifier

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

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

* update multiclass tests

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

* fix multiclass probabilities

* linting
Co-authored-by: José Morales <jmoralz92@gmail.com>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* [python-package][dask] handle failures parsing work host names

* add tests

* revert local testing changes

* unify values of `best_iteration` for sklearn and standard APIs

* update Dask test

* fix argument types in custom eval function for Dask estimators

* revert changes to docstrings

* fix argument names in Dask test

[python-package] Support 2d collections as input for `init_score` in multiclass classification task (#4150)

* initial implementation of init_score for multiclass classification

* check for 1d or 2d collection in init_score

* remove dataset import

* initial comments

* update dask test and docstrings

* update docstrings

* move logic to set_field. reshape back on get_field

* add type hints and update docstrings for dask. fix Dataset.set_field

* revert wrong docstrings and type hints

* add extra comma for consistency

* prefix private functions with underscore

add type hints to new functions

make commas consistent in dask and basic

* add missing spaces after type hint

* remove shape condition for dataframe in is_2d_collection
Co-authored-by: Nikita Titov <nekit94-12@hotmail.com>

Co-authored-by: Nikita Titov <nekit94-12@hotmail.com>

* reduce number of collisions tests

* measure tests execution time

* measure tests execution time in bdist task

* remove durations in bdist task

* find all needed ports in each worker at once

* lint

* better naming

* use _HostWorkers in test

[dask] Make output of feature contribution predictions for sparse matrices match those from sklearn estimators (fixes #3881) (#4378)

* test_classifier working

* adding tests

* docs

* tests

* revert unnecessary changes in tests

* test output type

* linting

* linting

* use from_delayed() instead

* docstring pycodestyle is happy with

* isort

* put pytest skips back

* respect sparse return type

* fix doc

* remove unnecessary dask_array_concatenate()

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

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

* update predict_proba() docstring

* remove unnecessary np.array()

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* fix assertion

* fix test use of len()

* restore np.array() in tests

* use np.asarray() instead

* use toarray()

* remove empty functions in compat
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* es WiP, need to add eval_sample_weight and eval_group

* add weight, group to dask es. WiP.

* dask es reorg

* Update python-package/lightgbm/dask.py

_train_part model.fit args to lines
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Update tests/python_package_test/test_dask.py

_train_part model.fit args to lines, pt2
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Update python-package/lightgbm/dask.py

_train_part model.fit args to lines pt3
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Update tests/python_package_test/test_dask.py

dask_model.fit args to lines
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Update tests/python_package_test/test_dask.py
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Update python-package/lightgbm/dask.py

use is instead of id()
Co-authored-by: James Lamb <jaylamb20@gmail.com>

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

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

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

* Update tests/python_package_test/test_dask.py
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Update tests/python_package_test/test_dask.py
Co-authored-by: James Lamb <jaylamb20@gmail.com>

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

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

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

* applying changes to eval_set PR WiP

* dask support for eval_names, eval_metric, eval_stopping_rounds

* add evals_result checks and other eval_set attribute-related test checks. need to merge master - WiP

* fix lint errors in test_dask.py

* drop group_shape from _lgbmmodel_doc_fit.format for non-rankers, add support for eval_at for dask ranker

* add eval_at to test_dask eval_set ranker tests

* add back group_shape to lgbmmmodel docs, tighten tests

* drop random eval weights from early stopping, probably causing training to terminate too early

* add eval data templates to sklearn fit docs, add eval data docs to dask

* add n_features to _create_data, eval_set tests stop w/ desirable tree counts

* import alphabetically

* add back get_worker for eval_set error handling

* test_dask argmin typo

* push forgotten eval_names bugfix

* eval_stopping_rounds -> early_stopping_rounds, fix failing non-es test

* change default eval_at to tuple 1-5

* re-drop get_worker

* drop early stopping support from eval_set commits, move eval_set worker check prior to client.submit

* add eval_class_weight and eval_init_score to lightgbm/dask, WiP

* clean up eval_set tests, allow user to specify fewer eval_names, clswghts than eval_sets

* remove redundant backslash

* lint fixes

* fix eval_at, eval_metric duplication, let eval_at be Iterable not just Tuple

* use all data_outputs for test_eval_set tests

* undo newlines from first pr

* add custom_eval_metric test, correct issue with eval_at and metric names

* move _constant_metric outside of test

* dataset reference names instead of __strings__

* add padding to eval_set parts makes each part has same len(eval_set)

* eval set code clean up

* revert n_evals to be max len eval_set across all parts on worker

* pylint errors in _DatasetNames

* more pylint fixes

* pylinting...

* add by pytest.mark, mistakenly deleted during merge conflict resolution

* address code review comments

* add _pad_eval_names to handle nondeterministic evals_result_ valid set names

* change not evaluated evals_result_ test criteria

* address fit eval docs issues, switch _DatasetNames to Enum

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* update eval_metrics, eval_at dask fit docstr to match sklearn, make tests reflect that l2 (rmse), logloss in evals_result_ by default

* address eval_set dict keys naming in docstr and training eval_set naming issue

* in test_dask check for obj-default metric names in eval_results, remove check for training key

* lint fixes for _pad_eval_names

* remove unnecessary breaklinen in _pad_eval_names docstr

* use Enum.member syntax not Enum.member.name

* remove str from supported eval_at types

* add whitespace and remove DaskDataframes mention from eval_ param docstrs in _train

* remove "of shape = [n_samples]" from group_shape docs

* add eval_at base_doc in DaskLGBMRanker.fit

* remove excess paren from eval_names docs in _train

* make requested changes to test_dask.py

* remove Optional() wrapper on eval_at

* add _lgbmmodel_doc_custom_eval_note to dask.py fit.__doc__

* fix ordering of .sklearn imports to attempt lint fix

* dask custom eval note to f-string pt1
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* dask custom eval note to f-string pt 2
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* dask custom eval note to f-string pt 3
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>
Co-authored-by: James Lamb <jaylamb20@gmail.com>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* [dask] pass predict() kwargs through when input is a Dask Array

* add tests

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

* add prediction early stopping params
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

[python] improving the syntax of the fstring in the file : tests/python_package_test/test_dask.py (#4358)

* updated the old syntax with fstrings

* Updated the strings with + catenation to fstrings

* Updated the strings with + catenation to fstrings

* Update tests/python_package_test/test_dask.py
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* run Dask tests on aarch64 architecture

* make random Dask test to fail

* Revert "make random Dask test to fail"

This reverts commit c43c98507f818994bb08b4f7d289ecad3b3449eb.

* empty commit

* empty commit

* empty commit

* empty commit
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* replace client fixture with cluster fixture

* wait on persist before rebalance

* include voting_parallel tree_learner in test_regressor, test_classifier and test_ranker

* remove test for warnings and test for error when using feature_parallel

* use real names for tree_learner intest and include test for aliases. use the error message in the test for error in feature parallel

* split all tests with rf in test_classifier

* remove task parametrization for tree_learner aliases test. smaller input data from feature_parallel error

* define task for tree_learner aliases