GitLab

* [python-package] fix mypy errors about scikit-learn properties

* Update python-package/lightgbm/sklearn.py

* add parameter data_sample_strategy

* abstract GOSS as a sample strategy(GOSS1), togetherwith origial GOSS (Normal Bagging has not been abstracted, so do NOT use it now)

* abstract Bagging as a subclass (BAGGING), but original Bagging members in GBDT are still kept

* fix some variables

* remove GOSS(as boost) and Bagging logic in GBDT

* rename GOSS1 to GOSS(as sample strategy)

* add warning about use GOSS as boosting_type

* a little ; bug

* remove CHECK when "gradients != nullptr"

* rename DataSampleStrategy to avoid confusion

* remove and add some ccomments, followingconvention

* fix bug about GBDT::ResetConfig (ObjectiveFunction inconsistencty bet…

* add std::ignore to avoid compiler warnings (anpotential fails)

* update Makevars and vcxproj

* handle constant hessian

move resize of gradient vectors out of sample strategy

* mark override for IsHessianChange

* fix lint errors

* rerun parameter_generator.py

* update config_auto.cpp

* delete redundant blank line

* update num_data_ when train_data_ is updated

set gradients and hessians when GOSS

* check bagging_freq is not zero

* reset config_ value

merge ResetBaggingConfig and ResetGOSS

* remove useless check

* add ttests in test_engine.py

* remove whitespace in blank line

* remove arguments verbose_eval and evals_result

* Update tests/python_package_test/test_engine.py

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

* Update tests/python_package_test/test_engine.py

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

* Update tests/python_package_test/test_engine.py

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

* Update tests/python_package_test/test_engine.py

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

* Update tests/python_package_test/test_engine.py

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

* Update tests/python_package_test/test_engine.py

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

* Update src/boosting/sample_strategy.cpp

modify warning about setting goss as `boosting_type`
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Update tests/python_package_test/test_engine.py

replace load_boston() with make_regression()

remove value checks of mean_squared_error in test_sample_strategy_with_boosting()

* Update tests/python_package_test/test_engine.py

add value checks of mean_squared_error in test_sample_strategy_with_boosting()

* Modify warnning about using goss as boosting type

* Update tests/python_package_test/test_engine.py

add random_state=42 for make_regression()

reduce the threshold of mean_square_error

* Update src/boosting/sample_strategy.cpp
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* remove goss from boosting types in documentation

* Update src/boosting/bagging.hpp
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update src/boosting/bagging.hpp
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update src/boosting/goss.hpp
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update src/boosting/goss.hpp
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* rename GOSS with GOSSStrategy

* update doc

* address comments

* fix table in doc

* Update include/LightGBM/config.h
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* update documentation

* update test case

* revert useless change in test_engine.py

* add tests for evaluation results in test_sample_strategy_with_boosting

* include <string>

* change to assert_allclose in test_goss_boosting_and_strategy_equivalent

* more tolerance in result checking, due to minor difference in results of gpu versions

* change == to np.testing.assert_allclose

* fix test case

* set gpu_use_dp to true

* change --report to --report-level for rstcheck

* use gpu_use_dp=true in test_goss_boosting_and_strategy_equivalent

* revert unexpected changes of non-ascii characters

* revert unexpected changes of non-ascii characters

* remove useless changes

* allocate gradients_pointer_ and hessians_pointer when necessary

* add spaces

* remove redundant virtual

* include <LightGBM/utils/log.h> for USE_CUDA

* check for  in test_goss_boosting_and_strategy_equivalent

* check for identity in test_sample_strategy_with_boosting

* remove cuda  option in test_sample_strategy_with_boosting

* Update tests/python_package_test/test_engine.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

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

* ResetGradientBuffers after ResetSampleConfig

* ResetGradientBuffers after ResetSampleConfig

* ResetGradientBuffers after bagging

* remove useless code

* check objective_function_ instead of gradients

* enable rf with goss

simplify params in test cases

* remove useless changes

* allow rf with feature subsampling alone

* change position of ResetGradientBuffers

* check for dask

* add parameter types for data_sample_strategy
Co-authored-by: Guangda Liu <v-guangdaliu@microsoft.com>
Co-authored-by: Yu Shi <shiyu_k1994@qq.com>
Co-authored-by: GuangdaLiu <90019144+GuangdaLiu@users.noreply.github.com>
Co-authored-by: James Lamb <jaylamb20@gmail.com>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

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

[python-package] Use scikit-learn interpretation of negative `n_jobs` and change default to number of cores (#5105)

* use joblib formula for negative n_jobs

* correction for n_jobs calculation

* use more robust cpu_count from joblib

* change default n_jobs to number of cores

* fix detection of num_threads under parameters

* better handling of n_jobs at prediction time

* fix incorrect usage of list.pop

* correct pop/remove yet again

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

* Update tests/python_package_test/test_sklearn.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update tests/python_package_test/test_sklearn.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* add comments clarifying negative n_jobs

* fix CI (code taken from PR comment)

* change default to n_jobs=None in dask interface

* corrections for handling of n_jobs

* linter

* corrections for predict-time n_jobs

* linter

* add more comments about n_jobs values

* linter

* more corrections

* linter

* linter

* linter

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

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

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

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

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

* workaround for passing test about outputs with multiple threads

* Update tests/python_package_test/test_sklearn.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update tests/python_package_test/test_sklearn.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* 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

* 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

* [python-package] fix mypy error about missing type hint in dask.py

* list of lists

* use different variable

* use append()

* Update test_plotting.py

* Update dask.py

* Update sklearn.py

* Update test_sklearn.py

* Update basic.py

* Update engine.py

* Update test_engine.py

* Update basic.py

* Update basic.py

* Update engine.py

* [python] add type hints for custom objective and metric functions in scikit-learn interface

* update type hints

* remote unnecessary input

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

* remove type hint on objective being callable
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* [docs] [dask] Add return information to Dask fit() docs (fixes #4402)

* put return before note

[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>

* deprecate `silent` and standalone `verbose` args. Prefer global `verbose` param

* simplify code

* Rephrase warning messages

* Reffer to string type as `str` and and commas in `list of ...` types

* update `libpath.py` too

* find all needed ports in each worker at once

* lint

* better naming

* use _HostWorkers in test

* call predict on one row of data to determine output shape

* make DaskLGBMRanker predict method equal to the others

* remove extra drop_axis

[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>