GitLab

* chanche MRO

* fix MRO resolution

* Migrate to f-strings in python-package/lightgbm/sklearn.py

* Apply suggestions from code review
Co-authored-by: James Lamb <jaylamb20@gmail.com>

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

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

* Add suggestions from code review

* resolve conflicts

* Apply suggestions from code review
Co-authored-by: James Lamb <jaylamb20@gmail.com>

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

* include support for init_score

* use dataframe from init_score and test difference with and without init_score in local model

* revert refactoring

* initial docs. test between distributed models with and without init_score

* remove ranker from tests

* test value for root node and change docs

* comma

* re-include parametrize

* fix incorrect merge

* use single init_score and the booster_ attribute

* use np.float64 instead of float

* run isort in CI linting job

* workaround conda compatibility issues

* minor mypy type errors fixed

* fix some warnings from mypy

* fix 3 mypy warnings

* selectively ignored some mypy errors

* minor mypy type errors fixed

* minor mypy type errors fixed

* minor mypy type errors fixed

* added import

* Update python-package/lightgbm/callback.py

* Apply suggestions from code review

* Apply suggestions from code review
Co-authored-by: James Lamb <jaylamb20@gmail.com>
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Update dask.py

* Update sklearn.py

* got fit() working

* add predict()

* predict_proba()

* remove custom objective docs

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

* fix capitalization

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

* Update sklearn.py

* Update dask.py

* initial Dask docs

* fix MRO

* address review comments

* centralize Python-package logging in one place

* continue

* fix test name

* removed unused import

* enhance test

* fix lint

* hotfix test

* workaround for GPU test

* remove custom logger from Dask-package

* replace one log func with flags by multiple funcs

* fix docs

* Update basic.py

* Update engine.py

* [python-package] expand documentation on 'group' for ranking task

* add R package

* update Query Data section

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

* fix typo in group example

* regenerate parameters

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

* regenerate R docs
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update setup.py

* Update .appveyor.yml

* Update .travis.yml

* Update .vsts-ci.yml

* Update __init__.py

* Update test.sh

* Update test_windows.ps1

* Update advanced_example.py

* Update requirements_base.txt

* Update conf.py

* Update conf.py

* Update test_engine.py

* Update utils.py

* Update dockerfile-r

* Update README.md

* Update dockerfile.gpu

* Update test_consistency.py

* Update basic.py

* Update compat.py

* Update engine.py

* Update sklearn.py

* Update sklearn.py

* Update callback.py

* Update setup.py

* Update __init__.py

* Update plotting.py

* Update sklearn.py

* Update engine.py

* Update compat.py

* Update callback.py

* Update basic.py

* Update compat.py

* Update basic.py

* Update basic.py

* Update compat.py

* Update compat.py

* Update plotting.py

* Update engine.py

* Update basic.py

* Update sklearn.py

* Update compat.py

* Update engine.py

* Update engine.py

* Update callback.py

* Update basic.py

* Update basic.py

* Update basic.py

* Update basic.py

* Update basic.py

* Update sklearn.py

* Update sklearn.py

* Update plotting.py

* Update sklearn.py

* Update compat.py

* Update compat.py

* Update engine.py

* Update plotting.py

* Update sklearn.py

* Update basic.py

* Update basic.py

* Update basic.py

* Update basic.py

* Update compat.py

* Update compat.py

* Update compat.py

* Update engine.py

* Update basic.py

* Update compat.py

* Update basic.py

* Update basic.py

* Update basic.py

* Update compat.py

* Update compat.py

* Update basic.py

* Update basic.py

* Update .vsts-ci.yml

* Update .vsts-ci.yml

* Update conf.py

* Revert "Update dockerfile-r"

This reverts commit 4ff6ffc7e3eeda24cc6a59a3bb0c973f02d9d71c.

* 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

* [python] fix dangerous default for eval_at in LGBMRanker

* use a tuple

* five

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

* Refactors sklearn API to allow a list of evaluation metrics in the parameter eval_metric of the class (and subclasses of) LGBMModel. Also adds unit tests for this functionality

* Simplify expression to check whether the user passed one or multiple metrics to eval_metric parameter

* Simplify new tests by using custom metrics already defined in the test file

* Update docstring to reflect the fact that the parameter "feval" from the "train" and "cv" functions can also receive a list of callables

* Remove oxford comma from docstrings

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

* Use named-parameters to make sure code is compatible with future versions of scikit-learn

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

* Remove throwaway return value to make code more succinct
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Move statement to group together the code related to feval

* Avoid modifying original args as it causes errors in scikit-learn tools

For details see: https://github.com/microsoft/LightGBM/pull/2619



* Consolidate multiple eval-metrics unit-tests into one test
Co-authored-by: German I Ramirez-Espinoza <gire@home>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

simplify start_iteration param for predict in Python and some code cleanup for start_iteration (#3288)

* simplify start_iteration param for predict in Python and some code cleanup for start_iteration

* revert docs changes about the prediction result shape

* [python] add start_iteration to python predict interface (#3058)

* Apply suggestions from code review

* Update lightgbm_R.h

* Apply suggestions from code review

* Apply suggestions from code review

* fix R interface

* update R documentation
Co-authored-by: Guolin Ke <guolin.ke@outlook.com>

[python][scikit-learn] Fixes a bug that prevented using multiple eval_metrics in LGBMClassifier (#3222)

* Fixes a bug that prevented using multiple eval_metrics in LGBMClassifier

* Move bug-fix test to the test_metrics unit-test

* Fix test to avoid issues with existing tests

* Fix coding-style error
Co-authored-by: German I Ramirez-Espinoza <gire@home>

* Update engine.py

* Update sklearn.py

* adding sparse support to TreeSHAP in lightgbm

* updating based on comments

* updated based on comments, used fromiter instead of frombuffer

* updated based on comments

* fixed limits import order

* fix sparse feature contribs to work with more than int32 max rows

* really fixed int64 max error and build warnings

* added sparse test with >int32 max rows

* fixed python side reshape check on sparse data

* updated based on latest comments

* fixed comments

* added CSC INT32_MAX validation to test, fixed comments

* modify attribute and include stacking tests

* backwards compatibility

* check sklearn version

* move stacking import

* Number of input features (#3173)

* Number of input features (#3173)

* Number of input features (#3173)

* Number of input features (#3173)

Split number of features and stacking tests.

* Number of input features (#3173)

Modify test name.

* Number of input features (#3173)

Update stacking tests for review comments.

* Number of input features (#3173)

* Number of input features (#3173)

* Number of input features (#3173)

* Number of input features (#3173)

Modify classifier test.

* Number of input features (#3173)

* Number of input features (#3173)

Check score.

* Revert "[ci][docs] temporarily pin Sphinx version (#3157)"

This reverts commit b3a84df5.

* removed duplicated docstrings

* update number of features attribute

Fixes issue related to https://github.com/scikit-learn/scikit-learn/issues/17353 (see SLEP010 https://scikit-learn-enhancement-proposals.readthedocs.io/en/latest/slep010/proposal.html

).

* Update sklearn.py

* set public attribute in fit method

Reverted ```n_features``` property, and inserted the public attribute ```n_features_in_```.

* Update documentation

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

* Revert "specify the last supported version of scikit-learn (#2637)"

This reverts commit d1002776.

* ban scikit-learn 0.22.0 and skip broken test

* fix updated test

* fix lint test

* Revert "fix lint test"

This reverts commit 8b4db0805fe7a9e7f7eb0be3eac231f85026d196.

* Add handling of RandomState object, which is standard for sklearn methods.

LightGBM expects an integer seed instead of an object.
If passed object is RandomState, we choose random integer based on its state to seed the underlying low level code.
While chosen random integer is only in the range between 1 and 1e10 I expect it to have enough entropy (?) to not matter in practice.

* Add RandomState object to random_state docstring.

* remove blank line

* Use property to handle setting random_state.
This enables setting cloned estimators with the set_params method in sklearn.

* Add docstring to attribute.

* Fix and simplify docstring.

* Add test case.

* Use maximal int for datatype in seed derivation.

* Replace random_state property with interfacing in fit method.
Derives int seed for C code only when fitting and keeps RandomState object as param.

* Adapt unit test to property change.

* Extended test case and docstring
Co-Authored-By: Nikita Titov <nekit94-08@mail.ru>

* Add more equality checks (feature importance, best iteration/score).

* Add equality comparison of boosters represented by strings.
Remove useless best_iteration_ comparison (we do not use early_stopping).

* fix whitespace

* Test if two subsequent fits produce different models

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

* add property: feature_name_ in lgb sklearn api

* modify the comments

* fix linting errors and add info about new attribute: feature_name_

* clean code

* clean code

* do not modify args in fit function

* added test

* allow python sklearn interface's fit() to pass init_model to train()

* Fix whitespace issues, and change ordering of parameters to be backward
compatible

* Formatting fixes

* allow python sklearn interface's fit() to pass init_model to train()

* Fix whitespace issues, and change ordering of parameters to be backward
compatible

* Formatting fixes

* Recognize LGBModel objects for init_model

* simplified condition

* updated docstring

* added test

* handle aliases centralized

* convert aliases dict to class