GitLab

* [python] add return_cvbooster flag to cv function and rename _CVBooster to make public (#283,#2105)

* [python] Reduce expected metric of unit testing

* [docs] add the CVBooster to the documentation

* [python] reflect the review comments

- Add some clarifications to the documentation
- Rename CVBooster.append to make private
- Decrease iteration rounds of testing to save CI time
- Use CVBooster as root member of lgb

* [python] add more checks in testing for cv
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* [python] add docstring for instance attributes of CVBooster
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* [python] fix docstring
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* [python][scikit-learn] New unit tests and maintenance

* Includes multioutput tests
* Includes RandomizedSearchCV test
* Updates dataset parameters to eliminate FutureWarning

* Change to n_class in load_digits

* Fix spacing

* Changes after review

* Also updates validation split in grid and random search

* Include skipif for classes_ attr

* Updates checks for classes and order
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

[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>

* 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

* Fix bug: crashes when interaction_constraints is nonempty and not all features are used.

* Fix python lint error.

* 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.

* Add interaction constraints functionality.

* Minor fixes.

* Minor fixes.

* Change lambda to function.

* Fix gpu bug, remove extra blank lines.

* Fix gpu bug.

* Fix style issues.

* Try to fix segfault on MACOS.

* Fix bug.

* Fix bug.

* Fix bugs.

* Change parameter format for R.

* Fix R style issues.

* Change string formatting code.

* Change docs to say R package not supported.

* Remove R functionality, moving to separate PR.

* Keep track of branch features in tree object.

* Only track branch features when feature interactions are enabled.

* Fix lint error.

* Update docs and simplify tests.

* Path smoothing

* Try to fix issue with gpu version.

* Fix failing CI for R package.

* Minor fixes.

* Minor refactor.

* Restore old code to get CI working.

* Fix style issues.

* Fix ci for R package.

* Minor fixes for docs and code style.

* Update docs.

* Fix loss computation

* fix test

* Support UTF-8 characters in feature name again

This commit reverts 0d59859c.
Also see:
- https://github.com/microsoft/LightGBM/issues/2226
- https://github.com/microsoft/LightGBM/issues/2478
- https://github.com/microsoft/LightGBM/pull/2229

I reproduced the issue and as @kidotaka gave us a great survey in #2226,
I don't conclude that the cause is UTF-8, but "an empty string (character)".
Therefore, I revert "throw error when meet non ascii (#2229)" whose commit hash
is 0d59859c, and add support feture names as UTF-8 again.

* add tests

* fix check-docs tests

* update

* fix tests

* update .travis.yml

* fix tests

* update test_r_package.sh

* update test_r_package.sh

* update test_r_package.sh

* add a test for R-package

* update test_r_package.sh

* update test_r_package.sh

* update test_r_package.sh

* fix test for R-package

* update test_r_package.sh

* update test_r_package.sh

* update test_r_package.sh

* update test_r_package.sh

* update

* updte

* update

* remove unneeded comments

* 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 the monotone penalty parameter to the config.

* Pass tree in the necessary functions so it can be used in ComputeBestSplitForFeature.

* Add monotone penalty.

* Added link to the original report.

* Add tests.

* Fix GPU.

* Revert "Pass tree in the necessary functions so it can be used in ComputeBestSplitForFeature."

This reverts commit 37757e8e8f3a2c82a604f4af9a926da616660d2e.

* Revert "Fix GPU."

This reverts commit e49eeee41c883f3c97fd5cdbd53c9288094bffb6.

* Added a shared pointer to the tree so the constraints can use it too.

* Moved check on monotone penalty to config.cpp.

* Python linting.

* Use AssertTrue instead of assert_.

* Fix penalization in test.

* Make GPU deterministic in tests.

* Rename tree to tree_ in monotone constraints.

* Replaced epsilon by kEplison.

* Typo.

* Make tree pointer const.

* Update src/treelearner/monotone_constraints.hpp
Co-Authored-By: Guolin Ke <guolin.ke@outlook.com>

* Update src/treelearner/monotone_constraints.hpp
Co-Authored-By: Guolin Ke <guolin.ke@outlook.com>

* Added alias for the penalty.

* Remove useless comment.

* Save CI time.

* Refactor test_monotone_penalty_max.

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

* Fix doc to be in line with previous config change commit.
Co-authored-by: Charles Auguste <auguste@dubquantdev801.ire.susq.com>
Co-authored-by: Guolin Ke <guolin.ke@outlook.com>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Add util functions.

* Added monotone_constraints_method as a parameter.

* Add the intermediate constraining method.

* Updated tests.

* Minor fixes.

* Typo.

* Linting.

* Ran the parameter generator for the doc.

* Removed usage of the FeatureMonotone function.

* more fixes

* Fix.

* Remove duplicated code.

* Add debug checks.

* Typo.

* Bug fix.

* Disable the use of intermediate monotone constraints and feature sampling at the same time.

* Added an alias for monotone constraining method.

* Use the right variable to get the number of threads.

* Fix DEBUG checks.

* Add back check to determine if histogram is splittable.

* Added forgotten override keywords.

* Perform monotone constraint update only when necessary.

* Small refactor of FastLeafConstraints.

* Post rebase commit.

* Small refactor.

* Typo.

* Added comment and slightly improved logic of monotone constraints.

* Forgot a const.

* Vectors that are to be modified need to be pointers.

* Rename FastLeafConstraints to IntermediateLeafConstraints to match documentation.

* Remove overload of GoUpToFindLeavesToUpdate.

* Stop memory leaking.

* Fix cpplint issues.

* Fix checks.

* Fix more cpplint issues.

* Refactor config monotone constraints method.

* Typos.

* Remove useless empty lines.

* Add new line to separate includes.

* Replace unsigned ind by size_t.

* Reduce number of trials in tests to decrease CI time.

* Specify monotone constraints better in tests.

* Removed outer loop in test of monotone constraints.

* Added categorical features to the monotone constraints tests.

* Add blank line.

* Regenerate parameters automatically.

* Speed up ShouldKeepGoingLeftRight.
Co-authored-by: Charles Auguste <auguste@dubquantdev801.ire.susq.com>
Co-authored-by: guolinke <guolin.ke@outlook.com>

* 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>

* fix

* fix return

* fix test

* fix test

* fix predictor is none

* Apply suggestions from code review

* Update basic.py

* Update basic.py

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

* fix the bug when use different params with reference

* fix

* Update basic.py

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

* Update basic.py

* add test

* Apply suggestions from code review

* added asserts in test
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>
Co-authored-by: StrikerRUS <nekit94-12@hotmail.com>

* save all param values into model file

* revert storing predict params

* do not save params for predict and convert tasks

* fixed test: 10 is found successfully for default 100

* specify more params as no-save

* speed up for const hessian

* rename template

* some refactorings

* refine

* refine

* simplify codes

* fix random in feature histogram

* code refine

* refine

* try fix

* make gcc happy

* remove timer

* rollback some changes

* more templates

* fix a bug

* reduce the cost of timer

* fix gpu

* fix bug

* fix gpu

* avoid most_freq_bin to be 0 in categorical features

* Apply suggestions from code review

* add tests

* update test

* Apply suggestions from code review

* Apply suggestions from code review

* code refactoring

* update vcproject

* refine

* fix test

* Update tests/python_package_test/test_sklearn.py

* fix test

* fxied pandas deprecation warning in tests

* support old versions of pandas

* fix bug when nan bin is most freq bin

* fix naming

* fix bug

* add test

* Apply suggestions from code review

* fix more bugs

* Add capability to get possible max and min values for a model

* Change implementation to have return value in tree.cpp, change naming to upper and lower bound, move implementation to gdbt.cpp

* Update include/LightGBM/c_api.h
Co-Authored-By: Nikita Titov <nekit94-08@mail.ru>

* Change iteration to avoid potential overflow, add bindings to R and Python and a basic test

* Adjust test values

* Consider const correctness and multithreading protection

* Update test values

* Update test values

* Add test to check that model is exactly the same in all platforms

* Try to parse the model to get the expected values

* Try to parse the model to get the expected values

* Fix implementation, num_leaves can be lower than the leaf_value_ size

* Do not check for num_leaves to be smaller than actual size and get back to test with hardcoded value

* Change test order

* Add gpu_use_dp option in test

* Remove helper test method

* Update src/c_api.cpp
Co-Authored-By: Nikita Titov <nekit94-08@mail.ru>

* Update src/io/tree.cpp
Co-Authored-By: Nikita Titov <nekit94-08@mail.ru>

* Update src/io/tree.cpp
Co-Authored-By: Nikita Titov <nekit94-08@mail.ru>

* Update tests/python_package_test/test_basic.py
Co-Authored-By: Nikita Titov <nekit94-08@mail.ru>

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

* reset

* fix a bug

* fix test

* Update c_api.h

* support to no filter features by min_data

* add warning in reset config

* refine warnings for override dataset's parameter

* some cleans

* clean code

* clean code

* refine C API function doxygen comments

* refined new param description

* refined doxygen comments for R API function

* removed stuff related to int8

* break long line in warning message

* removed tests which results cannot be validated anymore

* added test for warnings about unchangeable params

* write parameter from dataset to booster

* consider free_raw_data.

* fix params

* fix bug

* implementing R

* fix typo

* filter params in R

* fix R

* not min_data

* refined tests

* fixed linting

* refine

* pilint

* add docstring

* fix docstring

* R lint

* updated description for C API function

* use param aliases in Python

* fixed typo

* fixed typo

* added more params to test

* removed debug print

* fix dataset construct place

* fix merge bug

* Update feature_histogram.hpp

* add is_sparse back

* remove unused parameters

* fix lint

* add data random seed

* update

* [R-package] centrallized Dataset parameter aliases and added tests on Dataset parameter updating (#2767)
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Add extra-trees functionality.

* Remove unnecessary code.

* Update docs.

* Use template for FindBestThresholdSequence.

* Use separate random seed. Fix bug.

* Update test_engine.py

* Update test_sklearn.py

* commit

* fix a bug

* fix bug

* reset to track changes

* refine the auto choose logic

* sort the time stats output

* fix include

* change  multi_val_bin_sparse_threshold

* add cmake

* add _mm_malloc and _mm_free for cross platform

* fix cmake bug

* timer for split

* try to fix cmake

* fix tests

* refactor DataPartition::Split

* fix test

* typo

* formating

* Revert "formating"

This reverts commit 5b8de4f7fb9d975ee23701d276a66d40ee6d4222.

* add document

* [R-package] Added tests on use of force_col_wise and force_row_wise in training (#2719)

* naming

* fix gpu code

* Update include/LightGBM/bin.h
Co-Authored-By: James Lamb <jaylamb20@gmail.com>

* Update src/treelearner/ocl/histogram16.cl

* test: swap compilers for CI

* fix omp

* not avx2

* no aligned for feature histogram

* Revert "refactor DataPartition::Split"

This reverts commit 256e6d9641ade966a1f54da1752e998a1149b6f8.

* slightly refactor data partition

* reduce the memory cost
Co-authored-by: James Lamb <jaylamb20@gmail.com>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Implementation of XE_NDCG loss function for ranking.

* Add citation

* Check in example usage for xe_ndcg loss.

* Seed the generator when a seed is provided in the config. Add unit-tests for xe_ndcg

* Update documentation

* Fix indentation

* Address issues raised by reviewers.

* Clean up include statements.

* Fix issues raised by reviewers.

* Regenerate parameters.rst

* Add a note to explain that reproducing xe_ndcg results requires num_threads to be one.

* Introduce objective_seed and use that in rank_xendcg instead of directly using seed

* Change default value of objective_seed

* transfer and enhance test for trees_to_dataframe

* fixed bug in Python 2

* trees_to_df method and unit test added. PEP 8 fixes for integration.

* Co-Authored-By: Nikita Titov <nekit94-08@mail.ru>

Post-review changes

* changes from second round of reviews from striker

* third round of review. formatting and added 2 more tests

* replaced pandas dot attribute accessor with string attribute accessor

* dealt with single tree edge case and minor refactor of tests

* slight refactor for checking if tree is a single node

* Fix bug where small values of max_bin cause crash.

* Revert "Fix bug where small values of max_bin cause crash."

This reverts commit fe5c8e2547057c1fa5750bcddd359dd7708fab4b.

* Add auc-mu multiclass metric.

* Fix bug where scores are equal.

* Merge.

* Change name to auc_mu everywhere (instead of auc-mu).

* Fix comparison between signed and unsigned int.

* Change name to AUC-mu in docs and output messages.

* Improve test.

* Use prefix increment.

* Update R package.

* Fix style issues.

* Tidy up test code.

* Read all lines first then process.

* Allow passing AUC-mu weights directly as a list in parameters.

* Remove unused code, improve example and docs.

* 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

* speed up tests

* more updates

* fixed pylint

* updated tests

* Update test_sklearn.py

* test that indices are sorted internally

* Update sparse_bin.hpp

* check sorted in c_api

* fix python package

* fix tests

* fix test

* std::is_sorted

* Update basic.py

* check the shape for mat, csr and csc

* guess from csr

* support file checking

* better error msg

* grammar

* clean code

* code clean

* check range for CSR

* Update test_.py

* Update test_.py

* added tests