GitLab

* Update test_engine.py

* Update test_sklearn.py

* Update test_engine.py

* Update test_sklearn.py

* Update test_sklearn.py

* Update test_sklearn.py

* Update test_sklearn.py

* Update test_engine.py

* Update .vsts-ci.yml

* Update .vsts-ci.yml

* Update test_engine.py

* Update test_dual.py

* Update test_engine.py

* Update .vsts-ci.yml

* Update .vsts-ci.yml

* TST Migrates tset_engine.py to pytest

* ENH Apply suggestions

* ENH Uses temp path

* ENH Fixes typos

* [R-package] allow access to params in Booster

* remove unnecessary whitespace

* fix test on resetting params

* remove pytest_cache

* Update R-package/tests/testthat/test_custom_objective.R

* small code and docs refactoring

* Update CMakeLists.txt

* Update .vsts-ci.yml

* Update test.sh

* continue

* continue

* revert stable sort for all-unique values

* Add Eigen library.

* Working for simple test.

* Apply changes to config params.

* Handle nan data.

* Update docs.

* Add test.

* Only load raw data if boosting=gbdt_linear

* Remove unneeded code.

* Minor updates.

* Update to work with sk-learn interface.

* Update to work with chunked datasets.

* Throw error if we try to create a Booster with an already-constructed dataset having incompatible parameters.

* Save raw data in binary dataset file.

* Update docs and fix parameter checking.

* Fix dataset loading.

* Add test for regularization.

* Fix bugs when saving and loading tree.

* Add test for load/save linear model.

* Remove unneeded code.

* Fix case where not enough leaf data for linear model.

* Simplify code.

* Speed up code.

* Speed up code.

* Simplify code.

* Speed up code.

* Fix bugs.

* Working version.

* Store feature data column-wise (not fully working yet).

* Fix bugs.

* Speed up.

* Speed up.

* Remove unneeded code.

* Small speedup.

* Speed up.

* Minor updates.

* Remove unneeded code.

* Fix bug.

* Fix bug.

* Speed up.

* Speed up.

* Simplify code.

* Remove unneeded code.

* Fix bug, add more tests.

* Fix bug and add test.

* Only store numerical features

* Fix bug and speed up using templates.

* Speed up prediction.

* Fix bug with regularisation

* Visual studio files.

* Working version

* Only check nans if necessary

* Store coeff matrix as an array.

* Align cache lines

* Align cache lines

* Preallocation coefficient calculation matrices

* Small speedups

* Small speedup

* Reverse cache alignment changes

* Change to dynamic schedule

* Update docs.

* Refactor so that linear tree learner is not a separate class.

* Add refit capability.

* Speed up

* Small speedups.

* Speed up add prediction to score.

* Fix bug

* Fix bug and speed up.

* Speed up dataload.

* Speed up dataload

* Use vectors instead of pointers

* Fix bug

* Add OMP exception handling.

* Change return type of LGBM_BoosterGetLinear to bool

* Change return type of LGBM_BoosterGetLinear back to int, only parameter type needed to change

* Remove unused internal_parent_ property of tree

* Remove unused parameter to CreateTreeLearner

* Remove reference to LinearTreeLearner

* Minor style issues

* Remove unneeded check

* Reverse temporary testing change

* Fix Visual Studio project files

* Restore LightGBM.vcxproj.filters

* Speed up

* Speed up

* Simplify code

* Update docs

* Simplify code

* Initialise storage space for max num threads

* Move Eigen to include directory and delete unused files

* Remove old files.

* Fix so it compiles with mingw

* Fix gpu tree learner

* Change AddPredictionToScore back to const

* Fix python lint error

* Fix C++ lint errors

* Change eigen to a submodule

* Update comment

* Add the eigen folder

* Try to fix build issues with eigen

* Remove eigen files

* Add eigen as submodule

* Fix include paths

* Exclude eigen files from Python linter

* Ignore eigen folders for pydocstyle

* Fix C++ linting errors

* Fix docs

* Fix docs

* Exclude eigen directories from doxygen

* Update manifest to include eigen

* Update build_r to include eigen files

* Fix compiler warnings

* Store raw feature data as float

* Use float for calculating linear coefficients

* Remove eigen directory from GLOB

* Don't compile linear model code when building R package

* Fix doxygen issue

* Fix lint issue

* Fix lint issue

* Remove uneeded code

* Restore delected lines

* Restore delected lines

* Change return type of has_raw to bool

* Update docs

* Rename some variables and functions for readability

* Make tree_learner parameter const in AddScore

* Fix style issues

* Pass vectors as const reference when setting tree properties

* Make temporary storage of serial_tree_learner mutable so we can make the object's methods const

* Remove get_raw_size, use num_numeric_features instead

* Fix typo

* Make contains_nan_ and any_nan_ properties immutable again

* Remove data_has_nan_ property of tree

* Remove temporary test code

* Make linear_tree a dataset param

* Fix lint error

* Make LinearTreeLearner a separate class

* Fix lint errors

* Fix lint error

* Add linear_tree_learner.o

* Simulate omp_get_max_threads if openmp is not available

* Update PushOneData to also store raw data.

* Cast size to int

* Fix bug in ReshapeRaw

* Speed up code with multithreading

* Use OMP_NUM_THREADS

* Speed up with multithreading

* Update to use ArrayToString

* Fix tests

* Fix test

* Fix bug introduced in merge

* Minor updates

* Update docs

* 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.

* [ci] [python] reduce unnecessary data loading in tests

* add profiling files to gitignore

* just use cache()

* default on cache size

* patch lru_cache on Python 2.7

* linting

* reduce duplicated code

* missing warnings

* fix imports

* fix lru_cache backport

* missing kwargs

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

* reduce duplicated code

* cache in test_plotting
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Implement average precision score

* Fix lint errors

* Change name to average_precision

* Add to R-package list of metrics

* Empty commit to trigger CI jobs

* Change name to average_precision

* No need to pass the tree to all fuctions related to monotone constraints because the pointer is shared.

* Fix OppositeChildShouldBeUpdated numerical split optimisation.

* No need to use constraints when computing the output of the root.

* Refactor existing constraints.

* Add advanced constraints method.

* Update tests.

* Add override.

* linting.

* Add override.

* Simplify condition in LeftRightContainsRelevantInformation.

* Add virtual destructor to FeatureConstraint.

* Remove redundant blank line.

* linting of else.

* Indentation.

* Lint else.

* Replaced non-const reference by pointers.

* Forgotten reference.

* Leverage USE_MC for efficiency.

* Make constraints const again in feature_histogram.hpp.

* Update docs.

* Add "advanced" to the monotone constraints options.

* Update monotone constraints restrictions.

* Fix loop iterator.
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Fix loop iterator.
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Remove superfluous parenthesis.

* Fix loop iterator.
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Fix loop iterator.
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Fix loop iterator.
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Fix loop iterator.
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Fix loop iterator.
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Fix loop iterator.
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Fix loop iterator.
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Fix loop iterator.
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Remove std namespace qualifier.

* Fix unsigned_int size_t comparison.

* Set num_features as int for consistency with the rest of the codebase.

* Make sure constraints exist before recomputing them.

* Initialize previous constraints in UpdateConstraints.

* Update monotone constraints restrictions.

* Refactor UpdateConstraints loop.

* Update src/io/config.cpp
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Delete white spaces.
Co-authored-by: Charles Auguste <charles.auguste@sig.com>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update auc_mu metric to use data weights if provided

* Calculate class sizes and total weights in Init so we only do it once

* Fix lint error

* Empty commit to trigger CI jobs

* 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] 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>

* 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.

* 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.

* 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

* 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>

* 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

* 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

* 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>

* transfer and enhance test for trees_to_dataframe

* fixed bug in Python 2

* 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.