GitLab

[tests][python-package] change boston dataset to synthetic dataset in tests that don't check score (#4895)

* change boston dataset to synthetic dataset in tests that don't evaluate score

* format imports

* Update test_sklearn.py

* Update python_package.yml

* Update python_package.yml

* Update callback.py

* Update callback.py

* Update sklearn.py

* Update sklearn.py

* Update test_sklearn.py

* in predict(), respect params set via `set_params()` after fit()

* continue

* add test

* fix return name

* hotfix

* simplify

* 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

* respect objective aliases

* Update test_sklearn.py

* revert removal of blank lines

* add argument name which is being overwritten in warning message

* add n_estimators_ and n_iter_ post-fit attributes

* address review comments

* Update test_sklearn.py

* Update test_basic.py

* Update dask.py

* Update basic.py

* Update basic.py

* Update basic.py

* Update basic.py

* Update callback.py

* include support for column array as label

* remove nested ifs

* fix linting errors

* include tests for sklearn regressors

* include docstring for numpy_1d_array_to_dtype

* include . at end of docstring

* remove pandas import and test for regression, classification and ranking

* check predictions of sklearn models as well

* test training only in dask. drop pandas series tests

* use PANDAS_INSTALLED and pd_Series

* inline imports

* use col array in fit for test_dask

* include review comments

* run isort in CI linting job

* workaround conda compatibility issues

* 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 test_sklearn.py to pytest

* STY Fixes linting

* FIX Adds reason

* ENH Address comments

* 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* remove outdated comment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

* [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>

* Add support to optimize for NDCG at a given truncation level

In order to correctly optimize for NDCG@_k_, one should exclude pairs containing both documents beyond the top-_k_ (as they don't affect NDCG@_k_ when swapped).

* Update rank_objective.hpp

* Apply suggestions from code review
Co-authored-by: Guolin Ke <guolin.ke@outlook.com>

* Update rank_objective.hpp

remove the additional branching: get high_rank and low_rank by one "if".

* Update config.h

add description to lambdarank_truncation_level parameter

* Update Parameters.rst

* Update test_sklearn.py

update expected NDCG value for a test, as it was affected by the underlying change in the algorithm

* Update test_sklearn.py

update NDCG@3 reference value

* fix R learning-to-rank tests

* Update rank_objective.hpp

* Update include/LightGBM/config.h
Co-authored-by: Guolin Ke <guolin.ke@outlook.com>

* Update Parameters.rst
Co-authored-by: Guolin Ke <guolin.ke@outlook.com>
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* 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>

* [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] 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>

* 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.

* Fix loss computation

* fix test

* 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>

* 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

* 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

* 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