GitLab

* 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>

* [python-package] add type hints on Booster eval methods

* remove unnecessary changes

* fix hints

* [python-package] remove inner function _construct_dataset() in LGBMModel.fit()

* switch order

* [python-package] add type hints on predict() methods

* formatting

* allow custom weighing in sklearn api

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

* check feature names and order in predict with dataframe

* slice df in predict to remove the target

* scramble features

* handle int column names

* only change column order when needed

* include validate_features param in booster and sklearn estimators

* document validate_features argument

* use all_close in preds checks and check for assertion error to compare different arrays

* perform remapping and checks in cpp

* remove extra logs

* fixes

* revert cpp

* proposal

* remove extra arg

* lint

* restore _data_from_pandas arguments

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

* move data conversion to Predictor.predict

* use Vector2Ptr
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

Simplify params handling in `predict()` with `_choose_param_value()`

[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>

[python-package] remove 'fobj' in favor of passing custom objective function in params (fixes #3244) (#5052)

* feat: support custom metrics in params

* feat: support objective in params

* test: custom objective and metric

* fix: imports are incorrectly sorted

* feat: convert eval metrics str and set to list

* feat: convert single callable eval_metric to list

* test: single callable objective in params
Signed-off-by: Miguel Trejo <armando.trejo.marrufo@gmail.com>

* feat: callable fobj in basic cv function
Signed-off-by: Miguel Trejo <armando.trejo.marrufo@gmail.com>

* test: cv support objective callable
Signed-off-by: Miguel Trejo <armando.trejo.marrufo@gmail.com>

* fix: assert in cv_res
Signed-off-by: Miguel Trejo <armando.trejo.marrufo@gmail.com>

* docs: objective callable in params
Signed-off-by: Miguel Trejo <armando.trejo.marrufo@gmail.com>

* recover test_boost_from_average_with_single_leaf_trees
Signed-off-by: Miguel Trejo <armando.trejo.marrufo@gmail.com>

* linters fail
Signed-off-by: Miguel Trejo <armando.trejo.marrufo@gmail.com>

* remove metrics helper functions
Signed-off-by: Miguel Trejo <armando.trejo.marrufo@gmail.com>

* feat: choose objective through _choose_param_values
Signed-off-by: Miguel Trejo <armando.trejo.marrufo@gmail.com>

* test: test objective through _choose_param_values
Signed-off-by: Miguel Trejo <armando.trejo.marrufo@gmail.com>

* test: test objective is callabe in train
Signed-off-by: Miguel Trejo <armando.trejo.marrufo@gmail.com>

* test: parametrize choose_param_value with objective aliases
Signed-off-by: Miguel Trejo <armando.trejo.marrufo@gmail.com>

* test: cv booster metric is none
Signed-off-by: Miguel Trejo <armando.trejo.marrufo@gmail.com>

* fix: if string and callable choose callable
Signed-off-by: Miguel Trejo <armando.trejo.marrufo@gmail.com>

* test train uses custom objective metrics
Signed-off-by: Miguel Trejo <armando.trejo.marrufo@gmail.com>

* test: cv uses custom objective metrics
Signed-off-by: Miguel Trejo <armando.trejo.marrufo@gmail.com>

* refactor: remove fobj parameter in train and cv
Signed-off-by: Miguel Trejo <armando.trejo.marrufo@gmail.com>

* refactor: objective through params in sklearn API
Signed-off-by: Miguel Trejo <armando.trejo.marrufo@gmail.com>

* custom objective function in advanced_example
Signed-off-by: Miguel Trejo <armando.trejo.marrufo@gmail.com>

* fix whitespackes lint

* objective is none not a particular case for predict method
Signed-off-by: Miguel Trejo <armando.trejo.marrufo@gmail.com>

* replace scipy.expit with custom implementation
Signed-off-by: Miguel Trejo <armando.trejo.marrufo@gmail.com>

* test: set num_boost_round value to 20
Signed-off-by: Miguel Trejo <armando.trejo.marrufo@gmail.com>

* fix: custom objective default_value is none
Signed-off-by: Miguel Trejo <armando.trejo.marrufo@gmail.com>

* refactor: remove self._fobj
Signed-off-by: Miguel Trejo <armando.trejo.marrufo@gmail.com>

* custom_objective default value is None
Signed-off-by: Miguel Trejo <armando.trejo.marrufo@gmail.com>

* refactor: variables name reference dummy_obj
Signed-off-by: Miguel Trejo <armando.trejo.marrufo@gmail.com>

* linter errors

* fix: process objective parameter when calling predict
Signed-off-by: Miguel Trejo <armando.trejo.marrufo@gmail.com>

* linter errors

* fix: objective is None during predict call
Signed-off-by: Miguel Trejo <armando.trejo.marrufo@gmail.com>

[python] fixes for supporting 2d numpy arrays for predictions, grads and hess in multiclass custom objective and eval (#5030)

* fixes for supporting 2d numpy arrays for predictions, grads and hess in multiclass custom objective

* Apply suggestions from code review
Co-authored-by: José Morales <jmoralz92@gmail.com>
Co-authored-by: José Morales <jmoralz92@gmail.com>

* 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

[python-package] use 2d collections for predictions, grads and hess in multiclass custom objective (#4925)

* reshape predictions, grad and hess in multiclass custom objective

* add sklearn test. move custom obj to utils. docs for numpy

* use num_model_per_iteration to get num_classes

* update docs and dask multiclass custom objective test

* move reshaping to __inner_predict. add test for feval

* add missing note. remove extra line

* clarify that categoricals will be converted to ints and not that they should be ints in the input data

* update remaining sections

* update config.h

* add suggestions

* Update sklearn.py

* Update sklearn.py

* Update test_sklearn.py

* unify values of `best_iteration` for sklearn and standard APIs

* update Dask test

* [python-package] fix mypy errors in sklearn.py

* use ignore comments

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

* continue

* add test

* fix return name

* hotfix

* simplify

* Update sklearn.py

* Update engine.py

* Update sklearn.py

* Update engine.py

* Update basic.py

* Update engine.py

* 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>

* 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

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

* extract docs changes

[python][sklearn] add `__sklearn_is_fitted__()` method to be better compatible with scikit-learn API (#4636)

[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>