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* 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

* 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.

* 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>

* [doc] better doc for `keep_training_booster`

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

* [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>

* Update engine.py

* Update sklearn.py

* 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

* 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>

* handle aliases centralized

* convert aliases dict to class

* Update sparse_bin.hpp

* check sorted in c_api

* fix python package

* fix tests

* fix test

* std::is_sorted

* Update basic.py

* avoid copy where possible

* use precise type for importance type

* removed pointless code

* simplify sparse pandas Series conversion

* more memory savings

* always force type conversion for 1-D arrays

* one more copy=False

* Bug fix for first_metric_only if the first metric is train metric.

* Update bug fix for feval issue.

* Disable feval for first_metric_only.

* Additional test items.

* Fix wrong assertEqual settings & formating.

* Change dataset of test.

* Fix random seed for test.

* Modiry assumed test result due to different sklearn verion between CI and local.

* Remove f-string

* Applying variable  assumed test result for test.

* Fix flake8 error.

* Modifying  in accordance with review comments.

* Modifying for pylint.

* simplified tests

* Deleting error criteria `if eval_metric is None`.

* Delete test items of classification.

* Simplifying if condition.

* Applying first_metric_only for sklearn wrapper.

* Modifying test_sklearn for comforming to python 2.x

* Fix flake8 error.

* Additional fix for sklearn and add tests.

* Bug fix and add test cases.

* some refactor

* fixed lint

* fixed lint

* Fix duplicated metrics scores to pass the test.

* Fix the case first_metric_only not in params.

* Converting metrics aliases.

* Add comment.

* Modify comment for pylint.

* Modify comment for pydocstyle.

* Using split test set for two eval_set.

* added test case for metric aliases and length checks

* minor style fixes

* fixed rmse name and alias position

* Fix the case metric=[]

* Fix using env.model._train_data_name

* Fix wrong test condition.

* Move initial process to _init() func.

* Modify test setting for test_sklearn & training data matching on callback.py

* test_sklearn.py
-> A test case for training is wrong, so fixed.

* callback.py
-> A condition of if statement for detecting test dataset is wrong, so fixed.

* Support composite name metrics.

* Remove metric check process & reduce redundant test cases.

For #2273 fixed not only the order of metrics in cpp, removing metric check process at callback.py

* Revised according to the matters pointed out on a review.

* increased code readability

* Fix the issue of order of validation set.

* Changing to OrderdDict from default dict for score result.

* added missed check in cv function for first_metric_only and feval co-occurrence

* keep order only for metrics but not for datasets in best_score

* move OrderedDict initialization to init phase

* fixed minor printing issues

* move first metric detection to init phase and split can be performed without checks

* split only once during callback

* removed excess code

* fixed typo in variable name and squashed ifs

* use setdefault

* hotfix

* fixed failing test

* refined tests

* refined sklearn test

* Making "feval" effective on early stopping.

* allow feval and first_metric_only for cv

* removed unused code

* added tests for feval

* fixed printing

* add note about whitespaces in feval name

* Modifying final iteration process in case valid set is  training data.

* Some basic changes to the plot of the trees to make them readable.

* Squeezed the information in the nodes.

* Added colouring when a dictionnary mapping the features to the constraints is passed.

* Fix spaces.

* Added data percentage as an option in the nodes.

* Squeezed the information in the leaves.

* Important information is now in bold.

* Added a legend for the color of monotone splits.

* Changed "split_gain" to "gain" and "internal_value" to "value".

* Sqeezed leaves a bit more.

* Changed description in the legend.

* Revert "Sqeezed leaves a bit more."

This reverts commit dd8bf14a3ba604b0dfae3b7bb1c64b6784d15e03.

* Increased the readability for the gain.

* Tidied up the legend.

* Added the data percentage in the leaves.

* Added the monotone constraints to the dumped model.

* Monotone constraints are now specified automatically when plotting trees.

* Raise an exception instead of the bug that was here before.

* Removed operators on the branches for a clearer design.

* Small cleaning of the code.

* Setting a monotone constraint on a categorical feature now returns an exception instead of doing nothing.

* Fix bug when monotone constraints are empty.

* Fix another bug when monotone constraints are empty.

* Variable name change.

* Added is / isn't on every edge of the trees.

* Fix test "tree_create_digraph".

* Add new test for plotting trees with monotone constraints.

* Typo.

* Update documentation of categorical features.

* Typo.

* Information in nodes more explicit.

* Used regular strings instead of raw strings.

* Small refactoring.

* Some cleaning.

* Added future statement.

* Changed output for consistency.

* Updated documentation.

* Added comments for colors.

* Changed text on edges for more clarity.

* Small refactoring.

* Modified text in leaves for consistency with nodes.

* Updated default values and documentaton for consistency.

* Replaced CHECK with Log::Fatal for user-friendliness.

* Updated tests.

* Typo.

* Simplify imports.

* Swapped count and weight to improve readibility of the leaves in the plotted trees.

* Thresholds in bold.

* Made information in nodes written in a specific order.

* Added information to clarify legend.

* Code cleaning.

Replace "Traninig" with "Training"

[python] fixed picklability of sklearn models with custom obj and updated docstings for custom obj (#2191)

* refactored joblib test

* fixed picklability of sklearn models with custom obj and updated docstings for custom obj

* pickled model should be able to predict without refitting

* added ability to pass first_metric_only in params

* simplified tests

* fixed test

* fixed punctuation

* ignore pandas ordered categorical columns by default

* fix tests

* fix tests

* added comments

* [python] displaying train loss during training with lgb.cv

* modifying only display running type when disp_train_loss==True

* Add test for display train loss

* del .idea files

* Rename disp_train_loss to show_train_loss and revise comment.

* Change aug name show_train_loss -> eval_train_metric , and add a test item.

* Modifying comment of eval_train_metric.

* fixed Python intro

* fixed typos

* scikit-learn added support of https

* Use first_metric_only flag for early_stopping function.

In order to apply early stopping with only first metric, applying first_metric_only flag for early_stopping function.

* upcate comment

* Revert "upcate comment"

This reverts commit 1e75a1a415cc16cfbe795181e148ebfe91469be4.

* added test

* fixed docstring

* cut comment and save one line

* document new feature

[tests][python] added tests for metrics' behavior and fixed case for multiclass task with custom objective (#1954)

* added metrics test for standard interface

* simplified code

* less trees

* less trees

* use dummy custom objective and metric

* added tests for multiclass metrics aliases

* fixed bug in case of custom obj and num_class > 1

* added metric test for sklearn wrapper

* removed misleading note about best_iteration

* Update engine.py

* Update Python-Intro.rst

* Updated Engine.py

* Updated Python-Intro.rst

* add article 'the best', break huge line and remove excess empty line

* added docstring style test and fixed errors in existing docstrings

* hotfix

* hotfix

* fix grammar

* hotfix

* Fixed some typos in Python API Docs

* FixTypo changed validation set -> sets

* break extremely large lines in basic.py

* break extremely large lines in callback.py

* break extremely large lines in engine.py

* break extremely large lines in sklearn.py

* hotfixes

* added sklearn splitter classes in cv function

* added tests

* warning on categorical feature with sparse values

* [docs] categorical features note

* pass params to _InnerPredictor in train and cv

* fixed verbosity param description

* treat silent param as Fatal log level

* create Dataset in refit method silently

* do not overwrite verbose param by silent argument

* bring consistency and clearness into early_stopping_rounds desc, metric desc and implementation

* hotfix

* hotfix

* used NDCG as default metric for lambdarank task

* fixed missed methods at ReadTheDocs and changed default eval_metric

* leaved only unique metrics

* fixed comment

* add start_iteration in model saving

* fix test

* shuffle models ability

* fix bug

* update document

* refine

* Update engine.py

* Update basic.py

* fix comments

* fix comment