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* add get params

clean codes

* check duplicate params

* Revert "add get params"

This reverts commit ec1d8dd17aa2c83dd0d5f7716c59b6a6fb94102c.

* set priority by length & check duplicate

* rename function

* Silent parameter does not exist anymore

* Fix Microsoft/LightGBM#913

* Generate Rd files for R documentation

* Update dataset.cpp

* Update dataset.cpp

* added doc test

* fixed 404

* prevented maito: ref creation

* added rst structure test

* fixed unknown directives

* added code for the future check for html syntax

* replaced problem link

fix typos in callback.R and lgb.cv.R, closing #946 and #947

* Remove signed/unsigned integer compare warning with g++

* minor changes for robustness and efficiency

* fix charcter typo

* don't use lgb.is.* in filter

* revert to cat instead of message; don't use mget

* fix bugs in previous commits

* should be good to go now

* gpu enabled docker file with python bindings

* update readme to reference master

* added python version badge

* fixed typos

* fixed links

* readthedocs doesn't support links with anchor out of box

* fixed table rendering at ReadTheDocs #776#issuecomment-319851551

* fixed table rendering at ReadTheDocs

* added link to Key-Events page

* fixed links

* hotfix

* fixed markdown

* refine categorical split

* add test

* better log for "Check max_depth and num_leaves"

The default log said "[LightGBM] [Warning] Accuarcy may be bad since you didn't set num_leaves." but the case max_depth²>num_leaves could also trigger this log.
Correction of a typo too : Accuarcy > Accuracy

* Update config.cpp

While in parallel training, when one worker have 0 data, then it will not execute ConstructHistogram.
if so, ptr_smaller_leaf_hist_data will not be 0 but the old data. that will get the wrong Histogram, and the wrong split info.

* add python examples to test [test should fail]

* fix example && move some to advanced_example

* fix last line

* refine test python version

* added test for sklearn handle categorical features

* use raw X, y in sklearn wrapper in case of pandas.DataFrame

* fixed probs

The example page state that "simple_example.py" should feature an example of json dump, but that was missing.

* Fix feature attributions for regression models and add Python bindings

* Address pylint issue

* Lazy fix missing tree depth info

* travis cleanup

* removed precompiled files in windows folder from sdist command

* removed rubbish from install folder

* added compute folder

* disabled logs from compilers; fixed #874

* fixed safe clear_fplder

* added windows folder to manifest.in

* added windows folder to build

* added library path

* added compilation with MSBuild from .sln-file

* fixed unknown PlatformToolset returns exitcode 0

* hotfix

* updated Readme

* removed return

* added installation with mingw test to appveyor

* let's test appveyor with both VS 2015 and VS 2017; but MinGW isn't installed on VS 2017 image

* fixed built-in name 'file'

* simplified appveyor

* removed excess data_files

* fixed unreadable paths

* separated exceptions for cmake and mingw

* refactored silent_call

* don't create artifacts with VS 2015 and mingw

* be more precise with python versioning in Travis

* removed unnecessary if statement

* added classifiers for PyPI and python versions badge

* changed python version in travis

* added support of scikit-learn 0.18.x

* added more python versions to Travis

* added more python versions to Appveyor

* reduced number of tests in Travis

* Travis trick is not needed anymore

* attempt to fix according to https://github.com/Microsoft/LightGBM/pull/880#discussion_r137438856

* fixed description of Dataset

* fixed description of Booster

* fixed description of train

* fixed description of cv

* fixed description of callbacks

* fixed description of plotting

* hotfixes

This commit will regain backward compatibility that has been broken
after 015c8fff.

* fixed sklearn test on python 2.7

* commit to show that problem has been solved

* come back to python 3.6

* removed warnings check

* improved sklearn interface; added sklearns' tests

* moved best_score into the if statement

* improved docstrings; simplified LGBMCheckConsistentLength

* fixed typo

* pylint

* updated example

* fixed Ranker interface

* added missed boosting_type

* fixed more comfortable autocomplete without unused objects

* removed check for None of eval_at

* fixed according to review

* fixed typo

* added description of fit return type

* dictionary->dict for short

* markdown cleanup

* Add efficient demo for LightGBM

* Add bank dataset and more examples

* Add bank manual