GitLab

* 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

* broadcast info about negative values in categorical features to python package

* update link to categorical_feature parameter

* added new aliases for params

* run helper/parameter_generator.py

* removed useless test

The document of `early_stopping_rounds` says it will check all of
eval_set. But, this is not true. It doesn't check the dataset
specified as the training data.

This change appends an extra phrase "except the training data" to all
of the sentences "If there's more than one, will check all of them" in
documents.

* added checks for categorical features > max_int32

* added tests

* fixed pylint

* removed warnings about overridden categorical features

* Revert "removed warnings about overridden categorical features"

This reverts commit 289a426c700ce8934a526cc456a1b1cd5c621db9.

* a little bit more efficient checks

* added notes about max values in categorical features

* Revert "a little bit more efficient checks"

This reverts commit bed88830243da21a2db454873c0e308126e05732.

* Revert "fixed pylint"

This reverts commit a229e1563b0abc1b13de6358577abf90bd529015.

* Revert "added tests"

This reverts commit 299e001b7550111555b80730d673d4f225cf5f74.

* Revert "added checks for categorical features > max_int32"

This reverts commit 2cc7afacde7c6366644f6988ccedc344752b68c7.

* Clarify docs for feval

* docs- adds that feval can be a string

* docs- deletes extraneous space

fix #1288

* Add n_estimators as num_iteration alias

Scikit-Learn's ensemble methods use the term `n_estimators` for the number of
iterations of training models. To make it more accessible for newcomers who are
familiar with Scikit-Learn, it would help if the Parameters page mentioned
`n_estimators` and what parameter that maps to within LightGBM's API.

Addresses discussion brought up in #954

* Add n_estimators as num_iterations alias

Adds n_estimators as an alias for num_iterations in the CLI as well as Python
libs. Additionally bumps the default for n_estimators in the Sklearn API to 100
estimators.

* made max_bin parameter deprecated

* fixed accidental docstrings in Sphinx

* concrete version when deprecated stuff will be removed

* added warnings in case of duplicated params to Dataset

* fixed indents in docs

* add init_score & test cpp and python result consistency

* try fix common.h

* Fix tests (#3)

* update atof

* fix bug

* fix tests.

* fix bug

* fix dtypes

* fix categorical feature override

* fix protobuf on vs build (#1004)

* [optional] support protobuf

* fix windows/LightGBM.vcxproj

* add doc

* fix doc

* fix vs support (#2)

* fix vs support

* fix cmake

* fix #1012

* [python] add network config api  (#1019)

* add network

* update doc

* add float tolerance in bin finder.

* fix a bug

* update tests

* add double torelance on tree model

* fix tests

* simplify the double comparison

* fix lightsvm zero base

* move double tolerance to the bin finder.

* fix pylint

* clean test.sh

* add sklearn test

* remove underline

* clean codes

* set random_state=None

* add last line

* fix doc

* rename file

* try fix test

* fixed Python-API references

* moved Features section to ReadTheDocs

* fixed index of ReadTheDocs

* moved Experiments section to ReadTheDocs

* fixed capital letter

* fixed citing

* moved Parallel Learning section to ReadTheDocs

* fixed markdown

* fixed Python-API

* fixed link to Quick-Start

* fixed gpu docker README

* moved Installation Guide from wiki to ReadTheDocs

* removed references to wiki

* fixed capital letters in headings

* hotfixes

* fixed non-Unicode symbols and reference to Python API

* fixed citing references

* fixed links in .md files

* fixed links in .rst files

* store images locally in the repo

* fixed missed word

* fixed indent in Experiments.rst

* fixed 'Duplicate implicit target name' message which is successfully
resolved by adding anchors

* less verbose

* prevented maito: ref creation

* fixed indents

* fixed 404

* fixed 403

* fixed 301

* fixed fake anchors

* fixed file extentions

* fixed Sphinx warnings

* added StrikerRUS profile link to FAQ

* added henry0312 profile link to FAQ

* 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

* 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

* check params

* add test case

* fix pylint

* params back up

* refine logic

* add keep_training_booster

* use model string

* reset handle; free dataset

* make test fail

* change default best_iteration to 0

* fix test

* change data_splitter to folds in cv

* update docs

* Python: Fix RandomState issue #376

* Add test case for Python's Shuffle=True

np.random.seed(seed) in _make_n_folds(...) has a side effect setting random seed for outer scope