GitLab

* Update Features.rst

* Update .appveyor.yml

* Update test_windows.ps1

* Update test_windows.ps1

* Update .appveyor.yml

* Update test_windows.ps1

* Update .appveyor.yml

* disable default pandas cat features if cat features were explicitly provided

* added assertion for cat features

* ignore pandas ordered categorical columns by default

* fix tests

* fix tests

* added comments

* Update doc string for pred_contrib

See comments at the end of #1969

* Update basic.py

* Update basic.py

* update doc strings

* update equals sign in doc string

* strip whitespace and gen rst

* strip whitespace

* minor fixed in CMakeLists

* state what message is about

* [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.

* added all necessary includes - fixed build/include_what_you_use error

* fixed the order of includes (build/include_order)

* updated HDFS guide

* updated guide

* no info about Clang

* pass paths in quotes

* Update README.rst

* added fix for OpenMP on macOS into test script

* test: AppleClang on Travis

* use Mojave on Travis

* bash hotfix

* get back to gcc compiler on Travis macOS

* fixed Python intro

* fixed typos

* scikit-learn added support of https

* updated boost submodule

* updated docker with new stable Clang and CMake

* switch to dev docker

* updated setup script

* updated MinGW on Appveyor

* updated Azure config to use docker for GPU task

* do not upgrade gcc - takes too long

* test: switch compilers

* switch compilers back

* get back to main docker

* Add configuration parameters for CEGB.

* Add skeleton CEGB tree learner

Like the original CEGB version, this inherits from SerialTreeLearner.
Currently, it changes nothing from the original.

* Track features used in CEGB tree learner.

* Pull CEGB tradeoff and coupled feature penalty from config.

* Implement finding best splits for CEGB

This is heavily based on the serial version, but just adds using the coupled penalties.

* Set proper defaults for cegb parameters.

* Ensure sanity checks don't switch off CEGB.

* Implement per-data-point feature penalties in CEGB.

* Implement split penalty and remove unused parameters.

* Merge changes from CEGB tree learner into serial tree learner

* Represent features_used_in_data by a bitset, to reduce the memory overhead of CEGB, and add sanity checks for the lengths of the penalty vectors.

* Fix bug where CEGB would incorrectly penalise a previously used feature

The tree learner did not update the gains of previously computed leaf splits when splitting a leaf elsewhere in the tree.
This caused it to prefer new features due to incorrectly penalising splitting on previously used features.

* Document CEGB parameters and add them to the appropriate section.

* Remove leftover reference to cegb tree learner.

* Remove outdated diff.

* Fix warnings

* Fix minor issues identified by @StrikerRUS.

* Add docs section on CEGB, including citation.

* Fix link.

* Fix CI failure.

* Add some unit tests

* Fix pylint issues.

* Fix remaining pylint issue

* updated gitignore to ignore files created by local python installation

* moved sections around in gitignore

* Small aesthetic improvements to RTD docs

* fixed markdown table in Development-Guide

* removed unnecessary blank line in conf.py

* Fix index out-of-range exception generated by BaggingHelper on small datasets.

Prior to this change, the line "score_t threshold = tmp_gradients[top_k - 1];" would generate an exception, since tmp_gradients would be empty when the cnt input value to the function is zero.

* Update goss.hpp

* Update goss.hpp

* Add API method LGBM_BoosterPredictForMats which runs prediction on a data set given as of array of pointers to rows (as opposed to existing method LGBM_BoosterPredictForMat which requires data given as contiguous array)

* Fix incorrect upstream merge

* Add link to LightGBM.NET

* Fix indenting to 2 spaces

* Dummy edit to trigger CI

* Dummy edit to trigger CI

* 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

* added API changes required for JNI performance optimizations (e.g. predict is 3-4x faster)

* removed commented variables

* removed commented header

* renamed method to make it obvious it is created for Spark

* fixed comment alignment

* replaced GetPrimitiveArrayCritical with GetIntArrayElements for training. fixed dead-lock on databricks

* lightgbm SWIG Java wrapper changes needed to add early stopping in mmlspark

* updated based on comments

* ci fix

* ci fix for Appveyor

* actually firx Appveyor

* disabled split value histogram for categorical features

* updated test for cat. feature

* updated docs

* updated gitignore

* updated tree index with cat feature

* added get_split_value_histogram method

* added param for ordinary return value