GitLab

* add quantile metric.

* first draft.

* add "sqrt" transform in regression objective function.

* fix a bug.

* update parameter doc

* fix doc

* 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

* [optional] support protobuf

* fix windows/LightGBM.vcxproj

* add doc

* fix doc

* fix vs support (#2)

* fix vs support

* fix cmake

This reverts commit 53b99854.

commit c9e123f24fcbb159c04e6694c7f830530bb2f27e
Author: Guolin Ke <i@yumumu.me>
Date:   Wed Oct 18 10:00:19 2017 +0800

    change default max_cat_to_onehot

commit 805a5c3125b9979d634922e1708877fa0fec80c6
Author: Guolin Ke <i@yumumu.me>
Date:   Tue Oct 17 22:57:18 2017 +0800

    use one hot coding for the small cats

* add feature

* add label idx move func to .cpp file

* fix

* move func to predictor

* restore files

* fix by commits

* move the non-used features to the end of the feature vector

* fix by commits

* fix bug by commits

* many fixes for categorical feature

* add l2 to categorcial split.

* remove useless file

* update version

* add cat_l2

* update appveyor verison

* remove file

* fix tests.

* change default cat_l2 value

* fix a bug in bin finder

* change default cat_smooth_ratio

* refine categorical split

* a bug fix

* fix a bug

* add network apis.

* support parallel loading dataset in c api.

* fix bug

* fix bug

* thread local for the network interface.

* fix bug.

* Bounds checking of used_row_indices (#959)

* add Common::check_int32_elements_interval_closed function.

* insert a bounds checking code in LGBM_DatasetGetSubset function of C API.

* Delete a needless function

* delete the function check_int32_elements_interval_closed

* Fix typos

* Bounds checking of used_row_indices (#959)

* add Common::check_int32_elements_interval_closed function.

* insert a bounds checking code in LGBM_DatasetGetSubset function of C API.

* Delete a needless function

* delete the function check_int32_elements_interval_closed

* Fix coding style (#969)

Function names must be in the "Pascal Case" style.

* check_elements_interval_closed to CheckElementsIntervalClosed

* obtain_min_max_sum to ObtainMinMaxSum

* Change functions in common.h into template functions (#969)

* CheckElementsIntervalClosed

* ObtainMinMaxSum

These two functions were changed into template functions.

* Remove an unpreferable overload

* remove an overload of the function ObtainMinMaxSum

* Use stringstream to format T type

Function names must be in the "Pascal Case" style.

* check_elements_interval_closed to CheckElementsIntervalClosed

* obtain_min_max_sum to ObtainMinMaxSum

* add get params

clean codes

* check duplicate params

* Revert "add get params"

This reverts commit ec1d8dd17aa2c83dd0d5f7716c59b6a6fb94102c.

* set priority by length & check duplicate

* rename function

* Update dataset.cpp

* Update dataset.cpp

* 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.

* Fix feature attributions for regression models and add Python bindings

* Address pylint issue

* Lazy fix missing tree depth info

* Attempt to address issue #807 Poisson regression objective.

* Fix coding style.

* Added label non-negative safety check.

* expose feature importance to c_api

* support type=gain

* remove dump model from examples and tests temporarily because it's unstable

* use double instead of float