GitLab

* Fix feature attributions for regression models and add Python bindings

* Address pylint issue

* Lazy fix missing tree depth info

* 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

* 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

* try to fix python memory leak

* fix parameter error.

* free old handle in _load_model_from_string .

* fix best_score & best_iteration

* refine logics.

* fix tests.

* params back up

* refine logic

* refine python codes

* fix appveryor test

* add note to feature_importances

* added get_ref_chain function to Dataset object, used it in train() to compare if training and validation have common reference ancestors

* moved check of common ancestor reference from train() in engine.py to set_reference() in Dataset

* moved check of common ancestor reference from train() in engine.py to set_reference() in Dataset

* removed check for handle = None

missing parameter "self" in signature of Booster.get_leaf_output

* add keep_training_booster

* use model string

* reset handle; free dataset

* handle list for prediction

* fix error msg

* fix multi-threading.

* fix name style.

* support in CLI version.

* remove warnings.

* Not default parameters.

* fix if...else... .

* fix bug.

* fix warning.

* refine c_api.

* fix R-package.

* fix R's warning.

* fix tests.

* fix pep8 .

* Add early stopping for prediction

* Fix GBDT if-else prediction with early stopping

* Small C++ embelishments to early stopping API and functions

* Fix early stopping efficiency issue by creating a singleton for no early stopping

* Python improvements to early stopping API

* Add assertion check for binary and multiclass prediction score length

* Update vcxproj and vcxproj.filters with new early stopping files

* Remove inline from PredictRaw(), the linker was not able to find it otherwise

* change whitespace to underline in feature names

* add test

* fix bug

* fix bug

* warning -> fatal

* refine plot

* use warnings

* refine  logic

* revert 'move to compat.py'

* use json instead of repr/eval for pandas_categorical

* fix json dumps with numpy data

* add more test cases

* expose save model to string to c_api

* add 'save-model-to-string' to python; use it to copy model

* remove boosting_type

* add plot importance

* add plot example

* fix bug for categorical_feature

* add test on load model with categorical feature

* add unseen category in test dataset

* save/load pandas_categorical to model

* fix logic

* cast pandas columns to string

* add load pandas_categorical from file to _InnerPredictor init

* suppprt pandas categorical

* refine logic

* make default=auto

* fix train/valid categorical codes

* add test

* unify set _predictor

* fix tests

* fix warning

* support feature_name=int