GitLab

* finish R's c_api

* clean code

* fix sizeof pointer in 32bit system.

* add predictor class

* add Dataset class

* format code

* add booster

* add type check for expose function

* add a simple callback

* add all callbacks

* finish the basic training logic

* update docs

* add an simple training interface

* add basic test

* adapt the changes in c_api

* add test for Dataset

* add test for custom obj/eval functions

* fix python test

* fix bug in metadata init

* fix R CMD check

* fix reset parameter

* redefine CVBooster

* env.model won't be None

* update env.params

file as input and output a directory with pmml files. Each file can be given as input to
pmml consumers. You can test the files using jpmml-evaluator. Please refer to this the
data mining group's website for the pmml specifcation (http://dmg.org/pmml/pmml-v4-3.html).
Also refer to the README.md file in the pmml directory to see how to test the pmml output
with jpmml-evaluator.

* simplify Dataset class

* simplify check output; fix deprecated warning

* format python code with pep8

* **DO NOT MERGE** deliberately break rules to see what will happen during check

* Revert "**DO NOT MERGE** deliberately break rules to see what will happen during check"

This reverts commit 0db93cd7a43c7efa43a2112ada43d46c6f9115d9.

* fix format in test.py

* add docs for pep-8

* add @property to sklearn interface

* add deprecated; fix binary_metric

This commit fixes https://github.com/Microsoft/LightGBM/issues/154#issuecomment-269908741.

* support pickle

* add pickle/joblib test; change test_basic to unittest

* remove file for deepcopy

* fix tests

* test basic predict from file

* Revert "test basic predict from file"

This reverts commit 60d2c3158537fd56081f60f1d6d120cedd782887.

* test predict from file

* use tempfile for copy & pickle

* use tempfile w/o binary mode

* clean test

1. add csc support
2. some data type from float to double

* fix creating valid_sets

* support both list and dict

* update Python-API.md

* remove other parameters in sklearn fit

* add dart parameters to sklearn init