GitLab

* 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

* add compat.py

* unify types

* range/xrange -> range_

* move argc_ to compat.py; add is_numeric error type

* use simplejson for json

* move json to compat.py

* move pandas to compat.py

* move sklearn to compat.py

* remove unused function

* fix 'unify types'

* argc_ (lambda -> def)

http://research.microsoft.com/en-us/um/people/zhang/INRIA/Publis/Tutorial-Estim/node24.html

* Add Huber loss

https://en.wikipedia.org/wiki/Huber_loss

* update

* update docs/Parameters.md

* update

* use handle is not None for _is_constructed

* sort imports; clean code; move FAQ to docs

* 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

* 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

* add troubleshooting

* use unittest

* update unittest version

* fix test_engine.py

* fix test_sklearn.py

* default eval_metric by subclass

* add test grid search

* remove verbose_eval

* add python doc

* add boosting_type to python sklearn

* fix error

Provide a high level Dataset class for easy use.

* add feature importances in python; add pandas support

* solve best_iteration issue

Categorical feature support (#108)

1. merge python-package
2. add dump model to json
3. fix bugs
4. clean code with pylint
5. update python examples