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np.random.seed(seed) in _make_n_folds(...) has a side effect setting random seed for outer scope

* add data_splitter for cv

* update gitignore

* clean code

When setting the parameter 'shuffle' of cv method to False ,the program raises the error below:
UnboundLocalError:local variable 'randidx' referenced before assignment

fixed.

* 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

* 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)

* use handle is not None for _is_constructed

* sort imports; clean code; move FAQ to docs

* fix reset parameter

* redefine CVBooster

* env.model won't be None

* update env.params

* 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 learning rate to dart (#133)

* add gridsearch example for python sklearn

* reset_learning_rate->reset_parameter

* make callbacks public

Provide a high level Dataset class for easy use.

current problem:
callback order: user-defined in callback parameter -> reset_learning_rate/print_evaluation/record_evaluation/early_stop
user can't insert a callback between last 4 callbacks

solution:
set order variable:
reset_learning_rate = 10
print_evaluation = 10
record_evaluation = 20
early_stop = 30
user-defined = some int
default: according to index in callback parameter list, = index - len(callbacks) (all < 0)

current callback order:
before iter: user-defined -> reset_learning_rate -> user-defined
after iter: user-defined -> print_evaluation -> user-defined -> record_evaluation -> user-defined -> early_stop -> user-defined

* 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