Add random_state parameter to stacking cv estimators

[qiagu]

Description

The idea just came to my mind today. Since the latest check_cv from scikit-learn supports random_state, stacking CV estimators can have the parameter now.

Related issues or pull requests

Pull Request Checklist

• Added a note about the modification or contribution to the ./docs/sources/CHANGELOG.md file (if applicable)
• Added appropriate unit test functions in the ./mlxtend/*/tests directories (if applicable)
• Modify documentation in the corresponding Jupyter Notebook under mlxtend/docs/sources/ (if applicable)
• Ran nosetests ./mlxtend -sv and make sure that all unit tests pass (for small modifications, it might be sufficient to only run the specific test file, e.g., nosetests ./mlxtend/classifier/tests/test_stacking_cv_classifier.py -sv)
• Checked for style issues by running flake8 ./mlxtend

[coveralls]

Coverage decreased (-0.001%) to 91.546% when pulling 34ddf02 on qiagu:stacking into c338a1f on rasbt:master.

[rasbt]

Great point. I think this is a relatively new feature and I didn't know it would work. While this is certainly great, one little request though,

random_state : int, RandomState instance or None, optional (default: 0)

     Constrols the randomness of the cv splitter. Used when `cv` is
     integer and `shuffle=True`. New in v0.16.0.

Can we change that to None as default setting (while I personally prefer setting random seeds explicitly, I think this is a scikit-learn default to use None.)

[rasbt]

I actually meant the random_state to be random_state=None, but good that you caught the n_jobs=None thing as well (which is another sklearn convention)

[qiagu]

The random_state default was given by not None because the shuffle was True and also it may save typing in many cases. I'm fine with None and don't have strong inclinations. I'll follow your decision. @rasbt

[rasbt]

I see. I think shuffle=True would still work with random_state=None -- as far as I know, random_state=None will just use the default random seed. I don't have a strong preference, but for consistency with sklearn, maybe random_state=None is the slightly better choice.

[qiagu]

I agree. Updated.

[rasbt]

That's great, happy to merge this. Thanks a lot!