[WIP] Support pandas DataFrames and feature names in ExhaustiveFeatureSelector

[rasbt]

Description

Adds support for a feature names and pandas DataFrames in the ExhaustiveFeatureSelector. In particular, the EFS methods (fit, transform, etc.) now support pandas DataFrames in addition to NumPy(-like) arrays. Also, the feature names will be recorded in self.k_feature_names_ as well as self.subsets_['feature_names']. If a pandas DataFrame is provided as input, these feature names are correspond to the column names. Otherwise, the column indices as string representation will be used as a placeholder.

Finally, an optional feature_names parameter is added to the ExhaustiveFeatureSelector constructor, which allows users to pass custom feature names corresponding to column indices to improve the interpretability of the selected feature subsets via self.subsets_['feature_names'] and self.k_feature_names_. Note that user-provided feature names have precedence over feature names based on column indices or pandas DataFrame columns but are only used for labeling purposes.

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 increased (+0.08%) to 91.157% when pulling e922687 on exh-featsele into 3b9dfa9 on master.