[WIP] Support pandas DataFrames and feature names in ExhaustiveFeatur…

…eSelector (#380)

* update efs

* update

* add docs

* docs

docs/sources/CHANGELOG.md

@@ -20,13 +20,14 @@ The CHANGELOG for the current development version is available at
 - The `SequentialFeatureSelector` now accepts custom feature names via the `fit` method for more interpretable feature subset reports. ([#379](https://github.com/rasbt/mlxtend/pull/379))
 - The `SequentialFeatureSelector` is now also compatible with Pandas DataFrames and uses DataFrame column-names for more interpretable feature subset reports. ([#379](https://github.com/rasbt/mlxtend/pull/379))
 - `ColumnSelector` now works with Pandas DataFrames columns. ([#378](https://github.com/rasbt/mlxtend/pull/378) by [Manuel Garrido](https://github.com/manugarri))
+- The `ExhaustiveFeatureSelector` estimator in `mlxtend.feature_selection` now is safely stoppable mid-process by control+c. ([#380](https://github.com/rasbt/mlxtend/pull/380))
 
 
 ##### Changes
 
-
 - -
 
+
 ##### Bug Fixes
 
 - Allow mlxtend estimators to be cloned via scikit-learn's `clone` function. ([#374](https://github.com/rasbt/mlxtend/pull/374))
@@ -303,7 +304,7 @@ Note that this didn't cause any difference in performance on any of the test sce
 - The `StackingClassifier` has a new parameter `average_probas` that is set to `True` by default to maintain the current behavior. A deprecation warning was added though, and it will default to `False` in future releases (0.6.0); `average_probas=False` will result in stacking of the level-1 predicted probabilities rather than averaging these.
 - New `StackingCVClassifier` estimator in 'mlxtend.classifier' for implementing a stacking ensemble that uses cross-validation techniques for training the meta-estimator to avoid overfitting ([Reiichiro Nakano](https://github.com/reiinakano))
 - New `OnehotTransactions` encoder class added to the `preprocessing` submodule for transforming transaction data into a one-hot encoded array
-- The `SequentialFeatureSelector` estimator in `mlxtend.feature_selection` now is safely stoppable mid-process by control+c, and deprecated print_progress in favor of a more tunable verbose parameter ([Will McGinnis](https://github.com/wdm0006))
+- The `SequentialFeatureSelector` estimator in `mlxtend.feature_selection` now is safely stoppable mid-process by control+c, and deprecated `print_progress` in favor of a more tunable `verbose` parameter ([Will McGinnis](https://github.com/wdm0006))
 - New `apriori` function in `association` to extract frequent itemsets from transaction data for association rule mining
 - New `checkerboard_plot` function in `plotting` to plot checkerboard tables / heat maps
 - New `mcnemar_table` and `mcnemar` functions in `evaluate` to compute 2x2 contingency tables and McNemar's test