Feature importance from DataFrame

[GilesStrong]

Current state

Ensemble and Model classes have get_feat_importance methods to compute the permutation importance of input features. Currently the the input data must be supplied as a FoldYielder object. There are occasions when one may wish to evaluate the feature importance on only a subset of the data (e.g. only on 2-jet events). This then requires saving the subset to a foldfile and instantiating a new FoldYielder to point to the subset of data.

Probable solution

The get_feat_importance methods are extend to take pandas.DataFrame objects as inputs. This will no doubt impact certain aspects of the returned information, such as averaging over folds and computing uncertainties. I think it is reasonable that if the user really wants this extra information, they can export a new foldfile. This extension would simply be for getting the rough information quickly and producing an informative plot which wouldn't necessarily be used for publication.