[WIP] Towards sklearn compatible subset featurizer.

I'm slowly reworking #133 to be more compatible with sklearn.  

@rmcgibbo are there issues with the way we almost always use `X_list` for our estimators while sklearn tends to use `X`?  Or that the first step in our pipeline involves MDTraj objects?  It seems like we might have some cleanup to do for maximum sklearn compatibility.  (For example, we currently build TICA models by repeated calls to `partial_fit`, so it's not clear how that works with the Pipeline and GridSearch).

(Ignore `selector.py` for now, that's just copy pasted from the previous PR and hasn't yet been updated.)