Identify Data Vectors with New Attributes and/or Values
Not with anything built into scikit-learn, as removing rows is something that is not easily done in the current API.
It should be quite easy to write a custom function / class that does that based on the output of DictVectorizer.
I am setting up a classification system using scikit-learn.  After training a classifier I would like to save it for reuse along with the necessary transforms such as the DictVectorizer.  
I am looking for a way to filter the incoming stream of unclassified data that will feed into the feature transforms and classifier.  Ideally, I would like to remove and flag vectors that contain new values for categorical attributes and/or altogether new attributes.  
I have used the DictVectorizer.restrict() method to filter input data but this only results in the vectorizer filtering new attributes and zeroing new values, where I would also like to put aside inconsistent data.  Is there an easy way to pull out rows with values and attribute that were not in the initial data set?