IDFModel.transform() add support for single vector

For now when using the tfidf implementation of mllib you have no other possibility to map your data back onto i.e. labels or ids than use a hackish way with ziping:

1. Persist input RDD. 2. Transform it to just vectors and apply IDFModel 3. zip with original RDD 4. transform label and new vector to LabeledPoint

Source:http://stackoverflow.com/questions/26897908/spark-mllib-tfidf-implementation-for-logisticregression

I think as in production alot of users want to map their data back to some identifier, it would be a good imporvement to allow using a single vector on IDFModel.transform()