Fastest Count Vectorizer Implementation
Have you tried HashingVectorizer? It's slightly faster (up to 2X if I remember correctly). Next step is to profile the code, strip the features of CountVectorizer or HashingVectorizer that you don't use and rewrite the remaining part in optimized Cython code (after profiling again).
Vowpal Wabbit's bare-bone feature processing that uses the hashing trick by default might give you a hint of what is achievable.
I'm looking for an implementation of n-grams count vectorization that is more efficient than scikit-learn's CountVectorizer.  I've identified the CountVectorizer.transform() call as a huge bottleneck in a bit of software, and can dramatically increase model throughput if we're able to make this part of the pipeline more efficient.  Fit time is not important, we're only concerned with transform time.  The end output must be a scipy.sparse vector.  If anyone has any leads for potential alternatives it would be much appreciated.