Pacakge bits

Merged in @audy's pass, and put some structure in place for the code. The api/cli distinction feels nice here, though I think there is more room to decompose... was using this as an opportunity to play with some ideas prior to dropping click into qiime

Note, travis and coveragerc are included but no unit tests have been written and haven't hooked up services yet

@audy, using the `HashingVectorizer` is _awesome_. I'm not seeing the memory spike you mentioned, but instead seeing a runtime issue that is driven by the serialization (profiling show'd stupid amounts of time being spent in `ravel` in `_mul_multivector` in scipy's `compressed.py` or something. The notebook was a few orders of magnitude faster by doing the kword transform external, but I agree with you that this is likely a bug. 

Last, two things, the resulting classifications aren't great yet but we're near in a place to really start poking and proding and this is currently dependent on biocore/scikit-bio#643
The resulting probabilties are a float vector of length N where N is the number of labels. Would need to be a more complex structure returned by sklearn to have the label

good catch on the type