Naive Bayes Classification in Ruby using Hadoop and HBase

Posted 02 Dec 2010 to ankusa, hbase, hadoop, ruby and has Comments

One of the problems I’ve run into recently at work is that we have quite a bit of text that needs to be classified. My first thought was to use one of the simplest classification methods, a naive bayes classifier. I couldn’t find anything that could possibly handle many terabytes of data, though. Most Ruby implementations, like the classifier gem, have only a simplistic implementation (for instance, the classifier gem doesn’t actually provide a true naive bayes implementation in that it ignores prior probabilities). I decided to create a better naive bayes implementation (for instance, using a Laplacian smoother) that could also handle up to many terabytes of corpus data.

We already have a Hadoop cluster with HBase running, and HBase is perfect for storing data like word counts. The HBaseRb gem provides an easy interface for Ruby to interact with HBase.

I spent today implementing the classifier, and have released the code in the ankusa gem. Unlike other classifiers written in Ruby, ankusa has a fairly abstract storage class that can easily be implemented for other storage solutions. For instance, the two that come with the gem provide both HBase storage and in memory storage.

To use the gem:

gem install ankusa


require 'rubygems'
require 'ankusa'

# connect to HBase 
storage = 'localhost', 9090
# or use in-memory storage
storage =

c = storage

c.train :spam, "This is some spammy text"
c.train :good, "This is not the bad stuff"

# This will return the most likely class (as symbol)
puts c.classify "This is some spammy text"

# This will return Hash with classes as keys and 
# membership probability as values
puts c.classifications "This is some spammy text"

# get a list of all classes
puts c.classes

# close connection

The classifier does return probabilities (when you use the classifications method, unlike the classifier gem which only returns log likelihoods). Additionally, the classifier has no limitations on the size of the corpora (HBase can handle petabytes of data depending on your cluster size), so realistically your training set can be as large as you need it to be.