Jruby mahout

This is how I managed to set up JRuby and Mahout 0.8 on Mac OSX 10.9 – Without any gems.

Part of the reason for this is jruby_mahout currently only works with Mahout 0.7, and I was too lazy and impatient to get it to work with Mahout 0.8.

I am especially excited because this I think exhibits one of the best use-cases for JRuby – Interoping with existing (awesome) Java libraries with minimum hassle.

Step 1a. Installing JRuby with rbenv

Grab the latest rbenv

> brew upgrade rbenv
> brew upgrade ruby-build

Let’s pick the latest JRuby version.

> rbenv install -l | grep 'jruby'`
> ...
    jruby-1.7.4
    jruby-1.7.5
    jruby-1.7.6
    jruby-1.7.7
    jruby-1.7.8
    jruby-1.7.9
> rbenv install jruby-1.7.9

Then we switch to JRuby.

> rbenv shell jruby-1.7.9

Note that this command would swith to jruby-1.7.9 only for the current terminal window.

Step 1b. Installing JRuby with rvm

> rvm install jruby
> rvm use jruby

Step 2. Installing Mahout

brew install mahout

Step 3: export the Mahout directory

In your ~/.bashrc or ~/.zshrc, add this line:

export MAHOUT_DIR="/usr/local/Cellar/mahout/0.8/"

Remember to source ~/.bashrc or source ~/.zshrc for the changes to be picked up.

To double check:

> echo $MAHOUT_DIR
/usr/local/Cellar/mahout/0.8/

Step 4: Taking Mahout for a spin

Create a file data.csv and populate it with the following data:

4   1
7   2
4   4
1   4
4   3
8   1
8   3
4   5
4   6
6   6

Then create mahout_test_run.rb:

Dir.glob("#{ENV['MAHOUT_DIR']}/libexec/*.jar").each { |d| require d }

MahoutFile = org.apache.mahout.cf.taste.impl.model.file
model = MahoutFile.FileDataModel.new(java.io.File.new("data.csv"))

MahoutSimilarity = org.apache.mahout.cf.taste.impl.similarity
similarity = MahoutSimilarity.TanimotoCoefficientSimilarity.new(model)

MahoutNeighborhood = org.apache.mahout.cf.taste.impl.neighborhood
neighborhood = MahoutNeighborhood.NearestNUserNeighborhood.new(5, similarity, model)

MahoutRecommender = org.apache.mahout.cf.taste.impl.recommender
recommender = MahoutRecommender.GenericBooleanPrefUserBasedRecommender.new(model, neighborhood, similarity)
recommendations = recommender.recommend(8, 5)

puts recommendations

Next run the script:

> ruby mahout_test_run.rb

If everything went rosy, you would get:

Jan 26, 2014 12:36:52 PM org.slf4j.impl.JCLLoggerAdapter info
INFO: Creating FileDataModel for file data.csv
Jan 26, 2014 12:36:52 PM org.slf4j.impl.JCLLoggerAdapter info
INFO: Reading file info...
Jan 26, 2014 12:36:52 PM org.slf4j.impl.JCLLoggerAdapter info
INFO: Read lines: 10
[RecommendedItem[item:4, value:0.4], RecommendedItem[item:5, value:0.4], RecommendedItem[item:6, value:0.4]]

Step 5: Profit!

Thanks for reading! Go forth and build awesome recommendation systems! :)