Tyler Clemons » Data Mining

Weka, Ruby, Association Mining

Tyler — Sat, 15 May 2010 21:09:17 +0000

Weka is a collection of machine learning tools used for data mining. Weka is written in Java however it is possible to use Weka’s libraries inside Ruby. To do this, we must install the Java, Rjb, and of course obtain the Weka source code. In this example, I look at Associating Mining.

Refer to my previous example for setup instructions.

Running Weka

The following is an example of a frequent itemset finder.



require 'rjb'

#———————-
def asscm()

#Load Java Jar
dir = “./weka.jar”
#Have Rjb load the jar file, and pass Java command line arguments
Rjb::load(dir, jvmargs=["-Xmx1000M"])

#make initial association Miner
obj = Rjb::import(“weka.associations.Apriori”)
assc= obj.new

#load the data using Java and Weka
weather_src = Rjb::import(“java.io.FileReader”).new(“weather.nominal.arff”)
weather_data = Rjb::import(“weka.core.Instances”).new(weather_src)

#Find the frequent itemsets
assc.setCar(true) #mines for class association
assc.setLowerBoundMinSupport(0.25) #set minimum support
assc.buildAssociations(weather_data)
puts assc.toString

end

#———————-
asscm()

We first tell Rjb to load the specified classpath, for us it’s our Jar file. I passed command line arguments that specify the amount of RAM to use.

Rjb::import loads specific classes. These are relative to our classpath.

I call the constructor for the new classes by using the .new method from Ruby. Afterward, I can use the new object like any other Ruby object. The method names are as they are found in their Java source files. For an explanation of data type conversions, click here.

The dataset I used is different from the previous because we can’t use numerical values. The dataset is found inside the data folder downloaded with weka.jar.

As I said before, this can be done in JRuby. A great example can be found at this great blog post, which inspired my post.

Weka, Ruby, Decision Trees

Tyler — Sat, 15 May 2010 20:40:26 +0000

Refer to my previous example for setup instructions.

Running Weka

The following is an example of running a Decision Tree.



require 'rjb'

#———————-
def dtree()

#Load Java Jar
dir = “./weka.jar”
#Have Rjb load the jar file, and pass Java command line arguments
Rjb::load(dir, jvmargs=["-Xmx1000M"])

#make the initial tree
obj = Rjb::import(“weka.classifiers.trees.J48″)
dtree = obj.new

#load the data using Java and Weka
labor_src = Rjb::import(“java.io.FileReader”).new(“labor.arff”)
labor_data = Rjb::import(“weka.core.Instances”).new(labor_src)

#set the class attribute, here it’s the last value, and then build the classifier
labor_data.setClassIndex(labor_data.numAttributes() – 1)
dtree.buildClassifier(labor_data)
puts dtree.toString

#examine the particular datapoints
points = labor_data.numInstances
points.times {|instance|

theclass = dtree.classifyInstance(labor_data.instance(instance))
point = labor_data.instance(instance).toString
puts “#{point} \t #{theclass}”

}

end

#———————-
dtree()

We first tell Rjb to load the specified classpath, for us it’s our Jar file. I passed command line arguments that specify the amount of RAM to use.

Rjb::import loads specific classes. These are relative to our classpath.

The dataset is found inside the data folder downloaded with weka.jar.

As I said before, this can be done in JRuby. A great example can be found at this great blog post, which inspired my post.

Weka and Ruby

Tyler — Thu, 06 May 2010 17:21:15 +0000

Setup

The first step is to install Java. That’s very simple, just follow this link to download Java. be sure to download the JDK.

Getting Rjb, which stands for Ruby Java Bridge, is a ruby-gem. You can either install the gem using the command line:



gem install rjb

or, we can download the gem from this link. The readme inside the gem is very easy to follow.

The last step in setup is obtaining Weka. On the download page of Weka, choose “Other platforms (Linux, etc.)“ Inside, we find a jar file called “weka.jar” This is what we need to continue.

Running Weka

The following is an example of running the SimpleKmeans classifier.



require 'rjb'

#———————-
def kmeans()

#Load Java Jar
dir = “./weka.jar”
#Have Rjb load the jar file, and pass Java command line arguments
Rjb::load(dir, jvmargs=["-Xmx1000M"])

#make k-means classifier
obj = Rjb::import(“weka.clusterers.SimpleKMeans”)
kmeans = obj.new

#load the data using Java and Weka
labor_src = Rjb::import(“java.io.FileReader”).new(“labor.arff”)
labor_data = Rjb::import(“weka.core.Instances”).new(labor_src)

#build the cluster and output the k-means data
kmeans.buildClusterer(labor_data)
puts kmeans.toString

#examine the particular datapoints
points = labor_data.numInstances
points.times {|instance|

cluster = kmeans.clusterInstance(labor_data.instance(instance))
point = labor_data.instance(instance).toString
puts “#{point} \t #{cluster}”

}

end

#———————-
kmeans()

This is a simple example of how to use K-means. We first tell Rjb to load the specified classpath, for us it’s our Jar file. I passed command line arguments that specify the amount of RAM to use.

Rjb::import loads specific classes. These are relative to our classpath.

The dataset is found inside the data folder downloaded with weka.jar.

I will probably look at the other classifiers and post some more example. As I said before, this can be done in JRuby. A great example can be found at this great blog post, which inspired my post.