CSC/ECE 517 Fall 2013/ch1 1w46 ka: Difference between revisions
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We can see that the WEKA api makes it easy to pass in a data file. Data can be in a number of formats, including ARFF and CSV. When run on the weather.arff example (in WEKA's 'data' folder), the output looks like the following: | We can see that the WEKA api makes it easy to pass in a data file. Data can be in a number of formats, including ARFF and CSV. When run on the weather.arff example (in WEKA's 'data' folder), the output looks like the following: | ||
= Advantages of using Weka from jRuby= | |||
One of the advantages of using a language like jruby to talk to WEKA is that we should have more control on how our data is constructed and passed to the machine-learning algorithms. A good start is how to construct our own set of instances, rather than reading them directly in from file. There are some quirks to WEKA's construction of a set of Instances. In particular, each attribute must be defined through an instance of the Attribute class. This class gives a string name to the attribute and, if the attribute is a nominal attribute, the class also holds a vector of the nominal values. Each instance can then be constructed and added to the growing set of Instances. The code below shows how to 'by-hand' construct a dataset which can then be passed to one of WEKA's learning algorithms. | One of the advantages of using a language like jruby to talk to WEKA is that we should have more control on how our data is constructed and passed to the machine-learning algorithms. A good start is how to construct our own set of instances, rather than reading them directly in from file. There are some quirks to WEKA's construction of a set of Instances. In particular, each attribute must be defined through an instance of the Attribute class. This class gives a string name to the attribute and, if the attribute is a nominal attribute, the class also holds a vector of the nominal values. Each instance can then be constructed and added to the growing set of Instances. The code below shows how to 'by-hand' construct a dataset which can then be passed to one of WEKA's learning algorithms. | ||
{| border="1" | |||
|-Number of iterations: 3 | |||
|Within cluster sum of squared errors: 16.237456311387238 | |||
|-Missing values globally replaced with mean/mode | |||
| | |||
|-Cluster centroids: | |||
| Cluster# | |||
|-Attribute Full Data 0 1 | |||
| (14) (9) (5) | |||
| | |||
|-outlook sunny sunny overcast | |||
|temperature 73.5714 75.8889 69.4 | |||
|humidity 81.6429 84.1111 77.2 | |||
|windy FALSE FALSE TRUE | |||
|play yes yes yes | |||
|} | |||
Revision as of 22:14, 5 October 2013
Weka and Ruby
Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from an embedded code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. 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. We use JRuby and this is illustrated as follows:
Clustering Data using WEKA from jRuby
jRuby provides easy access to Java classes and methods, and WEKA is no exception. The following program builds a simple kmeans clusterer on a supplied input file, and then prints out the assigned cluster for each data instance. The 'include_class' statements are there to simplify references to classes in the API. When classifying each instance, we must watch for the exception thrown in case a classification cannot be made. Finally, notice that the filename is passed as a command-line parameter: the parameters after the name of the jRuby program are packaged up into ARGV in the usual ruby style. Assuming weka.jar, jruby.jar, and your program are in the same folder, a sample Ruby example is shown bellow:
We can see that the WEKA api makes it easy to pass in a data file. Data can be in a number of formats, including ARFF and CSV. When run on the weather.arff example (in WEKA's 'data' folder), the output looks like the following:
= Advantages of using Weka from jRuby= One of the advantages of using a language like jruby to talk to WEKA is that we should have more control on how our data is constructed and passed to the machine-learning algorithms. A good start is how to construct our own set of instances, rather than reading them directly in from file. There are some quirks to WEKA's construction of a set of Instances. In particular, each attribute must be defined through an instance of the Attribute class. This class gives a string name to the attribute and, if the attribute is a nominal attribute, the class also holds a vector of the nominal values. Each instance can then be constructed and added to the growing set of Instances. The code below shows how to 'by-hand' construct a dataset which can then be passed to one of WEKA's learning algorithms.
| Within cluster sum of squared errors: 16.237456311387238 | |||
| Cluster# | |||
| (14) (9) (5) | |||
| temperature 73.5714 75.8889 69.4 | humidity 81.6429 84.1111 77.2 | windy FALSE FALSE TRUE | play yes yes yes |