weka_dataset_creation.java

weka.java

import weka.classifiers.Evaluation;
import weka.classifiers.bayes.NaiveBayes;
import weka.core.Instances;
import weka.core.Instance;
import weka.core.converters.ConverterUtils.DataSource;
import weka.core.Attribute;
import weka.core.DenseInstance;

import java.io.File;
import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;

import java.util.List;
import java.util.ArrayList;

public class Example {

	public String readFile (String filename)  throws IOException
	{
	    String content = null;
	    File file = new File(filename); //for ex foo.txt
	    FileReader reader = null;
	    try {
	        reader = new FileReader(file);
	        char[] chars = new char[(int) file.length()];
	        reader.read(chars);
	        content = new String(chars);
	        reader.close();
	    } catch (IOException e) {
	        e.printStackTrace();
	    } finally {
	        if(reader !=null){reader.close();}
	    }
	    return content;
	}


	public static void main(String[] args) throws Exception{

		// Declare text attribute
		 Attribute attribute_text = new Attribute("text",(List<String>) null);

		 // Declare the class attribute along with its values
		 ArrayList<String> classAttributeValues = new ArrayList<String>();
		 classAttributeValues.add("spam");
		 classAttributeValues.add("ham");

		 // FastVector fvClassVal = new FastVector(2);

		 // fvClassVal.addElement("spam");
		 // fvClassVal.addElement("ham");

		 Attribute classAttribute = new Attribute("label", classAttributeValues);

		  // Declare the feature vector
		 ArrayList<Attribute> fvWekaAttributes = new ArrayList<Attribute>();
		 fvWekaAttributes.add(classAttribute);
		 fvWekaAttributes.add(attribute_text);


		 /* 
		    Create an empty training set
			name the relation “Rel”.
			set capacity of 10
		*/	
		 Instances trainingSet = new Instances("Rel", fvWekaAttributes, 10);
		 // Set class index
		 trainingSet.setClassIndex(0);

		try(BufferedReader br = new BufferedReader(new FileReader("data/train.txt"))) {
		    for(String line; (line = br.readLine()) != null; ) {
		      // System.out.println(line);
		    	try{
		        	String parts[] = line.split("\\s+",2);
		        	 // Create the instance
		        	if (!parts[0].isEmpty() && !parts[1].isEmpty()){
		        	  Instance row = new DenseInstance(2);
					  System.out.println(String.format("class: %s\n", parts[0]));
					  row.setValue(fvWekaAttributes.get(0), parts[0]);
					  row.setValue(fvWekaAttributes.get(1), parts[1]);	
					  					 // add the instance
					trainingSet.add(row);
		        	}

					 

					// System.out.println(String.format("class: %s,\n text: %s", parts[0], parts[1]));
		    	}
		    	catch (ArrayIndexOutOfBoundsException e){
					System.out.println("invalid row");
				}

		    }
		}
		catch (IOException e){
			e.printStackTrace();
		}

		System.out.println(trainingSet);



		// DataSource source = new DataSource("iris.arff");
		// Instances dataset = source.getDataSet();
		// dataset.setClassIndex(dataset.numAttributes()-1);
		// NaiveBayes nb = new NaiveBayes();
		// nb.buildClassifier(dataset);


		// // evaluation set
		// Evaluation eval = new Evaluation(dataset);
		// eval.evaluateModel(nb, dataset);
		// System.out.println(eval.toSummaryString());
	}
}