create weka Classifier - Java Machine Learning AI

Java examples for Machine Learning AI:weka

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Description

create weka Classifier

Demo Code



import weka.core.Instances;
import java.io.BufferedReader;
import java.io.BufferedWriter;
import java.io.File;
import java.io.FileReader;
import java.io.FileWriter;
import java.io.Writer;
import java.util.Random;

import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.bayes.NaiveBayes;
import weka.classifiers.misc.VFI;
import weka.classifiers.trees.J48;

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

    BufferedReader reader = new BufferedReader(new FileReader("test.arff"));
    Instances data = new Instances(reader);
    reader.close();//from   w  w  w .  ja va 2 s .  c  o  m
    data.setClassIndex(data.numAttributes() - 1);

    int i = data.numInstances();
    int j = data.numAttributes() - 1;

    File file = new File("table4.csv");
    Writer output = null;
    output = new BufferedWriter(new FileWriter(file));
    output.write("%missing,auc1,correct1,fmeasure1,auc2,correct2,fmeasure2,auc3,correct3,fmeasure3\n");

    Random randomGenerator = new Random();
    int numBlock = data.numInstances() / 2;

    double perc = 100;
    {
      Instances mdata = new Instances(data);
      int numMissing = (int) (perc * numBlock / 100);
      double y11[] = new double[2];
      double y21[] = new double[2];
      double y31[] = new double[2];
      double y12[] = new double[2];
      double y22[] = new double[2];
      double y32[] = new double[2];
      double y13[] = new double[2];
      double y23[] = new double[2];
      double y33[] = new double[2];
      int p = 0;// for (int p=0;p<2;p++)
      {
        Instances traindata = mdata.trainCV(2, p);
        Instances testdata = mdata.testCV(2, p);


        Instances trainwithmissing = new Instances(traindata);
        Instances testwithmissing = new Instances(testdata);
        for (int q = 0; q < j; q++) {
          int r = randomGenerator.nextInt((int) i / 2);

          for (int k = 0; k < numMissing; k++) {
            trainwithmissing.instance((r + k) % numBlock).setMissing(q);
            testwithmissing.instance((r + k) % numBlock).setMissing(q);
          }

        }
        Classifier cModel = (Classifier) new VFI();
        cModel.buildClassifier(trainwithmissing);
        Evaluation eTest1 = new Evaluation(trainwithmissing);
        eTest1.evaluateModel(cModel, testdata);
        y11[p] = eTest1.areaUnderROC(0);
        y21[p] = eTest1.correct();
        y31[p] = eTest1.fMeasure(0);
        testwithmissing.deleteWithMissing(0); 
        Classifier cModel2 = (Classifier) new VFI();
        cModel2.buildClassifier(traindata);
        Evaluation eTest2 = new Evaluation(traindata);
        eTest2.evaluateModel(cModel2, testwithmissing);
        y12[p] = eTest2.areaUnderROC(0);
        y22[p] = eTest2.correct();
        y32[p] = eTest2.fMeasure(0);

        Classifier cModel3 = (Classifier) new VFI();
        cModel3.buildClassifier(trainwithmissing);
        Evaluation eTest3 = new Evaluation(trainwithmissing);
        eTest3.evaluateModel(cModel3, testwithmissing);
        y13[p] = eTest3.areaUnderROC(0);
        y23[p] = eTest3.correct();
        y33[p] = eTest3.fMeasure(0);

      }
      double auc1 = (y11[0] + y11[1]) / 2;
      double auc2 = (y12[0] + y12[1]) / 2;
      double auc3 = (y13[0] + y13[1]) / 2;
      double corr1 = (y21[0] + y21[1]);
      double corr2 = (y22[0] + y22[1]);
      double corr3 = (y23[0] + y23[1]);
      double fm1 = (y31[0] + y31[1]) / 2;
      double fm2 = (y32[0] + y32[1]) / 2;
      double fm3 = (y33[0] + y33[1]) / 2;
      output.write(perc + "," + auc1 + "," + corr1 + "," + fm1 + "," + auc2 + "," + corr2 + "," + fm2 + "," + auc3 + ","
          + corr3 + "," + fm3 + "\n");
      System.out.println(auc1 + "," + corr1 + "," + fm1 + "\n");
      Instances md = new Instances(data);
      Classifier cM = (Classifier) new VFI();
      Evaluation eT = new Evaluation(md);
      eT.crossValidateModel(cM, md, 2, md.getRandomNumberGenerator(1));
      double y1 = eT.areaUnderROC(0);
      double y2 = eT.correct();
      double y3 = eT.fMeasure(0);
      System.out.println(y1 + "," + y2 + "," + y3 + "\n");
    }
    output.close();
  }
}

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