epsi.i5.datamining.Weka.java Source code

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Here is the source code for epsi.i5.datamining.Weka.java

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package epsi.i5.datamining;

import java.io.BufferedReader;
import java.io.File;
import java.io.FileNotFoundException;
import java.io.FileReader;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import java.util.Random;
import weka.attributeSelection.AttributeSelection;
import weka.classifiers.Evaluation;
import weka.classifiers.bayes.NaiveBayes;
import weka.classifiers.functions.LinearRegression;
import weka.classifiers.rules.DecisionTable;
import weka.classifiers.rules.M5Rules;
import weka.classifiers.rules.OneR;
import weka.classifiers.trees.J48;
import weka.core.Attribute;
import weka.core.DenseInstance;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.converters.ArffSaver;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.StringToWordVector;

/**
 *
 * @author Sinys
 */
public class Weka {

    //Listes des attributs
    ArrayList<Attribute> attInfo = new ArrayList<>();
    //Cration de l'instance
    Instances instances;
    String fileOne = "NewARFFOne";

    public Weka() {

    }

    public void setFileName(String file) {
        fileOne = file;
    }

    public void chargementData(List<DataEntity> ListEntity) {

        //En tte class Weka
        List<String> listCat = new ArrayList();
        for (DataEntity entity : ListEntity) {
            for (String laCat : entity.getListeCategorie()) {
                if (!listCat.contains(laCat)) {
                    listCat.add(laCat);
                }
            }
        }

        //Cration des attributs
        attInfo.add(new Attribute("commentaire", (FastVector) null));
        attInfo.add(new Attribute("categorie", listCat));
        attInfo.add(new Attribute("polarit"));
        instances = new Instances("ClasserSE", attInfo, 0);

        //Generation des datas
        double[] vals = new double[instances.numAttributes()];

        for (DataEntity entity : ListEntity) {

            for (String categ : entity.getListeCategorie()) {
                //System.out.println(categ);
                vals[0] = instances.attribute(0).addStringValue(entity.getCommentaireTrie());
                vals[1] = listCat.indexOf(categ);//instances.classAttribute().indexOfValue(categ);
                vals[2] = Double.parseDouble((String) entity.getPolarite());
                instances.add(new DenseInstance(1, vals));
                //Purge vals
                vals = new double[instances.numAttributes()];

            }
        }
    }

    public void generationArff() throws IOException {
        ArffSaver saver = new ArffSaver();
        saver.setInstances(instances);
        saver.setFile(new File("src/epsi/i5/data/" + fileOne + ".arff"));
        saver.writeBatch();
    }

    public void generationArffFilter() throws IOException, Exception {

        BufferedReader reader = new BufferedReader(new FileReader("src/epsi/i5/data/" + fileOne + ".arff"));
        Instances data = new Instances(reader);
        reader.close();

        StringToWordVector filter = new StringToWordVector();
        filter.setInputFormat(data);
        instances = Filter.useFilter(data, filter);
        fileOne = fileOne + "Two";
        generationArff();
    }

    public void excutionAlgo() throws FileNotFoundException, IOException, Exception {
        BufferedReader reader = new BufferedReader(new FileReader("src/epsi/i5/data/" + fileOne + ".arff"));
        Instances data = new Instances(reader);
        reader.close();
        //System.out.println(data.attribute(0));
        data.setClass(data.attribute(0));
        NaiveBayes NB = new NaiveBayes();
        NB.buildClassifier(data);
        Evaluation naiveBayes = new Evaluation(data);
        naiveBayes.crossValidateModel(NB, data, 10, new Random(1));
        naiveBayes.evaluateModel(NB, data);
        //System.out.println(test.confusionMatrix() + "1");
        //System.out.println(test.correct() + "2");
        System.out.println("*****************************");
        System.out.println("******** Naive Bayes ********");
        System.out.println(naiveBayes.toMatrixString());
        System.out.println("*****************************");
        System.out.println("**** Pourcentage Correct ****");
        System.out.println(naiveBayes.pctCorrect());
        System.out.println("");
        J48 j = new J48();
        j.buildClassifier(data);
        Evaluation jeval = new Evaluation(data);
        jeval.crossValidateModel(j, data, 10, new Random(1));
        jeval.evaluateModel(j, data);
        System.out.println("*****************************");
        System.out.println("************ J48 ************");
        System.out.println(jeval.toMatrixString());
        System.out.println("*****************************");
        System.out.println("**** Pourcentage Correct ****");
        System.out.println(jeval.pctCorrect());
        System.out.println("");
        DecisionTable DT = new DecisionTable();
        DT.buildClassifier(data);
        Evaluation decisionTable = new Evaluation(data);
        decisionTable.crossValidateModel(DT, data, 10, new Random(1));
        decisionTable.evaluateModel(DT, data);
        System.out.println("*****************************");
        System.out.println("******* DecisionTable *******");
        System.out.println(decisionTable.toMatrixString());
        System.out.println("*****************************");
        System.out.println("**** Pourcentage Correct ****");
        System.out.println(decisionTable.pctCorrect());
        System.out.println("");
        OneR OR = new OneR();
        OR.buildClassifier(data);
        Evaluation oneR = new Evaluation(data);
        oneR.crossValidateModel(OR, data, 10, new Random(1));
        oneR.evaluateModel(OR, data);
        System.out.println("*****************************");
        System.out.println("************ OneR ***********");
        System.out.println(oneR.toMatrixString());
        System.out.println("*****************************");
        System.out.println("**** Pourcentage Correct ****");
        System.out.println(oneR.pctCorrect());

        //Polarit
        data.setClass(data.attribute(1));
        System.out.println("");
        M5Rules MR = new M5Rules();
        MR.buildClassifier(data);
        Evaluation m5rules = new Evaluation(data);
        m5rules.crossValidateModel(MR, data, 10, new Random(1));
        m5rules.evaluateModel(MR, data);
        System.out.println("*****************************");
        System.out.println("********** M5Rules **********");
        System.out.println(m5rules.correlationCoefficient());

        System.out.println("");
        LinearRegression LR = new LinearRegression();
        LR.buildClassifier(data);
        Evaluation linearR = new Evaluation(data);
        linearR.crossValidateModel(LR, data, 10, new Random(1));
        linearR.evaluateModel(LR, data);
        System.out.println("*****************************");
        System.out.println("********** linearR **********");
        System.out.println(linearR.correlationCoefficient());
    }

}