lu.lippmann.cdb.dt.RegressionTreeFactory.java Source code

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Here is the source code for lu.lippmann.cdb.dt.RegressionTreeFactory.java

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/**
 * Copyright 2014-2016 LIST (Luxembourg Institute of Science and Technology), all right reserved.
 * Authorship : Olivier PARISOT, Yoanne DIDRY
 * Licensed under GNU General Public License version 3
 */
package lu.lippmann.cdb.dt;

import java.io.File;

import lu.lippmann.cdb.common.FormatterUtil;
import lu.lippmann.cdb.dsl.ASCIIGraphDsl;
import lu.lippmann.cdb.graph.GraphUtil;
import lu.lippmann.cdb.models.history.GraphWithOperations;
import lu.lippmann.cdb.weka.WekaDataAccessUtil;
import weka.classifiers.Evaluation;
import weka.classifiers.trees.REPTree;
import weka.core.Instances;

/**
 * RegressionTreeFactory.
 * 
 * @author
 */
public class RegressionTreeFactory {
    /**
     * Main method.
     * @param args command line arguments
     */
    public static void main(final String[] args) {
        try {
            final String f = "./samples/csv/uci/winequality-red.csv";
            //final String f="./samples/arff/UCI/crimepredict.arff";
            final Instances dataSet = WekaDataAccessUtil.loadInstancesFromARFFOrCSVFile(new File(f));
            System.out.println(dataSet.classAttribute().isNumeric());

            final REPTree rt = new REPTree();
            rt.setMaxDepth(3);
            rt.buildClassifier(dataSet);

            System.out.println(rt);

            //System.out.println(rt.graph());

            final GraphWithOperations gwo = GraphUtil.buildGraphWithOperationsFromWekaRegressionString(rt.graph());
            System.out.println(gwo);
            System.out.println(new ASCIIGraphDsl().getDslString(gwo));

            final Evaluation eval = new Evaluation(dataSet);

            /*Field privateStringField = Evaluation.class.getDeclaredField("m_CoverageStatisticsAvailable");
            privateStringField.setAccessible(true);
            //privateStringField.get
            boolean fieldValue = privateStringField.getBoolean(eval);
            System.out.println("fieldValue = " + fieldValue);*/

            double[] d = eval.evaluateModel(rt, dataSet);
            System.out.println("PREDICTED -> " + FormatterUtil.buildStringFromArrayOfDoubles(d));

            System.out.println(eval.errorRate());
            System.out.println(eval.sizeOfPredictedRegions());

            System.out.println(eval.toSummaryString("", true));

            /*final String f2="./samples/csv/salary.csv";
            final Instances dataSet2=WekaDataAccessUtil.loadInstancesFromARFFOrCSVFile(new File(f2));
                
            final J48 j48=new J48();
            j48.buildClassifier(dataSet2);
            System.out.println(j48.graph());
            final GraphWithOperations gwo2=GraphUtil.buildGraphWithOperationsFromWekaString(j48.graph(),false);
            System.out.println(gwo2);*/

            System.out.println(new DecisionTree(gwo, eval.errorRate()));
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}