tutorial.exampleMIMLBinaryRelevance.java Source code

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Here is the source code for tutorial.exampleMIMLBinaryRelevance.java

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/*    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 2 of the License, or
 *    (at your option) any later version.
 *
 *    This program is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */
package tutorial;

import java.io.File;

import data.Bag;
import data.MIMLInstances;
import mimlclassifier.MIMLBinaryRelevance;
import mulan.classifier.MultiLabelOutput;
import mulan.evaluation.Evaluation;
import mulan.evaluation.Evaluator;
import mulan.evaluation.MultipleEvaluation;
import weka.classifiers.Classifier;
import weka.classifiers.mi.MIBoost;

/**
 * 
 * Class for basic handling of the classifier {@link MIMLBinaryRelevance}.
 * 
 * @author Ana I. Reyes Melero
 * @author Eva Gibaja
 * @author Amelia Zafra
 * @version 20170507
 *
 */
public class exampleMIMLBinaryRelevance {
    /** Shows the help on command line. */
    public static void showUse() {
        System.out.println("Program parameters:");
        System.out.println("\t-f arffPathFile Name -> path of arff file for train.");
        System.out.println("\t-g arffPathFile Name -> path of arff file for test");
        System.out.println("\t-x xmlPathFileName -> path of xml file.");
        System.out.println("Example:");
        System.out.println("\tjava -jar exampleMIMLBinaryRelevance -f data" + File.separator
                + "toyTrain.arff -g data" + File.separator + " -x data" + File.separator + "toy.xml");
        System.exit(-1);
    }

    public static void main(String[] args) {

        try {
            // String arffFileNameTrain = Utils.getOption("f", args);
            // String arffFileNameTest = Utils.getOption("g",args);
            // String xmlFileName = Utils.getOption("x", args);

            String arffFileNameTrain = "data" + File.separator + "miml_text_data_random_80train.arff";
            String arffFileNameTest = "data" + File.separator + "miml_text_data_random_20test.arff";
            String xmlFileName = "data" + File.separator + "miml_text_data.xml";

            // Parameter checking
            if (arffFileNameTrain.isEmpty()) {
                System.out.println("Arff pathName must be specified.");
                showUse();
            }
            if (arffFileNameTest.isEmpty()) {
                System.out.println("Arff pathName must be specified.");
                showUse();
            }
            if (xmlFileName.isEmpty()) {
                System.out.println("Xml pathName must be specified.");
                showUse();
            }

            // Loads the dataset
            System.out.println("Loading the dataset....");

            MIMLInstances mimlTrain = new MIMLInstances(arffFileNameTrain, xmlFileName);
            MIMLInstances mimlTest = new MIMLInstances(arffFileNameTest, xmlFileName);
            Classifier baseLearner = new MIBoost();

            MIMLBinaryRelevance MIMLBR = new MIMLBinaryRelevance(baseLearner);

            MIMLBR.setDebug(true);
            MIMLBR.build(mimlTrain);

            // Evaluates a single instance
            Bag bag = mimlTrain.getBag(1);
            MultiLabelOutput prediction = MIMLBR.makePrediction(bag);
            System.out.println("\nPrediction on a single instance:\n\t" + prediction.toString());

            // Performs a cross validation evaluation
            Evaluator evalCV = new Evaluator();
            MultipleEvaluation resultsCV;
            int numFolds = 2;
            System.out.println("\nPerforming " + numFolds + "-fold cross-validation:\n");
            resultsCV = evalCV.crossValidate(MIMLBR, mimlTrain, numFolds);
            System.out.println("\nResults on cross validation evaluation:\n" + resultsCV);

            // Performs a train-test evaluation
            Evaluator evalTT = new Evaluator();
            System.out.println("\nPerforming train-test evaluation:\n");
            Evaluation resultsTT = evalTT.evaluate(MIMLBR, mimlTest, mimlTrain);
            System.out.println("\nResults on train test evaluation:\n" + resultsTT);

            System.out.println("The program has finished.");

        } catch (IndexOutOfBoundsException ioobe) {
            System.err.println("Exception: Incorrect index of Bag.");
        } catch (Exception e) {
            e.printStackTrace();
        }
    }

}