net.sf.jclal.examples.HoldOutExample.java Source code

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Here is the source code for net.sf.jclal.examples.HoldOutExample.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., 59 Temple Place - Suite 330, Boston, MA  02111-1307, USA.
 */
package net.sf.jclal.examples;

import net.sf.jclal.activelearning.algorithm.ClassicalALAlgorithm;
import net.sf.jclal.activelearning.batchmode.QBestBatchMode;
import net.sf.jclal.activelearning.oracle.SimulatedOracle;
import net.sf.jclal.activelearning.singlelabel.querystrategy.EntropySamplingQueryStrategy;
import net.sf.jclal.activelearning.stopcriteria.MaxIteration;
import net.sf.jclal.activelearning.stopcriteria.UnlabeledSetEmpty;
import net.sf.jclal.activelearning.scenario.PoolBasedSamplingScenario;
import net.sf.jclal.classifier.WekaClassifier;
import net.sf.jclal.core.IClassifier;
import net.sf.jclal.core.IQueryStrategy;
import net.sf.jclal.core.IStopCriterion;
import net.sf.jclal.evaluation.method.HoldOut;
import net.sf.jclal.evaluation.method.HoldOut1;
import net.sf.jclal.listener.GraphicalReporterListener;
import net.sf.jclal.sampling.supervised.Resample;
import net.sf.jclal.util.random.RanecuFactory;
import weka.classifiers.Classifier;
import weka.classifiers.bayes.NaiveBayes;

/**
 * Example of hold out experiment that uses the entropy sampling as query
 * strategy, pool-base as scenario and the classical AL process.
 *
 * @author Oscar Gabriel Reyes Pupo
 * @author Eduardo Perez Perdomo
 *
 *
 */
public class HoldOutExample {

    /**
     * @param args
     *            the command line arguments
     */
    public static void main(String[] args) {

        String fileName = "datasets/iris/iris.arff";

        // The initial labeled set from the training set is randomly
        // selected
        Resample sampling = new Resample();

        sampling.setNoReplacement(false);
        sampling.setInvertSelection(false);

        sampling.setPercentageInstancesToLabelled(5);

        // Set the scenario to use
        PoolBasedSamplingScenario scenario = new PoolBasedSamplingScenario();
        QBestBatchMode batchMode = new QBestBatchMode();
        batchMode.setBatchSize(1);

        scenario.setBatchMode(batchMode);

        // Set the oracle
        SimulatedOracle oracle = new SimulatedOracle();

        scenario.setOracle(oracle);

        // Set the query strategy to use
        IQueryStrategy queryStrategy = new EntropySamplingQueryStrategy();

        // Set the base classifier to use in the query strategy
        IClassifier model = new WekaClassifier();

        Classifier classifier = new NaiveBayes();

        ((WekaClassifier) model).setClassifier(classifier);

        // Set the model into the query strategy
        queryStrategy.setClassifier(model);

        // Set the query strategy into the scenario
        scenario.setQueryStrategy(queryStrategy);

        // Set the algorithm's listeners
        GraphicalReporterListener visual = new GraphicalReporterListener();
        visual.setReportOnFile(true);
        visual.setShowSeparateWindow(true);
        visual.setReportFrequency(1);

        // Construct the AL algorithm
        ClassicalALAlgorithm algorithm = new ClassicalALAlgorithm();

        // Set the listener for the algorithm
        algorithm.addListener(visual);

        // Set the stop criteria

        MaxIteration stop1 = new MaxIteration();
        stop1.setMaxIteration(45);

        UnlabeledSetEmpty stop2 = new UnlabeledSetEmpty();

        algorithm.addStopCriterion(stop1);
        algorithm.addStopCriterion(stop2);

        // Set the scenario into the algorithm
        algorithm.setScenario(scenario);

        // Set the evaluation method to use
        HoldOut method = new HoldOut();

        // Set the sampling strategy into the algorithm
        method.setSamplingStrategy(sampling);

        // Set the path of the dataset
        method.setFileDataset(fileName);

        // Set the 66% of the total of instances to train the model
        method.setPercentageToSplit(66);

        RanecuFactory random = new RanecuFactory();
        random.setSeed(9871234);

        method.setRandGenFactory(random);

        // Set the algorithm into the evaluation method
        method.setAlgorithm(algorithm);

        // To evaluate the algorithm
        method.evaluate();

    }
}