net.sf.jclal.evaluation.method.RealScenario.java Source code

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Here is the source code for net.sf.jclal.evaluation.method.RealScenario.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.evaluation.method;

import java.util.logging.Level;
import java.util.logging.Logger;
import net.sf.jclal.activelearning.algorithm.AbstractALAlgorithm;
import net.sf.jclal.core.IAlgorithm;
import net.sf.jclal.dataset.AbstractDataset;
import org.apache.commons.configuration.Configuration;
import org.apache.commons.configuration.ConfigurationRuntimeException;

/**
 * Class that represents a real scenario where a tagger labels the unlabeled
 * instances in each iteration.
 *
 * @author Oscar Gabriel Reyes Pupo
 * @author Eduardo Perez Perdomo
 */
public class RealScenario extends AbstractEvaluationMethod {

    private static final long serialVersionUID = -6154654610498415157L;

    /**
     * Constructor
     *
     * @param algorithm
     *            The algorithm.
     * @param dataset
     *            The dataset.
     * @param percentageToTrain
     *            The percentage of instances for training the initial
     *            classifier.
     */
    public RealScenario(AbstractALAlgorithm algorithm, AbstractDataset dataset, double percentageToTrain) {

        super(algorithm, dataset);

    }

    /**
     * Empty(default) constructor.
     */
    public RealScenario() {
    }

    /**
     * Executes the process of evaluation
     */
    @Override
    public void evaluate() {

        try {
            // Load the initial data
            loadData();

            IAlgorithm algorithmCopy = getAlgorithm().makeCopy();

            algorithmCopy.setLabeledDataSet(getLabeledDataset());
            algorithmCopy.setUnlabeledDataSet(getUnlabeledDataset());
            algorithmCopy.setTestDataSet(getTestDataset());

            // Executes the algorithm
            algorithmCopy.execute();

            setFinalEvaluations(algorithmCopy.getScenario().getQueryStrategy().getEvaluations());

        } catch (Exception e) {
            Logger.getLogger(RealScenario.class.getName()).log(Level.SEVERE, null, e);
        }

    }

    /**
     * @param configuration
     *            The configuration of Hold Out, support datasets and algorithm
     *            configuration.
     */
    @Override
    public void configure(Configuration configuration) {

        setDataConfiguration(configuration);

        super.setAlgorithmConfiguration(configuration);

    }

    @Override
    protected void setDataConfiguration(Configuration configuration) {
        // Set multiLabel flag
        boolean multi = configuration.getBoolean("multi-label", false);
        setMultiLabel(multi);

        // Set multiInstance flag
        boolean multiInstance = configuration.getBoolean("multi-instance", false);
        setMultiInstance(multiInstance);

        // Set the xml file, it is used in the case of a multi-label
        // dataset
        String xml = configuration.getString("file-xml", "");
        setXmlPath(xml);

        // the multi-label elements are verified
        if (multi && xml.isEmpty()) {
            throw new ConfigurationRuntimeException(
                    "\nThe multi-label flag is " + "enabled and the xml path is empty. "
                            + "<multi-label>true</multi-label>" + " <file-xml></file-xml>");
        }

        // Set file labeled
        String fileLabeled = configuration.getString("file-labeled", "");
        setFileLabeledDataset(fileLabeled);

        // Set file unlabeled
        String fileUnlabeled = configuration.getString("file-unlabeled", "");
        setFileUnlabeledDataset(fileUnlabeled);

        if (fileLabeled.isEmpty() || fileUnlabeled.isEmpty()) {
            throw new ConfigurationRuntimeException("\n <file-labeled> and <file-unlabeled> tags must be defined.");
        }

        // Set file test
        String fileTest = configuration.getString("file-test", "");

        if (fileTest.isEmpty()) {
            /*
             * Logger.getLogger(RealScenario.class.getName()).log(Level.INFO,
             * "The param <file-test> is empty, the active learning algorithm require this property "
             * +
             * "for evaluating the constructed model, but in real scenario is not really necessary. In this case, "
             * + "we assign the <file-unlabeled> as <file-test>.");
             */
            fileTest = fileUnlabeled;
        }

        setFileTestDataset(fileTest);

        // Set class attribute
        int classAttributeT = configuration.getInt("class-attribute", -1);

        setClassAttribute(classAttributeT);
    }
}