wekaclassifier.java

著名的开源仿真软件yale

/*
 *  YALE - Yet Another Learning Environment
 *  Copyright (C) 2002, 2003
 *      Simon Fischer, Ralf Klinkenberg, Ingo Mierswa, 
 *          Katharina Morik, Oliver Ritthoff
 *      Artificial Intelligence Unit
 *      Computer Science Department
 *      University of Dortmund
 *      44221 Dortmund,  Germany
 *  email: [e-mail]
 *  web:   http://yale.cs.uni-dortmund.de/
 *
 *  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 edu.udo.cs.yale.operator.learner;

import edu.udo.cs.yale.operator.OperatorException;
import edu.udo.cs.yale.example.Attribute;
import edu.udo.cs.yale.example.Example;
import edu.udo.cs.yale.example.ExampleSet;
import edu.udo.cs.yale.tools.LogService;

import weka.core.Instance;
import weka.core.Drawable;
import weka.gui.treevisualizer.TreeDisplayListener;
import weka.gui.treevisualizer.TreeDisplayEvent;
import weka.gui.treevisualizer.PlaceNode2;
import weka.gui.treevisualizer.TreeVisualizer;
import weka.classifiers.Classifier;
import weka.classifiers.DistributionClassifier;

import java.awt.Component;

/** A Weka {@link weka.classifiers.Classifier} which can be used to classify
 *  {@link Example}s. It is learned by a {@link WekaLearner}.
 *
 *  @author ingo 
 *  @version $Id: WekaClassifier.java,v 2.5 2003/09/03 14:27:27 fischer Exp $
 */
public class WekaClassifier extends WekaModel {

    /** The used weka classifier. */
    private Classifier classifier;

    /** Set to true iff {@link weka.classifiers.DistributionClassifier} implements the 
     *  Weka DistributionClassifier and the predicted label should be the confidence
     *  (and not the class index). */
    private boolean useDistributionClassifier = false;

    public WekaClassifier(Classifier classifier) {
	this(classifier, false);
    }

    public WekaClassifier(Classifier classifier, boolean setConfidence) {
	this.classifier = classifier;
	this.useDistributionClassifier = setConfidence;
	if (useDistributionClassifier && !(classifier instanceof DistributionClassifier))
	    throw new IllegalArgumentException("setConfidence must only be true if classifier is instance of DistributionClassifier!");
    }

    
    public void apply(ExampleSet exampleSet) throws OperatorException {
	if (useDistributionClassifier) {
	    exampleSet.getPredictedLabel().setMinimum(0);
	    exampleSet.getPredictedLabel().setMaximum(1);
	}
	super.apply(exampleSet);
    }

    /** Classifies ervery weka instance and sets the result as predicted label of the current example.
     */
    public void applyModelForInstance(Instance instance, Example e) {
	double predictedLabel = Double.NaN;
	try {
	    if (useDistributionClassifier) {
		double confidence[] = ((DistributionClassifier)classifier).distributionForInstance(instance); 
		predictedLabel = confidence[0];
	    } else {
		predictedLabel = classifier.classifyInstance(instance)+Attribute.FIRST_CLASS_INDEX; 
	    }
	} catch (Exception exc) {
	    LogService.logMessage("Exception occured while classifying example:"+exc.getMessage(), 
				  LogService.ERROR);
	}
	e.setPredictedLabel(predictedLabel);
    }   

    public String toString() {
	return "Weka model ("+classifier.getClass().getName()+")";
    }

    public String toResultString() {
	return classifier.toString();
    }

    public Component getVisualisationComponent() {
	if (classifier instanceof Drawable) {
	    try {
		Drawable drawable = (Drawable)classifier;
		return new TreeVisualizer(new TreeDisplayListener() {
			public void userCommand(TreeDisplayEvent e) {
			    //System.out.println("TreeDisplayEvent: "+e);
			}
		    },
					  drawable.graph(),
					  new PlaceNode2());
	    } catch (Exception e) {
		e.printStackTrace();
		return super.getVisualisationComponent();
	    }
	} else {
	    return super.getVisualisationComponent();
	}
    }

    public boolean equals(Object o) {
	if (!super.equals(o)) return false;
	WekaClassifier other = (WekaClassifier)o;
	if (other.useDistributionClassifier != this.useDistributionClassifier) return false;
	if (!other.classifier.getClass().equals(this.classifier.getClass())) return false;
	return true;
    }

}