multiclassificationperformance.java

一个很好的LIBSVM的JAVA源码。对于要研究和改进SVM算法的学者。可以参考。来自数据挖掘工具YALE工具包。

/*
 *  YALE - Yet Another Learning Environment
 *  Copyright (C) 2001-2004
 *      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.performance;

import edu.udo.cs.yale.tools.math.Averagable;
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 java.util.Collection;
import java.util.Iterator;

/** Measures the accuracy and classification error for both binary classification problems and multi class problems.
 *
 *  @version $Id: MultiClassificationPerformance.java,v 2.4 2004/08/28 17:04:48 ingomierswa Exp $
 */
public class MultiClassificationPerformance extends MeasuredPerformance {

    /** Indicates an undefined type (should not happen). */
    public static final int UNDEFINED = -1;
    /** Indicates accuracy. */
    public static final int ACCURACY  =  0;
    /** Indicates classification error. */
    public static final int ERROR     =  1;

    /** The names of the criteria. */
    public static final String[] NAME = { "accuracy", "classification_error" };

    /** The descriptions of the criteria. */
    public static final String[] DESCRIPTION = { 
	"Relative number of correctly classified examples", 
	"Relative number of misclassified examples" 
    };


    /** The counter for true labels and the prediction. First: true label, second: predicted label. */
    private int[][] counter;
    /** The class names of the label. Used for logging and result display. */
    private String[] classNames;

    /** The type of this performance: accuracy or classification error. */
    private int type = ACCURACY;


    /** Creates a MultiClassificationPerformance with undefined type. */
    public MultiClassificationPerformance() { this(UNDEFINED); }

    /** Creates a MultiClassificationPerformance with the given type. */
    public MultiClassificationPerformance(int type) { this.type = type; }
    

    /** Creates a MultiClassificationPerformance with the given type. */
    public static MultiClassificationPerformance newInstance(String name) {
	for (int i = 0; i < NAME.length; i++) {
	    if (NAME[i].equals(name)) return new MultiClassificationPerformance(i);
	}
	return null;
    }

    /** Initializes the criterion and sets the label. */
    public void startCounting(ExampleSet eSet) {
	Collection values = eSet.getLabel().getValues();
	this.counter = new int[values.size()][values.size()];
	this.classNames = new String[values.size()];
	Iterator i = values.iterator();
	int n = 0;
	while (i.hasNext()) {
	    classNames[n] = (String)i.next();
	    n++;
	}
    }

    /** Increases the prediction value in the matrix. */
    public void countExample(Example example) {
	int label = (int)example.getLabel();
	int plabel = (int)example.getPredictedLabel();
	counter[label][plabel]++;
    }

    /** Returns either the accuracy or the classification error. */
    public double getValue() {
	int x=0, y=0;
	for (int i = 0; i < counter.length; i++) {
	    x += counter[i][i]; 
	    for (int j = 0; j < counter[i].length; j++)
		y += counter[i][j];
	}
	
	if (y == 0) return Double.NaN;

	// returns either the accuracy or the error
	if (type == ACCURACY)
	    return (double)x/(double)y;
	else
	    return (1.0d - (double)x/(double)y);
    }

    /** Returns true. */
    public boolean formatPercent() { return true; }

    public double getVariance() {
	return Double.NaN;
    }

    /** Returns the name. */
    public String getName() { 
	return NAME[type]; 
    }

    /** Returns the description. */
    public String getDescription() {
	return DESCRIPTION[type];
    }


    // ================================================================================

    /** Returns the accuracy or 1 - error. */
    public double getFitness() {
	if (type == ACCURACY)
	    return getValue();
	else 
	    return (1.0d - getValue());
    }

    /** Returns 1. */
    public double getMaxFitness() { 
	return 1.0d;
    }
    
    protected void cloneAveragable(Averagable newPC) {	
	MultiClassificationPerformance newMulti = (MultiClassificationPerformance)newPC;
	this.type       = newMulti.type;
	this.classNames = new String[newMulti.classNames.length];
	for (int i = 0; i < this.classNames.length; i++)
	    this.classNames[i] = newMulti.classNames[i];
	this.counter    = new int[newMulti.counter.length][newMulti.counter.length];
	for (int i = 0; i < this.counter.length; i++)
	    for (int j = 0; j < this.counter[i].length; j++)
		this.counter[i][j] = newMulti.counter[i][j];
    }

    public void buildAverage(Averagable performance) {
	super.buildAverage(performance);
	MultiClassificationPerformance other = (MultiClassificationPerformance)performance;
	for (int i = 0; i < this.counter.length; i++)
	    for (int j = 0; j < this.counter[i].length; j++)
		this.counter[i][j] += other.counter[i][j];
    }


    // ================================================================================

    public String toString() {
	StringBuffer result = new StringBuffer(super.toString());
	result.append("\nConfusionMatrix:\nTrue:");
	for (int i = 0; i < this.counter.length; i++)
	    result.append("\t" + classNames[i]);
	
	for (int i = 0; i < this.counter.length; i++) {
	    result.append("\n" + classNames[i] + ":");
	    for (int j = 0; j < this.counter[i].length; j++) {
		result.append("\t" + this.counter[j][i]);
	    }
	}
	
	result.append("\nper class:");
	for (int i = 0; i < this.counter.length; i++) {
	    int total = 0;
	    for (int j = 0; j < this.counter[i].length; j++)
		total += counter[i][j];
	    result.append("\t" + formatValue(counter[i][i] / (double)total));
	}
	return result.toString();
    }


    /** Returns a HTML table for the confusion matrix. */
    public String toHTML() {
	StringBuffer result = new StringBuffer(super.toString());
	
	result.append("<table bgcolor=\"#E3D8C3\" border=\"1\"><tr bgcolor=\"#ccccff\"><td></td>");
	for (int i = 0; i < this.counter.length; i++)
	    result.append("<td><b>true " + classNames[i] + "</b></td>");
	result.append("</tr>");

	for (int i = 0; i < this.counter.length; i++) {
	    result.append("<tr><td bgcolor=\"#ccccff\"><b>pred. " + classNames[i] + "</b></td>");
	    for (int j = 0; j < this.counter[i].length; j++) {
		result.append("<td>" + this.counter[j][i] + "</td>");
	    }
	    result.append("</tr>");
	}

	result.append("<tr bgcolor=\"#ccccff\"><td><b>per class:</b></td>");
	for (int i = 0; i < this.counter.length; i++) {
	    int total = 0;
	    for (int j = 0; j < this.counter[i].length; j++)
		total += counter[i][j];
	    if (type == ACCURACY)
		result.append("<td>" + formatValue(counter[i][i] / (double)total) + "</td>");
	    else
		result.append("<td>" + formatValue(1.0d - counter[i][i] / (double)total) + "</td>");
	}
	result.append("</tr>");

	result.append("</table>");
	return result.toString();
    }
}