wekalearner.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.parameter.*;
import edu.udo.cs.yale.operator.OperatorException;
import edu.udo.cs.yale.operator.UserError;
import edu.udo.cs.yale.example.Attribute;
import edu.udo.cs.yale.example.Example;
import edu.udo.cs.yale.example.ExampleReader;
import edu.udo.cs.yale.example.ExampleSet;
import edu.udo.cs.yale.tools.Ontology;
import edu.udo.cs.yale.tools.LogService;
import edu.udo.cs.yale.tools.WekaTools;

import weka.classifiers.Classifier;
import weka.core.Instances;

import java.util.List;
import java.util.Iterator;

/** This operator can build all classifiers from the 
 *  <a href="http://www.cs.waikato.ac.nz/~ml/weka/">Weka</a> package.<br/>
 *  The classifier type can be selected by the parameter <var>weka_learner_name</var>. 
 *  Parameters can be passed to the Weka classifier using the Yale parameter
 *  <var>weka_parameters</var>. See the Weka javadoc for classifier and parameter 
 *  descriptions.
 *
 *  @yale.xmlclass WekaLearner
 *  @version $Id: WekaLearner.java,v 2.7 2003/08/14 10:24:57 fischer Exp $
 */
public class WekaLearner extends Learner {

    public static final String[] WEKA_CLASSIFIERS = WekaTools.getWekaClasses(weka.classifiers.Classifier.class, 
									     "weka.classifiers");


    public Model learn(ExampleSet exampleSet) throws OperatorException {
	String operatorName = getParameterAsString("weka_learner_name");
	List wekaParameters = getParameterList("weka_parameters");

	String[] parameters = WekaTools.getWekaParameters(wekaParameters);
	Classifier classifier = null;
	try {
	    classifier = Classifier.forName(operatorName, parameters);
	} catch (Exception e) {
	    throw new UserError(this, e, 904, new Object[] { operatorName, e});
	}

	LogService.logMessage(getName() + ": Converting to Weka instances.", LogService.MINIMUM);
	Instances instances = WekaTools.toWekaInstances(exampleSet, "TempInstances");
	try {
	    LogService.logMessage(getName() + ": Building Weka classifier.", LogService.MINIMUM);
	    classifier.buildClassifier(instances);
	} catch (Exception e) {
	    throw new UserError(this, e, 905, new Object[] {operatorName, e});
	}
	boolean useDist = getParameterAsBoolean("use_distribution");
	if (useDist && !(classifier instanceof weka.classifiers.DistributionClassifier)) {
	    LogService.logMessage("use_distribution must only be true if the classifier is a DistributionClassifier", LogService.ERROR);
	}
	return new WekaClassifier(classifier, useDist);
    }


    public List getParameterTypes() {
	List types = super.getParameterTypes();
	types.add(new ParameterTypeStringCategory("weka_learner_name", "The fully qualified classname of the weka classifier.", WEKA_CLASSIFIERS));
	types.add(new ParameterTypeList("weka_parameters", "Parameters for the Weka classifier as described in the Weka manual.", new ParameterTypeString(null, null)));
	types.add(new ParameterTypeBoolean("use_distribution", "If set to true, the prediction of the model will not be the class, but the confidence for that class.", false));
	return types;
    }
}