wekaattributeweighting.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.features.weighting;

import edu.udo.cs.yale.operator.Operator;
import edu.udo.cs.yale.operator.OperatorException;
import edu.udo.cs.yale.operator.UserError;
import edu.udo.cs.yale.operator.IOObject;
import edu.udo.cs.yale.operator.parameter.*;
import edu.udo.cs.yale.example.ExampleSet;
import edu.udo.cs.yale.example.Attribute;
import edu.udo.cs.yale.example.AttributeWeights;
import edu.udo.cs.yale.tools.WekaTools;
import edu.udo.cs.yale.tools.LogService;

import weka.attributeSelection.AttributeEvaluator;
import weka.attributeSelection.ASEvaluation;
import weka.core.Instances;

import java.util.List;

/** Performs one of Weka's AttributeEvaluator classes to determine a sort of attribute relevance. 
 *  These relevance values build an instance of AttributeWeights. Therefore, they can be used by 
 *  other operators which make use of such weights, like weight based selection or search heuristics
 *  which use attribute weights to speed up the search.
 *   
 *  @version $Id: WekaAttributeWeighting.java,v 1.2 2004/09/01 12:39:50 ingomierswa Exp $
 */
public class WekaAttributeWeighting extends Operator {

    public static final String[] WEKA_ATTRIBUTE_EVALUATORS = WekaTools.getWekaClasses(AttributeEvaluator.class);

    public IOObject[] apply() throws OperatorException {
	ExampleSet exampleSet = (ExampleSet)getInput(ExampleSet.class);
	AttributeWeights weights = new AttributeWeights();

	String operatorName = getWekaLearnerName();
	String[] parameters = getWekaParameters();
	AttributeEvaluator evaluator = null;
	try {
	    evaluator = (AttributeEvaluator)ASEvaluation.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", exampleSet.getLabel(), true);
	try {
	    LogService.logMessage(getName() + ": Building Weka attribute evaluator.", LogService.MINIMUM);
	    evaluator.buildEvaluator(instances);
	} catch (Exception e) {
	    throw new UserError(this, e, 905, new Object[] {operatorName, e});
	}

	for (int i = 0; i < exampleSet.getNumberOfAttributes(); i++) {
	    Attribute attribute = exampleSet.getAttribute(i);
	    try {
		double result = evaluator.evaluateAttribute(i);
		weights.setWeight(attribute.getName(), result);
	    } catch (Exception e) {
		LogService.logMessage(getName() + ": Cannot evaluate attribute '" + attribute.getName() +
				      "', use unknown weight.", LogService.WARNING);
	    }
	}

	return new IOObject[] { exampleSet, weights };
    }

    public Class[] getOutputClasses() {
	return new Class[] { ExampleSet.class, AttributeWeights.class };
    }

    public Class[] getInputClasses() {
	return new Class[] { ExampleSet.class };
    }

    public String getWekaLearnerName() {
	return getParameterAsString("weka_attribute_evaluator");
    }
    
    public String[] getWekaParameters() {
	List wekaParameters = getParameterList("weka_parameters");
	return WekaTools.getWekaParameters(wekaParameters);
    }
    
    public List getParameterTypes() {
	List types = super.getParameterTypes();
	ParameterType type = new ParameterTypeStringCategory("weka_attribute_evaluator", 
							     "The fully qualified classname of the Weka attribute evaluator.", 
							     WEKA_ATTRIBUTE_EVALUATORS);
	type.setExpert(false);
	types.add(type);
	types.add(new ParameterTypeList("weka_parameters", "Parameters for the Weka attribute evaluator as described in the Weka manual.", new ParameterTypeString(null, null)));
	return types;
    }
}