methodvalidationchain.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.validation;

import edu.udo.cs.yale.operator.Operator;
import edu.udo.cs.yale.operator.OperatorChain;
import edu.udo.cs.yale.operator.IOContainer;
import edu.udo.cs.yale.operator.OperatorException;
import edu.udo.cs.yale.operator.IllegalInputException;
import edu.udo.cs.yale.operator.IODescription;
import edu.udo.cs.yale.operator.IOObject;
import edu.udo.cs.yale.operator.MissingIOObjectException;
import edu.udo.cs.yale.operator.Value;
import edu.udo.cs.yale.tools.LogService;
import edu.udo.cs.yale.example.ExampleSet;
import edu.udo.cs.yale.example.AttributeVector;
import edu.udo.cs.yale.operator.learner.Model;
import edu.udo.cs.yale.operator.performance.*;
import edu.udo.cs.yale.tools.ParameterService;

/** This operator evaluates the performance of algorithms, e.g. feature selection algorithms. The first
 *  inner operator is the algorithm to be evaluated itself. It must return an example set which is in turn
 *  used to create a new model using the second inner operator and retrieve a performance vector using
 *  the third inner operator. This performance vector serves as a performance indicator for the actual algorithm.
 *  
 *  @author ingo
 *  @version $Id: MethodValidationChain.java,v 1.3 2004/09/12 11:09:52 ingomierswa Exp $
 */
public abstract class MethodValidationChain extends OperatorChain {

    private static final Class[] OUTPUT_CLASSES = { PerformanceVector.class, AttributeVector.class };
    private static final Class[] INPUT_CLASSES =  { ExampleSet.class };

    private PerformanceCriterion lastPerformance;
    private IOContainer learnResult;
    private IOContainer methodResult;
    
    public MethodValidationChain() {
	addValue(new Value("performance", "The last performance (main criterion).") {
		public double getValue() {
		    if (lastPerformance != null)
			return lastPerformance.getValue();
		    else
			return Double.NaN;
		}
	    });
	addValue(new Value("variance", "The variance of the last performance (main criterion).") {
		public double getValue() {
		    if (lastPerformance != null)
			return lastPerformance.getVariance();
		    else
			return Double.NaN;
		}
	    });
    }

    /** Returns the maximum number of innner operators. */
    public int getMaxNumberOfInnerOperators() { return 3; }
    /** Returns the minimum number of innner operators. */
    public int getMinNumberOfInnerOperators() { return 3; }

    public Class[] getOutputClasses() { return OUTPUT_CLASSES; }
    public Class[] getInputClasses() { return INPUT_CLASSES; }

    /** Ok if the first inner operator returns a example set, the second returns model and the third a performance vector. 
     */
    public Class[] checkIO(Class[] input) throws IllegalInputException {
	Operator method    = getMethod();
	Operator learner   = getLearner();
	Operator evaluator = getEvaluator();

	//input = method.getIODescription().getOutputClasses(input);
	input = method.checkIO(input);
	if (!IODescription.containsClass(ExampleSet.class, input))
	    throw new IllegalInputException(this, learner, ExampleSet.class);

	//input = learner.getIODescription().getOutputClasses(input);
	input = learner.checkIO(input);
	if (!IODescription.containsClass(Model.class, input))
	    throw new IllegalInputException(this, learner, Model.class);
	
	// GUT?
	Class[] newInput = new Class[input.length+1];
	for (int i = 0; i < input.length; i++) {
	    newInput[i] = input[i];
	}
	newInput[newInput.length-1] = ExampleSet.class;
	input = evaluator.checkIO(newInput);
	// ???

	//input = evaluator.getIODescription().getOutputClasses(input);
	if (!IODescription.containsClass(PerformanceVector.class, input))
	    throw new IllegalInputException(this, evaluator, PerformanceVector.class);

	return new Class[] { PerformanceVector.class, AttributeVector.class };
    }

    private Operator getMethod() { return getOperator(0); }
    private Operator getLearner() { return getOperator(1); }
    private Operator getEvaluator() { return getOperator(2); }	

    /** Can be used by subclasses to set the performance of the example set. */
    void setResult(PerformanceCriterion pc) { lastPerformance = pc; }

    /** Applies the method.
     */
    IOContainer useMethod(ExampleSet methodTrainingSet) throws OperatorException {
	methodTrainingSet.recalculateAllAttributeStatistics();
	return methodResult = getMethod().apply(getInput().append(new IOObject[] { methodTrainingSet }));
    }

    /** Applies the learner. 
     */
    IOContainer learn(ExampleSet trainingSet) throws OperatorException {
	if (methodResult == null) {
	    throw new RuntimeException("Wrong use of MethodEvaluator.evaluate(ExampleSet): No preceding invocation of useMethod(ExampleSet)!");
	}
	trainingSet.recalculateAllAttributeStatistics();
	learnResult = getLearner().apply(getInput().append(new IOObject[] { trainingSet }));
	methodResult = null;
	return learnResult;
    }

    /** Applies the applier and evaluator. 
     */
    IOContainer evaluate(ExampleSet testSet) throws OperatorException {
	if (learnResult == null) {
	    throw new RuntimeException("Wrong use of ValidationChain.evaluate(ExampleSet): No preceding invocation of learn(ExampleSet)!");
	}
	testSet.recalculateAllAttributeStatistics();
	IOContainer result = getEvaluator().apply(learnResult.append(new IOObject[] { testSet }));
	learnResult = null;
	return result;
    }

    void setLastPerformance(PerformanceCriterion pc) {
	lastPerformance = pc;
    }

    public abstract int getNumberOfValidationSteps();

    public int getNumberOfSteps() {
	return getNumberOfValidationSteps() * super.getNumberOfChildrensSteps();
    }

}