sampletest.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.test;

import edu.udo.cs.yale.Yale;
import edu.udo.cs.yale.operator.IOContainer;
import edu.udo.cs.yale.operator.MissingIOObjectException;
import edu.udo.cs.yale.tools.LogService;
import edu.udo.cs.yale.tools.ParameterService;

import junit.framework.AssertionFailedError;
import junit.framework.Test;
import junit.framework.TestSuite;
import java.io.File;

/** Extends the JUnit test case by a method for checking the output of an experiment.
 *
 *  @version $Id: SampleTest.java,v 2.18 2004/09/12 11:09:52 ingomierswa Exp $
 */
public abstract class SampleTest extends TestCase {

    private String file;

    public SampleTest(String file) {
	super("sampleTest");
	this.file = file;
    }

    public String getName() {
	return "Sample '"+file+"'";
    }

    public void setUp() throws Exception {
	super.setUp();
	LogService.setVerbosityLevel(LogService.WARNING);
	Yale.init();
	LogService.setVerbosityLevel(LogService.WARNING);
    }

    public void sampleTest() throws Exception {

  	File experimentFile = new File(ParameterService.getYaleHome(), "sample"+File.separator+file);
	
  	Yale.readExperimentFile(experimentFile);
  	LogService.init(Yale.getExperiment());
	LogService.setVerbosityLevel(LogService.WARNING);
  	Yale.getExperiment().prepareRun();
  	IOContainer output = Yale.getExperiment().run();
	checkOutput(output);
    }


    public abstract void checkOutput(IOContainer output) throws MissingIOObjectException;


    public static Test suite() {
	TestSuite suite = new TestSuite("Sample test");
  	suite.addTest(new SimpleSampleTest("Empty.xml"));
  	suite.addTest(new ExampleSetSampleTest("Input.xml", 14, 4));
  	suite.addTest(new ExampleSetSampleTest("ArffTest.xml", 150, 4));
  	suite.addTest(new IOObjectSampleTest("C45ExampleSource.xml", new Class[] { edu.udo.cs.yale.operator.learner.Model.class }));
  	suite.addTest(new ExampleSetSampleTest("Sparse.xml", 4, 30));

  	suite.addTest(new IOObjectSampleTest("DecisionTree.xml", new Class[] { edu.udo.cs.yale.operator.learner.Model.class }));
  	suite.addTest(new PerformanceSampleTest("Weka.xml", 
						new String[] { "accuracy", "precision", "recall" }, 
						new double[] { 0.642857, 0.700000, 0.777778 }));
  	suite.addTest(new IOObjectSampleTest("Bagging.xml", new Class[] { edu.udo.cs.yale.operator.learner.Model.class }));
  	suite.addTest(new IOObjectSampleTest("WekaMetaLearning.xml", new Class[] { edu.udo.cs.yale.operator.learner.Model.class }));

	suite.addTest(new IOObjectSampleTest("ModelWriter.xml", new Class[] { edu.udo.cs.yale.operator.learner.Model.class }));
	suite.addTest(new ExampleSetSampleTest("ModelLoader.xml", 14, 4, 
					       new String[] { edu.udo.cs.yale.example.ExampleSet.PREDICTION_NAME }));
  	suite.addTest(new PerformanceSampleTest("XValidation.xml",
						new String[] { "absolute", "scaled", "squared", "relative" },
						new double[] { 5.155480, 0.008305, 51.974253, 0.3653 }));
  	suite.addTest(new PerformanceSampleTest("MultiClass.xml",
						new String[] { "accuracy", "classification_error" },
						new double[] { 0.755555555, 0.244444444 }));
  	suite.addTest(new PerformanceSampleTest("EnsembleLearning.xml",
						new String[] { "absolute", "scaled" },
						new double[] { 22.321238, 0.012639 }));
  	suite.addTest(new SimpleSampleTest("LearningCurve.xml"));

  	suite.addTest(new ExampleSetSampleTest("MissingValueReplenishment.xml", 40, 16));
  	suite.addTest(new ExampleSetSampleTest("Noise.xml", 200, 8));
  	suite.addTest(new PerformanceSampleTest("ForwardSelection.xml",
						new String[] { "relative" },
						new double[] { 0.39618193 }));
  	suite.addTest(new ExampleSetSampleTest("FeatureGeneration.xml", 200, 8));
  	suite.addTest(new PerformanceSampleTest("FeatureWeighting.xml",
						new String[] { "accuracy", "precision", "recall" },
						new double[] { 0.910000, 0.91416309, 0.894957 }));
	suite.addTest(new PerformanceSampleTest("YAGGA.xml",
						new String[] { "relative" },
						new double[] { 0.386400 }));	
	suite.addTest(new ExampleSetSampleTest("YAGGAResultAttributeSetting.xml", 200, 8));
	suite.addTest(new ExampleSetSampleTest("PrincipalComponents.xml", 150, 2));
	suite.addTest(new ExampleSetSampleTest("Obfuscation.xml", 14, 4));
	suite.addTest(new IOObjectSampleTest("WekaAttributeWeighting.xml", 
					     new Class[] { 
						 edu.udo.cs.yale.example.ExampleSet.class,
						 edu.udo.cs.yale.example.AttributeWeights.class
					     }));
	
  	suite.addTest(new PerformanceSampleTest("ParameterOptimization.xml",
						new String[] { "absolute", "scaled", "squared" },
						new double[] { 8.377710, 0.016019, 169.584314 }));
  	suite.addTest(new SimpleSampleTest("ParameterSetter.xml"));

	return suite;
    }

}