classifiersubseteval.java

这是关于数据挖掘的一些算法

/*
 *    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., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 *    ClassifierSubsetEval.java
 *    Copyright (C) 2000 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.attributeSelection;

import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.rules.ZeroR;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.Utils;
import weka.core.Capabilities.Capability;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Remove;

import java.io.File;
import java.util.BitSet;
import java.util.Enumeration;
import java.util.Vector;


/**
 <!-- globalinfo-start -->
 * Classifier subset evaluator:<br/>
 * <br/>
 * Evaluates attribute subsets on training data or a seperate hold out testing set. Uses a classifier to estimate the 'merit' of a set of attributes.
 * <p/>
 <!-- globalinfo-end -->
 *
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -B &lt;classifier&gt;
 *  class name of the classifier to use for accuracy estimation.
 *  Place any classifier options LAST on the command line
 *  following a "--". eg.:
 *   -B weka.classifiers.bayes.NaiveBayes ... -- -K
 *  (default: weka.classifiers.rules.ZeroR)</pre>
 * 
 * <pre> -T
 *  Use the training data to estimate accuracy.</pre>
 * 
 * <pre> -H &lt;filename&gt;
 *  Name of the hold out/test set to 
 *  estimate accuracy on.</pre>
 * 
 * <pre> 
 * Options specific to scheme weka.classifiers.rules.ZeroR:
 * </pre>
 * 
 * <pre> -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console</pre>
 * 
 <!-- options-end -->
 *
 * @author Mark Hall ([e-mail])
 * @version $Revision: 1.17 $
 */
public class ClassifierSubsetEval 
  extends HoldOutSubsetEvaluator
  implements OptionHandler, ErrorBasedMeritEvaluator {
  
  /** for serialization */
  static final long serialVersionUID = 7532217899385278710L;

  /** training instances */
  private Instances m_trainingInstances;

  /** class index */
  private int m_classIndex;

  /** number of attributes in the training data */
  private int m_numAttribs;
  
  /** number of training instances */
  private int m_numInstances;

  /** holds the classifier to use for error estimates */
  private Classifier m_Classifier = new ZeroR();

  /** holds the evaluation object to use for evaluating the classifier */
  private Evaluation m_Evaluation;

  /** the file that containts hold out/test instances */
  private File m_holdOutFile = new File("Click to set hold out or "
					+"test instances");

  /** the instances to test on */
  private Instances m_holdOutInstances = null;

  /** evaluate on training data rather than seperate hold out/test set */
  private boolean m_useTraining = true;

  /**
   * Returns a string describing this attribute evaluator
   * @return a description of the evaluator suitable for
   * displaying in the explorer/experimenter gui
   */
  public String globalInfo() {
    return 
        "Classifier subset evaluator:\n\nEvaluates attribute subsets on training data or a seperate "
      + "hold out testing set. Uses a classifier to estimate the 'merit' of a set of attributes.";
  }

  /**
   * Returns an enumeration describing the available options.
   *
   * @return an enumeration of all the available options.
   **/
  public Enumeration listOptions () {
    Vector newVector = new Vector(3);
    
    newVector.addElement(new Option(
	"\tclass name of the classifier to use for accuracy estimation.\n"
	+ "\tPlace any classifier options LAST on the command line\n"
	+ "\tfollowing a \"--\". eg.:\n"
	+ "\t\t-B weka.classifiers.bayes.NaiveBayes ... -- -K\n"
	+ "\t(default: weka.classifiers.rules.ZeroR)", 
	"B", 1, "-B <classifier>"));
    
    newVector.addElement(new Option(
	"\tUse the training data to estimate"
	+" accuracy.",
	"T",0,"-T"));
    
    newVector.addElement(new Option(
	"\tName of the hold out/test set to "
	+"\n\testimate accuracy on.",
	"H", 1,"-H <filename>"));

    if ((m_Classifier != null) && 
	(m_Classifier instanceof OptionHandler)) {
      newVector.addElement(new Option("", "", 0, "\nOptions specific to " 
				      + "scheme " 
				      + m_Classifier.getClass().getName() 
				      + ":"));
      Enumeration enu = ((OptionHandler)m_Classifier).listOptions();

      while (enu.hasMoreElements()) {
        newVector.addElement(enu.nextElement());
      }
    }

    return  newVector.elements();
  }

  /**
   * Parses a given list of options. <p/>
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -B &lt;classifier&gt;
   *  class name of the classifier to use for accuracy estimation.
   *  Place any classifier options LAST on the command line
   *  following a "--". eg.:
   *   -B weka.classifiers.bayes.NaiveBayes ... -- -K
   *  (default: weka.classifiers.rules.ZeroR)</pre>
   * 
   * <pre> -T
   *  Use the training data to estimate accuracy.</pre>
   * 
   * <pre> -H &lt;filename&gt;
   *  Name of the hold out/test set to 
   *  estimate accuracy on.</pre>
   * 
   * <pre> 
   * Options specific to scheme weka.classifiers.rules.ZeroR:
   * </pre>
   * 
   * <pre> -D
   *  If set, classifier is run in debug mode and
   *  may output additional info to the console</pre>
   * 
   <!-- options-end -->
   *
   * @param options the list of options as an array of strings
   * @throws Exception if an option is not supported
   */
  public void setOptions (String[] options)
    throws Exception {
    String optionString;
    resetOptions();

    optionString = Utils.getOption('B', options);
    if (optionString.length() == 0)
      optionString = ZeroR.class.getName();
    setClassifier(Classifier.forName(optionString,
				     Utils.partitionOptions(options)));

    optionString = Utils.getOption('H',options);
    if (optionString.length() != 0) {
      setHoldOutFile(new File(optionString));
    }

    setUseTraining(Utils.getFlag('T',options));
  }

    /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String classifierTipText() {
    return "Classifier to use for estimating the accuracy of subsets";
  }

  /**
   * Set the classifier to use for accuracy estimation
   *
   * @param newClassifier the Classifier to use.
   */
  public void setClassifier (Classifier newClassifier) {
    m_Classifier = newClassifier;
  }


  /**
   * Get the classifier used as the base learner.
   *
   * @return the classifier used as the classifier
   */
  public Classifier getClassifier () {
    return  m_Classifier;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String holdOutFileTipText() {
    return "File containing hold out/test instances.";
  }

  /**
   * Gets the file that holds hold out/test instances.
   * @return File that contains hold out instances
   */
  public File getHoldOutFile() {
    return m_holdOutFile;
  }


  /**
   * Set the file that contains hold out/test instances
   * @param h the hold out file
   */
  public void setHoldOutFile(File h) {
    m_holdOutFile = h;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String useTrainingTipText() {
    return "Use training data instead of hold out/test instances.";
  }

  /**
   * Get if training data is to be used instead of hold out/test data
   * @return true if training data is to be used instead of hold out data
   */
  public boolean getUseTraining() {
    return m_useTraining;
  }

  /**
   * Set if training data is to be used instead of hold out/test data
   * @param t true if training data is to be used instead of hold out data
   */
  public void setUseTraining(boolean t) {
    m_useTraining = t;
  }

  /**
   * Gets the current settings of ClassifierSubsetEval
   *
   * @return an array of strings suitable for passing to setOptions()
   */
  public String[] getOptions () {
    String[] classifierOptions = new String[0];

    if ((m_Classifier != null) && 
	(m_Classifier instanceof OptionHandler)) {
      classifierOptions = ((OptionHandler)m_Classifier).getOptions();
    }

    String[] options = new String[6 + classifierOptions.length];
    int current = 0;

    if (getClassifier() != null) {
      options[current++] = "-B";
      options[current++] = getClassifier().getClass().getName();
    }

    if (getUseTraining()) {
      options[current++] = "-T";
    }
    options[current++] = "-H"; options[current++] = getHoldOutFile().getPath();

    if (classifierOptions.length > 0) {
      options[current++] = "--";
      System.arraycopy(classifierOptions, 0, options, current, 
	  classifierOptions.length);
      current += classifierOptions.length;
    }

    while (current < options.length) {
	options[current++] = "";
    }

    return  options;
  }

  /**
   * Returns the capabilities of this evaluator.
   *
   * @return            the capabilities of this evaluator
   * @see               Capabilities