part.java

代码是一个分类器的实现,其中使用了部分weka的源代码。可以将项目导入eclipse运行

/*
 *    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.
 */

/*
 *    PART.java
 *    Copyright (C) 1999 Eibe Frank
 *
 */

package weka.classifiers.rules;

import weka.classifiers.Classifier;
import weka.classifiers.rules.part.MakeDecList;
import weka.classifiers.trees.j48.BinC45ModelSelection;
import weka.classifiers.trees.j48.C45ModelSelection;
import weka.classifiers.trees.j48.ModelSelection;
import weka.core.AdditionalMeasureProducer;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.Summarizable;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;

import java.util.Enumeration;
import java.util.Vector;

/**
 <!-- globalinfo-start -->
 * Class for generating a PART decision list. Uses separate-and-conquer. Builds a partial C4.5 decision tree in each iteration and makes the "best" leaf into a rule.<br/>
 * <br/>
 * For more information, see:<br/>
 * <br/>
 * Eibe Frank, Ian H. Witten: Generating Accurate Rule Sets Without Global Optimization. In: Fifteenth International Conference on Machine Learning, 144-151, 1998.
 * <p/>
 <!-- globalinfo-end -->
 *
 <!-- technical-bibtex-start -->
 * BibTeX:
 * <pre>
 * &#64;inproceedings{Frank1998,
 *    author = {Eibe Frank and Ian H. Witten},
 *    booktitle = {Fifteenth International Conference on Machine Learning},
 *    editor = {J. Shavlik},
 *    pages = {144-151},
 *    publisher = {Morgan Kaufmann},
 *    title = {Generating Accurate Rule Sets Without Global Optimization},
 *    year = {1998},
 *    PS = {http://www.cs.waikato.ac.nz/~eibe/pubs/ML98-57.ps.gz}
 * }
 * </pre>
 * <p/>
 <!-- technical-bibtex-end -->
 *
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -C &lt;pruning confidence&gt;
 *  Set confidence threshold for pruning.
 *  (default 0.25)</pre>
 * 
 * <pre> -M &lt;minimum number of objects&gt;
 *  Set minimum number of objects per leaf.
 *  (default 2)</pre>
 * 
 * <pre> -R
 *  Use reduced error pruning.</pre>
 * 
 * <pre> -N &lt;number of folds&gt;
 *  Set number of folds for reduced error
 *  pruning. One fold is used as pruning set.
 *  (default 3)</pre>
 * 
 * <pre> -B
 *  Use binary splits only.</pre>
 * 
 * <pre> -U
 *  Generate unpruned decision list.</pre>
 * 
 * <pre> -Q &lt;seed&gt;
 *  Seed for random data shuffling (default 1).</pre>
 * 
 <!-- options-end -->
 *
 * @author Eibe Frank ([e-mail])
 * @version $Revision: 1.7 $
 */
public class PART 
  extends Classifier 
  implements OptionHandler, WeightedInstancesHandler, Summarizable, 
             AdditionalMeasureProducer, TechnicalInformationHandler {

  /** for serialization */
  static final long serialVersionUID = 8121455039782598361L;
  
  /** The decision list */
  private MakeDecList m_root;

  /** Confidence level */
  private float m_CF = 0.25f;

  /** Minimum number of objects */
  private int m_minNumObj = 2;

  /** Use reduced error pruning? */
  private boolean m_reducedErrorPruning = false;

  /** Number of folds for reduced error pruning. */
  private int m_numFolds = 3;

  /** Binary splits on nominal attributes? */
  private boolean m_binarySplits = false;
  
  /** Generate unpruned list? */
  private boolean m_unpruned = false;

  /** The seed for random number generation. */
  private int m_Seed = 1;
    
  /**
   * Returns a string describing classifier
   * @return a description suitable for
   * displaying in the explorer/experimenter gui
   */
  public String globalInfo() {

    return  "Class for generating a PART decision list. Uses "
      + "separate-and-conquer. Builds a partial C4.5 decision tree "
      + "in each iteration and makes the \"best\" leaf into a rule.\n\n"
      + "For more information, see:\n\n"
      + getTechnicalInformation().toString();
  }

  /**
   * Returns an instance of a TechnicalInformation object, containing 
   * detailed information about the technical background of this class,
   * e.g., paper reference or book this class is based on.
   * 
   * @return the technical information about this class
   */
  public TechnicalInformation getTechnicalInformation() {
    TechnicalInformation 	result;
    
    result = new TechnicalInformation(Type.INPROCEEDINGS);
    result.setValue(Field.AUTHOR, "Eibe Frank and Ian H. Witten");
    result.setValue(Field.TITLE, "Generating Accurate Rule Sets Without Global Optimization");
    result.setValue(Field.BOOKTITLE, "Fifteenth International Conference on Machine Learning");
    result.setValue(Field.EDITOR, "J. Shavlik");
    result.setValue(Field.YEAR, "1998");
    result.setValue(Field.PAGES, "144-151");
    result.setValue(Field.PUBLISHER, "Morgan Kaufmann");
    result.setValue(Field.PS, "http://www.cs.waikato.ac.nz/~eibe/pubs/ML98-57.ps.gz");
    
    return result;
  }

  /**
   * Returns default capabilities of the classifier.
   *
   * @return      the capabilities of this classifier
   */
  public Capabilities getCapabilities() {
    Capabilities      result;

    if (m_unpruned) 
      result = new MakeDecList(null, m_minNumObj).getCapabilities();
    else if (m_reducedErrorPruning) 
      result = new MakeDecList(null, m_numFolds, m_minNumObj, m_Seed).getCapabilities();
    else
      result = new MakeDecList(null, m_CF, m_minNumObj).getCapabilities();
    
    return result;
  }

  /**
   * Generates the classifier.
   *
   * @param instances the data to train with
   * @throws Exception if classifier can't be built successfully
   */
  public void buildClassifier(Instances instances) 
       throws Exception {

    // can classifier handle the data?
    getCapabilities().testWithFail(instances);

    // remove instances with missing class
    instances = new Instances(instances);
    instances.deleteWithMissingClass();
    
    ModelSelection modSelection;	 

    if (m_binarySplits)
      modSelection = new BinC45ModelSelection(m_minNumObj, instances);
    else
      modSelection = new C45ModelSelection(m_minNumObj, instances);
    if (m_unpruned) 
      m_root = new MakeDecList(modSelection, m_minNumObj);
    else if (m_reducedErrorPruning) 
      m_root = new MakeDecList(modSelection, m_numFolds, m_minNumObj, m_Seed);
    else
      m_root = new MakeDecList(modSelection, m_CF, m_minNumObj);
    m_root.buildClassifier(instances);
    if (m_binarySplits) {
      ((BinC45ModelSelection)modSelection).cleanup();
    } else {
      ((C45ModelSelection)modSelection).cleanup();
    }
  }

  /**
   * Classifies an instance.
   *
   * @param instance the instance to classify
   * @return the classification
   * @throws Exception if instance can't be classified successfully
   */
  public double classifyInstance(Instance instance) 
       throws Exception {

    return m_root.classifyInstance(instance);
  }

  /** 
   * Returns class probabilities for an instance.
   *
   * @param instance the instance to get the distribution for
   * @return the class probabilities
   * @throws Exception if the distribution can't be computed successfully
   */
  public final double [] distributionForInstance(Instance instance) 
       throws Exception {

    return m_root.distributionForInstance(instance);
  }

  /**
   * Returns an enumeration describing the available options.
   *
   * Valid options are: <p>
   *
   * -C confidence <br>
   * Set confidence threshold for pruning. (Default: 0.25) <p>
   *
   * -M number <br>
   * Set minimum number of instances per leaf. (Default: 2) <p>
   *
   * -R <br>
   * Use reduced error pruning. <p>
   *
   * -N number <br>
   * Set number of folds for reduced error pruning. One fold is
   * used as the pruning set. (Default: 3) <p>
   *
   * -B <br>
   * Use binary splits for nominal attributes. <p>
   *
   * -U <br>
   * Generate unpruned decision list. <p>
   *
   * -Q <br>
   * The seed for reduced-error pruning. <p>
   *
   * @return an enumeration of all the available options.
   */
  public Enumeration listOptions() {

    Vector newVector = new Vector(7);

    newVector.
	addElement(new Option("\tSet confidence threshold for pruning.\n" +
			      "\t(default 0.25)",
			      "C", 1, "-C <pruning confidence>"));
    newVector.
	addElement(new Option("\tSet minimum number of objects per leaf.\n" +
			      "\t(default 2)",
			      "M", 1, "-M <minimum number of objects>"));
    newVector.
	addElement(new Option("\tUse reduced error pruning.",
			      "R", 0, "-R"));
    newVector.
	addElement(new Option("\tSet number of folds for reduced error\n" +
			      "\tpruning. One fold is used as pruning set.\n" +
			      "\t(default 3)",
			      "N", 1, "-N <number of folds>"));
    newVector.
	addElement(new Option("\tUse binary splits only.",
			      "B", 0, "-B"));
    newVector.
	addElement(new Option("\tGenerate unpruned decision list.",
			      "U", 0, "-U"));
    newVector.
      addElement(new Option("\tSeed for random data shuffling (default 1).",
			    "Q", 1, "-Q <seed>"));

    return newVector.elements();
  }

  /**
   * Parses a given list of options. <p/>
   * 
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -C &lt;pruning confidence&gt;
   *  Set confidence threshold for pruning.
   *  (default 0.25)</pre>
   * 
   * <pre> -M &lt;minimum number of objects&gt;
   *  Set minimum number of objects per leaf.
   *  (default 2)</pre>
   * 
   * <pre> -R
   *  Use reduced error pruning.</pre>
   * 
   * <pre> -N &lt;number of folds&gt;
   *  Set number of folds for reduced error
   *  pruning. One fold is used as pruning set.
   *  (default 3)</pre>
   * 
   * <pre> -B
   *  Use binary splits only.</pre>
   * 
   * <pre> -U
   *  Generate unpruned decision list.</pre>
   * 
   * <pre> -Q &lt;seed&gt;
   *  Seed for random data shuffling (default 1).</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 {