discretize.java

一个数据挖掘软件ALPHAMINERR的整个过程的JAVA版源代码

/*
 *    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.
 */

/*
 *    Discretize.java
 *    Copyright (C) 1999 Eibe Frank,Len Trigg
 *
 */


package weka.filters.supervised.attribute;

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

import weka.core.Attribute;
import weka.core.ContingencyTables;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.Range;
import weka.core.SparseInstance;
import weka.core.SpecialFunctions;
import weka.core.UnassignedClassException;
import weka.core.UnsupportedClassTypeException;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;
import weka.filters.Filter;
import weka.filters.SupervisedFilter;

/** 
 * An instance filter that discretizes a range of numeric attributes in 
 * the dataset into nominal attributes. Discretization is by 
 * Fayyad & Irani's MDL method (the default).<p>
 *
 * Valid filter-specific options are: <p>
 *
 * -R col1,col2-col4,... <br>
 * Specifies list of columns to Discretize. First
 * and last are valid indexes. (default: none) <p>
 *
 * -V <br>
 * Invert matching sense.<p>
 *
 * -D <br>
 * Make binary nominal attributes. <p>
 *
 * -E <br>
 * Use better encoding of split point for MDL. <p>
 *   
 * -K <br>
 * Use Kononeko's MDL criterion. <p>
 * 
 * @author Len Trigg ([e-mail])
 * @author Eibe Frank ([e-mail])
 * @version $Revision$
 */
public class Discretize extends Filter 
  implements SupervisedFilter, OptionHandler, WeightedInstancesHandler {

  /** Stores which columns to Discretize */
  protected Range m_DiscretizeCols = new Range();

  /** Store the current cutpoints */
  protected double [][] m_CutPoints = null;

  /** Output binary attributes for discretized attributes. */
  protected boolean m_MakeBinary = false;

  /** Use better encoding of split point for MDL. */
  protected boolean m_UseBetterEncoding = false;

  /** Use Kononenko's MDL criterion instead of Fayyad et al.'s */
  protected boolean m_UseKononenko = false;

  /** Constructor - initialises the filter */
  public Discretize() {

    setAttributeIndices("first-last");
  }


  /**
   * Gets an enumeration describing the available options.
   *
   * @return an enumeration of all the available options.
   */
  public Enumeration listOptions() {

    Vector newVector = new Vector(7);

    newVector.addElement(new Option(
              "\tSpecifies list of columns to Discretize. First"
	      + " and last are valid indexes.\n"
	      + "\t(default none)",
              "R", 1, "-R <col1,col2-col4,...>"));

    newVector.addElement(new Option(
              "\tInvert matching sense of column indexes.",
              "V", 0, "-V"));

    newVector.addElement(new Option(
              "\tOutput binary attributes for discretized attributes.",
              "D", 0, "-D"));

    newVector.addElement(new Option(
              "\tUse better encoding of split point for MDL.",
              "E", 0, "-E"));

    newVector.addElement(new Option(
              "\tUse Kononenko's MDL criterion.",
              "K", 0, "-K"));

    return newVector.elements();
  }


  /**
   * Parses the options for this object. Valid options are: <p>
   *
   * -R col1,col2-col4,... <br>
   * Specifies list of columns to Discretize. First
   * and last are valid indexes. (default none) <p>
   *
   * -V <br>
   * Invert matching sense.<p>
   *
   * -D <br>
   * Make binary nominal attributes. <p>
   *
   * -E <br>
   * Use better encoding of split point for MDL. <p>
   *   
   * -K <br>
   * Use Kononeko's MDL criterion. <p>
   * 
   * @param options the list of options as an array of strings
   * @exception Exception if an option is not supported
   */
  public void setOptions(String[] options) throws Exception {

    setMakeBinary(Utils.getFlag('D', options));
    setUseBetterEncoding(Utils.getFlag('E', options));
    setUseKononenko(Utils.getFlag('K', options));
    setInvertSelection(Utils.getFlag('V', options));
    
    String convertList = Utils.getOption('R', options);
    if (convertList.length() != 0) {
      setAttributeIndices(convertList);
    } else {
      setAttributeIndices("first-last");
    }

    if (getInputFormat() != null) {
      setInputFormat(getInputFormat());
    }
  }
  /**
   * Gets the current settings of the filter.
   *
   * @return an array of strings suitable for passing to setOptions
   */
  public String [] getOptions() {

    String [] options = new String [12];
    int current = 0;

    if (getMakeBinary()) {
      options[current++] = "-D";
    }
    if (getUseBetterEncoding()) {
      options[current++] = "-E";
    }
    if (getUseKononenko()) {
      options[current++] = "-K";
    }
    if (getInvertSelection()) {
      options[current++] = "-V";
    }
    if (!getAttributeIndices().equals("")) {
      options[current++] = "-R"; options[current++] = getAttributeIndices();
    }
    while (current < options.length) {
      options[current++] = "";
    }
    return options;
  }


  /**
   * Sets the format of the input instances.
   *
   * @param instanceInfo an Instances object containing the input instance
   * structure (any instances contained in the object are ignored - only the
   * structure is required).
   * @return true if the outputFormat may be collected immediately
   * @exception Exception if the input format can't be set successfully
   */
  public boolean setInputFormat(Instances instanceInfo) throws Exception {

    super.setInputFormat(instanceInfo);

    m_DiscretizeCols.setUpper(instanceInfo.numAttributes() - 1);
    m_CutPoints = null;
    
    if (instanceInfo.classIndex() < 0) {
      throw new UnassignedClassException("Cannot use class-based discretization: "
					 + "no class assigned to the dataset");
    }
    if (!instanceInfo.classAttribute().isNominal()) {
      throw new UnsupportedClassTypeException("Supervised discretization not possible:"
					      + " class is not nominal!");
    }

    // If we implement loading cutfiles, then load 
    //them here and set the output format
    return false;
  }

  

  /**
   * Input an instance for filtering. Ordinarily the instance is processed
   * and made available for output immediately. Some filters require all
   * instances be read before producing output.
   *
   * @param instance the input instance
   * @return true if the filtered instance may now be
   * collected with output().
   * @exception IllegalStateException if no input format has been defined.
   */
  public boolean input(Instance instance) {

    if (getInputFormat() == null) {
      throw new IllegalStateException("No input instance format defined");
    }
    if (m_NewBatch) {
      resetQueue();
      m_NewBatch = false;
    }
    
    if (m_CutPoints != null) {
      convertInstance(instance);
      return true;
    }

    bufferInput(instance);
    return false;
  }


  /**
   * Signifies that this batch of input to the filter is finished. If the 
   * filter requires all instances prior to filtering, output() may now 
   * be called to retrieve the filtered instances.
   *
   * @return true if there are instances pending output
   * @exception IllegalStateException if no input structure has been defined
   */
  public boolean batchFinished() {

    if (getInputFormat() == null) {
      throw new IllegalStateException("No input instance format defined");
    }
    if (m_CutPoints == null) {
      calculateCutPoints();

      setOutputFormat();

      // If we implement saving cutfiles, save the cuts here

      // Convert pending input instances
      for(int i = 0; i < getInputFormat().numInstances(); i++) {
	convertInstance(getInputFormat().instance(i));
      }
    } 
    flushInput();

    m_NewBatch = true;
    return (numPendingOutput() != 0);
  }

  /**
   * Returns a string describing this filter
   *
   * @return a description of the filter suitable for
   * displaying in the explorer/experimenter gui
   */
  public String globalInfo() {

    return "An instance filter that discretizes a range of numeric"
      + " attributes in the dataset into nominal attributes."
      + " Discretization is by Fayyad & Irani's MDL method (the default).";
  }
  
  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String makeBinaryTipText() {

    return "Make resulting attributes binary.";
  }

  /**
   * Gets whether binary attributes should be made for discretized ones.
   *
   * @return true if attributes will be binarized
   */
  public boolean getMakeBinary() {

    return m_MakeBinary;
  }

  /** 
   * Sets whether binary attributes should be made for discretized ones.
   *
   * @param makeBinary if binary attributes are to be made
   */
  public void setMakeBinary(boolean makeBinary) {

    m_MakeBinary = makeBinary;
  }

  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String useKononenkoTipText() {

    return "Use Kononenko's MDL criterion. If set to false"
      + " uses the Fayyad & Irani criterion.";
  }
  
  /**
   * Gets whether Kononenko's MDL criterion is to be used.
   *
   * @return true if Kononenko's criterion will be used.
   */
  public boolean getUseKononenko() {

    return m_UseKononenko;
  }

  /** 
   * Sets whether Kononenko's MDL criterion is to be used.
   *
   * @param useKon true if Kononenko's one is to be used
   */
  public void setUseKononenko(boolean useKon) {

    m_UseKononenko = useKon;
  }

  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String useBetterEncodingTipText() {

    return "Uses a more efficient split point encoding.";
  }

  /**
   * Gets whether better encoding is to be used for MDL.
   *
   * @return true if the better MDL encoding will be used
   */
  public boolean getUseBetterEncoding() {

    return m_UseBetterEncoding;
  }

  /** 
   * Sets whether better encoding is to be used for MDL.
   *
   * @param useBetterEncoding true if better encoding to be used.
   */
  public void setUseBetterEncoding(boolean useBetterEncoding) {

    m_UseBetterEncoding = useBetterEncoding;
  }

  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String invertSelectionTipText() {

    return "Set attribute selection mode. If false, only selected"
      + " (numeric) attributes in the range will be discretized; if"
      + " true, only non-selected attributes will be discretized.";
  }

  /**
   * Gets whether the supplied columns are to be removed or kept
   *
   * @return true if the supplied columns will be kept
   */
  public boolean getInvertSelection() {

    return m_DiscretizeCols.getInvert();
  }

  /**
   * Sets whether selected columns should be removed or kept. If true the 
   * selected columns are kept and unselected columns are deleted. If false
   * selected columns are deleted and unselected columns are kept.
   *
   * @param invert the new invert setting
   */
  public void setInvertSelection(boolean invert) {

    m_DiscretizeCols.setInvert(invert);
  }

  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String attributeIndicesTipText() {
    return "Specify range of attributes to act on."
      + " This is a comma separated list of attribute indices, with"
      + " \"first\" and \"last\" valid values. Specify an inclusive"
      + " range with \"-\". E.g: \"first-3,5,6-10,last\".";
  }

  /**
   * Gets the current range selection
   *
   * @return a string containing a comma separated list of ranges
   */
  public String getAttributeIndices() {

    return m_DiscretizeCols.getRanges();