spreadsubsample.java

wekaUT是 university texas austin 开发的基于weka的半指导学习(semi supervised learning)的分类器

/*
 *    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.
 */

/*
 *    SpreadSubsample.java
 *    Copyright (C) 2002 University of Waikato
 *
 */


package weka.filters.supervised.instance;

import weka.filters.*;
import java.util.Enumeration;
import java.util.Hashtable;
import java.util.Random;
import java.util.Vector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.Utils;
import weka.core.UnsupportedClassTypeException;

/** 
 * Produces a random subsample of a dataset. The original dataset must
 * fit entirely in memory. This filter allows you to specify the maximum
 * "spread" between the rarest and most common class. For example, you may
 * specify that there be at most a 2:1 difference in class frequencies.
 * When used in batch mode, subsequent batches are
 * <b>not</b> resampled.
 *
 * Valid options are:<p>
 *
 * -S num <br>
 * Specify the random number seed (default 1).<p>
 *
 * -M num <br>
 *  The maximum class distribution spread. <br>
 *  0 = no maximum spread, 1 = uniform distribution, 10 = allow at most a
 *  10:1 ratio between the classes (default 0)
 *  <p>
 *
 * -X num <br>
 *  The maximum count for any class value. <br>
 *  (default 0 = unlimited)
 *  <p>
 *
 * -W <br>
 *  Adjust weights so that total weight per class is maintained. Individual
 *  instance weighting is not preserved. (default no weights adjustment)
 *  <p>
 *
 * @author Stuart Inglis ([e-mail])
 * @version $Revision: 1.1.1.1 $ 
 **/
public class SpreadSubsample extends Filter implements SupervisedFilter,
						       OptionHandler {

  /** The random number generator seed */
  private int m_RandomSeed = 1;

  /** The maximum count of any class */
  private int m_MaxCount;
  
  /** True if the first batch has been done */
  private boolean m_FirstBatchDone = false;

  /** True if the first batch has been done */
  private double m_DistributionSpread = 0;

  /**
   * True if instance weights will be adjusted to maintain
   * total weight per class.
   */
  private boolean m_AdjustWeights = false;
  
  /**
   * Returns true if instance  weights will be adjusted to maintain
   * total weight per class.
   *
   * @return true if instance weights will be adjusted to maintain
   * total weight per class.
   */
  public boolean getAdjustWeights() {

    return m_AdjustWeights;
  }
  
  /**
   * Sets whether the instance weights will be adjusted to maintain
   * total weight per class.
   *
   * @param newAdjustWeights
   */
  public void setAdjustWeights(boolean newAdjustWeights) {

    m_AdjustWeights = newAdjustWeights;
  }
  
  /**
   * Returns an enumeration describing the available options.
   *
   * @return an enumeration of all the available options.
   */
  public Enumeration listOptions() {

    Vector newVector = new Vector(4);

    newVector.addElement(new Option(
              "\tSpecify the random number seed (default 1)",
              "S", 1, "-S <num>"));
    newVector.addElement(new Option(
              "\tThe maximum class distribution spread.\n"
              +"\t0 = no maximum spread, 1 = uniform distribution, 10 = allow at most\n"
	      +"\ta 10:1 ratio between the classes (default 0)",
              "M", 1, "-M <num>"));
    newVector.addElement(new Option(
              "\tAdjust weights so that total weight per class is maintained.\n"
              +"\tIndividual instance weighting is not preserved. (default no\n"
              +"\tweights adjustment",
              "W", 0, "-W"));
    newVector.addElement(new Option(
	      "\tThe maximum count for any class value (default 0 = unlimited).\n",
              "X", 0, "-X <num>"));

    return newVector.elements();
  }


  /**
   * Parses a list of options for this object. Valid options are:<p>
   *
   * -S num <br>
   * Specify the random number seed (default 1).<p>
   *
   * -M num <br>
   *  The maximum class distribution spread. <br>
   *  0 = no maximum spread, 1 = uniform distribution, 10 = allow at most a
   *  10:1 ratio between the classes (default 0)
   *  <p>
   *
   * -X num <br>
   *  The maximum count for any class value. <br>
   *  (default 0 = unlimited)
   *  <p>
   *
   * -W <br>
   *  Adjust weights so that total weight per class is maintained. Individual
   *  instance weighting is not preserved. (default no weights adjustment)
   *  <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 {
    
    String seedString = Utils.getOption('S', options);
    if (seedString.length() != 0) {
      setRandomSeed(Integer.parseInt(seedString));
    } else {
      setRandomSeed(1);
    }

    String maxString = Utils.getOption('M', options);
    if (maxString.length() != 0) {
      setDistributionSpread(Double.valueOf(maxString).doubleValue());
    } else {
      setDistributionSpread(0);
    }

    String maxCount = Utils.getOption('X', options);
    if (maxCount.length() != 0) {
      setMaxCount(Double.valueOf(maxCount).doubleValue());
    } else {
      setMaxCount(0);
    }

    setAdjustWeights(Utils.getFlag('W', options));

    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 [7];
    int current = 0;

    options[current++] = "-M"; 
    options[current++] = "" + getDistributionSpread();

    options[current++] = "-X"; 
    options[current++] = "" + getMaxCount();

    options[current++] = "-S"; 
    options[current++] = "" + getRandomSeed();

    if (getAdjustWeights()) {
      options[current++] = "-W";
    }

    while (current < options.length) {
      options[current++] = "";
    }
    return options;
  }
  
  
  /**
   * Sets the value for the distribution spread
   *
   * @param spread the new distribution spread
   */
  public void setDistributionSpread(double spread) {

    m_DistributionSpread = spread;
  }

  /**
   * Gets the value for the distribution spread
   *
   * @return the distribution spread
   */    
  public double getDistributionSpread() {

    return m_DistributionSpread;
  }
  
  /**
   * Sets the value for the max count
   *
   * @param spread the new max count
   */
  public void setMaxCount(double maxcount) {

    m_MaxCount = (int)maxcount;
  }

  /**
   * Gets the value for the max count
   *
   * @return the max count
   */    
  public double getMaxCount() {

    return m_MaxCount;
  }
  
  /**
   * Gets the random number seed.
   *
   * @return the random number seed.
   */
  public int getRandomSeed() {

    return m_RandomSeed;
  }
  
  /**
   * Sets the random number seed.
   *
   * @param newSeed the new random number seed.
   */
  public void setRandomSeed(int newSeed) {

    m_RandomSeed = newSeed;
  }
  
  /**
   * 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 UnassignedClassException if no class attribute has been set.
   * @exception UnsupportedClassTypeException if the class attribute
   * is not nominal. 
   */
  public boolean setInputFormat(Instances instanceInfo) 
       throws Exception {

    super.setInputFormat(instanceInfo);
    if (instanceInfo.classAttribute().isNominal() == false) {
      throw new UnsupportedClassTypeException("The class attribute must be nominal.");
    }
    setOutputFormat(instanceInfo);
    m_FirstBatchDone = false;
    return true;
  }

  /**
   * Input an instance for filtering. Filter requires all
   * training 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 structure 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_FirstBatchDone) {
      push(instance);
      return true;
    } else {
      bufferInput(instance);
      return false;
    }
  }

  /**
   * Signify 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_FirstBatchDone) {
      // Do the subsample, and clear the input instances.
      createSubsample();
    }

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


  /**
   * Creates a subsample of the current set of input instances. The output
   * instances are pushed onto the output queue for collection.
   */
  private void createSubsample() {

    int classI = getInputFormat().classIndex();
    // Sort according to class attribute.
    getInputFormat().sort(classI);
    // Determine where each class starts in the sorted dataset
    int [] classIndices = getClassIndices();

    // Get the existing class distribution
    int [] counts = new int [getInputFormat().numClasses()];
    double [] weights = new double [getInputFormat().numClasses()];
    int min = -1;
    for (int i = 0; i < getInputFormat().numInstances(); i++) {
      Instance current = getInputFormat().instance(i);
      if (current.classIsMissing() == false) {
        counts[(int)current.classValue()]++;
        weights[(int)current.classValue()]+= current.weight();
      }
    }

    // Convert from total weight to average weight
    for (int i = 0; i < counts.length; i++) {
      if (counts[i] > 0) {
        weights[i] = weights[i] / counts[i];
      }
      /*
      System.err.println("Class:" + i + " " + getInputFormat().classAttribute().value(i)
                         + " Count:" + counts[i]
                         + " Total:" + weights[i] * counts[i]
                         + " Avg:" + weights[i]);
      */
    }
    
    // find the class with the minimum number of instances
    for (int i = 0; i < counts.length; i++) {
      if ( (min < 0) && (counts[i] > 0) ) {
        min = counts[i];
      } else if ((counts[i] < min) && (counts[i] > 0)) {
        min = counts[i];
      }
    }

    if (min < 0) { 
	System.err.println("SpreadSubsample: *warning* none of the classes have any values in them.");
	return;
    }

    // determine the new distribution 
    int [] new_counts = new int [getInputFormat().numClasses()];
    for (int i = 0; i < counts.length; i++) {
      new_counts[i] = (int)Math.abs(Math.min(counts[i],
                                             min * m_DistributionSpread));
      if (m_DistributionSpread == 0) {
        new_counts[i] = counts[i];
      }

      if (m_MaxCount > 0) {
        new_counts[i] = Math.min(new_counts[i], m_MaxCount);
      }
    }

    // Sample without replacement
    Random random = new Random(m_RandomSeed);
    Hashtable t = new Hashtable();
    for (int j = 0; j < new_counts.length; j++) {
      double newWeight = 1.0;
      if (m_AdjustWeights && (new_counts[j] > 0)) {
        newWeight = weights[j] * counts[j] / new_counts[j];
        /*
        System.err.println("Class:" + j + " " + getInputFormat().classAttribute().value(j) 
                           + " Count:" + counts[j]
                           + " Total:" + weights[j] * counts[j]
                           + " Avg:" + weights[j]
                           + " NewCount:" + new_counts[j]
                           + " NewAvg:" + newWeight);
        */
      }
      for (int k = 0; k < new_counts[j]; k++) {
        boolean ok = false;
        do {
	  int index = classIndices[j] + (Math.abs(random.nextInt()) 
                                         % (classIndices[j + 1] - classIndices[j])) ;
	  // Have we used this instance before?
          if (t.get("" + index) == null) {
            // if not, add it to the hashtable and use it
            t.put("" + index, "");
            ok = true;
	    if(index >= 0) {
              Instance newInst = (Instance)getInputFormat().instance(index).copy();
              if (m_AdjustWeights) {
                newInst.setWeight(newWeight);
              }
              push(newInst);
            }
          }
        } while (!ok);
      }
    }
  }

  /**
   * Creates an index containing the position where each class starts in 
   * the getInputFormat(). m_InputFormat must be sorted on the class attribute.
   */
  private int []getClassIndices() {

    // Create an index of where each class value starts
    int [] classIndices = new int [getInputFormat().numClasses() + 1];
    int currentClass = 0;
    classIndices[currentClass] = 0;
    for (int i = 0; i < getInputFormat().numInstances(); i++) {
      Instance current = getInputFormat().instance(i);
      if (current.classIsMissing()) {
        for (int j = currentClass + 1; j < classIndices.length; j++) {
          classIndices[j] = i;
        }
        break;
      } else if (current.classValue() != currentClass) {
        for (int j = currentClass + 1; j <= current.classValue(); j++) {
          classIndices[j] = i;
        }          
        currentClass = (int) current.classValue();
      }
    }
    if (currentClass <= getInputFormat().numClasses()) {
      for (int j = currentClass + 1; j < classIndices.length; j++) {
        classIndices[j] = getInputFormat().numInstances();
      }
    }
    return classIndices;
  }


  /**
   * Main method for testing this class.
   *
   * @param argv should contain arguments to the filter: 
   * use -h for help
   */
  public static void main(String [] argv) {

    try {
      if (Utils.getFlag('b', argv)) {
 	Filter.batchFilterFile(new SpreadSubsample(), argv);
      } else {
	Filter.filterFile(new SpreadSubsample(), argv);
      }
    } catch (Exception ex) {
		ex.printStackTrace();
      System.out.println(ex.getMessage());
    }
  }
}