reservoirsample.java

来自「Weka」· Java 代码 · 共 398 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.
 */

/*
 *    ReservoirSample.java
 *    Copyright (C) 2007 Pentaho Corporation
 *
 */

package weka.filters.unsupervised.instance;

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.UnsupervisedFilter;

import java.util.Collections;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;

/** 
 <!-- globalinfo-start -->
 * Produces a random subsample of a dataset using the reservoir sampling 
 * algorithm "R" of Vitter. The original data set does not have to fit 
 * into main memory, only the resevoir does.
 * <br/>
 * For more information, see:<br/>
 * <br/>
 *  Vitter, J. S. Random Sampling with a Reservoir. ACM
 * Transactions on Mathematical Software, Vol. 11, No. 1,
 * March 1985. Pages 37-57.
 * <p/>
 <!-- globalinfo-end -->
 * 
 <!-- technical-bibtex-start -->
 * BibTeX:
 * <pre>
 * &#64;article{Vitter1985,
 *    author = {J. S. Vitter},
 *    journal = {ACM Transactions on Mathematical Software},
 *    number = {1}
 *    volume = {11}
 *    pages = {37-57},
 *    title = {Random Sampling with a Reservoir},
 *    year = {1985}
 * }
 * </pre>
 * </p>
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -S &lt;num&gt;
 *  Specify the random number seed (default 1)</pre>
 * 
 * <pre> -Z &lt;num&gt;
 *  The size of the output dataset, as a percentage of
 *  the input dataset (default 100)</pre>
 * 
 * 
 <!-- options-end -->
 *
 * @author Mark Hall (mhall{[at]}pentaho{[dot]}org)
 * @version $Revision: 1.2 $ 
 */
public class ReservoirSample 
  extends Filter 
  implements UnsupervisedFilter, OptionHandler {
  
  /** for serialization */
  static final long serialVersionUID = 3119607037607101160L;

  /** The subsample size, number of instances% */
  protected int m_SampleSize = 100;

  /** Holds the sub-sample (reservoir) */
  protected Instance[] m_subSample;

  /** The current instance being processed */
  protected int m_currentInst;
  
  /** The random number generator seed */
  protected int m_RandomSeed = 1;

  /** The random number generator */
  protected Random m_random;
  
  /**
   * Returns a string describing this filter
   * @return a description of the classifier suitable for
   * displaying in the explorer/experimenter gui
   */
  public String globalInfo() {
    return 
        "Produces a random subsample of a dataset using the reservoir sampling "
      + "Algorithm \"R\" by Vitter. The original data set does not have to fit "
      + "into main memory, but the reservoir does. ";
  }

  /**
   * Returns an enumeration describing the available options.
   *
   * @return an enumeration of all the available options.
   */
  public Enumeration listOptions() {
    Vector result = new Vector();

    result.addElement(new Option(
	"\tSpecify the random number seed (default 1)",
	"S", 1, "-S <num>"));

    result.addElement(new Option(
	"\tThe size of the output dataset - number of instances\n"
	+"\t(default 100)",
	"Z", 1, "-Z <num>"));

    return result.elements();
  }


  /**
   * Parses a given list of options. <p/>
   * 
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -S &lt;num&gt;
   *  Specify the random number seed (default 1)</pre>
   * 
   * <pre> -Z &lt;num&gt;
   *  The size of the output dataset, as a percentage of
   *  the input dataset (default 100)</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	tmpStr;
    
    tmpStr = Utils.getOption('S', options);
    if (tmpStr.length() != 0) {
      setRandomSeed(Integer.parseInt(tmpStr));
    } else {
      setRandomSeed(1);
    }

    tmpStr = Utils.getOption('Z', options);
    if (tmpStr.length() != 0) {
      setSampleSize(Integer.parseInt(tmpStr));
    } else {
      setSampleSize(100);
    }
  }

  /**
   * Gets the current settings of the filter.
   *
   * @return an array of strings suitable for passing to setOptions
   */
  public String [] getOptions() {
    Vector<String>	result;

    result = new Vector<String>();

    result.add("-S");
    result.add("" + getRandomSeed());

    result.add("-Z");
    result.add("" + getSampleSize());
    
    return result.toArray(new String[result.size()]);
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String randomSeedTipText() {
    return "The seed used for random sampling.";
  }

  /**
   * 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;
  }
  
  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String sampleSizeTipText() {
    return "Size of the subsample (reservoir). i.e. the number of instances.";
  }

  /**
   * Gets the subsample size.
   *
   * @return the subsample size
   */
  public int getSampleSize() {
    return m_SampleSize;
  }
  
  /**
   * Sets the size of the subsample.
   *
   * @param newSampleSize size of the subsample.
   */
  public void setSampleSize(int newSampleSize) {
    m_SampleSize = newSampleSize;
  }
  
  
  /** 
   * Returns the Capabilities of this filter.
   *
   * @return            the capabilities of this object
   * @see               Capabilities
   */
  public Capabilities getCapabilities() {
    Capabilities result = super.getCapabilities();

    // attributes
    result.enableAllAttributes();
    result.enable(Capability.MISSING_VALUES);
    
    // class
    result.enableAllClasses();
    result.enable(Capability.MISSING_CLASS_VALUES);
    result.enable(Capability.NO_CLASS);
    
    return result;
  }

  /**
   * 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
   * @throws Exception if the input format can't be set 
   * successfully
   */
  public boolean setInputFormat(Instances instanceInfo) 
       throws Exception {

    super.setInputFormat(instanceInfo);
    setOutputFormat(instanceInfo);

    m_subSample = new Instance[m_SampleSize];
    m_currentInst = 0;
    m_random = new Random(m_RandomSeed);

    return true;
  }

  /**
   * Decides whether the current instance gets retained in the
   * reservoir.
   *
   * @param instance the Instance to potentially retain
   */
  protected void processInstance(Instance instance) {
    if (m_currentInst < m_SampleSize) {
      m_subSample[m_currentInst] = (Instance)instance.copy();
    } else {
      double r = m_random.nextDouble();
      if (r < ((double)m_SampleSize / (double)m_currentInst)) {
        r = m_random.nextDouble();
        int replace = (int)((double)m_SampleSize * r);
        m_subSample[replace] = (Instance)instance.copy();
      }
    }
    m_currentInst++;
  }

  /**
   * 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().
   * @throws 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 (isFirstBatchDone()) {
      push(instance);
      return true;
    } else {
      //      bufferInput(instance);
      copyValues(instance, false);
      processInstance(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
   * @throws IllegalStateException if no input structure has been defined
   */
  public boolean batchFinished() {

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

    if (!isFirstBatchDone()) {
      // 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.
   */
  protected void createSubsample() {

    for (int i = 0; i < m_SampleSize; i++) {
      if (m_subSample[i] != null) {
        Instance copy = (Instance) m_subSample[i].copy();
        push(copy);
      } else {
        // less data in the original than was asked for
        // as a sample.
        break;
      }
    }
    m_subSample = null;
  }
  
  /**
   * Main method for testing this class.
   *
   * @param argv should contain arguments to the filter: 
   * use -h for help
   */
  public static void main(String [] argv) {
    runFilter(new ReservoirSample(), argv);
  }
}