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The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This version represents the developer version, the "bleeding edge" of development, you could say. New functionality gets added to this version.
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/*
 *   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 3 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, see .
 */

/*
 *    RandomSubSpace.java
 *    Copyright (C) 2006-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.meta;

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

import weka.classifiers.Classifier;
import weka.classifiers.RandomizableParallelIteratedSingleClassifierEnhancer;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.Randomizable;
import weka.core.RevisionUtils;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;
import weka.filters.unsupervised.attribute.Remove;

/**
 
 * This method constructs a decision tree based classifier that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity. The classifier consists of multiple trees constructed systematically by pseudorandomly selecting subsets of components of the feature vector, that is, trees constructed in randomly chosen subspaces.

 * 

 * For more information, see

 * 

 * Tin Kam Ho (1998). The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20(8):832-844. URL http://citeseer.ist.psu.edu/ho98random.html.
 * 
 
 *
 
 * BibTeX:
 * 
 * @article{Ho1998,
 *    author = {Tin Kam Ho},
 *    journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
 *    number = {8},
 *    pages = {832-844},
 *    title = {The Random Subspace Method for Constructing Decision Forests},
 *    volume = {20},
 *    year = {1998},
 *    ISSN = {0162-8828},
 *    URL = {http://citeseer.ist.psu.edu/ho98random.html}
 * }
 * 
 * 
 
 *
 
 * Valid options are: 

 * 
 * 
 -P
 *  Size of each subspace:
 *   < 1: percentage of the number of attributes
 *   >=1: absolute number of attributes
 * 
 * 
 *  -S <num>
 *  Random number seed.
 *  (default 1)
 * 
 *  -I <num>
 *  Number of iterations.
 *  (default 10)
 * 
 *  -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
 * 
 *  -W
 *  Full name of base classifier.
 *  (default: weka.classifiers.trees.REPTree)
 * 
 *  
 * Options specific to classifier weka.classifiers.trees.REPTree:
 * 
 * 
 *  -M <minimum number of instances>
 *  Set minimum number of instances per leaf (default 2).
 * 
 *  -V <minimum variance for split>
 *  Set minimum numeric class variance proportion
 *  of train variance for split (default 1e-3).
 * 
 *  -N <number of folds>
 *  Number of folds for reduced error pruning (default 3).
 * 
 *  -S <seed>
 *  Seed for random data shuffling (default 1).
 * 
 *  -P
 *  No pruning.
 * 
 *  -L
 *  Maximum tree depth (default -1, no maximum)
 * 
 
 *
 * Options after -- are passed to the designated classifier.
 *
 * @author Bernhard Pfahringer ([email protected])
 * @author Peter Reutemann ([email protected])
 * @version $Revision: 10470 $
 */
public class RandomSubSpace
  extends RandomizableParallelIteratedSingleClassifierEnhancer 
  implements WeightedInstancesHandler, TechnicalInformationHandler {

  /** for serialization */
  private static final long serialVersionUID = 1278172513912424947L;
  
  /** The size of each bag sample, as a percentage of the training size */
  protected double m_SubSpaceSize = 0.5;

  /** a ZeroR model in case no model can be built from the data */
  protected Classifier m_ZeroR;
  
  /** Training data */
  protected Instances m_data;
    
  /**
   * Constructor.
   */
  public RandomSubSpace() {
    super();
    
    m_Classifier = new weka.classifiers.trees.REPTree();
  }
  
  /**
   * Returns a string describing classifier
   * 
   * @return 		a description suitable for
   * 			displaying in the explorer/experimenter gui
   */
  public String globalInfo() {
    return 
        "This method constructs a decision tree based classifier that "
      + "maintains highest accuracy on training data and improves on "
      + "generalization accuracy as it grows in complexity. The classifier "
      + "consists of multiple trees constructed systematically by "
      + "pseudorandomly selecting subsets of components of the feature vector, "
      + "that is, trees constructed in randomly chosen subspaces.\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.ARTICLE);
    result.setValue(Field.AUTHOR, "Tin Kam Ho");
    result.setValue(Field.YEAR, "1998");
    result.setValue(Field.TITLE, "The Random Subspace Method for Constructing Decision Forests");
    result.setValue(Field.JOURNAL, "IEEE Transactions on Pattern Analysis and Machine Intelligence");
    result.setValue(Field.VOLUME, "20");
    result.setValue(Field.NUMBER, "8");
    result.setValue(Field.PAGES, "832-844");
    result.setValue(Field.URL, "http://citeseer.ist.psu.edu/ho98random.html");
    result.setValue(Field.ISSN, "0162-8828");
    
    return result;
  }

  /**
   * String describing default classifier.
   * 
   * @return 		the default classifier classname
   */
  protected String defaultClassifierString() {
    return "weka.classifiers.trees.REPTree";
  }

  /**
   * Returns an enumeration describing the available options.
   *
   * @return 		an enumeration of all the available options.
   */
  public Enumeration

   *
   
   * Valid options are: 

   * 
   * 
 -P
   *  Size of each subspace:
   *   < 1: percentage of the number of attributes
   *   >=1: absolute number of attributes
   * 
   * 
   *  -S <num>
   *  Random number seed.
   *  (default 1)
   * 
   *  -I <num>
   *  Number of iterations.
   *  (default 10)
   * 
   *  -D
   *  If set, classifier is run in debug mode and
   *  may output additional info to the console
   * 
   *  -W
   *  Full name of base classifier.
   *  (default: weka.classifiers.trees.REPTree)
   * 
   *  
   * Options specific to classifier weka.classifiers.trees.REPTree:
   * 
   * 
   *  -M <minimum number of instances>
   *  Set minimum number of instances per leaf (default 2).
   * 
   *  -V <minimum variance for split>
   *  Set minimum numeric class variance proportion
   *  of train variance for split (default 1e-3).
   * 
   *  -N <number of folds>
   *  Number of folds for reduced error pruning (default 3).
   * 
   *  -S <seed>
   *  Seed for random data shuffling (default 1).
   * 
   *  -P
   *  No pruning.
   * 
   *  -L
   *  Maximum tree depth (default -1, no maximum)
   * 
   
   *
   * Options after -- are passed to the designated classifier.
   *
   * @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('P', options);
    if (tmpStr.length() != 0)
      setSubSpaceSize(Double.parseDouble(tmpStr));
    else
      setSubSpaceSize(0.5);

    super.setOptions(options);
    
    Utils.checkForRemainingOptions(options);
  }

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

    result.add("-P");
    result.add("" + getSubSpaceSize());
    
    Collections.addAll(result, super.getOptions());

    return (String[]) 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 subSpaceSizeTipText() {
    return 
        "Size of each subSpace: if less than 1 as a percentage of the "
      + "number of attributes, otherwise the absolute number of attributes.";
  }

  /**
   * Gets the size of each subSpace, as a percentage of the training set size.
   *
   * @return 		the subSpace size, as a percentage.
   */
  public double getSubSpaceSize() {
    return m_SubSpaceSize;
  }
  
  /**
   * Sets the size of each subSpace, as a percentage of the training set size.
   *
   * @param value 	the subSpace size, as a percentage.
   */
  public void setSubSpaceSize(double value) {
    m_SubSpaceSize = value;
  }

  /**
   * calculates the number of attributes
   * 
   * @param total	the available number of attributes
   * @param fraction	the fraction - if less than 1 it represents the
   * 			percentage, otherwise the absolute number of attributes
   * @return		the number of attributes to use
   */
  protected int numberOfAttributes(int total, double fraction) {
    int k = (int) Math.round((fraction < 1.0) ? total*fraction : fraction);
    
    if (k > total)
      k = total;
    if (k < 1)
      k = 1;

    return k;
  }

  /**
   * generates an index string describing a random subspace, suitable for
   * the Remove filter.
   * 
   * @param indices		the attribute indices
   * @param subSpaceSize	the size of the subspace
   * @param classIndex		the class index
   * @param random		the random number generator
   * @return			the generated string describing the subspace
   */
  protected String randomSubSpace(Integer[] indices, int subSpaceSize, int classIndex, Random random) {
    Collections.shuffle(Arrays.asList(indices), random);
    StringBuffer sb = new StringBuffer("");
    for(int i = 0; i < subSpaceSize; i++) {
      sb.append(indices[i]+",");
    }
    sb.append(classIndex);
    
    if (getDebug())
      System.out.println("subSPACE = " + sb);

    return sb.toString();
  }

  /**
   * builds the classifier.
   *
   * @param data 	the training data to be used for generating the
   * 			classifier.
   * @throws Exception 	if the classifier could not be built successfully
   */
  public void buildClassifier(Instances data) throws Exception {

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

    // remove instances with missing class
    m_data = new Instances(data);
    m_data.deleteWithMissingClass();
    
    // only class? -> build ZeroR model
    if (m_data.numAttributes() == 1) {
      System.err.println(
	  "Cannot build model (only class attribute present in data!), "
	  + "using ZeroR model instead!");
      m_ZeroR = new weka.classifiers.rules.ZeroR();
      m_ZeroR.buildClassifier(m_data);
      return;
    }
    else {
      m_ZeroR = null;
    }
    
    super.buildClassifier(data);

    Integer[] indices = new Integer[data.numAttributes()-1];
    int classIndex = data.classIndex();
    int offset = 0;
    for(int i = 0; i < indices.length+1; i++) {
      if (i != classIndex) {
	indices[offset++] = i+1;
      }
    }
    int subSpaceSize = numberOfAttributes(indices.length, getSubSpaceSize());
    Random random = data.getRandomNumberGenerator(m_Seed);
    
    for (int j = 0; j < m_Classifiers.length; j++) {
      if (m_Classifier instanceof Randomizable) {
	((Randomizable) m_Classifiers[j]).setSeed(random.nextInt());
      }
      FilteredClassifier fc = new FilteredClassifier();
      fc.setClassifier(m_Classifiers[j]);
      m_Classifiers[j] = fc;
      Remove rm = new Remove();
      rm.setOptions(new String[]{"-V", "-R", randomSubSpace(indices,subSpaceSize,classIndex+1,random)});
      fc.setFilter(rm);

      // build the classifier
      //m_Classifiers[j].buildClassifier(m_data);
    }
    
    buildClassifiers();
    
    // save memory
    m_data = null;
  }
  
  /**
   * Returns a training set for a particular iteration.
   * 
   * @param iteration the number of the iteration for the requested training set.
   * @return the training set for the supplied iteration number
   * @throws Exception if something goes wrong when generating a training set.
   */
  protected synchronized Instances getTrainingSet(int iteration) throws Exception {
    
    // We don't manipulate the training data in any way. The FilteredClassifiers
    // take care of generating the sub-spaces.
    return m_data;
  }

  /**
   * Calculates the class membership probabilities for the given test
   * instance.
   *
   * @param instance 	the instance to be classified
   * @return 		preedicted class probability distribution
   * @throws Exception 	if distribution can't be computed successfully 
   */
  public double[] distributionForInstance(Instance instance) throws Exception {

    // default model?
    if (m_ZeroR != null) {
      return m_ZeroR.distributionForInstance(instance);
    }
    
    double[] sums = new double [instance.numClasses()], newProbs; 
    
    double numPreds = 0;
    for (int i = 0; i < m_NumIterations; i++) {
      if (instance.classAttribute().isNumeric() == true) {
        double pred = m_Classifiers[i].classifyInstance(instance);
        if (!Utils.isMissingValue(pred)) {
          sums[0] += pred;
          numPreds++;
        }
      } else {
	newProbs = m_Classifiers[i].distributionForInstance(instance);
	for (int j = 0; j < newProbs.length; j++)
	  sums[j] += newProbs[j];
      }
    }
    if (instance.classAttribute().isNumeric() == true) {
      if (numPreds == 0) {
        sums[0] = Utils.missingValue();
      } else {
        sums[0] /= numPreds;
      }
      return sums;
    } else if (Utils.eq(Utils.sum(sums), 0)) {
      return sums;
    } else {
      Utils.normalize(sums);
      return sums;
    }
  }

  /**
   * Returns description of the bagged classifier.
   *
   * @return 		description of the bagged classifier as a string
   */
  public String toString() {
    
    // only ZeroR model?
    if (m_ZeroR != null) {
      StringBuffer buf = new StringBuffer();
      buf.append(this.getClass().getName().replaceAll(".*\\.", "") + "\n");
      buf.append(this.getClass().getName().replaceAll(".*\\.", "").replaceAll(".", "=") + "\n\n");
      buf.append("Warning: No model could be built, hence ZeroR model is used:\n\n");
      buf.append(m_ZeroR.toString());
      return buf.toString();
    }
    
    if (m_Classifiers == null) {
      return "RandomSubSpace: No model built yet.";
    }
    StringBuffer text = new StringBuffer();
    text.append("All the base classifiers: \n\n");
    for (int i = 0; i < m_Classifiers.length; i++)
      text.append(m_Classifiers[i].toString() + "\n\n");

    return text.toString();
  }
  
  /**
   * Returns the revision string.
   * 
   * @return		the revision
   */
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 10470 $");
  }

  /**
   * Main method for testing this class.
   *
   * @param args 	the options
   */
  public static void main(String[] args) {
    runClassifier(new RandomSubSpace(), args);
  }
}