content: /*
   *    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.
   */
  
  /*
   *    RandomForest.java
   *    Copyright (C) 2001 University of Waikato, Hamilton, New Zealand
   *
   */
  
  package weka.classifiers.trees;
  
  import weka.classifiers.Classifier;
  import weka.classifiers.AbstractClassifier;
  import weka.classifiers.meta.Bagging;
  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.Randomizable;
  import weka.core.RevisionUtils;
  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 constructing a forest of random trees.<br/>
   * <br/>
   * For more information see: <br/>
   * <br/>
   * Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.
   * <p/>
   <!-- globalinfo-end -->
   *
   <!-- technical-bibtex-start -->
   * BibTeX:
   * <pre>
   * &#64;article{Breiman2001,
   *    author = {Leo Breiman},
   *    journal = {Machine Learning},
   *    number = {1},
   *    pages = {5-32},
   *    title = {Random Forests},
   *    volume = {45},
   *    year = {2001}
   * }
   * </pre>
   * <p/>
   <!-- technical-bibtex-end -->
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -I &lt;number of trees&gt;
   *  Number of trees to build.</pre>
   * 
   * <pre> -K &lt;number of features&gt;
   *  Number of features to consider (&lt;1=int(logM+1)).</pre>
   * 
   * <pre> -S
   *  Seed for random number generator.
   *  (default 1)</pre>
   * 
   * <pre> -depth &lt;num&gt;
   *  The maximum depth of the trees, 0 for unlimited.
   *  (default 0)</pre>
   * 
   * <pre> -num-slots &lt;num&gt;
   *  Number of execution slots.
   *  (default 1 - i.e. no parallelism)</pre>
   * 
   * <pre> -D
   *  If set, classifier is run in debug mode and
   *  may output additional info to the console</pre>
   * 
   <!-- options-end -->
   *
   * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)
   * @version $Revision: 7372 $
   */
  public class RandomForest 
    extends AbstractClassifier 
    implements OptionHandler, Randomizable, WeightedInstancesHandler, 
               AdditionalMeasureProducer, TechnicalInformationHandler {
  
    /** for serialization */
    static final long serialVersionUID = 4216839470751428698L;
    
    /** Number of trees in forest. */
    protected int m_numTrees = 10;
  
    /** Number of features to consider in random feature selection.
        If less than 1 will use int(logM+1) ) */
    protected int m_numFeatures = 0;
  
    /** The random seed. */
    protected int m_randomSeed = 1;  
  
    /** Final number of features that were considered in last build. */
    protected int m_KValue = 0;
  
    /** The bagger. */
    protected Bagging m_bagger = null;
    
    /** The maximum depth of the trees (0 = unlimited) */
    protected int m_MaxDepth = 0;
    
    /** The number of threads to have executing at any one time */
    protected int m_numExecutionSlots = 1;
  
    /**
     * Returns a string describing classifier
     * @return a description suitable for
     * displaying in the explorer/experimenter gui
     */
    public String globalInfo() {
  
      return  
          "Class for constructing a forest of random trees.\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, "Leo Breiman");
      result.setValue(Field.YEAR, "2001");
      result.setValue(Field.TITLE, "Random Forests");
      result.setValue(Field.JOURNAL, "Machine Learning");
      result.setValue(Field.VOLUME, "45");
      result.setValue(Field.NUMBER, "1");
      result.setValue(Field.PAGES, "5-32");
      
      return result;
    }
    
    /**
     * Returns the tip text for this property
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String numTreesTipText() {
      return "The number of trees to be generated.";
    }
  
    /**
     * Get the value of numTrees.
     *
     * @return Value of numTrees.
     */
    public int getNumTrees() {
      
      return m_numTrees;
    }
    
    /**
     * Set the value of numTrees.
     *
     * @param newNumTrees Value to assign to numTrees.
     */
    public void setNumTrees(int newNumTrees) {
      
      m_numTrees = newNumTrees;
    }
    
    /**
     * Returns the tip text for this property
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String numFeaturesTipText() {
      return "The number of attributes to be used in random selection (see RandomTree).";
    }
  
    /**
     * Get the number of features used in random selection.
     *
     * @return Value of numFeatures.
     */
    public int getNumFeatures() {
      
      return m_numFeatures;
    }
    
    /**
     * Set the number of features to use in random selection.
     *
     * @param newNumFeatures Value to assign to numFeatures.
     */
    public void setNumFeatures(int newNumFeatures) {
      
      m_numFeatures = newNumFeatures;
    }
    
    /**
     * Returns the tip text for this property
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String seedTipText() {
      return "The random number seed to be used.";
    }
  
    /**
     * Set the seed for random number generation.
     *
     * @param seed the seed 
     */
    public void setSeed(int seed) {
  
      m_randomSeed = seed;
    }
    
    /**
     * Gets the seed for the random number generations
     *
     * @return the seed for the random number generation
     */
    public int getSeed() {
  
      return m_randomSeed;
    }
    
    /**
     * Returns the tip text for this property
     * 
     * @return 		tip text for this property suitable for
     * 			displaying in the explorer/experimenter gui
     */
    public String maxDepthTipText() {
      return "The maximum depth of the trees, 0 for unlimited.";
    }
  
    /**
     * Get the maximum depth of trh tree, 0 for unlimited.
     *
     * @return 		the maximum depth.
     */
    public int getMaxDepth() {
      return m_MaxDepth;
    }
    
    /**
     * Set the maximum depth of the tree, 0 for unlimited.
     *
     * @param value 	the maximum depth.
     */
    public void setMaxDepth(int value) {
      m_MaxDepth = value;
    }
  
    /**
     * Gets the out of bag error that was calculated as the classifier was built.
     *
     * @return the out of bag error
     */
    public double measureOutOfBagError() {
      
      if (m_bagger != null) {
        return m_bagger.measureOutOfBagError();
      } else return Double.NaN;
    }
    
    /**
     * Set the number of execution slots (threads) to use for building the
     * members of the ensemble.
     *
     * @param numSlots the number of slots to use.
     */
    public void setNumExecutionSlots(int numSlots) {
      m_numExecutionSlots = numSlots;
    }
  
    /**
     * Get the number of execution slots (threads) to use for building
     * the members of the ensemble.
     *
     * @return the number of slots to use
     */
    public int getNumExecutionSlots() {
      return m_numExecutionSlots;
    }
  
    /**
     * Returns the tip text for this property
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String numExecutionSlotsTipText() {
      return "The number of execution slots (threads) to use for " +
        "constructing the ensemble.";
    }
    
    /**
     * Returns an enumeration of the additional measure names.
     *
     * @return an enumeration of the measure names
     */
    public Enumeration enumerateMeasures() {
      
      Vector newVector = new Vector(1);
      newVector.addElement("measureOutOfBagError");
      return newVector.elements();
    }
    
    /**
     * Returns the value of the named measure.
     *
     * @param additionalMeasureName the name of the measure to query for its value
     * @return the value of the named measure
     * @throws IllegalArgumentException if the named measure is not supported
     */
    public double getMeasure(String additionalMeasureName) {
      
      if (additionalMeasureName.equalsIgnoreCase("measureOutOfBagError")) {
        return measureOutOfBagError();
      }
      else {throw new IllegalArgumentException(additionalMeasureName 
  					     + " not supported (RandomForest)");
      }
    }
  
    /**
     * Returns an enumeration describing the available options.
     *
     * @return an enumeration of all the available options
     */
    public Enumeration listOptions() {
      
      Vector newVector = new Vector();
  
      newVector.addElement(new Option(
  	"\tNumber of trees to build.",
  	"I", 1, "-I <number of trees>"));
      
      newVector.addElement(new Option(
  	"\tNumber of features to consider (<1=int(logM+1)).",
  	"K", 1, "-K <number of features>"));
      
      newVector.addElement(new Option(
  	"\tSeed for random number generator.\n"
  	+ "\t(default 1)",
  	"S", 1, "-S"));
  
      newVector.addElement(new Option(
  	"\tThe maximum depth of the trees, 0 for unlimited.\n"
  	+ "\t(default 0)",
  	"depth", 1, "-depth <num>"));
      
      newVector.addElement(new Option(
          "\tNumber of execution slots.\n"
          + "\t(default 1 - i.e. no parallelism)",
          "num-slots", 1, "-num-slots <num>"));
  
      Enumeration enu = super.listOptions();
      while (enu.hasMoreElements()) {
        newVector.addElement(enu.nextElement());
      }
  
      return newVector.elements();
    }
  
    /**
     * Gets the current settings of the forest.
     *
     * @return an array of strings suitable for passing to setOptions()
     */
    public String[] getOptions() {
      Vector        result;
      String[]      options;
      int           i;
      
      result = new Vector();
      
      result.add("-I");
      result.add("" + getNumTrees());
      
      result.add("-K");
      result.add("" + getNumFeatures());
      
      result.add("-S");
      result.add("" + getSeed());
      
      if (getMaxDepth() > 0) {
        result.add("-depth");
        result.add("" + getMaxDepth());
      }
      
      result.add("-num-slots");
      result.add("" + getNumExecutionSlots());
      
      options = super.getOptions();
      for (i = 0; i < options.length; i++)
        result.add(options[i]);
      
      return (String[]) result.toArray(new String[result.size()]);
    }
  
    /**
     * Parses a given list of options. <p/>
     * 
     <!-- options-start -->
     * Valid options are: <p/>
     * 
     * <pre> -I &lt;number of trees&gt;
     *  Number of trees to build.</pre>
     * 
     * <pre> -K &lt;number of features&gt;
     *  Number of features to consider (&lt;1=int(logM+1)).</pre>
     * 
     * <pre> -S
     *  Seed for random number generator.
     *  (default 1)</pre>
     * 
     * <pre> -depth &lt;num&gt;
     *  The maximum depth of the trees, 0 for unlimited.
     *  (default 0)</pre>
     * 
     * <pre> -num-slots &lt;num&gt;
     *  Number of execution slots.
     *  (default 1 - i.e. no parallelism)</pre>
     * 
     * <pre> -D
     *  If set, classifier is run in debug mode and
     *  may output additional info to the console</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('I', options);
      if (tmpStr.length() != 0) {
        m_numTrees = Integer.parseInt(tmpStr);
      } else {
        m_numTrees = 10;
      }
      
      tmpStr = Utils.getOption('K', options);
      if (tmpStr.length() != 0) {
        m_numFeatures = Integer.parseInt(tmpStr);
      } else {
        m_numFeatures = 0;
      }
      
      tmpStr = Utils.getOption('S', options);
      if (tmpStr.length() != 0) {
        setSeed(Integer.parseInt(tmpStr));
      } else {
        setSeed(1);
      }
      
      tmpStr = Utils.getOption("depth", options);
      if (tmpStr.length() != 0) {
        setMaxDepth(Integer.parseInt(tmpStr));
      } else {
        setMaxDepth(0);
      }
      
      tmpStr = Utils.getOption("num-slots", options);
      if (tmpStr.length() > 0) {
        setNumExecutionSlots(Integer.parseInt(tmpStr));
      } else {
        setNumExecutionSlots(1);
      }
      
      super.setOptions(options);
      
      Utils.checkForRemainingOptions(options);
    }  
  
    /**
     * Returns default capabilities of the classifier.
     *
     * @return      the capabilities of this classifier
     */
    public Capabilities getCapabilities() {
      return new RandomTree().getCapabilities();
    }
  
    /**
     * Builds a classifier for a set of instances.
     *
     * @param data the instances to train the classifier with
     * @throws Exception if something goes wrong
     */
    public void buildClassifier(Instances data) throws Exception {
  
      // can classifier handle the data?
      getCapabilities().testWithFail(data);
  
      // remove instances with missing class
      data = new Instances(data);
      data.deleteWithMissingClass();
      
      m_bagger = new Bagging();
      RandomTree rTree = new RandomTree();
  
      // set up the random tree options
      m_KValue = m_numFeatures;
      if (m_KValue < 1) m_KValue = (int) Utils.log2(data.numAttributes())+1;
      rTree.setKValue(m_KValue);
      rTree.setMaxDepth(getMaxDepth());
  
      // set up the bagger and build the forest
      m_bagger.setClassifier(rTree);
      m_bagger.setSeed(m_randomSeed);
      m_bagger.setNumIterations(m_numTrees);
      m_bagger.setCalcOutOfBag(true);
      m_bagger.setNumExecutionSlots(m_numExecutionSlots);
      m_bagger.buildClassifier(data);
    }
  
    /**
     * Returns the class probability distribution for an instance.
     *
     * @param instance the instance to be classified
     * @return the distribution the forest generates for the instance
     * @throws Exception if computation fails
     */
    public double[] distributionForInstance(Instance instance) throws Exception {
  
      return m_bagger.distributionForInstance(instance);
    }
  
    /**
     * Outputs a description of this classifier.
     *
     * @return a string containing a description of the classifier
     */
    public String toString() {
  
      if (m_bagger == null) 
        return "Random forest not built yet";
      else 
        return "Random forest of " + m_numTrees
  	   + " trees, each constructed while considering "
  	   + m_KValue + " random feature" + (m_KValue==1 ? "" : "s") + ".\n"
  	   + "Out of bag error: "
  	   + Utils.doubleToString(m_bagger.measureOutOfBagError(), 4) + "\n"
  	   + (getMaxDepth() > 0 ? ("Max. depth of trees: " + getMaxDepth() + "\n") : (""))
  	   + "\n";
    }
    
    /**
     * Returns the revision string.
     * 
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 7372 $");
    }
  
    /**
     * Main method for this class.
     *
     * @param argv the options
     */
    public static void main(String[] argv) {
      runClassifier(new RandomForest(), argv);
    }
  }
  
  