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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 .
 */

/*
 *    RandomForest.java
 *    Copyright (C) 2001-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.trees;

import weka.classifiers.AbstractClassifier;
import weka.classifiers.Classifier;
import weka.classifiers.meta.Bagging;
import weka.core.Capabilities;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.Utils;
import weka.core.WekaException;
import weka.gui.ProgrammaticProperty;

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

/**
 *  Class for constructing a forest of random trees.

 * 

 * For more information see: 

 * 

 * Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32. 

 * 

 * 
 * 
 *  BibTeX:
 * 
 *  * @article{Breiman2001,
 *    author = {Leo Breiman},
 *    journal = {Machine Learning},
 *    number = {1},
 *    pages = {5-32},
 *    title = {Random Forests},
 *    volume = {45},
 *    year = {2001}
 * }
 * 
 * 
 * 

 * 

 * 
 * 
 *  Valid options are:
 * 
 * 
 * 
 * -P
 *  Size of each bag, as a percentage of the
 *  training set size. (default 100)
 * 
 * 
 *  * -O
 *  Calculate the out of bag error.
 * 
 * 
 *  * -store-out-of-bag-predictions
 *  Whether to store out of bag predictions in internal evaluation object.
 * 
 * 
 *  * -output-out-of-bag-complexity-statistics
 *  Whether to output complexity-based statistics when out-of-bag evaluation is performed.
 * 
 * 
 *  * -print
 *  Print the individual classifiers in the output
 * 
 * 
 *  * -attribute-importance
 *  Compute and output attribute importance (mean impurity decrease method)
 * 
 * 
 *  * -I <num>
 *  Number of iterations.
 *  (current value 100)
 * 
 * 
 *  * -num-slots <num>
 *  Number of execution slots.
 *  (default 1 - i.e. no parallelism)
 *  (use 0 to auto-detect number of cores)
 * 
 * 
 *  * -K <number of attributes>
 *  Number of attributes to randomly investigate. (default 0)
 *  (<1 = int(log_2(#predictors)+1)).
 * 
 * 
 *  * -M <minimum number of instances>
 *  Set minimum number of instances per leaf.
 *  (default 1)
 * 
 * 
 *  * -V <minimum variance for split>
 *  Set minimum numeric class variance proportion
 *  of train variance for split (default 1e-3).
 * 
 * 
 *  * -S <num>
 *  Seed for random number generator.
 *  (default 1)
 * 
 * 
 *  * -depth <num>
 *  The maximum depth of the tree, 0 for unlimited.
 *  (default 0)
 * 
 * 
 *  * -N <num>
 *  Number of folds for backfitting (default 0, no backfitting).
 * 
 * 
 *  * -U
 *  Allow unclassified instances.
 * 
 * 
 *  * -B
 *  Break ties randomly when several attributes look equally good.
 * 
 * 
 *  * -output-debug-info
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
 * 
 * 
 *  * -do-not-check-capabilities
 *  If set, classifier capabilities are not checked before classifier is built
 *  (use with caution).
 * 
 * 
 *  * -num-decimal-places
 *  The number of decimal places for the output of numbers in the model (default 2).
 * 
 * 
 *  * -batch-size
 *  The desired batch size for batch prediction  (default 100).
 * 
 * 
 * 
 * 
 * @author Richard Kirkby ([email protected])
 * @version $Revision: 13294 $
 */
public class RandomForest extends Bagging {

  /** for serialization */
  static final long serialVersionUID = 1116839470751428698L;

  /** True to compute attribute importance */
  protected boolean m_computeAttributeImportance;

  /**
   * The default number of iterations to perform.
   */
  @Override
  protected int defaultNumberOfIterations() {
    return 100;
  }

  /**
   * Constructor that sets base classifier for bagging to RandomTre and default
   * number of iterations to 100.
   */
  public RandomForest() {

    RandomTree rTree = new RandomTree();
    rTree.setDoNotCheckCapabilities(true);
    super.setClassifier(rTree);
    super.setRepresentCopiesUsingWeights(true);
    setNumIterations(defaultNumberOfIterations());
  }

  /**
   * Returns default capabilities of the base classifier.
   *
   * @return the capabilities of the base classifier
   */
  public Capabilities getCapabilities() {

    // Cannot use the main RandomTree object because capabilities checking has
    // been turned off
    // for that object.
    return (new RandomTree()).getCapabilities();
  }

  /**
   * String describing default classifier.
   *
   * @return the default classifier classname
   */
  @Override
  protected String defaultClassifierString() {

    return "weka.classifiers.trees.RandomTree";
  }

  /**
   * String describing default classifier options.
   *
   * @return the default classifier options
   */
  @Override
  protected String[] defaultClassifierOptions() {

    String[] args = { "-do-not-check-capabilities" };
    return args;
  }

  /**
   * 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
   */
  @Override
  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;
  }

  /**
   * This method only accepts RandomTree arguments.
   *
   * @param newClassifier the RandomTree to use.
   * @exception if argument is not a RandomTree
   */
  @Override
  @ProgrammaticProperty
  public void setClassifier(Classifier newClassifier) {
    if (!(newClassifier instanceof RandomTree)) {
      throw new IllegalArgumentException(
        "RandomForest: Argument of setClassifier() must be a RandomTree.");
    }
    super.setClassifier(newClassifier);
  }

  /**
   * This method only accepts true as its argument
   *
   * @param representUsingWeights must be set to true.
   * @exception if argument is not true
   */
  @Override
  @ProgrammaticProperty
  public void setRepresentCopiesUsingWeights(boolean representUsingWeights) {
    if (!representUsingWeights) {
      throw new IllegalArgumentException(
        "RandomForest: Argument of setRepresentCopiesUsingWeights() must be true.");
    }
    super.setRepresentCopiesUsingWeights(representUsingWeights);
  }

  /**
   * 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 ((RandomTree) getClassifier()).KValueTipText();
  }

  /**
   * Get the number of features used in random selection.
   *
   * @return Value of numFeatures.
   */
  public int getNumFeatures() {

    return ((RandomTree) getClassifier()).getKValue();
  }

  /**
   * Set the number of features to use in random selection.
   *
   * @param newNumFeatures Value to assign to numFeatures.
   */
  public void setNumFeatures(int newNumFeatures) {

    ((RandomTree) getClassifier()).setKValue(newNumFeatures);
  }

  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String computeAttributeImportanceTipText() {
    return "Compute attribute importance via mean impurity decrease";
  }

  /**
   * Set whether to compute and output attribute importance scores
   *
   * @param computeAttributeImportance true to compute attribute importance
   *          scores
   */
  public void setComputeAttributeImportance(boolean computeAttributeImportance) {
    m_computeAttributeImportance = computeAttributeImportance;
    ((RandomTree)m_Classifier).setComputeImpurityDecreases(computeAttributeImportance);
  }

  /**
   * Get whether to compute and output attribute importance scores
   *
   * @return true if computing attribute importance scores
   */
  public boolean getComputeAttributeImportance() {
    return m_computeAttributeImportance;
  }

  /**
   * 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 ((RandomTree) getClassifier()).maxDepthTipText();
  }

  /**
   * Get the maximum depth of trh tree, 0 for unlimited.
   *
   * @return the maximum depth.
   */
  public int getMaxDepth() {
    return ((RandomTree) getClassifier()).getMaxDepth();
  }

  /**
   * Set the maximum depth of the tree, 0 for unlimited.
   *
   * @param value the maximum depth.
   */
  public void setMaxDepth(int value) {
    ((RandomTree) getClassifier()).setMaxDepth(value);
  }

  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String breakTiesRandomlyTipText() {
    return ((RandomTree) getClassifier()).breakTiesRandomlyTipText();
  }

  /**
   * Get whether to break ties randomly.
   *
   * @return true if ties are to be broken randomly.
   */
  public boolean getBreakTiesRandomly() {

    return ((RandomTree) getClassifier()).getBreakTiesRandomly();
  }

  /**
   * Set whether to break ties randomly.
   *
   * @param newBreakTiesRandomly true if ties are to be broken randomly
   */
  public void setBreakTiesRandomly(boolean newBreakTiesRandomly) {

    ((RandomTree) getClassifier()).setBreakTiesRandomly(newBreakTiesRandomly);
  }

  /**
   * Set debugging mode.
   *
   * @param debug true if debug output should be printed
   */
  public void setDebug(boolean debug) {

    super.setDebug(debug);
    ((RandomTree) getClassifier()).setDebug(debug);
  }

  /**
   * Set the number of decimal places.
   */
  public void setNumDecimalPlaces(int num) {

    super.setNumDecimalPlaces(num);
    ((RandomTree) getClassifier()).setNumDecimalPlaces(num);
  }

  /**
   * Set the preferred batch size for batch prediction.
   *
   * @param size the batch size to use
   */
  @Override
  public void setBatchSize(String size) {

    super.setBatchSize(size);
    ((RandomTree) getClassifier()).setBatchSize(size);
  }

  /**
   * Sets the seed for the random number generator.
   *
   * @param s the seed to be used
   */
  public void setSeed(int s) {

    super.setSeed(s);
    ((RandomTree) getClassifier()).setSeed(s);
  }

  /**
   * Returns description of the bagged classifier.
   *
   * @return description of the bagged classifier as a string
   */
  @Override
  public String toString() {

    if (m_Classifiers == null) {
      return "RandomForest: No model built yet.";
    }
    StringBuilder buffer = new StringBuilder("RandomForest\n\n");
    buffer.append(super.toString());

    if (getComputeAttributeImportance()) {
      try {
        double[] nodeCounts = new double[m_data.numAttributes()];
        double[] impurityScores =
          computeAverageImpurityDecreasePerAttribute(nodeCounts);
        int[] sortedIndices = Utils.sort(impurityScores);
        buffer
          .append("\n\nAttribute importance based on average impurity decrease "
            + "(and number of nodes using that attribute)\n\n");
        for (int i = sortedIndices.length - 1; i >= 0; i--) {
          int index = sortedIndices[i];
          if (index != m_data.classIndex()) {
            buffer
              .append(
                Utils.doubleToString(impurityScores[index], 10,
                  getNumDecimalPlaces())).append(" (")
              .append(Utils.doubleToString(nodeCounts[index], 6, 0))
              .append(")  ").append(m_data.attribute(index).name())
              .append("\n");
          }
        }
      } catch (WekaException ex) {
        // ignore
      }
    }

    return buffer.toString();
  }

  /**
   * Computes the average impurity decrease per attribute over the trees
   *
   * @param nodeCounts an optional array that, if non-null, will hold the count
   *          of the number of nodes at which each attribute was used for
   *          splitting
   * @return the average impurity decrease per attribute over the trees
   */
  public double[] computeAverageImpurityDecreasePerAttribute(
    double[] nodeCounts) throws WekaException {

    if (m_Classifiers == null) {
      throw new WekaException("Classifier has not been built yet!");
    }

    if (!getComputeAttributeImportance()) {
      throw new WekaException("Stats for attribute importance have not "
        + "been collected!");
    }

    double[] impurityDecreases = new double[m_data.numAttributes()];
    if (nodeCounts == null) {
      nodeCounts = new double[m_data.numAttributes()];
    }
    for (Classifier c : m_Classifiers) {
      double[][] forClassifier = ((RandomTree) c).getImpurityDecreases();
      for (int i = 0; i < m_data.numAttributes(); i++) {
        impurityDecreases[i] += forClassifier[i][0];
        nodeCounts[i] += forClassifier[i][1];
      }
    }
    for (int i = 0; i < m_data.numAttributes(); i++) {
      if (nodeCounts[i] > 0) {
        impurityDecreases[i] /= nodeCounts[i];
      }
    }

    return impurityDecreases;
  }

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