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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 .
 */

/*
 *    AdditiveRegression.java
 *    Copyright (C) 2000-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.meta;

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

import weka.classifiers.Classifier;
import weka.classifiers.AbstractClassifier;
import weka.classifiers.IteratedSingleClassifierEnhancer;
import weka.classifiers.IterativeClassifier;
import weka.classifiers.rules.ZeroR;

import weka.core.AdditionalMeasureProducer;
import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.Instance;
import weka.core.Instances;
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.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;
import weka.core.UnassignedClassException;

/**
 
 * Meta classifier that enhances the performance of a regression base classifier. Each iteration fits a model to the residuals left by the classifier on the previous iteration. Prediction is accomplished by adding the predictions of each classifier. Reducing the shrinkage (learning rate) parameter helps prevent overfitting and has a smoothing effect but increases the learning time.

 * 

 * For more information see:

 * 

 * J.H. Friedman (1999). Stochastic Gradient Boosting.
 * 
 
 *
 
 * BibTeX:
 * 
 * @techreport{Friedman1999,
 *    author = {J.H. Friedman},
 *    institution = {Stanford University},
 *    title = {Stochastic Gradient Boosting},
 *    year = {1999},
 *    PS = {http://www-stat.stanford.edu/\~jhf/ftp/stobst.ps}
 * }
 * 
 * 
 
 *
 
 * Valid options are: 

 * 
 * 
 -S
 *  Specify shrinkage rate. (default = 1.0, ie. no shrinkage)
 * 
 * 
 *  -I <num>
 *  Number of iterations.
 *  (default 10)
 *
 *  -A
 *  Minimize absolute error instead of squared error (assumes that base learner minimizes absolute error).
 * 
 *  -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.DecisionStump)
 * 
 *  
 * Options specific to classifier weka.classifiers.trees.DecisionStump:
 * 
 * 
 *  -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
 * 
 
 *
 * @author Mark Hall ([email protected])
 * @version $Revision: 12091 $
 */
public class AdditiveRegression extends IteratedSingleClassifierEnhancer implements OptionHandler, 
        AdditionalMeasureProducer, WeightedInstancesHandler, TechnicalInformationHandler, IterativeClassifier {

  /** for serialization */
  static final long serialVersionUID = -2368937577670527151L;

  /** ArrayList for storing the generated base classifiers. 
   Note: we are hiding the variable from IteratedSingleClassifierEnhancer*/
  protected ArrayList m_Classifiers;
  
  /** Shrinkage (Learning rate). Default = no shrinkage. */
  protected double m_shrinkage = 1.0;

  /** The mean or median */
  protected double m_InitialPrediction;

  /** whether we have suitable data or nor (if only mean/mode is used) */
  protected boolean m_SuitableData = true;

  /** The working data */
  protected Instances m_Data;

  /** The sum of (absolute or squared) residuals. */
  protected double m_Error;

  /** The improvement in the sum of (absolute or squared) residuals. */
  protected double m_Diff;
  
  /** Whether to minimise absolute error instead of squared error. */
  protected boolean m_MinimizeAbsoluteError = false;
  
  /**
   * Returns a string describing this attribute evaluator
   * @return a description of the evaluator suitable for
   * displaying in the explorer/experimenter gui
   */
  public String globalInfo() {
    return " Meta classifier that enhances the performance of a regression "
      +"base classifier. Each iteration fits a model to the residuals left "
      +"by the classifier on the previous iteration. Prediction is "
      +"accomplished by adding the predictions of each classifier. "
      +"Reducing the shrinkage (learning rate) parameter helps prevent "
      +"overfitting and has a smoothing effect but increases the learning "
      +"time.\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.TECHREPORT);
    result.setValue(Field.AUTHOR, "J.H. Friedman");
    result.setValue(Field.YEAR, "1999");
    result.setValue(Field.TITLE, "Stochastic Gradient Boosting");
    result.setValue(Field.INSTITUTION, "Stanford University");
    result.setValue(Field.PS, "http://www-stat.stanford.edu/~jhf/ftp/stobst.ps");
    
    return result;
  }

  /**
   * Default constructor specifying DecisionStump as the classifier
   */
  public AdditiveRegression() {

    this(new weka.classifiers.trees.DecisionStump());
  }

  /**
   * Constructor which takes base classifier as argument.
   *
   * @param classifier the base classifier to use
   */
  public AdditiveRegression(Classifier classifier) {

    m_Classifier = classifier;
  }

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

  /**
   * Returns an enumeration describing the available options.
   *
   * @return an enumeration of all the available options.
   */
  public Enumeration -A
   *  Minimize absolute error instead of squared error (assumes that base learner minimizes absolute error).
   *  
   *  -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.DecisionStump)
   * 
   *  
   * Options specific to classifier weka.classifiers.trees.DecisionStump:
   * 
   * 
   *  -D
   *  If set, classifier is run in debug mode and
   *  may output additional info to the console
   * 
   
   *
   * @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 optionString = Utils.getOption('S', options);
    if (optionString.length() != 0) {
      Double temp = Double.valueOf(optionString);
      setShrinkage(temp.doubleValue());
    }
    setMinimizeAbsoluteError(Utils.getFlag('A', options));

    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 options = new Vector();

    options.add("-S"); options.add("" + getShrinkage());

    if (getMinimizeAbsoluteError()) {
      options.add("-A");
    }
    
    Collections.addAll(options, super.getOptions());
    
    return options.toArray(new String[0]);
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String shrinkageTipText() {
    return "Shrinkage rate. Smaller values help prevent overfitting and "
            + "have a smoothing effect (but increase learning time). "
            +"Default = 1.0, ie. no shrinkage.";
  }

  /**
   * Set the shrinkage parameter
   *
   * @param l the shrinkage rate.
   */
  public void setShrinkage(double l) {
    m_shrinkage = l;
  }

  /**
   * Get the shrinkage rate.
   *
   * @return the value of the learning rate
   */
  public double getShrinkage() {
    return m_shrinkage;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String minimizeAbsoluteErrorTipText() {
    return "Minimize absolute error instead of squared error (assume base learner minimizes absolute error)";
  }

  /**
   * Sets whether absolute error is to be minimized.
   *
   * @param f true if absolute error is to be minimized.
   */
  public void setMinimizeAbsoluteError(boolean f) {
    m_MinimizeAbsoluteError = f;
  }

  /**
   * Gets whether absolute error is to be minimized.
   *
   * @return true if absolute error is to be minimized
   */
  public boolean getMinimizeAbsoluteError() {
    return m_MinimizeAbsoluteError;
  }

  /**
   * Returns default capabilities of the classifier.
   *
   * @return      the capabilities of this classifier
   */
  public Capabilities getCapabilities() {
    Capabilities result = super.getCapabilities();

    // class
    result.disableAllClasses();
    result.disableAllClassDependencies();
    result.enable(Capability.NUMERIC_CLASS);
    result.enable(Capability.DATE_CLASS);
    
    return result;
  }

  /**
   * Method used to build the classifier.
   */
  public void buildClassifier(Instances data) throws Exception {

    // Initialize classifier
    initializeClassifier(data);

    // For the given number of iterations
    while (next()) {};

    // Clean up
    done();
  }

  /**
   * Initialize classifier.
   *
   * @param data the training data
   * @throws Exception if the classifier could not be initialized successfully
   */
  public void initializeClassifier(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();

    // Add the model for the mean first
    if (getMinimizeAbsoluteError()) {
      m_InitialPrediction = m_Data.kthSmallestValue(m_Data.classIndex(), m_Data.numInstances() / 2);
    } else {
      m_InitialPrediction = m_Data.meanOrMode(m_Data.classIndex());
    }
    
    // only class? -> use only ZeroR model
    if (m_Data.numAttributes() == 1) {
      System.err.println("Cannot build non-trivial model (only class attribute present in data!).");
      m_SuitableData = false;
      return;
    } else {
      m_SuitableData = true;
    }
   
    // Initialize list of classifiers and data
    m_Classifiers = new ArrayList(m_NumIterations);
    m_Data = residualReplace(m_Data, m_InitialPrediction);

    // Calculate error
    m_Error = 0;
    m_Diff = Double.MAX_VALUE;
    for (int i = 0; i < m_Data.numInstances(); i++) {
      if (getMinimizeAbsoluteError()) {
        m_Error += m_Data.instance(i).weight() * Math.abs(m_Data.instance(i).classValue());
      } else {
        m_Error += m_Data.instance(i).weight() * m_Data.instance(i).classValue() * m_Data.instance(i).classValue();
      }
    }
    if (m_Debug) {
      if (getMinimizeAbsoluteError()) {
        System.err.println("Sum of absolute residuals (predicting the median) : " + m_Error);
      } else {
        System.err.println("Sum of squared residuals (predicting the mean) : " + m_Error);
      }
    }
  }

  /**
   * Perform another iteration.
   */
  public boolean next() throws Exception {

    if ((!m_SuitableData) || (m_Classifiers.size() >= m_NumIterations) ||
            (m_Diff <= Utils.SMALL)) {
      return false;
    }

    // Build the classifier
    m_Classifiers.add(AbstractClassifier.makeCopy(m_Classifier));
    m_Classifiers.get(m_Classifiers.size() - 1).buildClassifier(m_Data);

    m_Data = residualReplace(m_Data, m_Classifiers.get(m_Classifiers.size() - 1));
    double sum = 0;
    for (int i = 0; i < m_Data.numInstances(); i++) {
      if (getMinimizeAbsoluteError()) {
        sum += m_Data.instance(i).weight() * Math.abs(m_Data.instance(i).classValue());
      } else {
        sum += m_Data.instance(i).weight() * m_Data.instance(i).classValue() * m_Data.instance(i).classValue();
      }
    }
    if (m_Debug) {
      if (getMinimizeAbsoluteError()) {
        System.err.println("Sum of absolute residuals: " + sum);
      } else {
        System.err.println("Sum of squared residuals: " + sum);
      }
    }
  
    m_Diff = m_Error - sum;
    m_Error = sum;

    return true;
  }

  /**
   * Clean up.
   */
  public void done() {
    
    m_Data = null;
  }

  /**
   * Classify an instance.
   *
   * @param inst the instance to predict
   * @return a prediction for the instance
   * @throws Exception if an error occurs
   */
  public double classifyInstance(Instance inst) throws Exception {

    double prediction = m_InitialPrediction;

    // default model?
    if (!m_SuitableData) {
      return prediction;
    }
    
    for (Classifier classifier : m_Classifiers) {
      double toAdd = classifier.classifyInstance(inst);
      if (Utils.isMissingValue(toAdd)) {
        throw new UnassignedClassException("AdditiveRegression: base learner predicted missing value.");
      }
      prediction += (toAdd * getShrinkage());
    }

    return prediction;
  }

  /**
   * Replace the class values of the instances from the current iteration
   * with residuals after predicting with the supplied classifier.
   *
   * @param data the instances to predict
   * @param c the classifier to use
   * @return a new set of instances with class values replaced by residuals
   * @throws Exception if something goes wrong
   */
  private Instances residualReplace(Instances data, Classifier c) throws Exception {

    Instances newInst = new Instances(data);
    for (int i = 0; i < newInst.numInstances(); i++) {
      double pred = c.classifyInstance(newInst.instance(i));
      if (Utils.isMissingValue(pred)) {
        throw new UnassignedClassException("AdditiveRegression: base learner predicted missing value.");
      }
      newInst.instance(i).setClassValue(newInst.instance(i).classValue() - (pred * getShrinkage()));
    }
    return newInst;
  }

  /**
   * Replace the class values of the instances from the current iteration
   * with residuals after predicting the given constant.
   *
   * @param data the instances to predict
   * @param c the constant to use
   * @return a new set of instances with class values replaced by residuals
   * @throws Exception if something goes wrong
   */
  private Instances residualReplace(Instances data, double c) throws Exception {

    Instances newInst = new Instances(data);
    for (int i = 0; i < newInst.numInstances(); i++) {
      newInst.instance(i).setClassValue(newInst.instance(i).classValue() - c);
    }
    return newInst;
  }

  /**
   * 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("measureNumIterations");
    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.compareToIgnoreCase("measureNumIterations") == 0) {
      return measureNumIterations();
    } else {
      throw new IllegalArgumentException(additionalMeasureName 
			  + " not supported (AdditiveRegression)");
    }
  }

  /**
   * return the number of iterations (base classifiers) completed
   * @return the number of iterations (same as number of base classifier
   * models)
   */
  public double measureNumIterations() {
    return m_Classifiers.size();
  }

  /**
   * Returns textual description of the classifier.
   *
   * @return a description of the classifier as a string
   */
  public String toString() {
    StringBuffer text = new StringBuffer();
    
    if (m_SuitableData && m_Classifiers == null) {
      return "Classifier hasn't been built yet!";
    }

    // only ZeroR model?
    if (!m_SuitableData) {
      StringBuffer buf = new StringBuffer();
      buf.append(this.getClass().getName().replaceAll(".*\\.", "") + "\n");
      buf.append(this.getClass().getName().replaceAll(".*\\.", "").replaceAll(".", "=") + "\n\n");
      buf.append("Warning: Non-trivial model could not be built, initial prediction is: ");
      buf.append(m_InitialPrediction);
      return buf.toString();
    }

    text.append("Additive Regression\n\n");

    text.append("Initial prediction: " + m_InitialPrediction + "\n\n");

    text.append("Base classifier " 
		+ getClassifier().getClass().getName()
		+ "\n\n");
    text.append("" + m_Classifiers.size() + " models generated.\n");

    for (int i = 0; i < m_Classifiers.size(); i++) {
      text.append("\nModel number " + i + "\n\n" +
		  m_Classifiers.get(i) + "\n");
    }

    return text.toString();
  }
  
  /**
   * Returns the revision string.
   * 
   * @return		the revision
   */
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 12091 $");
  }

  /**
   * Main method for testing this class.
   *
   * @param argv should contain the following arguments:
   * -t training file [-T test file] [-c class index]
   */
  public static void main(String [] argv) {
    runClassifier(new AdditiveRegression(), argv);
  }
}