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.
   */
  
  /*
   *    MarginCurve.java
   *    Copyright (C) 2002 University of Waikato, Hamilton, New Zealand
   *
   */
  
  package weka.classifiers.evaluation;
  
  import weka.core.Attribute;
  import weka.core.FastVector;
  import weka.core.Instance;
  import weka.core.DenseInstance;
  import weka.core.Instances;
  import weka.core.RevisionHandler;
  import weka.core.RevisionUtils;
  import weka.core.Utils;
  
  /**
   * Generates points illustrating the prediction margin. The margin is defined
   * as the difference between the probability predicted for the actual class and
   * the highest probability predicted for the other classes. One hypothesis
   * as to the good performance of boosting algorithms is that they increaes the
   * margins on the training data and this gives better performance on test data.
   *
   * @author Len Trigg (len@reeltwo.com)
   * @version $Revision: 5987 $
   */
  public class MarginCurve
    implements RevisionHandler {
  
    /**
     * Calculates the cumulative margin distribution for the set of
     * predictions, returning the result as a set of Instances. The
     * structure of these Instances is as follows:<p> <ul> 
     * <li> <b>Margin</b> contains the margin value (which should be plotted
     * as an x-coordinate) 
     * <li> <b>Current</b> contains the count of instances with the current 
     * margin (plot as y axis)
     * <li> <b>Cumulative</b> contains the count of instances with margin
     * less than or equal to the current margin (plot as y axis)
     * </ul> <p>
     *
     * @return datapoints as a set of instances, null if no predictions
     * have been made.  
     */
    public Instances getCurve(FastVector predictions) {
  
      if (predictions.size() == 0) {
        return null;
      }
  
      Instances insts = makeHeader();
      double [] margins = getMargins(predictions);
      int [] sorted = Utils.sort(margins);
      int binMargin = 0;
      int totalMargin = 0;
      insts.add(makeInstance(-1, binMargin, totalMargin));
      for (int i = 0; i < sorted.length; i++) {
        double current = margins[sorted[i]];
        double weight = ((NominalPrediction)predictions.elementAt(sorted[i]))
          .weight();
        totalMargin += weight;
        binMargin += weight;
        if (true) {
          insts.add(makeInstance(current, binMargin, totalMargin));
          binMargin = 0;
        }
      }
      return insts;
    }
  
    /**
     * Pulls all the margin values out of a vector of NominalPredictions.
     *
     * @param predictions a FastVector containing NominalPredictions
     * @return an array of margin values.
     */
    private double [] getMargins(FastVector predictions) {
  
      // sort by predicted probability of the desired class.
      double [] margins = new double [predictions.size()];
      for (int i = 0; i < margins.length; i++) {
        NominalPrediction pred = (NominalPrediction)predictions.elementAt(i);
        margins[i] = pred.margin();
      }
      return margins;
    }
  
    /**
     * Creates an Instances object with the attributes we will be calculating.
     *
     * @return the Instances structure.
     */
    private Instances makeHeader() {
  
      FastVector fv = new FastVector();
      fv.addElement(new Attribute("Margin"));
      fv.addElement(new Attribute("Current"));
      fv.addElement(new Attribute("Cumulative"));
      return new Instances("MarginCurve", fv, 100);
    }
    
    /**
     * Creates an Instance object with the attributes calculated.
     *
     * @param margin the margin for this data point.
     * @param current the number of instances with this margin.
     * @param cumulative the number of instances with margin less than or equal
     * to this margin.
     * @return the Instance object.
     */
    private Instance makeInstance(double margin, int current, int cumulative) {
  
      int count = 0;
      double [] vals = new double[3];
      vals[count++] = margin;
      vals[count++] = current;
      vals[count++] = cumulative;
      return new DenseInstance(1.0, vals);
    }
    
    /**
     * Returns the revision string.
     * 
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 5987 $");
    }
    
    /**
     * Tests the MarginCurve generation from the command line.
     * The classifier is currently hardcoded. Pipe in an arff file.
     *
     * @param args currently ignored
     */
    public static void main(String [] args) {
  
      try {
        Utils.SMALL = 0;
        Instances inst = new Instances(new java.io.InputStreamReader(System.in));
        inst.setClassIndex(inst.numAttributes() - 1);
        MarginCurve tc = new MarginCurve();
        EvaluationUtils eu = new EvaluationUtils();
        weka.classifiers.meta.LogitBoost classifier 
          = new weka.classifiers.meta.LogitBoost();
        classifier.setNumIterations(20);
        FastVector predictions 
          = eu.getTrainTestPredictions(classifier, inst, inst);
        Instances result = tc.getCurve(predictions);
        System.out.println(result);
      } catch (Exception ex) {
        ex.printStackTrace();
      }
    }
  }
  