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• MIOptimalBall.java

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weka.classifiers.mi.MIOptimalBall Maven / Gradle / Ivy

/*
 *   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 .
 */

/*
 * MIOptimalBall.java
 * Copyright (C) 2005 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.mi;

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

import weka.classifiers.AbstractClassifier;
import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.MultiInstanceCapabilitiesHandler;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.SelectedTag;
import weka.core.Tag;
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.matrix.DoubleVector;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.MultiInstanceToPropositional;
import weka.filters.unsupervised.attribute.Normalize;
import weka.filters.unsupervised.attribute.PropositionalToMultiInstance;
import weka.filters.unsupervised.attribute.Standardize;

/**
 *  This classifier tries to find a suitable ball in
 * the multiple-instance space, with a certain data point in the instance space
 * as a ball center. The possible ball center is a certain instance in a
 * positive bag. The possible radiuses are those which can achieve the highest
 * classification accuracy. The model selects the maximum radius as the radius
 * of the optimal ball.

 * 

 * For more information about this algorithm, see:

 * 

 * Peter Auer, Ronald Ortner: A Boosting Approach to Multiple Instance Learning.
 * In: 15th European Conference on Machine Learning, 63-74, 2004.
 * 

 * 
 * 
 *  BibTeX:
 * 
 * 
 * @inproceedings{Auer2004,
 *    author = {Peter Auer and Ronald Ortner},
 *    booktitle = {15th European Conference on Machine Learning},
 *    note = {LNAI 3201},
 *    pages = {63-74},
 *    publisher = {Springer},
 *    title = {A Boosting Approach to Multiple Instance Learning},
 *    year = {2004}
 * }
 * 
 * 

 * 
 * 
 *  Valid options are:
 * 

 * 
 * 
 * -N <num>
 *  Whether to 0=normalize/1=standardize/2=neither. 
 *  (default 0=normalize)
 * 
 * 
 * 
 * 
 * @author Lin Dong ([email protected])
 * @version $Revision: 10369 $
 */
public class MIOptimalBall extends AbstractClassifier implements OptionHandler,
  WeightedInstancesHandler, MultiInstanceCapabilitiesHandler,
  TechnicalInformationHandler {

  /** for serialization */
  static final long serialVersionUID = -6465750129576777254L;

  /** center of the optimal ball */
  protected double[] m_Center;

  /** radius of the optimal ball */
  protected double m_Radius;

  /** the distances from each instance in a positive bag to each bag */
  protected double[][][] m_Distance;

  /** The filter used to standardize/normalize all values. */
  protected Filter m_Filter = null;

  /** Whether to normalize/standardize/neither */
  protected int m_filterType = FILTER_NORMALIZE;

  /** Normalize training data */
  public static final int FILTER_NORMALIZE = 0;
  /** Standardize training data */
  public static final int FILTER_STANDARDIZE = 1;
  /** No normalization/standardization */
  public static final int FILTER_NONE = 2;
  /** The filter to apply to the training data */
  public static final Tag[] TAGS_FILTER = {
    new Tag(FILTER_NORMALIZE, "Normalize training data"),
    new Tag(FILTER_STANDARDIZE, "Standardize training data"),
    new Tag(FILTER_NONE, "No normalization/standardization"), };

  /** filter used to convert the MI dataset into single-instance dataset */
  protected MultiInstanceToPropositional m_ConvertToSI = new MultiInstanceToPropositional();

  /** filter used to convert the single-instance dataset into MI dataset */
  protected PropositionalToMultiInstance m_ConvertToMI = new PropositionalToMultiInstance();

  /**
   * Returns a string describing this filter
   * 
   * @return a description of the filter suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String globalInfo() {
    return "This classifier tries to find a suitable ball in the "
      + "multiple-instance space, with a certain data point in the instance "
      + "space as a ball center. The possible ball center is a certain "
      + "instance in a positive bag. The possible radiuses are those which can "
      + "achieve the highest classification accuracy. The model selects the "
      + "maximum radius as the radius of the optimal ball.\n\n"
      + "For more information about this algorithm, 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.INPROCEEDINGS);
    result.setValue(Field.AUTHOR, "Peter Auer and Ronald Ortner");
    result.setValue(Field.TITLE,
      "A Boosting Approach to Multiple Instance Learning");
    result.setValue(Field.BOOKTITLE,
      "15th European Conference on Machine Learning");
    result.setValue(Field.YEAR, "2004");
    result.setValue(Field.PAGES, "63-74");
    result.setValue(Field.PUBLISHER, "Springer");
    result.setValue(Field.NOTE, "LNAI 3201");

    return result;
  }

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

    // attributes
    result.enable(Capability.NOMINAL_ATTRIBUTES);
    result.enable(Capability.RELATIONAL_ATTRIBUTES);

    // class
    result.enable(Capability.BINARY_CLASS);
    result.enable(Capability.MISSING_CLASS_VALUES);

    // other
    result.enable(Capability.ONLY_MULTIINSTANCE);

    return result;
  }

  /**
   * Returns the capabilities of this multi-instance classifier for the
   * relational data.
   * 
   * @return the capabilities of this object
   * @see Capabilities
   */
  @Override
  public Capabilities getMultiInstanceCapabilities() {
    Capabilities result = super.getCapabilities();
    result.disableAll();

    // attributes
    result.enable(Capability.NOMINAL_ATTRIBUTES);
    result.enable(Capability.NUMERIC_ATTRIBUTES);
    result.enable(Capability.DATE_ATTRIBUTES);
    result.enable(Capability.MISSING_VALUES);

    // class
    result.disableAllClasses();
    result.enable(Capability.NO_CLASS);

    return result;
  }

  /**
   * Builds the classifier
   * 
   * @param data the training data to be used for generating the boosted
   *          classifier.
   * @throws Exception if the classifier could not be built successfully
   */
  @Override
  public void buildClassifier(Instances data) throws Exception {
    // can classifier handle the data?
    getCapabilities().testWithFail(data);

    // remove instances with missing class
    Instances train = new Instances(data);
    train.deleteWithMissingClass();

    int numAttributes = train.attribute(1).relation().numAttributes();
    m_Center = new double[numAttributes];

    if (getDebug()) {
      System.out.println("Start training ...");
    }

    // convert the training dataset into single-instance dataset
    m_ConvertToSI.setInputFormat(train);
    train = Filter.useFilter(train, m_ConvertToSI);

    if (m_filterType == FILTER_STANDARDIZE) {
      m_Filter = new Standardize();
    } else if (m_filterType == FILTER_NORMALIZE) {
      m_Filter = new Normalize();
    } else {
      m_Filter = null;
    }

    if (m_Filter != null) {
      // normalize/standardize the converted training dataset
      m_Filter.setInputFormat(train);
      train = Filter.useFilter(train, m_Filter);
    }

    // convert the single-instance dataset into multi-instance dataset
    m_ConvertToMI.setInputFormat(train);
    train = Filter.useFilter(train, m_ConvertToMI);

    /*
     * calculate all the distances (and store them in m_Distance[][][]), which
     * are from each instance in all positive bags to all bags
     */
    calculateDistance(train);

    /*
     * find the suitable ball center (m_Center) and the corresponding radius
     * (m_Radius)
     */
    findRadius(train);

    if (getDebug()) {
      System.out.println("Finish building optimal ball model");
    }
  }

  /**
   * calculate the distances from each instance in a positive bag to each bag.
   * All result distances are stored in m_Distance[i][j][k], where
   * m_Distance[i][j][k] refers the distances from the jth instance in ith bag
   * to the kth bag
   * 
   * @param train the multi-instance dataset (with relational attribute)
   */
  public void calculateDistance(Instances train) {
    int numBags = train.numInstances();
    int numInstances;
    Instance tempCenter;

    m_Distance = new double[numBags][][];
    for (int i = 0; i < numBags; i++) {
      if (train.instance(i).classValue() == 1.0) { // positive bag
        numInstances = train.instance(i).relationalValue(1).numInstances();
        m_Distance[i] = new double[numInstances][];
        for (int j = 0; j < numInstances; j++) {
          tempCenter = train.instance(i).relationalValue(1).instance(j);
          m_Distance[i][j] = new double[numBags]; // store the distance from one
                                                  // center to all the bags
          for (int k = 0; k < numBags; k++) {
            if (i == k) {
              m_Distance[i][j][k] = 0;
            } else {
              m_Distance[i][j][k] = minBagDistance(tempCenter,
                train.instance(k));
            }
          }
        }
      }
    }
  }

  /**
   * Calculate the distance from one data point to a bag
   * 
   * @param center the data point in instance space
   * @param bag the bag
   * @return the double value as the distance.
   */
  public double minBagDistance(Instance center, Instance bag) {
    double distance;
    double minDistance = Double.MAX_VALUE;
    Instances temp = bag.relationalValue(1);
    // calculate the distance from the data point to each instance in the bag
    // and return the minimum distance
    for (int i = 0; i < temp.numInstances(); i++) {
      distance = 0;
      for (int j = 0; j < center.numAttributes(); j++) {
        distance += (center.value(j) - temp.instance(i).value(j))
          * (center.value(j) - temp.instance(i).value(j));
      }

      if (minDistance > distance) {
        minDistance = distance;
      }
    }
    return Math.sqrt(minDistance);
  }

  /**
   * Find the maximum radius for the optimal ball.
   * 
   * @param train the multi-instance data
   */
  public void findRadius(Instances train) {
    int numBags, numInstances;
    double radius, bagDistance;
    int highestCount = 0;

    numBags = train.numInstances();
    // try each instance in all positive bag as a ball center (tempCenter),
    for (int i = 0; i < numBags; i++) {
      if (train.instance(i).classValue() == 1.0) {// positive bag
        numInstances = train.instance(i).relationalValue(1).numInstances();
        for (int j = 0; j < numInstances; j++) {
          Instance tempCenter = train.instance(i).relationalValue(1)
            .instance(j);

          // set the possible set of ball radius corresponding to each
          // tempCenter,
          double sortedDistance[] = sortArray(m_Distance[i][j]); // sort the
                                                                 // distance
                                                                 // value
          for (int k = 1; k < sortedDistance.length; k++) {
            radius = sortedDistance[k]
              - (sortedDistance[k] - sortedDistance[k - 1]) / 2.0;

            // evaluate the performance on the training data according to
            // the curren selected tempCenter and the set of radius
            int correctCount = 0;
            for (int n = 0; n < numBags; n++) {
              bagDistance = m_Distance[i][j][n];
              if ((bagDistance <= radius && train.instance(n).classValue() == 1.0)
                || (bagDistance > radius && train.instance(n).classValue() == 0.0)) {
                correctCount += train.instance(n).weight();
              }

            }

            // and keep the track of the ball center and the maximum radius
            // which can achieve the highest accuracy.
            if (correctCount > highestCount
              || (correctCount == highestCount && radius > m_Radius)) {
              highestCount = correctCount;
              m_Radius = radius;
              for (int p = 0; p < tempCenter.numAttributes(); p++) {
                m_Center[p] = tempCenter.value(p);
              }
            }
          }
        }
      }
    }
  }

  /**
   * Sort the array.
   * 
   * @param distance the array need to be sorted
   * @return sorted array
   */
  public double[] sortArray(double[] distance) {
    double[] sorted = new double[distance.length];

    // make a copy of the array
    double[] disCopy = new double[distance.length];
    for (int i = 0; i < distance.length; i++) {
      disCopy[i] = distance[i];
    }

    DoubleVector sortVector = new DoubleVector(disCopy);
    sortVector.sort();
    sorted = sortVector.getArrayCopy();
    return sorted;
  }

  /**
   * Computes the distribution for a given multiple instance
   * 
   * @param newBag the instance for which distribution is computed
   * @return the distribution
   * @throws Exception if the distribution can't be computed successfully
   */
  @Override
  public double[] distributionForInstance(Instance newBag) throws Exception {

    double[] distribution = new double[2];
    double distance;
    distribution[0] = 0;
    distribution[1] = 0;

    Instances insts = new Instances(newBag.dataset(), 0);
    insts.add(newBag);

    // Filter instances
    insts = Filter.useFilter(insts, m_ConvertToSI);
    if (m_Filter != null) {
      insts = Filter.useFilter(insts, m_Filter);
    }

    // calculate the distance from each single instance to the ball center
    int numInsts = insts.numInstances();
    insts.deleteAttributeAt(0); // remove the bagIndex attribute, no use for the
                                // distance calculation

    for (int i = 0; i < numInsts; i++) {
      distance = 0;
      for (int j = 0; j < insts.numAttributes() - 1; j++) {
        distance += (insts.instance(i).value(j) - m_Center[j])
          * (insts.instance(i).value(j) - m_Center[j]);
      }

      if (distance <= m_Radius * m_Radius) { // check whether this single
                                             // instance is inside the ball
        distribution[1] = 1.0; // predicted as a positive bag
        break;
      }
    }

    distribution[0] = 1 - distribution[1];

    return distribution;
  }

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

    Vector result = new Vector();

    result.addElement(new Option(
      "\tWhether to 0=normalize/1=standardize/2=neither. \n"
        + "\t(default 0=normalize)", "N", 1, "-N "));

    result.addAll(Collections.list(super.listOptions()));

    return result.elements();
  }

  /**
   * Gets the current settings of the classifier.
   * 
   * @return an array of strings suitable for passing to setOptions
   */
  @Override
  public String[] getOptions() {

    Vector result = new Vector();

    result.add("-N");
    result.add("" + m_filterType);

    Collections.addAll(result, super.getOptions());

    return result.toArray(new String[result.size()]);
  }

  /**
   * Parses a given list of options.
   * 

   * 
   *  Valid options are:
   * 

   * 
   * 
   * -N <num>
   *  Whether to 0=normalize/1=standardize/2=neither. 
   *  (default 0=normalize)
   * 
   * 
   * 
   * 
   * @param options the list of options as an array of strings
   * @throws Exception if an option is not supported
   */
  @Override
  public void setOptions(String[] options) throws Exception {

    String nString = Utils.getOption('N', options);
    if (nString.length() != 0) {
      setFilterType(new SelectedTag(Integer.parseInt(nString), TAGS_FILTER));
    } else {
      setFilterType(new SelectedTag(FILTER_NORMALIZE, TAGS_FILTER));
    }

    super.setOptions(options);

    Utils.checkForRemainingOptions(options);
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String filterTypeTipText() {
    return "The filter type for transforming the training data.";
  }

  /**
   * Sets how the training data will be transformed. Should be one of
   * FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
   * 
   * @param newType the new filtering mode
   */
  public void setFilterType(SelectedTag newType) {

    if (newType.getTags() == TAGS_FILTER) {
      m_filterType = newType.getSelectedTag().getID();
    }
  }

  /**
   * Gets how the training data will be transformed. Will be one of
   * FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
   * 
   * @return the filtering mode
   */
  public SelectedTag getFilterType() {

    return new SelectedTag(m_filterType, TAGS_FILTER);
  }

  /**
   * Returns the revision string.
   * 
   * @return the revision
   */
  @Override
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 10369 $");
  }

  /**
   * Main method for testing this class.
   * 
   * @param argv should contain the command line arguments to the scheme (see
   *          Evaluation)
   */
  public static void main(String[] argv) {
    runClassifier(new MIOptimalBall(), argv);
  }
}