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weka.classifiers.bayes.net.search.ci.ICSSearchAlgorithm 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 .
 */

/*
 * ICSSearchAlgorithm.java
 * Copyright (C) 2004-2012 University of Waikato, Hamilton, New Zealand
 * 
 */

package weka.classifiers.bayes.net.search.ci;

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

import weka.classifiers.bayes.BayesNet;
import weka.classifiers.bayes.net.ParentSet;
import weka.core.Instances;
import weka.core.Option;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
import weka.core.Utils;

/**
 *  This Bayes Network learning algorithm uses
 * conditional independence tests to find a skeleton, finds V-nodes and applies
 * a set of rules to find the directions of the remaining arrows.
 * 

 * 
 * 
 *  Valid options are:
 * 

 * 
 * 
 * -cardinality <num>
 *  When determining whether an edge exists a search is performed 
 *  for a set Z that separates the nodes. MaxCardinality determines 
 *  the maximum size of the set Z. This greatly influences the 
 *  length of the search. (default 2)
 * 
 * 
 * 
 * -mbc
 *  Applies a Markov Blanket correction to the network structure, 
 *  after a network structure is learned. This ensures that all 
 *  nodes in the network are part of the Markov blanket of the 
 *  classifier node.
 * 
 * 
 * 
 * -S [BAYES|MDL|ENTROPY|AIC|CROSS_CLASSIC|CROSS_BAYES]
 *  Score type (BAYES, BDeu, MDL, ENTROPY and AIC)
 * 
 * 
 * 
 * 
 * @author Remco Bouckaert
 * @version $Revision: 10154 $
 */
public class ICSSearchAlgorithm extends CISearchAlgorithm {

  /** for serialization */
  static final long serialVersionUID = -2510985917284798576L;

  /**
   * returns the name of the attribute with the given index
   * 
   * @param iAttribute the index of the attribute
   * @return the name of the attribute
   */
  String name(int iAttribute) {
    return m_instances.attribute(iAttribute).name();
  }

  /**
   * returns the number of attributes
   * 
   * @return the number of attributes
   */
  int maxn() {
    return m_instances.numAttributes();
  }

  /** maximum size of separating set **/
  private int m_nMaxCardinality = 2;

  /**
   * sets the cardinality
   * 
   * @param nMaxCardinality the max cardinality
   */
  public void setMaxCardinality(int nMaxCardinality) {
    m_nMaxCardinality = nMaxCardinality;
  }

  /**
   * returns the max cardinality
   * 
   * @return the max cardinality
   */
  public int getMaxCardinality() {
    return m_nMaxCardinality;
  }

  class SeparationSet implements RevisionHandler {

    public int[] m_set;

    /**
     * constructor
     */
    public SeparationSet() {
      m_set = new int[getMaxCardinality() + 1];
    } // c'tor

    public boolean contains(int nItem) {
      for (int iItem = 0; iItem < getMaxCardinality() && m_set[iItem] != -1; iItem++) {
        if (m_set[iItem] == nItem) {
          return true;
        }
      }
      return false;
    } // contains

    /**
     * Returns the revision string.
     * 
     * @return the revision
     */
    @Override
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 10154 $");
    }

  } // class sepset

  /**
   * Search for Bayes network structure using ICS algorithm
   * 
   * @param bayesNet datastructure to build network structure for
   * @param instances data set to learn from
   * @throws Exception if something goes wrong
   */
  @Override
  protected void search(BayesNet bayesNet, Instances instances)
    throws Exception {
    // init
    m_BayesNet = bayesNet;
    m_instances = instances;

    boolean edges[][] = new boolean[maxn() + 1][];
    boolean[][] arrows = new boolean[maxn() + 1][];
    SeparationSet[][] sepsets = new SeparationSet[maxn() + 1][];
    for (int iNode = 0; iNode < maxn() + 1; iNode++) {
      edges[iNode] = new boolean[maxn()];
      arrows[iNode] = new boolean[maxn()];
      sepsets[iNode] = new SeparationSet[maxn()];
    }

    calcDependencyGraph(edges, sepsets);
    calcVeeNodes(edges, arrows, sepsets);
    calcArcDirections(edges, arrows);

    // transfrom into BayesNet datastructure
    for (int iNode = 0; iNode < maxn(); iNode++) {
      // clear parent set of AttributeX
      ParentSet oParentSet = m_BayesNet.getParentSet(iNode);
      while (oParentSet.getNrOfParents() > 0) {
        oParentSet.deleteLastParent(m_instances);
      }
      for (int iParent = 0; iParent < maxn(); iParent++) {
        if (arrows[iParent][iNode]) {
          oParentSet.addParent(iParent, m_instances);
        }
      }
    }
  } // search

  /**
   * CalcDependencyGraph determines the skeleton of the BayesNetwork by starting
   * with a complete graph and removing edges (a--b) if it can find a set Z such
   * that a and b conditionally independent given Z. The set Z is found by
   * trying all possible subsets of nodes adjacent to a and b, first of size 0,
   * then of size 1, etc. up to size m_nMaxCardinality
   * 
   * @param edges boolean matrix representing the edges
   * @param sepsets set of separating sets
   */
  void calcDependencyGraph(boolean[][] edges, SeparationSet[][] sepsets) {
    /* calc undirected graph a-b iff D(a,S,b) for all S) */
    SeparationSet oSepSet;

    for (int iNode1 = 0; iNode1 < maxn(); iNode1++) {
      /* start with complete graph */
      for (int iNode2 = 0; iNode2 < maxn(); iNode2++) {
        edges[iNode1][iNode2] = true;
      }
    }
    for (int iNode1 = 0; iNode1 < maxn(); iNode1++) {
      edges[iNode1][iNode1] = false;
    }

    for (int iCardinality = 0; iCardinality <= getMaxCardinality(); iCardinality++) {
      for (int iNode1 = 0; iNode1 <= maxn() - 2; iNode1++) {
        for (int iNode2 = iNode1 + 1; iNode2 < maxn(); iNode2++) {
          if (edges[iNode1][iNode2]) {
            oSepSet = existsSepSet(iNode1, iNode2, iCardinality, edges);
            if (oSepSet != null) {
              edges[iNode1][iNode2] = false;
              edges[iNode2][iNode1] = false;
              sepsets[iNode1][iNode2] = oSepSet;
              sepsets[iNode2][iNode1] = oSepSet;
              // report separating set
              System.err.print("I(" + name(iNode1) + ", {");
              for (int iNode3 = 0; iNode3 < iCardinality; iNode3++) {
                System.err.print(name(oSepSet.m_set[iNode3]) + " ");
              }
              System.err.print("} ," + name(iNode2) + ")\n");
            }
          }
        }
      }
      // report current state of dependency graph
      System.err.print(iCardinality + " ");
      for (int iNode1 = 0; iNode1 < maxn(); iNode1++) {
        System.err.print(name(iNode1) + " ");
      }
      System.err.print('\n');
      for (int iNode1 = 0; iNode1 < maxn(); iNode1++) {
        for (int iNode2 = 0; iNode2 < maxn(); iNode2++) {
          if (edges[iNode1][iNode2]) {
            System.err.print("X ");
          } else {
            System.err.print(". ");
          }
        }
        System.err.print(name(iNode1) + " ");
        System.err.print('\n');
      }
    }
  } /* CalcDependencyGraph */

  /**
   * ExistsSepSet tests if a separating set Z of node a and b exists of given
   * cardiniality exists. The set Z is found by trying all possible subsets of
   * nodes adjacent to both a and b of the requested cardinality.
   * 
   * @param iNode1 index of first node a
   * @param iNode2 index of second node b
   * @param nCardinality size of the separating set Z
   * @param edges
   * @return SeparationSet containing set that separates iNode1 and iNode2 or
   *         null if no such set exists
   */
  SeparationSet existsSepSet(int iNode1, int iNode2, int nCardinality,
    boolean[][] edges) {
    /* Test if a separating set of node d and e exists of cardiniality k */
    // int iNode1_, iNode2_;
    int iNode3, iZ;
    SeparationSet Z = new SeparationSet();
    Z.m_set[nCardinality] = -1;

    // iNode1_ = iNode1;
    // iNode2_ = iNode2;

    // find first candidate separating set Z
    if (nCardinality > 0) {
      Z.m_set[0] = next(-1, iNode1, iNode2, edges);
      iNode3 = 1;
      while (iNode3 < nCardinality) {
        Z.m_set[iNode3] = next(Z.m_set[iNode3 - 1], iNode1, iNode2, edges);
        iNode3++;
      }
    }

    if (nCardinality > 0) {
      iZ = maxn() - Z.m_set[nCardinality - 1] - 1;
    } else {
      iZ = 0;
    }

    while (iZ >= 0) {
      // check if candidate separating set makes iNode2_ and iNode1_ independent
      if (isConditionalIndependent(iNode2, iNode1, Z.m_set, nCardinality)) {
        return Z;
      }
      // calc next candidate separating set
      if (nCardinality > 0) {
        Z.m_set[nCardinality - 1] = next(Z.m_set[nCardinality - 1], iNode1,
          iNode2, edges);
      }
      iZ = nCardinality - 1;
      while (iZ >= 0 && Z.m_set[iZ] >= maxn()) {
        iZ = nCardinality - 1;
        while (iZ >= 0 && Z.m_set[iZ] >= maxn()) {
          iZ--;
        }
        if (iZ < 0) {
          break;
        }
        Z.m_set[iZ] = next(Z.m_set[iZ], iNode1, iNode2, edges);
        for (iNode3 = iZ + 1; iNode3 < nCardinality; iNode3++) {
          Z.m_set[iNode3] = next(Z.m_set[iNode3 - 1], iNode1, iNode2, edges);
        }
        iZ = nCardinality - 1;
      }
    }

    return null;
  } /* ExistsSepSet */

  /**
   * determine index of node that makes next candidate separating set adjacent
   * to iNode1 and iNode2, but not iNode2 itself
   * 
   * @param x index of current node
   * @param iNode1 first node
   * @param iNode2 second node (must be larger than iNode1)
   * @param edges skeleton so far
   * @return int index of next node adjacent to iNode1 after x
   */
  int next(int x, int iNode1, int iNode2, boolean[][] edges) {
    x++;
    while (x < maxn()
      && (!edges[iNode1][x] || !edges[iNode2][x] || x == iNode2)) {
      x++;
    }
    return x;
  } /* next */

  /**
   * CalcVeeNodes tries to find V-nodes, i.e. nodes a,b,c such that a->c<-b and
   * a-/-b. These nodes are identified by finding nodes a,b,c in the skeleton
   * such that a--c, c--b and a-/-b and furthermore c is not in the set Z that
   * separates a and b
   * 
   * @param edges skeleton
   * @param arrows resulting partially directed skeleton after all V-nodes have
   *          been identified
   * @param sepsets separating sets
   */
  void calcVeeNodes(boolean[][] edges, boolean[][] arrows,
    SeparationSet[][] sepsets) {

    // start with complete empty graph
    for (int iNode1 = 0; iNode1 < maxn(); iNode1++) {
      for (int iNode2 = 0; iNode2 < maxn(); iNode2++) {
        arrows[iNode1][iNode2] = false;
      }
    }

    for (int iNode1 = 0; iNode1 < maxn() - 1; iNode1++) {
      for (int iNode2 = iNode1 + 1; iNode2 < maxn(); iNode2++) {
        if (!edges[iNode1][iNode2]) { /* i nonadj j */
          for (int iNode3 = 0; iNode3 < maxn(); iNode3++) {
            if ((iNode3 != iNode1 && iNode3 != iNode2 && edges[iNode1][iNode3] && edges[iNode2][iNode3])
              & (!sepsets[iNode1][iNode2].contains(iNode3))) {
              arrows[iNode1][iNode3] = true; /* add arc i->k */
              arrows[iNode2][iNode3] = true; /* add arc j->k */
            }
          }
        }
      }
    }
  } // CalcVeeNodes

  /**
   * CalcArcDirections assigns directions to edges that remain after V-nodes
   * have been identified. The arcs are directed using the following rules: Rule
   * 1: i->j--k & i-/-k => j->k Rule 2: i->j->k & i--k => i->k Rule 3 m /|\ i |
   * k => m->j i->j<-k \|/ j
   * 
   * Rule 4 m / \ i---k => i->m & k->m i->j \ / j Rule 5: if no edges are
   * directed then take a random one (first we can find)
   * 
   * @param edges skeleton
   * @param arrows resulting fully directed DAG
   */
  void calcArcDirections(boolean[][] edges, boolean[][] arrows) {
    /* give direction to remaining arcs */
    int i, j, k, m;
    boolean bFound;

    do {
      bFound = false;

      /* Rule 1: i->j--k & i-/-k => j->k */

      for (i = 0; i < maxn(); i++) {
        for (j = 0; j < maxn(); j++) {
          if (i != j && arrows[i][j]) {
            for (k = 0; k < maxn(); k++) {
              if (i != k && j != k && edges[j][k] && !edges[i][k]
                && !arrows[j][k] && !arrows[k][j]) {
                arrows[j][k] = true;
                bFound = true;
              }
            }
          }
        }
      }

      /* Rule 2: i->j->k & i--k => i->k */

      for (i = 0; i < maxn(); i++) {
        for (j = 0; j < maxn(); j++) {
          if (i != j && arrows[i][j]) {
            for (k = 0; k < maxn(); k++) {
              if (i != k && j != k && edges[i][k] && arrows[j][k]
                && !arrows[i][k] && !arrows[k][i]) {
                arrows[i][k] = true;
                bFound = true;
              }
            }
          }
        }
      }

      /*
       * Rule 3 m /|\ i | k => m->j i->j<-k \|/ j
       */
      for (i = 0; i < maxn(); i++) {
        for (j = 0; j < maxn(); j++) {
          if (i != j && arrows[i][j]) {
            for (k = 0; k < maxn(); k++) {
              if (k != i && k != j && arrows[k][j] && !edges[k][i]) {
                for (m = 0; m < maxn(); m++) {
                  if (m != i && m != j && m != k && edges[m][i]
                    && !arrows[m][i] && !arrows[i][m] && edges[m][j]
                    && !arrows[m][j] && !arrows[j][m] && edges[m][k]
                    && !arrows[m][k] && !arrows[k][m]) {
                    arrows[m][j] = true;
                    bFound = true;
                  }
                }
              }
            }
          }
        }
      }

      /*
       * Rule 4 m / \ i---k => i->m & k->m i->j \ / j
       */
      for (i = 0; i < maxn(); i++) {
        for (j = 0; j < maxn(); j++) {
          if (i != j && arrows[j][i]) {
            for (k = 0; k < maxn(); k++) {
              if (k != i && k != j && edges[k][j] && !arrows[k][j]
                && !arrows[j][k] && edges[k][i] && !arrows[k][i]
                && !arrows[i][k]) {
                for (m = 0; m < maxn(); m++) {
                  if (m != i && m != j && m != k && edges[m][i]
                    && !arrows[m][i] && !arrows[i][m] && edges[m][k]
                    && !arrows[m][k] && !arrows[k][m]) {
                    arrows[i][m] = true;
                    arrows[k][m] = true;
                    bFound = true;
                  }
                }
              }
            }
          }
        }
      }

      /*
       * Rule 5: if no edges are directed then take a random one (first we can
       * find)
       */

      if (!bFound) {
        i = 0;
        while (!bFound && i < maxn()) {
          j = 0;
          while (!bFound && j < maxn()) {
            if (edges[i][j] && !arrows[i][j] && !arrows[j][i]) {
              arrows[i][j] = true;
              bFound = true;
            }
            j++;
          }
          i++;
        }
      }

    } while (bFound);

  } // CalcArcDirections

  /**
   * 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(
        "\tWhen determining whether an edge exists a search is performed \n"
          + "\tfor a set Z that separates the nodes. MaxCardinality determines \n"
          + "\tthe maximum size of the set Z. This greatly influences the \n"
          + "\tlength of the search. (default 2)", "cardinality", 1,
        "-cardinality "));

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

    return result.elements();
  } // listOption

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

   * 
   *  Valid options are:
   * 

   * 
   * 
   * -cardinality <num>
   *  When determining whether an edge exists a search is performed 
   *  for a set Z that separates the nodes. MaxCardinality determines 
   *  the maximum size of the set Z. This greatly influences the 
   *  length of the search. (default 2)
   * 
   * 
   * 
   * -mbc
   *  Applies a Markov Blanket correction to the network structure, 
   *  after a network structure is learned. This ensures that all 
   *  nodes in the network are part of the Markov blanket of the 
   *  classifier node.
   * 
   * 
   * 
   * -S [BAYES|MDL|ENTROPY|AIC|CROSS_CLASSIC|CROSS_BAYES]
   *  Score type (BAYES, BDeu, MDL, ENTROPY and AIC)
   * 
   * 
   * 
   * 
   * @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 tmpStr;

    tmpStr = Utils.getOption("cardinality", options);
    if (tmpStr.length() != 0) {
      setMaxCardinality(Integer.parseInt(tmpStr));
    } else {
      setMaxCardinality(2);
    }

    super.setOptions(options);
  } // setOptions

  /**
   * 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("-cardinality");
    result.add("" + getMaxCardinality());

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

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

  /**
   * @return a string to describe the MaxCardinality option.
   */
  public String maxCardinalityTipText() {
    return "When determining whether an edge exists a search is performed for a set Z "
      + "that separates the nodes. MaxCardinality determines the maximum size of the set Z. "
      + "This greatly influences the length of the search. Default value is 2.";
  } // maxCardinalityTipText

  /**
   * This will return a string describing the search algorithm.
   * 
   * @return The string.
   */
  @Override
  public String globalInfo() {
    return "This Bayes Network learning algorithm uses conditional independence tests "
      + "to find a skeleton, finds V-nodes and applies a set of rules to find the directions "
      + "of the remaining arrows.";
  }

  /**
   * Returns the revision string.
   * 
   * @return the revision
   */
  @Override
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 10154 $");
  }

  /**
   * for testing the class
   * 
   * @param argv the commandline parameters
   */
  static public void main(String[] argv) {
    try {
      BayesNet b = new BayesNet();
      b.setSearchAlgorithm(new ICSSearchAlgorithm());
      Instances instances = new Instances(new FileReader(
        "C:\\eclipse\\workspace\\weka\\data\\contact-lenses.arff"));
      instances.setClassIndex(instances.numAttributes() - 1);
      b.buildClassifier(instances);
      System.out.println(b.toString());
    } catch (Exception e) {
      e.printStackTrace();
    }
  } // main

} // class ICSSearchAlgorithm