/*
 *    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.
 */

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

package weka.associations;

import weka.core.Attribute;
import weka.core.FastVector;
import weka.core.Instances;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
import weka.core.UnassignedClassException;

import java.io.Serializable;
import java.util.Hashtable;
import java.util.TreeSet;


/**
 * Class implementing the rule generation procedure of the predictive apriori algorithm for class association rules.
 *
 * For association rules in gerneral the method is described in:
 * T. Scheffer (2001). <i>Finding Association Rules That Trade Support 
 * Optimally against Confidence</i>. Proc of the 5th European Conf.
 * on Principles and Practice of Knowledge Discovery in Databases (PKDD'01),
 * pp. 424-435. Freiburg, Germany: Springer-Verlag. <p>
 *
 * The implementation follows the paper expect for adding a rule to the output of the
 * <i>n</i> best rules. A rule is added if:
 * the expected predictive accuracy of this rule is among the <i>n</i> best and it is 
 * not subsumed by a rule with at least the same expected predictive accuracy
 * (out of an unpublished manuscript from T. Scheffer). 
 *
 * @author Stefan Mutter (mutter@cs.waikato.ac.nz)
 * @version $Revision: 1.4 $ */
public class CaRuleGeneration
  extends RuleGeneration
  implements Serializable, RevisionHandler {

  /** for serialization */
  private static final long serialVersionUID = 3065752149646517703L;

  /**
   * Constructor
   * @param itemSet the item set that forms the premise of the rule
   */
  public CaRuleGeneration(ItemSet itemSet){
    super(itemSet);  
  }

  /**
   * Generates all rules for an item set. The item set is the premise.
   * @param numRules the number of association rules the use wants to mine.
   * This number equals the size <i>n</i> of the list of the
   * best rules.
   * @param midPoints the mid points of the intervals
   * @param priors Hashtable that contains the prior probabilities
   * @param expectation the minimum value of the expected predictive accuracy
   * that is needed to get into the list of the best rules
   * @param instances the instances for which association rules are generated
   * @param best the list of the <i>n</i> best rules.
   * The list is implemented as a TreeSet
   * @param genTime the maximum time of generation
   * @return all the rules with minimum confidence for the given item set
   */
  public TreeSet generateRules(int numRules, double[] midPoints, Hashtable priors, double expectation, Instances instances, TreeSet best, int genTime) {

    boolean redundant = false;
    FastVector consequences = new FastVector();
    ItemSet premise;
    RuleItem current = null, old = null;

    Hashtable hashtable;

    m_change = false;
    m_midPoints = midPoints;
    m_priors = priors;
    m_best = best;
    m_expectation = expectation;
    m_count = genTime;
    m_instances = instances;

    //create rule body
    premise =null;
    premise = new ItemSet(m_totalTransactions);
    int[] premiseItems = new int[m_items.length];
    System.arraycopy(m_items, 0, premiseItems, 0, m_items.length);
    premise.setItem(premiseItems);
    premise.setCounter(m_counter);

    consequences = singleConsequence(instances);

    //create n best rules
    do{
      if(premise == null || consequences.size() == 0)
	return m_best;
      m_minRuleCount = 1;
      while(expectation((double)m_minRuleCount,premise.counter(),m_midPoints,m_priors) <= m_expectation){
	m_minRuleCount++;
	if(m_minRuleCount > premise.counter())
	  return m_best;
      }
      redundant = false;

      //create possible heads  
      FastVector allRuleItems = new FastVector();
      int h = 0;
      while(h < consequences.size()){
	RuleItem dummie = new RuleItem();
	m_count++;
	current = dummie.generateRuleItem(premise,(ItemSet)consequences.elementAt(h),instances,m_count,m_minRuleCount,m_midPoints,m_priors);
	if(current != null)
	  allRuleItems.addElement(current);
	h++;
      }

      //update best
      for(h =0; h< allRuleItems.size();h++){
	current = (RuleItem)allRuleItems.elementAt(h);
	if(m_best.size() < numRules){
	  m_change =true;
	  redundant = removeRedundant(current);  
	}
	else{
	  m_expectation = ((RuleItem)(m_best.first())).accuracy();
	  if(current.accuracy() > m_expectation){
	    boolean remove = m_best.remove(m_best.first());
	    m_change = true;
	    redundant = removeRedundant(current);
	    m_expectation = ((RuleItem)(m_best.first())).accuracy();
	    while(expectation((double)m_minRuleCount, (current.premise()).counter(),m_midPoints,m_priors) < m_expectation){
	      m_minRuleCount++;
	      if(m_minRuleCount > (current.premise()).counter())
		break;
	    } 
	  }  
	}   
      }   
    }while(redundant); 
    return m_best;
  }

  /**
   * Methods that decides whether or not rule a subsumes rule b.
   * The defintion of subsumption is:
   * Rule a subsumes rule b, if a subsumes b
   * AND
   * a has got least the same expected predictive accuracy as b.
   * @param a an association rule stored as a RuleItem
   * @param b an association rule stored as a RuleItem
   * @return true if rule a subsumes rule b or false otherwise.
   */  
  public static boolean aSubsumesB(RuleItem a, RuleItem b){

    if(!a.consequence().equals(b.consequence()))
      return false;
    if(a.accuracy() < b.accuracy())
      return false;
    for(int k = 0; k < ((a.premise()).items()).length;k++){
      if((a.premise()).itemAt(k) != (b.premise()).itemAt(k)){
	if(((a.premise()).itemAt(k) != -1 && (b.premise()).itemAt(k) != -1) || (b.premise()).itemAt(k) == -1)
	  return false;
      }
      /*if(a.m_consequence.m_items[k] != b.m_consequence.m_items[k]){
            if((a.m_consequence.m_items[k] != -1 && b.m_consequence.m_items[k] != -1) || a.m_consequence.m_items[k] == -1)
                return false;
        }*/
    }
    return true;

  }

  /**
   * Converts the header info of the given set of instances into a set 
   * of item sets (singletons). The ordering of values in the header file 
   * determines the lexicographic order.
   *
   * @param instances the set of instances whose header info is to be used
   * @return a set of item sets, each containing a single item
   * @exception Exception if singletons can't be generated successfully
   */
  public static FastVector singletons(Instances instances) throws Exception {

    FastVector setOfItemSets = new FastVector();
    ItemSet current;

    if(instances.classIndex() == -1)
      throw new UnassignedClassException("Class index is negative (not set)!");
    Attribute att = instances.classAttribute();
    for (int i = 0; i < instances.numAttributes(); i++) {
      if (instances.attribute(i).isNumeric())
	throw new Exception("Can't handle numeric attributes!");
      if(i != instances.classIndex()){
	for (int j = 0; j < instances.attribute(i).numValues(); j++) {
	  current = new ItemSet(instances.numInstances());
	  int[] currentItems = new int[instances.numAttributes()];
	  for (int k = 0; k < instances.numAttributes(); k++)
	    currentItems[k] = -1;
	  currentItems[i] = j;
	  current.setItem(currentItems);
	  setOfItemSets.addElement(current);
	}
      }
    }
    return setOfItemSets;
  }

  /**
   * generates a consequence of length 1 for a class association rule.
   * @param instances the instances under consideration
   * @return FastVector with consequences of length 1
   */  
  public static FastVector singleConsequence(Instances instances){

    ItemSet consequence;
    FastVector consequences = new FastVector();

    for (int j = 0; j < (instances.classAttribute()).numValues(); j++) {
      consequence = new ItemSet(instances.numInstances());
      int[] consequenceItems = new int[instances.numAttributes()];
      consequence.setItem(consequenceItems);
      for (int k = 0; k < instances.numAttributes(); k++) 
	consequence.setItemAt(-1,k);
      consequence.setItemAt(j,instances.classIndex());
      consequences.addElement(consequence);
    }
    return consequences;

  }

  /**
   * Returns the revision string.
   * 
   * @return		the revision
   */
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 1.4 $");
  }
}