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.
   */
  
  /*
   *    AprioriItemSet.java
   *    Copyright (C) 2004 University of Waikato, Hamilton, New Zealand
   *
   */
  
  package weka.associations;
  
  import weka.core.ContingencyTables;
  import weka.core.FastVector;
  import weka.core.Instances;
  import weka.core.RevisionHandler;
  import weka.core.RevisionUtils;
  
  import java.io.Serializable;
  import java.util.Enumeration;
  import java.util.Hashtable;
  
  
  /**
   * Class for storing a set of items. Item sets are stored in a lexicographic
   * order, which is determined by the header information of the set of instances
   * used for generating the set of items. All methods in this class assume that
   * item sets are stored in lexicographic order.
   * The class provides methods that are used in the Apriori algorithm to construct
   * association rules.
   *
   * @author Eibe Frank (eibe@cs.waikato.ac.nz)
   * @author Stefan Mutter (mutter@cs.waikato.ac.nz)
   * @version $Revision: 6555 $
   */
  public class AprioriItemSet 
    extends ItemSet 
    implements Serializable, RevisionHandler {
    
    /** for serialization */
    static final long serialVersionUID = 7684467755712672058L;
  
    /**
     * Constructor
     * 
     * @param totalTrans the total number of transactions in the data
     */
    public AprioriItemSet(int totalTrans) {
      super(totalTrans);
    }
  
    
    /**
     * Outputs the confidence for a rule.
     *
     * @param premise the premise of the rule
     * @param consequence the consequence of the rule
     * @return the confidence on the training data
     */
    public static double confidenceForRule(AprioriItemSet premise, 
  					 AprioriItemSet consequence) {
  
      return (double)consequence.m_counter/(double)premise.m_counter;
    }
  
    /**
     * Outputs the lift for a rule. Lift is defined as:<br>
     * confidence / prob(consequence)
     *
     * @param premise the premise of the rule
     * @param consequence the consequence of the rule
     * @param consequenceCount how many times the consequence occurs independent
     * of the premise
     * @return the lift on the training data
     */
    public double liftForRule(AprioriItemSet premise, 
  			    AprioriItemSet consequence,
  			    int consequenceCount) {
      double confidence = confidenceForRule(premise, consequence);
  
     return confidence / ((double)consequenceCount / 
  	  (double)m_totalTransactions);
    }
  
    /**
     * Outputs the leverage for a rule. Leverage is defined as: <br>
     * prob(premise & consequence) - (prob(premise) * prob(consequence))
     *
     * @param premise the premise of the rule
     * @param consequence the consequence of the rule
     * @param premiseCount how many times the premise occurs independent
     * of the consequent
     * @param consequenceCount how many times the consequence occurs independent
     * of the premise
     * @return the leverage on the training data
     */
    public double leverageForRule(AprioriItemSet premise,
  				AprioriItemSet consequence,
  				int premiseCount,
  				int consequenceCount) {
      double coverageForItemSet = (double)consequence.m_counter / 
        (double)m_totalTransactions;
      double expectedCoverageIfIndependent = 
        ((double)premiseCount / (double)m_totalTransactions) * 
        ((double)consequenceCount / (double)m_totalTransactions);
      double lev = coverageForItemSet - expectedCoverageIfIndependent;
      return lev;
    }
  
    /**
     * Outputs the conviction for a rule. Conviction is defined as: <br>
     * prob(premise) * prob(!consequence) / prob(premise & !consequence)
     *
     * @param premise the premise of the rule
     * @param consequence the consequence of the rule
     * @param premiseCount how many times the premise occurs independent
     * of the consequent
     * @param consequenceCount how many times the consequence occurs independent
     * of the premise
     * @return the conviction on the training data
     */
    public double convictionForRule(AprioriItemSet premise,
  				   AprioriItemSet consequence,
  				   int premiseCount,
  				   int consequenceCount) {
      double num = 
        (double)premiseCount * (double)(m_totalTransactions - consequenceCount) /
         (double)m_totalTransactions;
      double denom = 
        ((premiseCount - consequence.m_counter)+1);
      
      if (num < 0 || denom < 0) {
        System.err.println("*** "+num+" "+denom);
        System.err.println("premis count: "+premiseCount+" consequence count "+consequenceCount+" total trans "+m_totalTransactions);
      }
      return num / denom;
    }
  
    
    
    /**
     * Generates all rules for an item set.
     *
     * @param minConfidence the minimum confidence the rules have to have
     * @param hashtables containing all(!) previously generated
     * item sets
     * @param numItemsInSet the size of the item set for which the rules
     * are to be generated
     * @return all the rules with minimum confidence for the given item set
     */
    public FastVector[] generateRules(double minConfidence, 
  					  FastVector hashtables,
  					  int numItemsInSet) {
  
      FastVector premises = new FastVector(),consequences = new FastVector(),
        conf = new FastVector();
      // TODO
      FastVector lift = new FastVector(), lev = new FastVector(),
      conv = new FastVector();
      //TODO
      FastVector[] rules = new FastVector[6], moreResults;
      AprioriItemSet premise, consequence;
      Hashtable hashtable = (Hashtable)hashtables.elementAt(numItemsInSet - 2);
  
      // Generate all rules with one item in the consequence.
      for (int i = 0; i < m_items.length; i++) 
        if (m_items[i] != -1) {
          premise = new AprioriItemSet(m_totalTransactions);
          consequence = new AprioriItemSet(m_totalTransactions);
          premise.m_items = new int[m_items.length];
          consequence.m_items = new int[m_items.length];
          consequence.m_counter = m_counter;
  
          for (int j = 0; j < m_items.length; j++) 
            consequence.m_items[j] = -1;
          System.arraycopy(m_items, 0, premise.m_items, 0, m_items.length);
          premise.m_items[i] = -1;
  
          consequence.m_items[i] = m_items[i];
          premise.m_counter = ((Integer)hashtable.get(premise)).intValue();
  
          Hashtable hashtableForConsequence = 
            (Hashtable)hashtables.elementAt(0);
          int consequenceUnconditionedCounter =
            ((Integer)hashtableForConsequence.get(consequence)).intValue();
          
          premises.addElement(premise);
          consequences.addElement(consequence);
          conf.addElement(new Double(confidenceForRule(premise, consequence)));
          
  
          double tempLift = liftForRule(premise, consequence, 
              consequenceUnconditionedCounter);
          double tempLev = leverageForRule(premise, consequence,
              premise.m_counter,
              consequenceUnconditionedCounter);
          double tempConv = convictionForRule(premise, consequence,
              premise.m_counter,
              consequenceUnconditionedCounter);
          lift.addElement(new Double(tempLift));
          lev.addElement(new Double(tempLev));
          conv.addElement(new Double(tempConv));
        }
      rules[0] = premises;
      rules[1] = consequences;
      rules[2] = conf;
  
      rules[3] = lift;
      rules[4] = lev;
      rules[5] = conv;
  
      pruneRules(rules, minConfidence);
  
      // Generate all the other rules
      moreResults = moreComplexRules(rules, numItemsInSet, 1, minConfidence,
  				   hashtables);
      if (moreResults != null) 
        for (int i = 0; i < moreResults[0].size(); i++) {
  	rules[0].addElement(moreResults[0].elementAt(i));
  	rules[1].addElement(moreResults[1].elementAt(i));
  	rules[2].addElement(moreResults[2].elementAt(i));
  	
  	// TODO
  	rules[3].addElement(moreResults[3].elementAt(i));
  	rules[4].addElement(moreResults[4].elementAt(i));
  	rules[5].addElement(moreResults[5].elementAt(i));
        }
      return rules;
    }
  
  
    
    /**
     * Generates all significant rules for an item set.
     *
     * @param minMetric the minimum metric (confidence, lift, leverage, 
     * improvement) the rules have to have
     * @param metricType (confidence=0, lift, leverage, improvement)
     * @param hashtables containing all(!) previously generated
     * item sets
     * @param numItemsInSet the size of the item set for which the rules
     * are to be generated
     * @param numTransactions
     * @param significanceLevel the significance level for testing the rules
     * @return all the rules with minimum metric for the given item set
     * @exception Exception if something goes wrong
     */
    public final FastVector[] generateRulesBruteForce(double minMetric,
  						    int metricType,
  						FastVector hashtables,
  						int numItemsInSet,
  						int numTransactions,
  						double significanceLevel) 
    throws Exception {
  
      FastVector premises = new FastVector(),consequences = new FastVector(),
        conf = new FastVector(), lift = new FastVector(), lev = new FastVector(),
        conv = new FastVector(); 
      FastVector[] rules = new FastVector[6];
      AprioriItemSet premise, consequence;
      Hashtable hashtableForPremise, hashtableForConsequence;
      int numItemsInPremise, help, max, consequenceUnconditionedCounter;
      double[][] contingencyTable = new double[2][2];
      double metric, chiSquared;
  
      // Generate all possible rules for this item set and test their
      // significance.
      max = (int)Math.pow(2, numItemsInSet);
      for (int j = 1; j < max; j++) {
        numItemsInPremise = 0;
        help = j;
        while (help > 0) {
  	if (help % 2 == 1)
  	  numItemsInPremise++;
  	help /= 2;
        }
        if (numItemsInPremise < numItemsInSet) {
  	hashtableForPremise = 
  	  (Hashtable)hashtables.elementAt(numItemsInPremise-1);
  	hashtableForConsequence = 
  	  (Hashtable)hashtables.elementAt(numItemsInSet-numItemsInPremise-1);
  	premise = new AprioriItemSet(m_totalTransactions);
  	consequence = new AprioriItemSet(m_totalTransactions);
  	premise.m_items = new int[m_items.length];
          
  	consequence.m_items = new int[m_items.length];
  	consequence.m_counter = m_counter;
  	help = j;
  	for (int i = 0; i < m_items.length; i++) 
  	  if (m_items[i] != -1) {
  	    if (help % 2 == 1) {          
  	      premise.m_items[i] = m_items[i];
  	      consequence.m_items[i] = -1;
  	    } else {
  	      premise.m_items[i] = -1;
  	      consequence.m_items[i] = m_items[i];
  	    }
  	    help /= 2;
  	  } else {
  	    premise.m_items[i] = -1;
  	    consequence.m_items[i] = -1;
  	  }
  	premise.m_counter = ((Integer)hashtableForPremise.get(premise)).intValue();
  	consequenceUnconditionedCounter =
  	  ((Integer)hashtableForConsequence.get(consequence)).intValue();
  
  	if (metricType == 0) {
  	  contingencyTable[0][0] = (double)(consequence.m_counter);
  	  contingencyTable[0][1] = (double)(premise.m_counter - consequence.m_counter);
  	  contingencyTable[1][0] = (double)(consequenceUnconditionedCounter -
  					    consequence.m_counter);
  	  contingencyTable[1][1] = (double)(numTransactions - premise.m_counter -
  					    consequenceUnconditionedCounter +
  					    consequence.m_counter);
  	  chiSquared = ContingencyTables.chiSquared(contingencyTable, false);
  	
  	  metric = confidenceForRule(premise, consequence);
  	
  	  if ((!(metric < minMetric)) &&
  	      (!(chiSquared > significanceLevel))) {
  	    premises.addElement(premise);
  	    consequences.addElement(consequence);
  	    conf.addElement(new Double(metric));
  	    lift.addElement(new Double(liftForRule(premise, consequence, 
  				       consequenceUnconditionedCounter)));
  	    lev.addElement(new Double(leverageForRule(premise, consequence,
  				     premise.m_counter,
  				     consequenceUnconditionedCounter)));
  	    conv.addElement(new Double(convictionForRule(premise, consequence,
  				       premise.m_counter,
  				       consequenceUnconditionedCounter)));
  	  }
  	} else {
  	  double tempConf = confidenceForRule(premise, consequence);
  	  double tempLift = liftForRule(premise, consequence, 
  					consequenceUnconditionedCounter);
  	  double tempLev = leverageForRule(premise, consequence,
  					   premise.m_counter,
  					   consequenceUnconditionedCounter);
  	  double tempConv = convictionForRule(premise, consequence,
  					      premise.m_counter,
  					      consequenceUnconditionedCounter);
  	  switch(metricType) {
  	  case 1: 
  	    metric = tempLift;
  	    break;
  	  case 2:
  	    metric = tempLev;
  	    break;
  	  case 3: 
  	    metric = tempConv;
  	    break;
  	  default:
  	    throw new Exception("ItemSet: Unknown metric type!");
  	  }
  	  if (!(metric < minMetric)) {
  	    premises.addElement(premise);
  	    consequences.addElement(consequence);
  	    conf.addElement(new Double(tempConf));
  	    lift.addElement(new Double(tempLift));
  	    lev.addElement(new Double(tempLev));
  	    conv.addElement(new Double(tempConv));
  	  }
  	}
        }
      }
      rules[0] = premises;
      rules[1] = consequences;
      rules[2] = conf;
      rules[3] = lift;
      rules[4] = lev;
      rules[5] = conv;
      return rules;
    }
  
    /**
     * Subtracts an item set from another one.
     *
     * @param toSubtract the item set to be subtracted from this one.
     * @return an item set that only contains items form this item sets that
     * are not contained by toSubtract
     */
    public final AprioriItemSet subtract(AprioriItemSet toSubtract) {
  
      AprioriItemSet result = new AprioriItemSet(m_totalTransactions);
      
      result.m_items = new int[m_items.length];
     
      for (int i = 0; i < m_items.length; i++) 
        if (toSubtract.m_items[i] == -1)
  	result.m_items[i] = m_items[i];
        else
  	result.m_items[i] = -1;
      result.m_counter = 0;
      return result;
    }
  
  
    /**
     * Generates rules with more than one item in the consequence.
     *
     * @param rules all the rules having (k-1)-item sets as consequences
     * @param numItemsInSet the size of the item set for which the rules
     * are to be generated
     * @param numItemsInConsequence the value of (k-1)
     * @param minConfidence the minimum confidence a rule has to have
     * @param hashtables the hashtables containing all(!) previously generated
     * item sets
     * @return all the rules having (k)-item sets as consequences
     */
    private final FastVector[] moreComplexRules(FastVector[] rules, 
  					      int numItemsInSet, 
  					      int numItemsInConsequence,
  					      double minConfidence, 
  					      FastVector hashtables) {
  
      AprioriItemSet newPremise;
      FastVector[] result, moreResults;
      FastVector newConsequences, newPremises = new FastVector(), 
        newConf = new FastVector();
      Hashtable hashtable;
      
      FastVector newLift = null, newLev = null, newConv = null;
  //    if (rules.length > 3) {
        newLift = new FastVector(); newLev = new FastVector(); newConv = new FastVector();
  //    }
  
      if (numItemsInSet > numItemsInConsequence + 1) {
        hashtable =
  	(Hashtable)hashtables.elementAt(numItemsInSet - numItemsInConsequence - 2);
        newConsequences = mergeAllItemSets(rules[1], 
  					 numItemsInConsequence - 1,
  					 m_totalTransactions);
        int newNumInConsequence = numItemsInConsequence + 1;
  
        Hashtable hashtableForConsequence = (Hashtable)hashtables.elementAt(newNumInConsequence-1);
        
        Enumeration enu = newConsequences.elements();
        while (enu.hasMoreElements()) {
  	AprioriItemSet current = (AprioriItemSet)enu.nextElement();
  	int z =0;
  	for (int jj = 0; jj < current.m_items.length; jj++) {
  	  if (current.m_items[jj] != -1) {
  	    z++;
  	  }
  	}
  	
  	current.m_counter = m_counter;
  	newPremise = subtract(current);
  	newPremise.m_counter = ((Integer)hashtable.get(newPremise)).intValue();
  	newPremises.addElement(newPremise);
  	newConf.addElement(new Double(confidenceForRule(newPremise, current)));
  	
  //	if (rules.length > 3) {
  	  int consequenceUnconditionedCounter =
  	    ((Integer)hashtableForConsequence.get(current)).intValue();
  
  	  double tempLift = liftForRule(newPremise, current, 
  	      consequenceUnconditionedCounter);
  	  double tempLev = leverageForRule(newPremise, current,
  	      newPremise.m_counter,
  	      consequenceUnconditionedCounter);
  	  double tempConv = convictionForRule(newPremise, current,
  	      newPremise.m_counter,
  	      consequenceUnconditionedCounter);
  
  	  newLift.addElement(new Double(tempLift));
  	  newLev.addElement(new Double(tempLev));
  	  newConv.addElement(new Double(tempConv));
  //	}
        }
        result = new FastVector[rules.length];
        result[0] = newPremises;
        result[1] = newConsequences;
        result[2] = newConf;
        
    //    if (rules.length > 3) {
          result[3] = newLift; result[4] = newLev; result[5] = newConv;
  //      }
        pruneRules(result, minConfidence);
        moreResults = moreComplexRules(result,numItemsInSet,numItemsInConsequence+1,
  				     minConfidence, hashtables);
        if (moreResults != null) 
  	for (int i = 0; i < moreResults[0].size(); i++) {
  	  result[0].addElement(moreResults[0].elementAt(i));
  	  result[1].addElement(moreResults[1].elementAt(i));
  	  result[2].addElement(moreResults[2].elementAt(i));
  	  //
  	  result[3].addElement(moreResults[3].elementAt(i));
  	  result[4].addElement(moreResults[4].elementAt(i));
  	  result[5].addElement(moreResults[5].elementAt(i));
  	}
        return result;
      } else
        return null;
    }
    
    
     /**
     * Returns the contents of an item set as a string.
     *
     * @param instances contains the relevant header information
     * @return string describing the item set
     */
    public final String toString(Instances instances) {
     
        return super.toString(instances);
    }
    
    /**
     * 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,
        boolean treatZeroAsMissing) throws Exception {
  
      FastVector setOfItemSets = new FastVector();
      ItemSet current;
  
      for (int i = 0; i < instances.numAttributes(); i++) {
        if (instances.attribute(i).isNumeric())
  	throw new Exception("Can't handle numeric attributes!");
        int j = (treatZeroAsMissing) ? 1 : 0;
        for (; j < instances.attribute(i).numValues(); j++) {
  	current = new AprioriItemSet(instances.numInstances());
  	current.setTreatZeroAsMissing(treatZeroAsMissing);
  	current.m_items = new int[instances.numAttributes()];
  	for (int k = 0; k < instances.numAttributes(); k++)
  	  current.m_items[k] = -1;
  	current.m_items[i] = j;
  	setOfItemSets.addElement(current);
        }
      }
      return setOfItemSets;
    }
    
    /**
     * Merges all item sets in the set of (k-1)-item sets 
     * to create the (k)-item sets and updates the counters.
     *
     * @param itemSets the set of (k-1)-item sets
     * @param size the value of (k-1)
     * @param totalTrans the total number of transactions in the data
     * @return the generated (k)-item sets
     */
    public static FastVector mergeAllItemSets(FastVector itemSets, int size, 
  					    int totalTrans) {
  
      FastVector newVector = new FastVector();
      ItemSet result;
      int numFound, k;
  
      for (int i = 0; i < itemSets.size(); i++) {
        ItemSet first = (ItemSet)itemSets.elementAt(i);
      out:
        for (int j = i+1; j < itemSets.size(); j++) {
  	ItemSet second = (ItemSet)itemSets.elementAt(j);
  	result = new AprioriItemSet(totalTrans);
  	result.m_items = new int[first.m_items.length];
  
  	// Find and copy common prefix of size 'size'
  	numFound = 0;
  	k = 0;
  	while (numFound < size) {
  	  if (first.m_items[k] == second.m_items[k]) {
  	    if (first.m_items[k] != -1) 
  	      numFound++;
  	    result.m_items[k] = first.m_items[k];
  	  } else 
  	    break out;
  	  k++;
  	}
  	
  	// Check difference
  	while (k < first.m_items.length) {
  	  if ((first.m_items[k] != -1) && (second.m_items[k] != -1))
  	    break;
  	  else {
  	    if (first.m_items[k] != -1)
  	      result.m_items[k] = first.m_items[k];
  	    else
  	      result.m_items[k] = second.m_items[k];
  	  }
  	  k++;
  	}
  	if (k == first.m_items.length) {
  	  result.m_counter = 0;
  	  newVector.addElement(result);
  	}
        }
      }
      return newVector;
    }
    
    /**
     * Returns the revision string.
     * 
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 6555 $");
    }
  }
  