/* *    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. *//* *    ItemSet.java *    Copyright (C) 1999 Eibe Frank * */package weka.associations;import java.io.*;import java.util.*;import weka.core.*;/** * 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. * * @author Eibe Frank (eibe@cs.waikato.ac.nz) * @version $Revision: 1.8 $ */public class ItemSet implements Serializable {  /** The items stored as an array of of ints. */  protected int[] m_items;  /** Counter for how many transactions contain this item set. */  protected int m_counter;  /** The total number of transactions */  protected int m_totalTransactions;  /**   * Constructor   * @param totalTrans the total number of transactions in the data   */  public ItemSet(int totalTrans) {    m_totalTransactions = 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(ItemSet premise, 					 ItemSet 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(ItemSet premise, 			    ItemSet 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(ItemSet premise,				ItemSet 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(ItemSet premise,				   ItemSet 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;  }  /**   * Checks if an instance contains an item set.   *   * @param instance the instance to be tested   * @return true if the given instance contains this item set   */  public final boolean containedBy(Instance instance) {        for (int i = 0; i < instance.numAttributes(); i++)       if (m_items[i] > -1) {	if (instance.isMissing(i))	  return false;	if (m_items[i] != (int)instance.value(i))	  return false;      }    return true;  }  /**   * Deletes all item sets that don't have minimum support.   *   * @param itemSets the set of item sets to be pruned   * @param minSupport the minimum number of transactions to be covered   * @return the reduced set of item sets   */  public static FastVector deleteItemSets(FastVector itemSets, 					  int minSupport,					  int maxSupport) {    FastVector newVector = new FastVector(itemSets.size());    for (int i = 0; i < itemSets.size(); i++) {      ItemSet current = (ItemSet)itemSets.elementAt(i);      if ((current.m_counter >= minSupport) 	  && (current.m_counter <= maxSupport))	newVector.addElement(current);    }    return newVector;  }  /**   * Tests if two item sets are equal.   *   * @param itemSet another item set   * @return true if this item set contains the same items as the given one   */  public final boolean equals(Object itemSet) {    if ((itemSet == null) || !(itemSet.getClass().equals(this.getClass()))) {      return false;    }    if (m_items.length != ((ItemSet)itemSet).m_items.length)      return false;    for (int i = 0; i < m_items.length; i++)      if (m_items[i] != ((ItemSet)itemSet).m_items[i])	return false;    return true;  }  /**   * 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 final FastVector[] generateRules(double minConfidence, 					  FastVector hashtables,					  int numItemsInSet) {    FastVector premises = new FastVector(),consequences = new FastVector(),      conf = new FastVector();    FastVector[] rules = new FastVector[3], moreResults;    ItemSet 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 ItemSet(m_totalTransactions);	consequence = new ItemSet(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();	premises.addElement(premise);	consequences.addElement(consequence);	conf.addElement(new Double(confidenceForRule(premise, consequence)));      }    rules[0] = premises;    rules[1] = consequences;    rules[2] = conf;    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));      }    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 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];    ItemSet 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 ItemSet(m_totalTransactions);	consequence = new ItemSet(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,