/**
* Run the algorithm.
*
* @param minSupport Minsup as a percentage (ex: 0.05 = 5 %)
* @param minConfidence minimum confidence (a value between 0 and 1).
* @param input the input file path
* @param output the output file path
* @param windowSize a window size
* @throws IOException exception if there is an error reading/writing files
*/
public void runAlgorithm(
double minSupport, double minConfidence, String input, String output, int windowSize)
throws IOException {
// load the input file into memory
try {
this.database = new SequenceDatabase();
database.loadFile(input);
} catch (Exception e) {
e.printStackTrace();
}
// convert minimum support to a relative minimum support (integer)
this.minsuppRelative = (int) Math.ceil(minSupport * database.size());
// run the algorithm
runAlgorithm(input, output, minsuppRelative, minConfidence, windowSize);
}
/**
* This method calculate the frequency of each item in one database pass. Then it remove all items
* that are not frequent.
*
* @param database : a sequence database
* @return A map such that key = item value = a map where a key = tid and a value = Occurence This
* map allows knowing the frequency of each item and their first and last occurence in each
* sequence.
*/
private Map<String, Map<Integer, Occurence>> removeItemsThatAreNotFrequent(
SequenceDatabase database) {
// (1) Count the support of each item in the database in one database pass
mapItemCount = new HashMap<String, Map<Integer, Occurence>>(); // <item, Map<tid, occurence>>
// for each sequence
for (Sequence sequence : database.getSequences()) {
// for each itemset
for (short j = 0; j < sequence.getItemsets().size(); j++) {
List<String> itemset = sequence.get(j);
// for each item
for (int i = 0; i < itemset.size(); i++) {
String itemI = itemset.get(i);
Map<Integer, Occurence> occurences = mapItemCount.get(itemI);
if (occurences == null) {
occurences = new HashMap<Integer, Occurence>();
mapItemCount.put(itemI, occurences);
}
Occurence occurence = occurences.get(sequence.getId());
if (occurence == null) {
occurence = new Occurence(sequence.getId());
occurences.put(sequence.getId(), occurence);
}
occurence.add(j);
}
}
}
// System.out.println("NUMBER OF DIFFERENT ITEMS : " + mapItemCount.size());
// (2) remove all items that are not frequent from the database
for (Sequence sequence : database.getSequences()) {
int i = 0;
while (i < sequence.getItemsets().size()) {
List<String> itemset = sequence.getItemsets().get(i);
int j = 0;
while (j < itemset.size()) {
double count = mapItemCount.get(itemset.get(j)).size();
if (count < minsuppRelative) {
itemset.remove(j);
} else {
j++;
}
}
i++;
}
}
return mapItemCount;
}
/**
* For each item, calculate the sequence id of sequences containing that item
*
* @param database the current sequence database
* @return Map of items to sequence IDs that contains each item
*/
private Map<String, Set<Integer>> findSequencesContainingItems(SequenceDatabase contexte) {
// We use a map to store the sequence IDs where an item appear
// Key : item Value : a set of sequence IDs
Map<String, Set<Integer>> mapSequenceID =
new HashMap<
String, Set<Integer>>(); // pour conserver les ID des séquences: <Id Item, Set d'id de
// séquences>
// for each sequence in the current database
for (Sequence sequence : contexte.getSequences()) {
// for each itemset in this sequence
for (List<String> itemset : sequence.getItemsets()) {
// for each item
for (String item : itemset) {
// get the set of sequence IDs for this item until now
Set<Integer> sequenceIDs = mapSequenceID.get(item);
if (sequenceIDs == null) {
// if the set does not exist, create one
sequenceIDs = new HashSet<Integer>();
mapSequenceID.put(item, sequenceIDs);
}
// add the sequence ID of the current sequence to the
// set of sequences IDs of this item
sequenceIDs.add(sequence.getId());
// }
}
}
}
return mapSequenceID;
}
/**
* This is the main method for the PrefixSpan algorithm that is called to start the algorithm
*
* @param outputFilePath an output file path if the result should be saved to a file or null if
* the result should be saved to memory.
* @param database a sequence database
* @throws IOException exception if an error while writing the output file
*/
private void prefixSpan(SequenceDatabase database, String outputFilePath) throws IOException {
// if the user want to keep the result into memory
if (outputFilePath == null) {
writer = null;
patterns = new SequentialPatterns("FREQUENT SEQUENTIAL PATTERNS");
} else { // if the user want to save the result to a file
patterns = null;
writer = new BufferedWriter(new FileWriter(outputFilePath));
}
// We have to scan the database to find all frequent patterns of size 1.
// We note the sequences in which these patterns appear.
Map<String, Set<Integer>> mapSequenceID = findSequencesContainingItems(database);
// WE CONVERT THE DATABASE ITON A PSEUDO-DATABASE, AND REMOVE
// THE ITEMS OF SIZE 1 THAT ARE NOT FREQUENT, SO THAT THE ALGORITHM
// WILL NOT CONSIDER THEM ANYMORE. (OPTIMIZATION : OCTOBER-08 )
// Create a list of pseudosequence
List<PseudoSequence> initialContext = new ArrayList<PseudoSequence>();
// for each sequence in the database
for (Sequence sequence : database.getSequences()) {
// remove infrequent items
Sequence optimizedSequence = sequence.cloneSequenceMinusItems(mapSequenceID, minsuppAbsolute);
if (optimizedSequence.size() != 0) {
// if the size is > 0, create a pseudo sequence with this sequence
initialContext.add(new PseudoSequence(optimizedSequence, 0, 0));
}
}
// For each item
for (Entry<String, Set<Integer>> entry : mapSequenceID.entrySet()) {
// if the item is frequent (has a support >= minsup)
if (entry.getValue().size() >= minsuppAbsolute) { // if the item is frequent
// build the projected context
String item = entry.getKey();
List<PseudoSequence> projectedContext = buildProjectedContext(item, initialContext, false);
// Create the prefix for the projected context.
SequentialPattern prefix = new SequentialPattern(0);
prefix.addItemset(new Itemset(item));
prefix.setSequencesID(entry.getValue());
// The prefix is a frequent sequential pattern.
// We save it in the result.
savePattern(prefix); // we found a sequence.
// Recursive call !
recursion(prefix, projectedContext);
}
}
}
/**
* This method search for items for expanding left side of a rule I --> J with any item c. This
* results in rules of the form I --> J U�{c}. The method makes sure that: - c is not already
* included in I or J - c appear at least minsup time in tidsIJ after the first occurence of I - c
* is lexically bigger than all items in J
*
* @param mapWindowsJI
* @throws IOException
*/
private void expandRight(
String[] itemsetI,
String[] itemsetJ,
Set<Integer> tidsI,
Collection<Integer> tidsJ,
Collection<Integer> tidsIJ // ,
// Map<Integer, Occurence> occurencesI,
// Map<Integer, Occurence> occurencesJ
) throws IOException {
// // map-key: item map-value: set of tids containing the item
Map<String, Set<Integer>> frequentItemsC = new HashMap<String, Set<Integer>>();
// for each sequence containing I-->J
for (Integer tid : tidsIJ) {
Sequence sequence = database.getSequences().get(tid);
LinkedHashMap<String, Integer> mapMostRightFromI = new LinkedHashMap<String, Integer>();
LinkedHashMap<String, Integer> mapMostRightFromJ = new LinkedHashMap<String, Integer>();
LinkedHashMap<String, LinkedList<Integer>> mapMostLeftFromI =
new LinkedHashMap<String, LinkedList<Integer>>();
int lastItemsetScannedForC = Integer.MIN_VALUE;
// For each itemset starting from the first...
int k = 0;
do {
final int firstElementOfWindow = k - windowSize + 1;
int lastElementOfWindow = k;
// remove items from I that fall outside the time window
int previousISize = mapMostRightFromI.size();
removeElementOutsideWindowER(mapMostRightFromI, firstElementOfWindow);
// important: if I was all there, but become smaller we need to clear the
// hashmap for items of J.
int currentISize = mapMostRightFromI.size();
if (previousISize == itemsetJ.length && previousISize != currentISize) {
mapMostRightFromJ.clear();
}
// remove items from J that fall outside the time window
removeElementOutsideWindowER(mapMostRightFromJ, firstElementOfWindow);
// For each item of the current itemset
for (String item : sequence.get(k)) {
// record the first position until now of each item in I or J
if (mapMostRightFromI.size() == itemsetI.length && contains(itemsetJ, item)) {
addToLinked(mapMostRightFromJ, item, k);
} else if (contains(itemsetI, item)) {
addToLinked(mapMostRightFromI, item, k);
LinkedList<Integer> list = mapMostLeftFromI.get(item);
if (list == null) {
list = new LinkedList<Integer>();
addToLinked(mapMostLeftFromI, item, list);
}
list.add(k);
}
}
// if all the items of IJ are in the current window
if (mapMostRightFromI.size() == itemsetI.length
&& mapMostRightFromJ.size() == itemsetJ.length) {
// remove items from mostLeft that fall outside the time window.
// at the same time, calculate the minimum index for items of I.
int minimum = 1;
for (LinkedList<Integer> list : mapMostLeftFromI.values()) {
while (true) {
Integer last = list.getLast();
if (last < firstElementOfWindow) {
list.removeLast();
} else {
if (last > minimum) {
minimum = last + 1;
}
break;
}
}
}
// we need to scan for items C to extend the rule...
// Such item c has to appear in the window before the last occurence of J (before
// "minimum")
// and if it was scanned before, it should not be scanned again.
int itemsetC = minimum;
if (itemsetC < lastItemsetScannedForC) {
itemsetC = lastItemsetScannedForC + 1;
}
for (; itemsetC <= lastElementOfWindow; itemsetC++) {
for (String itemC : sequence.get(itemsetC)) {
// if lexical order is not respected or c is included in the rule
// already.
if (containsLEX(itemsetI, itemC) || containsLEXPlus(itemsetJ, itemC)) {
continue;
}
Set<Integer> tidsItemC = frequentItemsC.get(itemC);
if (tidsItemC == null) {
tidsItemC = new HashSet<Integer>();
frequentItemsC.put(itemC, tidsItemC);
}
tidsItemC.add(tid);
}
}
lastItemsetScannedForC = lastElementOfWindow;
}
k++;
} while (k < sequence.size() && lastItemsetScannedForC < sequence.size() - 1);
}
////////////////////////////////////////////////////////////////////////
// for each item c found, we create a rule
for (Entry<String, Set<Integer>> entry : frequentItemsC.entrySet()) {
Set<Integer> tidsI_JC = entry.getValue();
// if the support is enough Sup(R) = sup(IC -->J)
if (tidsI_JC.size() >= minsuppRelative) {
String itemC = entry.getKey();
String[] itemsetJC = new String[itemsetJ.length + 1];
System.arraycopy(itemsetJ, 0, itemsetJC, 0, itemsetJ.length);
itemsetJC[itemsetJ.length] = itemC;
//
// Itemset itemsetJC = new Itemset(ruleIJ.getItemset2());
// itemsetJC.addItem(itemC);
// ---- CALCULATE ALL THE TIDS CONTAINING JC WITHIN A TIME WINDOW ---
Set<Integer> tidsJC = new HashSet<Integer>();
loop1:
for (Integer tid : tidsJ) {
Sequence sequence = database.getSequences().get(tid);
// MAP: item : itemset index
LinkedHashMap<String, Integer> mapAlreadySeenFromJC =
new LinkedHashMap<String, Integer>();
// For each itemset
for (int k = 0; k < sequence.size(); k++) {
// For each item
for (String item : sequence.get(k)) {
if (contains(itemsetJC, item)) { // record the last position of each item in JC
addToLinked(mapAlreadySeenFromJC, item, k);
}
}
// remove items that fall outside the time window
Iterator<Entry<String, Integer>> iter = mapAlreadySeenFromJC.entrySet().iterator();
while (iter.hasNext()) {
Entry<String, Integer> entryMap = iter.next();
if (entryMap.getValue() < k - windowSize + 1) {
iter.remove();
} else {
break;
}
}
// if all the items of I are inside the current window, then record the tid
if (mapAlreadySeenFromJC.keySet().size() == itemsetJC.length) {
tidsJC.add(tid);
continue loop1;
}
}
}
// ---- ----
// Create rule and calculate its confidence: Conf(r) = sup(I-->JC) / sup(I)
double confI_JC = ((double) tidsI_JC.size()) / tidsI.size();
// Rule ruleI_JC = new Rule(ruleIJ.getItemset1(), itemsetJC, confI_JC, tidsI_JC.size());
// if the confidence is enough
if (confI_JC >= minconf) {
saveRule(tidsI_JC, confI_JC, itemsetI, itemsetJC);
}
expandRight(itemsetI, itemsetJC, tidsI, tidsJC, tidsI_JC); //
// recursive call to expand left and right side of the rule
expandLeft(itemsetI, itemsetJC, tidsI, tidsI_JC); // occurencesJ
}
}
MemoryLogger.getInstance().checkMemory();
}
/**
* Run the algorithm.
*
* @param relativeMinSupport minsup as a a relative value (integer)
* @param minConfidence minimum confidence (a value between 0 and 1).
* @param input the input file path
* @param output the output file path
* @param windowSize a window size
* @throws IOException exception if there is an error reading/writing files
*/
public void runAlgorithm(
String input, String output, int relativeMinSupport, double minConfidence, int windowSize)
throws IOException {
this.minconf = minConfidence;
// read the database into memory
if (database == null) {
try {
this.database = new SequenceDatabase();
database.loadFile(input);
} catch (Exception e) {
e.printStackTrace();
}
}
// IMPORTANT : THIS IS A FIX SO THAT THE DEFINITION IS THE SAME AS IN THE ARTICLE!!
this.windowSize = windowSize + 1;
// if minsup is 0, set it to 1
this.minsuppRelative = relativeMinSupport;
if (this.minsuppRelative == 0) { // protection
this.minsuppRelative = 1;
}
// reset the stats for memory usage
MemoryLogger.getInstance().reset();
// prepare the object for writing the output file
writer = new BufferedWriter(new FileWriter(output));
// save the start time
timeStart = System.currentTimeMillis(); // for stats
// remove infrequent items from the database
removeItemsThatAreNotFrequent(database);
// note frequent items in a list "listFrequents"
List<String> listFrequents = new ArrayList<String>();
// for each item
for (Entry<String, Map<Integer, Occurence>> entry : mapItemCount.entrySet()) {
// if it is frequent
if (entry.getValue().size() >= minsuppRelative) {
// add the item to the list
listFrequents.add(entry.getKey());
}
}
// FOR EACH FREQUENT ITEM WE COMPARE WITH EACH OTHER FREQUENT ITEM TO
// TRY TO GENERATE A RULE 1-1.
for (int i = 0; i < listFrequents.size(); i++) {
String intI = listFrequents.get(i);
Map<Integer, Occurence> occurencesI = mapItemCount.get(intI);
for (int j = i + 1; j < listFrequents.size(); j++) {
String intJ = listFrequents.get(j);
Map<Integer, Occurence> occurencesJ = mapItemCount.get(intJ);
// (1) Calculate tidsI, tidsJ, tidsJ-->J and tidsI->J
Set<Integer> tidsI = new HashSet<Integer>();
Set<Integer> tidsJ = null;
Set<Integer> tidsIJ = new HashSet<Integer>();
Set<Integer> tidsJI = new HashSet<Integer>();
// for each occurence of I
looptid:
for (Occurence occI : occurencesI.values()) {
// add the sequenceID to tidsI
tidsI.add(occI.sequenceID);
// if J does not appear in that sequence continue loop
Occurence occJ = occurencesJ.get(occI.sequenceID);
if (occJ == null) {
continue looptid;
}
// make a big loop to compare if I appears before
// J in that sequence and
// if J appears before I
boolean addedIJ = false;
boolean addedJI = false;
// for each occurence of I in that sequence
loopIJ:
for (Short posI : occI.occurences) {
// for each occurence of J in that sequence
for (Short posJ : occJ.occurences) {
if (!posI.equals(posJ) && Math.abs(posI - posJ) <= windowSize) {
if (posI <= posJ) {
// if I is before J
tidsIJ.add(occI.sequenceID);
addedIJ = true;
} else {
// if J is before I
tidsJI.add(occI.sequenceID);
addedJI = true;
}
// if we have found that I is before J and J is before I
// we don't need to continue.
if (addedIJ && addedJI) {
break loopIJ;
}
}
}
}
}
// END
// (2) check if the two itemsets have enough common tids
// if not, we don't need to generate a rule for them.
// create rule IJ
if (tidsIJ.size() >= minsuppRelative) {
// calculate the confidence of I ==> J
double confIJ = ((double) tidsIJ.size()) / occurencesI.size();
// create itemset of the rule I ==> J
String[] itemset1 = new String[] {intI};
String[] itemset2 = new String[] {intJ};
// if the confidence is high enough, save the rule
if (confIJ >= minConfidence) {
saveRule(tidsIJ, confIJ, itemset1, itemset2);
}
// Calculate tidsJ.
tidsJ = new HashSet<Integer>();
for (Occurence occJ : occurencesJ.values()) {
tidsJ.add(occJ.sequenceID);
}
// recursive call to try to expand the rule
expandLeft(itemset1, itemset2, tidsI, tidsIJ);
expandRight(itemset1, itemset2, tidsI, tidsJ, tidsIJ);
}
// create rule JI
if (tidsJI.size() >= minsuppRelative) {
double confJI = ((double) tidsJI.size()) / occurencesJ.size();
// create itemsets for that rule
String[] itemset1 = new String[] {intI};
String[] itemset2 = new String[] {intJ};
// if the rule has enough confidence, save it!
if (confJI >= minConfidence) {
saveRule(tidsJI, confJI, itemset2, itemset1);
// rules.addRule(ruleJI);
}
// Calculate tidsJ.
if (tidsJ == null) {
tidsJ = new HashSet<Integer>();
for (Occurence occJ : occurencesJ.values()) {
tidsJ.add(occJ.sequenceID);
}
}
// recursive call to try to expand the rule
expandRight(itemset2, itemset1, tidsJ, tidsI, tidsJI /*, occurencesJ, occurencesI*/);
expandLeft(itemset2, itemset1, tidsJ, tidsJI /*, occurencesI*/);
}
}
}
// save the end time for the execution of the algorithm
timeEnd = System.currentTimeMillis(); // for stats
// close the file
writer.close();
database = null;
}
