// this is the ap-genrules procedure that generates rules out
// of a frequent itemset.
private void ap_genrules(Itemset is_frequent, Vector consequents) {
if (consequents.size() == 0) return;
// the size of frequent must be bigger than the size of the itemsets
// in consequents by at least 2, in order to be able to generate
// a rule in this call
if (is_frequent.size() > ((Itemset) (consequents.get(0))).size() + 1) {
Vector new_consequents = apriori_gen(consequents);
AssociationRule ar;
for (int i = 0; i < new_consequents.size(); i++) {
Itemset is_consequent = (Itemset) new_consequents.get(i);
Itemset is_antecedent = is_frequent.subtract(is_consequent);
float antecedent_support = (float) 0.00001;
try {
antecedent_support = supports.getSupport(is_antecedent);
} catch (SETException e) {
System.err.println("Error geting support from SET!!!\n" + e);
}
float confidence = is_frequent.getSupport() / antecedent_support;
// if the rule satisfies our requirements we add it
// to our collection
if (confidence >= min_confidence)
rules.add(
new AssociationRule(
is_antecedent, is_consequent, is_frequent.getSupport(), confidence));
// otherwise we remove the consequent from the collection
// and we update the index such that we don't skip a consequent
else new_consequents.remove(i--);
}
ap_genrules(is_frequent, new_consequents);
}
}
/**
* Find association rules in a database, given the set of frequent itemsets.
*
* @param cacheReader the object used to read from the cache
* @param minSupport the minimum support
* @param minConfidence the minimum confidence
* @return a Vector containing all association rules found
*/
public Vector findAssociations(DBCacheReader cacheReader, float minSupport, float minConfidence) {
min_support = minSupport;
min_confidence = minConfidence;
// create the vector where we'll put the rules
rules = new Vector();
// read from cache supports of frequent itemsets
initializeSupports(cacheReader);
// get the frequent itemsets
Vector frequent = supports.getItemsets();
// generate rules from each frequent itemset
for (int i = 0; i < frequent.size(); i++) {
// get a frequent itemset
Itemset is_frequent = (Itemset) frequent.get(i);
// skip it if it's too small
if (is_frequent.size() <= 1) continue;
// get all possible 1 item consequents
Vector consequents = new Vector(is_frequent.size());
for (int k = 0; k < is_frequent.size(); k++) {
int item = is_frequent.getItem(k);
Itemset is_consequent = new Itemset(1);
is_consequent.addItem(item);
// is_consequent now contains a possible consequent
// verify now that the rule having this consequent
// satisfies our requirements
Itemset is_antecedent = is_frequent.subtract(is_consequent);
float antecedent_support = (float) 0.00001;
try {
antecedent_support = supports.getSupport(is_antecedent);
} catch (SETException e) {
System.err.println("Error geting support from SET!!!\n" + e);
}
float confidence = is_frequent.getSupport() / antecedent_support;
if (confidence >= min_confidence) {
consequents.add(is_consequent);
// we add the rule to our collection if it satisfies
// our conditions
rules.add(
new AssociationRule(
is_antecedent, is_consequent, is_frequent.getSupport(), confidence));
}
}
// call the ap_genrules procedure for generating all rules
// out of this frequent itemset
ap_genrules(is_frequent, consequents);
}
return rules;
}
// this is the ap-genrules procedure that generates rules out
// of a frequent itemset.
private void ap_genrules_constraint(Itemset is_frequent, Vector consequents) {
if (consequents.size() == 0) return;
// the size of frequent must be bigger than the size of the itemsets
// in consequents by at least 2, in order to be able to generate
// a rule in this call
if (is_frequent.size() > ((Itemset) (consequents.get(0))).size() + 1) {
Vector new_consequents = apriori_gen(consequents);
AssociationRule ar;
for (int i = 0; i < new_consequents.size(); i++) {
Itemset is_consequent = (Itemset) new_consequents.get(i);
Itemset is_antecedent = is_frequent.subtract(is_consequent);
float antecedent_support = (float) 0.00001;
try {
antecedent_support = supports.getSupport(is_antecedent);
} catch (SETException e) {
System.err.println("Error geting support from SET!!!\n" + e);
}
float confidence = is_frequent.getSupport() / antecedent_support;
// if the rule satisfies our confidence requirements
if (confidence >= min_confidence) {
// check whether it also satisfies our constraints
boolean approved = true;
if (approved && is_in_antecedent != null && !is_in_antecedent.isIncludedIn(is_antecedent))
approved = false;
if (approved && is_in_consequent != null && !is_in_consequent.isIncludedIn(is_consequent))
approved = false;
if (approved && max_antecedent > 0 && is_antecedent.size() > max_antecedent)
approved = false;
if (approved && min_consequent > 0 && is_consequent.size() < min_consequent)
approved = false;
// if the rule satisifes all requirements then
// we add it to the rules collection
if (approved)
rules.add(
new AssociationRule(
is_antecedent, is_consequent, is_frequent.getSupport(), confidence));
}
// otherwise we remove the consequent from the collection
// and we update the index such that we don't skip a consequent
else new_consequents.remove(i--);
}
ap_genrules_constraint(is_frequent, new_consequents);
}
}
private void generateRules(ArrayList<Itemset> Lk, int clas) {
int i, uncover;
Itemset itemset;
double confidence[] = new double[2];
double var1[] = new double[2];
double var2[] = new double[2];
for (i = Lk.size() - 1; i >= 0; i--) {
itemset = Lk.get(i);
var2 = itemset.getSupport();
if (var2[0] > 0.0) {
var1 = itemset.getSupportClass();
confidence[0] = var1[0] / var2[0];
confidence[1] = var1[1] / var2[1];
} else {
confidence[0] = confidence[1] = 0.0;
}
if (confidence[0] > 0.4) {
this.ruleBaseClase.add(itemset);
ruleStage1++;
}
if (confidence[0] > this.minconf) Lk.remove(i);
}
if (this.ruleBaseClase.size() > 500000) {
this.ruleBaseClase.reduceRules(clas);
// System.out.println("Number of rules: " + this.ruleBase.size());
System.gc();
}
}
/**
* It adds a rule to the rule base
*
* @param itemset itemset to be added
* @param time Time of the rule
*/
public void add(Itemset itemset, long time) {
int i;
Item item;
int[] antecedent = new int[n_variables];
for (i = 0; i < n_variables; i++) antecedent[i] = -1; // Don't care
for (i = 0; i < itemset.size(); i++) {
item = itemset.get(i);
antecedent[item.getVariable()] = item.getValue();
}
Rule r = new Rule(this.dataBase);
r.asignaAntecedente(antecedent);
r.setConsequent(itemset.getClas());
r.setConfidence(itemset.getSupportClass() / itemset.getSupport());
r.setSupport(itemset.getSupportClass());
r.setTime(time);
this.ruleBase.add(r);
}
// this method stores all frequent itemsets that have support
// greater than the minimum support in a SET for more efficient
// access times.
private void initializeSupports(DBCacheReader cacheReader) {
// create new SET
supports = new SET();
try {
Itemset is;
while (true) {
// get item from cache
is = cacheReader.getNextItemset();
// if item has support greater than the minimum support
// required then we add it to the SET
if (is.getSupport() >= min_support) {
supports.insert(is);
}
}
} catch (EOFException e) {
// do nothing, we just reached the EOF
} catch (IOException e) {
System.err.println("Error scanning cache!!!\n" + e);
} catch (ClassNotFoundException e) {
System.err.println("Error scanning cache!!!\n" + e);
}
}
/**
* Find association rules in a database, given the set of frequent itemsets and a set of
* restrictions.
*
* @param cacheReader the object used to read from the cache
* @param minSupport the minimum support
* @param minConfidence the minimum confidence
* @param inAntecedent the items that must appear in the antecedent of each rule, if null then
* this constraint is ignored
* @param inConsequent the items that must appear in the consequent of each rule, if null then
* this constraint is ignored
* @param ignored the items that should be ignored, if null then this constraint is ignored
* @param maxAntecedent the maximum number of items that can appear in the antecedent of each
* rule, if 0 then this constraint is ignored
* @param minConsequent the minimum number of items that should appear in the consequent of each
* rule, if 0 then this constraint is ignored
* @return a Vector containing all association rules found
*/
public Vector findAssociations(
DBCacheReader cacheReader,
float minSupport,
float minConfidence,
Itemset inAntecedent,
Itemset inConsequent,
Itemset ignored,
int maxAntecedent,
int minConsequent) {
min_support = minSupport;
min_confidence = minConfidence;
is_in_antecedent = inAntecedent;
is_in_consequent = inConsequent;
is_ignored = ignored;
max_antecedent = maxAntecedent;
min_consequent = minConsequent;
// create the vector where we'll put the rules
rules = new Vector();
// read from cache supports of frequent itemsets
initializeSupports(cacheReader);
// get the frequent itemsets
Vector frequent = supports.getItemsets();
if (frequent.size() == 0) return rules;
// if we need to ignore some items
if (ignored != null) {
// remove all frequent itemsets that contain
// items to be ignored; their subsets that do
// not contain those items will remain
for (int i = 0; i < frequent.size(); i++) {
Itemset is = (Itemset) frequent.get(i);
if (is.doesIntersect(ignored)) {
// replace this element with last, delete last,
// and don't advance index
frequent.set(i, frequent.lastElement());
frequent.remove(frequent.size() - 1);
i--;
}
}
if (frequent.size() == 0) return rules;
}
// if we need to have some items in the antecedent or consequent
if (inAntecedent != null || inConsequent != null) {
// remove frequent itemsets that don't have the
// required items
for (int i = 0; i < frequent.size(); i++) {
Itemset is = (Itemset) frequent.get(i);
if (inAntecedent != null && !inAntecedent.isIncludedIn(is)) {
// replace this element with last, delete last,
// and don't advance index
frequent.set(i, frequent.lastElement());
frequent.remove(frequent.size() - 1);
i--;
} else if (inConsequent != null && !inConsequent.isIncludedIn(is)) {
// replace this element with last, delete last,
// and don't advance index
frequent.set(i, frequent.lastElement());
frequent.remove(frequent.size() - 1);
i--;
}
}
if (frequent.size() == 0) return rules;
}
// generate rules from each frequent itemset
for (int i = 0; i < frequent.size(); i++) {
// get a frequent itemset
Itemset is_frequent = (Itemset) frequent.get(i);
// skip it if it's too small
if (is_frequent.size() <= 1 || is_frequent.size() <= minConsequent) continue;
// get all possible 1 item consequents
Vector consequents = new Vector(is_frequent.size());
for (int k = 0; k < is_frequent.size(); k++) {
int item = is_frequent.getItem(k);
Itemset is_consequent = new Itemset(1);
is_consequent.addItem(item);
// is_consequent now contains a possible consequent
// verify now that the rule having this consequent
// satisfies our requirements
Itemset is_antecedent = is_frequent.subtract(is_consequent);
float antecedent_support = (float) 0.00001;
try {
antecedent_support = supports.getSupport(is_antecedent);
} catch (SETException e) {
System.err.println("Error geting support from SET!!!\n" + e);
}
float confidence = is_frequent.getSupport() / antecedent_support;
if (confidence >= min_confidence) {
consequents.add(is_consequent);
// check whether it also satisfies our constraints
boolean approved = true;
if (approved && is_in_antecedent != null && !is_in_antecedent.isIncludedIn(is_antecedent))
approved = false;
if (approved && is_in_consequent != null && !is_in_consequent.isIncludedIn(is_consequent))
approved = false;
if (approved && max_antecedent > 0 && is_antecedent.size() > max_antecedent)
approved = false;
if (approved && min_consequent > 0 && is_consequent.size() < min_consequent)
approved = false;
// if the rule satisifes all requirements then
// we add it to the rules collection
if (approved)
rules.add(
new AssociationRule(
is_antecedent, is_consequent, is_frequent.getSupport(), confidence));
}
}
// call the ap-genrules procedure for generating all rules
// out of this frequent itemset
ap_genrules_constraint(is_frequent, consequents);
}
return rules;
}