def generateRules(self):
self.error()
self.warning(0)
if self.dataset:
if self.dataset and self.useSparseAlgorithm and not self.datasetIsSparse:
self.warning(
0,
"Using algorithm for sparse data, but data does not appear to be sparse!"
)
try:
num_steps = 20
for i in range(num_steps):
build_support = (i == num_steps -
1) and self.minSupport / 100. or (
1 - float(i) / num_steps *
(1 - self.minSupport / 100.0))
if self.useSparseAlgorithm:
rules = orange.AssociationRulesSparseInducer(
self.dataset,
support=build_support,
confidence=self.minConfidence / 100.,
storeExamples=True)
else:
rules = orange.AssociationRulesInducer(
self.dataset,
support=build_support,
confidence=self.minConfidence / 100.,
classificationRules=self.classificationRules,
storeExamples=True)
if len(rules) >= self.maxRules:
break
self.send("Association Rules", rules)
except orange.KernelException, (errValue):
self.error(str(errValue))
self.send("Association Rules", None)
def findItemsets(self):
self.error()
if self.dataset:
try:
if self.useSparseAlgorithm:
self.itemsets = orange.AssociationRulesSparseInducer(support = self.minSupport/100., storeExamples = True).getItemsets(self.dataset)
else:
self.itemsets = orange.AssociationRulesInducer(support = self.minSupport/100., storeExamples = True).getItemsets(self.dataset)
self.send("Itemsets", (self.dataset, self.itemsets))
except Exception, (errValue):
errValue = str(errValue)
if "non-discrete attributes" in errValue and not self.useSparseAlgorithm:
errValue += "\nTry using the algorithm for sparse data"
self.error(str(errValue))
self.send("Itemsets", None)
def generateRules(self):
self.error()
if self.dataset:
try:
num_steps = 20
for i in range(num_steps):
build_support = (i == num_steps-1) and self.minSupport/100. or (1 - float(i) / num_steps * (1 - self.minSupport/100.0))
if self.useSparseAlgorithm:
rules = orange.AssociationRulesSparseInducer(self.dataset, support = build_support, confidence = self.minConfidence/100., storeExamples = True)
else:
rules = orange.AssociationRulesInducer(self.dataset, support = build_support, confidence = self.minConfidence/100., classificationRules = self.classificationRules, storeExamples = True)
if len(rules) >= self.maxRules:
break
self.send("Association Rules", rules)
except orange.KernelException as errValue:
self.error(str(errValue))
self.send("Association Rules", None)
else:
self.send("Association Rules", None)
import orange
data = orange.ExampleTable("inquisition")
rules = orange.AssociationRulesSparseInducer(data,
support=0.5,
storeExamples=True)
print "%5s %5s" % ("supp", "conf")
for r in rules:
print "%5.3f %5.3f %s" % (r.support, r.confidence, r)
