def computeDists(data, weight=0, targetClass=0, N=100, learner=None):
""" Compute distributions of likelihood ratio statistics of extreme (best) rules. """
if not learner:
learner = createLearner()
#########################
## Learner preparation ##
#########################
oldStopper = learner.ruleFinder.ruleStoppingValidator
evaluator = learner.ruleFinder.evaluator
learner.ruleFinder.evaluator = orange.RuleEvaluator_LRS()
learner.ruleFinder.evaluator.storeRules = True
learner.ruleFinder.ruleStoppingValidator = orange.RuleValidator_LRS(
alpha=1.0)
learner.ruleFinder.ruleStoppingValidator.max_rule_complexity = 0
# loop through N (sampling repetitions)
maxVals = []
for d_i in range(N):
# create data set (remove and randomize)
tempData = createRandomDataSet(data)
learner.ruleFinder.evaluator.rules = orange.RuleList()
# Next, learn a rule
bestRule = learner.ruleFinder(tempData, weight, targetClass,
orange.RuleList())
maxVals.append(bestRule.quality)
extremeDists = [compParameters(maxVals, 1.0, 1.0)]
#####################
## Restore learner ##
#####################
learner.ruleFinder.evaluator = evaluator
learner.ruleFinder.ruleStoppingValidator = oldStopper
return extremeDists
def add_sub_rules(rules, examples, weight, learner, dists):
apriori = orange.Distribution(examples.domain.classVar, examples, weight)
newRules = orange.RuleList()
for r in rules:
newRules.append(r)
# loop through rules
for r in rules:
tmpList = orange.RuleList()
tmpRle = r.clone()
tmpRle.filter.conditions = []
tmpRle.parentRule = None
tmpRle.filterAndStore(examples, weight, r.classifier.defaultVal)
tmpList.append(tmpRle)
while tmpList and len(tmpList[0].filter.conditions) <= len(
r.filter.conditions):
tmpList2 = orange.RuleList()
for tmpRule in tmpList:
# evaluate tmpRule
oldREP = learner.ruleFinder.evaluator.returnExpectedProb
learner.ruleFinder.evaluator.returnExpectedProb = False
learner.ruleFinder.evaluator.evDistGetter.dists = createEVDistList(
dists[int(r.classifier.defaultVal)])
tmpRule.quality = learner.ruleFinder.evaluator(
tmpRule, examples, weight, r.classifier.defaultVal,
apriori)
learner.ruleFinder.evaluator.returnExpectedProb = oldREP
# if rule not in rules already, add it to the list
if not True in [rules_equal(ri, tmpRule)
for ri in newRules] and len(
tmpRule.filter.conditions
) > 0 and tmpRule.quality > apriori[
r.classifier.defaultVal] / apriori.abs:
newRules.append(tmpRule)
# create new tmpRules, set parent Rule, append them to tmpList2
if not True in [rules_equal(ri, tmpRule) for ri in newRules]:
for c in r.filter.conditions:
tmpRule2 = tmpRule.clone()
tmpRule2.parentRule = tmpRule
tmpRule2.filter.conditions.append(c)
tmpRule2.filterAndStore(examples, weight,
r.classifier.defaultVal)
if tmpRule2.classDistribution.abs < tmpRule.classDistribution.abs:
tmpList2.append(tmpRule2)
tmpList = tmpList2
for cl in examples.domain.classVar:
tmpRle = orange.Rule()
tmpRle.filter = orange.Filter_values(domain=examples.domain)
tmpRle.parentRule = None
tmpRle.filterAndStore(examples, weight, int(cl))
tmpRle.quality = tmpRle.classDistribution[int(
cl)] / tmpRle.classDistribution.abs
newRules.append(tmpRle)
return newRules
def sortRules(self, rules):
newRules = orange.RuleList()
foundRule = True
while foundRule:
foundRule = False
bestRule = None
for r in rules:
if r in newRules:
continue
if r.beta < 0.01 and r.beta > -0.01:
continue
if not bestRule:
bestRule = r
foundRule = True
continue
if len(r.filter.conditions) < len(bestRule.filter.conditions):
bestRule = r
foundRule = True
continue
if len(r.filter.conditions) == len(
bestRule.filter.conditions) and r.beta > bestRule.beta:
bestRule = r
foundRule = True
continue
if bestRule:
newRules.append(bestRule)
return newRules
def __call__(self, example, result_type=orange.GetValue, retRules=False):
def add(disc1, disc2, sumd):
disc = orange.DiscDistribution(disc1)
sumdisc = sumd
for i, d in enumerate(disc):
disc[i] += disc2[i]
sumdisc += disc2[i]
return disc, sumdisc
# create empty distribution
retDist = orange.DiscDistribution(self.examples.domain.classVar)
covRules = orange.RuleList()
# iterate through examples - add distributions
sumdisc = 0.
for r in self.rules:
if r(example) and r.classDistribution:
retDist, sumdisc = add(retDist, r.classDistribution, sumdisc)
covRules.append(r)
if not sumdisc:
retDist = self.prior
sumdisc = self.prior.abs
for c in self.examples.domain.classVar:
retDist[c] /= sumdisc
if retRules:
if result_type == orange.GetValue:
return (retDist.modus(), covRules)
if result_type == orange.GetProbabilities:
return (retDist, covRules)
return (retDist.modus(), retDist, covRules)
if result_type == orange.GetValue:
return retDist.modus()
if result_type == orange.GetProbabilities:
return retDist
return (retDist.modus(), retDist)
def __init__(self,
alpha=.05,
min_coverage=0,
max_rule_length=0,
rules=orange.RuleList()):
self.rules = rules
self.validator = orange.RuleValidator_LRS(
alpha=alpha,
min_coverage=min_coverage,
max_rule_length=max_rule_length)
def getBestRules(self, currentRules, examples, weightID):
bestRules = orange.RuleList()
for r in currentRules:
if hasattr(r.learner, "argumentRule") and not orngCN2.rule_in_set(
r, bestRules):
bestRules.append(r)
for r_i, r in enumerate(self.bestRule):
if r and not rule_in_set(r, bestRules) and examples[r_i].getclass(
) == r.classifier.defaultValue:
bestRules.append(r)
return bestRules
def __call__(self, examples, weight=0):
supervisedClassCheck(examples)
rules = orange.RuleList()
self.ruleStopping.apriori = orange.Distribution(
examples.domain.classVar, examples)
progress = getattr(self, "progressCallback", None)
if progress:
progress.start = 0.0
progress.end = 0.0
distrib = orange.Distribution(examples.domain.classVar, examples,
weight)
distrib.normalize()
for targetClass in examples.domain.classVar:
if progress:
progress.start = progress.end
progress.end += distrib[targetClass]
self.targetClass = targetClass
cl = orange.RuleLearner.__call__(self, examples, weight)
for r in cl.rules:
rules.append(r)
if progress:
progress(1.0, None)
return CN2UnorderedClassifier(rules, examples, weight)
def learnRule(self, examples, weightID, targetClass):
self.ruleFinder.evaluator.bestRule = None
rule = self.ruleFinder(examples, weightID, targetClass,
orange.RuleList())
return self.ruleFinder.evaluator.bestRule
def __call__(self, examples, weight=0):
supervisedClassCheck(examples)
apriori = orange.Distribution(examples.domain.classVar, examples,
weight)
ruleSet = orange.RuleList() # resulting set of rules
# Progress bar in widgets
progress = getattr(self, "progressCallback", None)
if progress:
self.progressCallback = progress
progress.start = 0.0
progress.end = 0.0
distrib = orange.Distribution(examples.domain.classVar, examples,
weightID)
distrib.normalize()
# Main Loop
temp_dists = []
for cl_i, cl in enumerate(examples.domain.classVar):
# rulesForClass ... rules for this class only
rulesForClass = orange.RuleList()
if progress:
progress.start = progress.end
progress.end += distrib[cl]
# Compute EVD distribution if not set
if getattr(self, "dists", None):
self.ruleFinder.evaluator.evDistGetter.dists = createEVDistList(
self.dists[cl_i])
temp_dists.append(self.dists[cl_i])
else:
ds = computeDists(examples,
weight=weight,
targetClass=cl_i,
N=self.N,
learner=self)
self.ruleFinder.evaluator.evDistGetter.dists = createEVDistList(
ds)
temp_dists.append(ds)
examples = self.coverAndRemove.initialize(examples, weight, cl,
apriori)
self.ruleFinder.evaluator.probVar = examples.domain.getmeta(
self.coverAndRemove.probAttribute)
self.targetClass = cl
# Learn rules
while not self.dataStopping(examples, weight, cl):
# Learn rule
rule = self.learnRule(examples, weight, cl)
if not rule or len(
rule.filter.conditions
) == 0: # stop learning if no rule has been learned
break
(examples,
weight) = self.coverAndRemove(rule, examples, weight, cl)
# add rule to rule set
if not rule_in_set(rule, rulesForClass):
rulesForClass.append(rule)
if progress:
progress(
self.coverAndRemove.remainingExamplesP(examples, cl),
None)
else:
print "%4.2f," % self.coverAndRemove.remainingExamplesP(
examples, cl),
if not progress:
print
ruleSet.extend(
self.coverAndRemove.getBestRules(rulesForClass, examples,
weight))
if progress:
progress(1.0, None)
self.ruleFinder.evaluator.probVar = None
if self.add_sub_rules:
ruleSet = add_sub_rules(ruleSet, examples, weight, self,
temp_dists)
return self.LCR(ruleSet, examples, weight)
