def setLearner(self):
if hasattr(self, "btnApply"):
self.btnApply.setFocus()
#progress bar
self.progressBarInit()
#learner / specific handling in case of EVC learning (completely different type of class)
if self.useMaxRuleLength:
maxRuleLength = self.MaxRuleLength
else:
maxRuleLength = -1
if self.QualityButton == 2:
self.learner=orngCN2.CN2EVCUnorderedLearner(width=self.BeamWidth, rule_sig=self.Alpha, att_sig=self.stepAlpha,
min_coverage = self.MinCoverage, max_rule_complexity = maxRuleLength)
if self.preprocessor:
self.learner = self.preprocessor.wrapLearner(self.learner)
self.learner.name = self.name
# self.learner.progressCallback=CN2ProgressBar(self)
self.send("Learner",self.learner)
else:
self.learner=orngCN2.CN2UnorderedLearner()
self.learner.name = self.name
# self.learner.progressCallback=CN2ProgressBar(self)
# self.send("Learner",self.learner)
ruleFinder=orange.RuleBeamFinder()
if self.QualityButton==0:
ruleFinder.evaluator=orange.RuleEvaluator_Laplace()
elif self.QualityButton==1:
ruleFinder.evaluator=orngCN2.mEstimate(self.m)
elif self.QualityButton==3:
ruleFinder.evaluator=orngCN2.WRACCEvaluator()
ruleFinder.ruleStoppingValidator=orange.RuleValidator_LRS(alpha=self.stepAlpha,
min_coverage=self.MinCoverage, max_rule_complexity=maxRuleLength)
ruleFinder.validator=orange.RuleValidator_LRS(alpha=self.Alpha,
min_coverage=self.MinCoverage, max_rule_complexity=maxRuleLength)
ruleFinder.ruleFilter=orange.RuleBeamFilter_Width(width=self.BeamWidth)
self.learner.ruleFinder=ruleFinder
if self.CoveringButton==0:
self.learner.coverAndRemove=orange.RuleCovererAndRemover_Default()
elif self.CoveringButton==1:
self.learner.coverAndRemove=orngCN2.CovererAndRemover_multWeights(mult=self.Weight)
if self.preprocessor:
self.learner = self.preprocessor.wrapLearner(self.learner)
self.learner.name = self.name
self.send("Learner", self.learner)
self.classifier=None
self.error()
if self.data:
oldDomain = orange.Domain(self.data.domain)
learnData = orange.ExampleTable(oldDomain, self.data)
self.learner.progressCallback=CN2ProgressBar(self)
self.classifier=self.learner(learnData)
self.learner.progressCallback=None
self.classifier.name=self.name
for r in self.classifier.rules:
r.examples = orange.ExampleTable(oldDomain, r.examples)
self.classifier.examples = orange.ExampleTable(oldDomain, self.classifier.examples)
self.classifier.setattr("data",self.classifier.examples)
self.error("")
## except orange.KernelException, (errValue):
## self.classifier=None
## self.error(errValue)
## except Exception:
## self.classifier=None
## if not self.data.domain.classVar:
## self.error("Classless domain.")
## elif self.data.domain.classVar.varType == orange.VarTypes.Continuous:
## self.error("CN2 can learn only from discrete class.")
## else:
## self.error("Unknown error")
self.send("Classifier", self.classifier)
self.send("Unordered CN2 Classifier", self.classifier)
self.progressBarFinished()
import orngCN2
data = orange.ExampleTable("titanic.tab")
# create learner
learner = orngCN2.CN2Learner()
cl = learner(data)
for r in cl.rules:
print orngCN2.ruleToString(r)
print "*****"
learner = orngCN2.CN2UnorderedLearner()
learner.ruleFinder = orange.RuleBeamFinder()
learner.ruleFinder.evaluator = orngCN2.mEstimate(m=50)
cl = learner(data)
for r in cl.rules:
print orngCN2.ruleToString(r)
print "****"
learner = orngCN2.CN2SDUnorderedLearner()
learner.ruleFinder.ruleStoppingValidator = orange.RuleValidator_LRS(
alpha=0.01, min_coverage=10, max_rule_complexity=2)
learner.ruleFinder.ruleFilter = orange.RuleBeamFilter_Width(width=50)
cl = learner(data)
for r in cl.rules:
print orngCN2.ruleToString(r)
def setLearner(self):
if hasattr(self, "btnApply"):
self.btnApply.setFocus()
#progress bar
self.progressBarInit()
#learner / specific handling in case of EVC learning (completely different type of class)
if self.useMaxRuleLength:
maxRuleLength = self.MaxRuleLength
else:
maxRuleLength = -1
if self.QualityButton == 2:
self.learner = orngCN2.CN2EVCUnorderedLearner(
width=self.BeamWidth,
rule_sig=self.Alpha,
att_sig=self.stepAlpha,
min_coverage=self.MinCoverage,
max_rule_complexity=maxRuleLength)
if self.preprocessor:
self.learner = self.preprocessor.wrapLearner(self.learner)
self.learner.name = self.name
# self.learner.progressCallback=CN2ProgressBar(self)
self.send("Learner", self.learner)
else:
self.learner = orngCN2.CN2UnorderedLearner()
self.learner.name = self.name
# self.learner.progressCallback=CN2ProgressBar(self)
# self.send("Learner",self.learner)
ruleFinder = orange.RuleBeamFinder()
if self.QualityButton == 0:
ruleFinder.evaluator = orange.RuleEvaluator_Laplace()
elif self.QualityButton == 1:
ruleFinder.evaluator = orngCN2.mEstimate(self.m)
elif self.QualityButton == 3:
ruleFinder.evaluator = orngCN2.WRACCEvaluator()
ruleFinder.ruleStoppingValidator = orange.RuleValidator_LRS(
alpha=self.stepAlpha,
min_coverage=self.MinCoverage,
max_rule_complexity=maxRuleLength)
ruleFinder.validator = orange.RuleValidator_LRS(
alpha=self.Alpha,
min_coverage=self.MinCoverage,
max_rule_complexity=maxRuleLength)
ruleFinder.ruleFilter = orange.RuleBeamFilter_Width(
width=self.BeamWidth)
self.learner.ruleFinder = ruleFinder
if self.CoveringButton == 0:
self.learner.coverAndRemove = orange.RuleCovererAndRemover_Default(
)
elif self.CoveringButton == 1:
self.learner.coverAndRemove = orngCN2.CovererAndRemover_multWeights(
mult=self.Weight)
if self.preprocessor:
self.learner = self.preprocessor.wrapLearner(self.learner)
self.learner.name = self.name
self.send("Learner", self.learner)
self.classifier = None
self.error()
if self.data:
oldDomain = orange.Domain(self.data.domain)
learnData = orange.ExampleTable(oldDomain, self.data)
self.learner.progressCallback = CN2ProgressBar(self)
self.classifier = self.learner(learnData)
self.learner.progressCallback = None
self.classifier.name = self.name
for r in self.classifier.rules:
r.examples = orange.ExampleTable(oldDomain, r.examples)
self.classifier.examples = orange.ExampleTable(
oldDomain, self.classifier.examples)
self.classifier.setattr("data", self.classifier.examples)
self.error("")
## except orange.KernelException, (errValue):
## self.classifier=None
## self.error(errValue)
## except Exception:
## self.classifier=None
## if not self.data.domain.classVar:
## self.error("Classless domain.")
## elif self.data.domain.classVar.varType == orange.VarTypes.Continuous:
## self.error("CN2 can learn only from discrete class.")
## else:
## self.error("Unknown error")
self.send("Classifier", self.classifier)
self.send("Unordered CN2 Classifier", self.classifier)
self.progressBarFinished()
import orngCN2
data = Orange.data.Table("titanic.tab")
# create learner
learner = orngCN2.CN2Learner()
cl = learner(data)
for r in cl.rules:
print orngCN2.ruleToString(r)
print "*****"
learner = orngCN2.CN2UnorderedLearner()
learner.ruleFinder = Orange.core.RuleBeamFinder()
learner.ruleFinder.evaluator = orngCN2.mEstimate(m=50)
cl = learner(data)
for r in cl.rules:
print orngCN2.ruleToString(r)
print "****"
learner = orngCN2.CN2SDUnorderedLearner()
learner.ruleFinder.ruleStoppingValidator = Orange.core.RuleValidator_LRS(alpha=0.01,min_coverage=10,max_rule_complexity = 2)
learner.ruleFinder.ruleFilter = Orange.core.RuleBeamFilter_Width(width = 50)
cl = learner(data)
for r in cl.rules:
print orngCN2.ruleToString(r)
print "****"
