def setPos(self, x, y):
if self.showText:
ty, ry = y, y + 18
else:
ty, ry = 0, y + 3
pos = 0
for r in self.rect:
r.setPos(x + pos, ry)
pos += r.rect().width()
self.text.setPos(x, ty)
def show(self):
for r in self.rect:
r.show()
if self.showText:
self.text.show()
if __name__ == "__main__":
ap = QApplication(sys.argv)
w = OWCN2RulesViewer()
#data=orange.ExampleTable("../../doc/datasets/car.tab")
data = orange.ExampleTable(r"E:\Development\Orange Datasets\UCI\car.tab")
l = orngCN2.CN2UnorderedLearner()
l.ruleFinder.ruleStoppingValidator = orange.RuleValidator_LRS()
w.setRuleClassifier(l(data))
w.setRuleClassifier(l(data))
w.show()
ap.exec_()
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()