def __call__(self, trainingData = None, weight = None):
if not trainingData:
print "AZBaseClasses ERROR: Missing training data!"
self.basicStat = None
return False
elif dataUtilities.findDuplicatedNames(trainingData.domain):
print "AZBaseClasses ERROR: Duplicated names found in the training data. Please use the method dataUtilities.DataTable() when loading a dataset in order to fix the duplicated names and avoid this error."
self.basicStat = None
return False
possibleMetas = dataUtilities.getPossibleMetas(trainingData)
if possibleMetas:
print "AZBaseClasses ERROR: Detected attributes that should be considered meta-attributes:"
for attr in possibleMetas:
print " ",attr
return False
#Get the Domain basic statistics and save only the desired info in self.basicStat
basicStat = orange.DomainBasicAttrStat(trainingData)
self.basicStat = {}
for attr in trainingData.domain:
if attr.varType == orange.VarTypes.Discrete:
self.basicStat[attr.name] = None
else:
self.basicStat[attr.name] = {"min":basicStat[attr].min, "max":basicStat[attr].max}
return True
def __call__(self, trainingData=None, weight=None, allowMetas=False):
self.basicStat = None
if not trainingData:
print "AZBaseClasses ERROR: Missing training data!"
return False
elif dataUtilities.findDuplicatedNames(trainingData.domain):
print "AZBaseClasses ERROR: Duplicated names found in the training data. Please use the method dataUtilities.DataTable() when loading a dataset in order to fix the duplicated names and avoid this error."
return False
elif not trainingData.domain.classVar:
print "AZBaseClasses ERROR: No class attribute found in training data!"
return False
elif not len(trainingData):
print "AZBaseClasses ERROR: No examples in training data!"
return False
elif not len(trainingData.domain.attributes):
print "AZBaseClasses ERROR: No attributes in training data!"
return False
possibleMetas = dataUtilities.getPossibleMetas(trainingData,
checkIndividuality=True)
if not allowMetas and possibleMetas:
msg = "\nAZBaseClasses ERROR: Detected attributes that should be considered meta-attributes:"
for attr in possibleMetas:
msg += "\n " + attr
raise Exception(msg)
#return False
#Get the Domain basic statistics and save only the desired info in self.basicStat
basicStat = orange.DomainBasicAttrStat(trainingData)
self.basicStat = {}
for attr in trainingData.domain:
if attr.varType in [
orange.VarTypes.Discrete, orange.VarTypes.String
]:
self.basicStat[attr.name] = None
else:
self.basicStat[attr.name] = {
"dev": basicStat[attr].dev,
"min": basicStat[attr].min,
"max": basicStat[attr].max,
"avg": basicStat[attr].avg
}
# Gather all the learner parameters to be stored along with the classifier
# Find the name of the Learner
learnerName = str(
self.__class__)[:str(self.__class__).rfind("'")].split(".")[-1]
self.parameters = {}
if learnerName != "ConsensusLearner":
# Load the AZLearnersParamsConfig.py from the AZORANGEHOME!
AZOLearnersConfig = imp.load_source(
"AZLearnersParamsConfig",
os.path.join(os.environ["AZORANGEHOME"], 'azorange',
"AZLearnersParamsConfig.py"))
pars = AZOLearnersConfig.API(learnerName)
if pars:
for par in pars.getParameterNames():
self.parameters[par] = getattr(self, par)
return True
def __call__(self, trainingData = None, weight = None):
self.basicStat = None
if not trainingData:
print "AZBaseClasses ERROR: Missing training data!"
return False
elif dataUtilities.findDuplicatedNames(trainingData.domain):
print "AZBaseClasses ERROR: Duplicated names found in the training data. Please use the method dataUtilities.DataTable() when loading a dataset in order to fix the duplicated names and avoid this error."
return False
elif not trainingData.domain.classVar:
print "AZBaseClasses ERROR: No class attribute found in training data!"
return False
elif not len(trainingData):
print "AZBaseClasses ERROR: No examples in training data!"
return False
elif not len(trainingData.domain.attributes):
print "AZBaseClasses ERROR: No attributes in training data!"
return False
possibleMetas = dataUtilities.getPossibleMetas(trainingData, checkIndividuality = True)
if possibleMetas:
msg="\nAZBaseClasses ERROR: Detected attributes that should be considered meta-attributes:"
for attr in possibleMetas:
msg += "\n "+attr
raise Exception(msg)
#return False
#Get the Domain basic statistics and save only the desired info in self.basicStat
basicStat = orange.DomainBasicAttrStat(trainingData)
self.basicStat = {}
for attr in trainingData.domain:
if attr.varType == orange.VarTypes.Discrete:
self.basicStat[attr.name] = None
else:
self.basicStat[attr.name] = {"min":basicStat[attr].min, "max":basicStat[attr].max, "avg":basicStat[attr].avg}
# Gather all the learner parameters to be stored along with the classifier
# Find the name of the Learner
learnerName = str(self.__class__)[:str(self.__class__).rfind("'")].split(".")[-1]
self.parameters = {}
if learnerName != "ConsensusLearner":
# Load the AZLearnersParamsConfig.py from the AZORANGEHOME!
AZOLearnersConfig = imp.load_source("AZLearnersParamsConfig", os.path.join(os.environ["AZORANGEHOME"],'azorange',"AZLearnersParamsConfig.py"))
pars = AZOLearnersConfig.API(learnerName)
if pars:
for par in pars.getParameterNames():
self.parameters[par] = getattr(self,par)
return True
