def __call__(self, examples, weight=0):
imputer = getattr(self, "imputer", None) or None
if getattr(self, "removeMissing", 0):
examples = orange.Preprocessor_dropMissing(examples)
## if hasDiscreteValues(examples.domain):
## examples = createNoDiscTable(examples)
if not len(examples):
return None
if getattr(self, "stepwiseLR", 0):
addCrit = getattr(self, "addCrit", 0.2)
removeCrit = getattr(self, "removeCrit", 0.3)
numAttr = getattr(self, "numAttr", -1)
attributes = StepWiseFSS(examples,
addCrit=addCrit,
deleteCrit=removeCrit,
imputer=imputer,
numAttr=numAttr)
tmpDomain = orange.Domain(attributes, examples.domain.classVar)
tmpDomain.addmetas(examples.domain.getmetas())
examples = examples.select(tmpDomain)
learner = orange.LogRegLearner()
learner.imputerConstructor = imputer
if imputer:
examples = self.imputer(examples)(examples)
examples = orange.Preprocessor_dropMissing(examples)
if self.fitter:
learner.fitter = self.fitter
if self.removeSingular:
lr = learner.fitModel(examples, weight)
else:
lr = learner(examples, weight)
while isinstance(lr, orange.Variable):
if isinstance(lr.getValueFrom,
orange.ClassifierFromVar) and isinstance(
lr.getValueFrom.transformer,
orange.Discrete2Continuous):
lr = lr.getValueFrom.variable
attributes = examples.domain.attributes[:]
if lr in attributes:
attributes.remove(lr)
else:
attributes.remove(lr.getValueFrom.variable)
newDomain = orange.Domain(attributes, examples.domain.classVar)
newDomain.addmetas(examples.domain.getmetas())
examples = examples.select(newDomain)
lr = learner.fitModel(examples, weight)
return lr
def logreg(input_dict):
import orange
output_dict = {}
output_dict['logregout'] = orange.LogRegLearner(
name="Logistic Regression (Orange)")
return output_dict