def __call__(self, data):
pp = Preprocessor_discretize(attributes=self.features, discretizeClass=self.discretize_class)
pp.method = self.method
ddata = pp(data)
if self.clean:
return ddata.select([x for x in ddata.domain.features if len(x.values)>1] + [ddata.domain.classVar])
else:
return ddata
def __call__(self, data):
pp = Preprocessor_discretize(attributes=self.features,
discretizeClass=self.discretize_class)
pp.method = self.method
ddata = pp(data)
if self.clean:
return ddata.select(
[x for x in ddata.domain.features if len(x.values) > 1] +
[ddata.domain.classVar])
else:
return ddata
def __call__(self, data):
pp = Preprocessor_discretize(attributes=self.features,
discretize_class=self.discretize_class)
pp.method = self.method
ddata = pp(data)
if self.clean:
features = [x for x in ddata.domain.features if len(x.values) > 1]
domain = Orange.data.Domain(features, ddata.domain.class_var,
class_vars=ddata.domain.class_vars)
return Orange.data.Table(domain, ddata)
else:
return ddata
def __call__(self, data):
pp = Preprocessor_discretize(attributes=self.features,
discretize_class=self.discretize_class)
pp.method = self.method
ddata = pp(data)
if self.clean:
features = [x for x in ddata.domain.features if len(x.values) > 1]
domain = Orange.data.Domain(features,
ddata.domain.class_var,
class_vars=ddata.domain.class_vars)
return Orange.data.Table(domain, ddata)
else:
return ddata
def __call__(self, data, weight=None):
# filter the data and then learn
from Orange.preprocess import Preprocessor_discretize
ddata = Preprocessor_discretize(data, method=self.discretizer)
if weight <> None:
model = self.baseLearner(ddata, weight)
else:
model = self.baseLearner(ddata)
dcl = DiscretizedClassifier(classifier=model)
if hasattr(model, "domain"):
dcl.domain = model.domain
if hasattr(model, "name"):
dcl.name = model.name
return dcl