def initialize(self):
"""
Initialize the widget's state.
"""
learner = NaiveBayesLearner()
learner.name = self.learner_name
self.send("Learner", learner)
self.send("Classifier", None)
def initialize(self):
"""
Initialize the widget's state.
"""
learner = NaiveBayesLearner()
learner.name = self.learner_name
self.send("Learner", learner)
self.send("Classifier", None)
def apply(self):
learner = NaiveBayesLearner(preprocessors=self.preprocessors)
learner.name = self.learner_name
classifier = None
if self.data is not None:
self.error(0)
if not learner.check_learner_adequacy(self.data.domain):
self.error(0, learner.learner_adequacy_err_msg)
else:
classifier = learner(self.data)
classifier.name = self.learner_name
self.send("Learner", learner)
self.send("Classifier", classifier)
def setUp(self):
self.widget = self.create_widget(OWLoadModel) # type: OWLoadModel
data = Table("iris")
self.model = NaiveBayesLearner()(data)
with NamedTemporaryFile(suffix=".pkcls", delete=False) as f:
self.filename = f.name
pickle.dump(self.model, f)
def apply(self):
learner = NaiveBayesLearner(
preprocessors=self.preprocessors
)
learner.name = self.learner_name
classifier = None
if self.data is not None:
self.error(0)
if not learner.check_learner_adequacy(self.data.domain):
self.error(0, learner.learner_adequacy_err_msg)
else:
classifier = learner(self.data)
classifier.name = self.learner_name
self.send("Learner", learner)
self.send("Classifier", classifier)
def column_imputer_by_model(variable, table, *, learner=NaiveBayesLearner()):
model = learn_model_for(learner, variable, table)
assert model.domain.class_vars == (variable, )
return ColumnImputerFromModel(table.domain, model.domain.class_vars, model)
