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)