def create_learner(self):
alpha = self.alphas[self.alpha_index]
preprocessors = self.preprocessors
args = {"preprocessors": preprocessors}
if self.reg_type == OWLinearRegression.OLS:
learner = LinearRegressionLearner(**args)
elif self.reg_type == OWLinearRegression.Ridge:
learner = RidgeRegressionLearner(alpha=alpha, **args)
elif self.reg_type == OWLinearRegression.Lasso:
learner = LassoRegressionLearner(alpha=alpha, **args)
elif self.reg_type == OWLinearRegression.Elastic:
learner = ElasticNetLearner(alpha=alpha,
l1_ratio=1 - self.l2_ratio, **args)
return learner
def commit(self):
alpha = self.alphas[self.alpha_index]
preprocessors = self.preprocessors
args = {"preprocessors": preprocessors}
if self.reg_type == OWLinearRegression.OLS:
learner = LinearRegressionLearner(**args)
elif self.reg_type == OWLinearRegression.Ridge:
learner = RidgeRegressionLearner(alpha=alpha, **args)
elif self.reg_type == OWLinearRegression.Lasso:
learner = LassoRegressionLearner(alpha=alpha, **args)
elif self.reg_type == OWLinearRegression.Elastic:
learner = ElasticNetLearner(alpha=alpha,
l1_ratio=self.l1_ratio,
**args)
learner.name = self.learner_name
predictor = None
coef_table = None
self.error(0)
if self.data is not None:
if not learner.check_learner_adequacy(self.data.domain):
self.error(0, learner.learner_adequacy_err_msg)
else:
predictor = learner(self.data)
predictor.name = self.learner_name
domain = Domain(
[ContinuousVariable("coef", number_of_decimals=7)],
metas=[StringVariable("name")])
coefs = [predictor.intercept] + list(predictor.coefficients)
names = ["intercept"] + \
[attr.name for attr in predictor.domain.attributes]
coef_table = Table(domain, list(zip(coefs, names)))
coef_table.name = "coefficients"
self.send("Linear Regression", learner)
self.send("Model", predictor)
self.send("Coefficients", coef_table)