def apply(self):
learner = self.learner
predictor = None
if self.data is not None:
if self.learner is None:
learner = LinearRegressionLearner(
preprocessors=self.preprocessors)
attributes = self.x_var_model[self.x_var_index]
class_var = self.y_var_model[self.y_var_index]
data_table = Table(Domain([attributes], class_vars=[class_var]),
self.data)
degree = int(self.polynomialexpansion)
learner = PolynomialLearner(learner, degree=degree)
learner.name = self.learner_name
predictor = learner(data_table)
x = data_table.X.ravel()
y = data_table.Y.ravel()
linspace = np.linspace(min(x), max(x), 1000).reshape(-1, 1)
values = predictor(linspace, predictor.Value)
self.plot_scatter_points(x, y)
self.plot_regression_line(linspace.ravel(), values.ravel())
x_label = self.x_var_model[self.x_var_index]
axis = self.plot.getAxis("bottom")
axis.setLabel(x_label)
y_label = self.y_var_model[self.y_var_index]
axis = self.plot.getAxis("left")
axis.setLabel(y_label)
self.set_range(x, y)
self.send("Learner", learner)
self.send("Predictor", predictor)
model = None
if predictor is not None:
model = predictor.model
if hasattr(model, "model"):
model = model.model
elif hasattr(model, "skl_model"):
model = model.skl_model
if model is not None and hasattr(model, "coef_"):
domain = Domain([ContinuousVariable("coef", number_of_decimals=7)],
metas=[StringVariable("name")])
coefs = [model.intercept_ + model.coef_[0]] + list(model.coef_[1:])
names = ["1", x_label] + \
["{}^{}".format(x_label, i) for i in range(2, degree + 1)]
coef_table = Table(domain, list(zip(coefs, names)))
self.send("Coefficients", coef_table)
else:
self.send("Coefficients", None)
def apply(self):
learner = self.learner
predictor = None
if self.data is not None:
if self.learner is None:
learner = LinearRegressionLearner(preprocessors=self.preprocessors)
attributes = self.x_var_model[self.x_var_index]
class_var = self.y_var_model[self.y_var_index]
data_table = Table(Domain([attributes], class_vars=[class_var]), self.data)
degree = int(self.polynomialexpansion)
learner = PolynomialLearner(learner, degree=degree)
learner.name = self.learner_name
predictor = learner(data_table)
x = data_table.X.ravel()
y = data_table.Y.ravel()
linspace = np.linspace(min(x), max(x), 1000).reshape(-1,1)
values = predictor(linspace, predictor.Value)
self.plot_scatter_points(x, y)
self.plot_regression_line(linspace.ravel(), values.ravel())
x_label = self.x_var_model[self.x_var_index]
axis = self.plot.getAxis("bottom")
axis.setLabel(x_label)
y_label = self.y_var_model[self.y_var_index]
axis = self.plot.getAxis("left")
axis.setLabel(y_label)
self.set_range(x, y)
self.send("Learner", learner)
self.send("Predictor", predictor)
model = None
if predictor is not None:
model = predictor.model
if hasattr(model, "model"):
model = model.model
elif hasattr(model, "skl_model"):
model = model.skl_model
if model is not None and hasattr(model, "coef_"):
domain = Domain([ContinuousVariable("coef", number_of_decimals=7)],
metas=[StringVariable("name")])
coefs = [model.intercept_ + model.coef_[0]] + list(model.coef_[1:])
names = ["1", x_label] + \
["{}^{}".format(x_label, i) for i in range(2, degree + 1)]
coef_table = Table(domain, list(zip(coefs, names)))
self.send("Coefficients", coef_table)
else:
self.send("Coefficients", None)
def commit(self):
alpha = self.alphas[self.alpha_index]
preprocessors = self.preprocessors
if self.data is not None and np.isnan(self.data.Y).any():
self.warning(0, "Missing values of target variable(s)")
if not self.preprocessors:
if self.reg_type == OWLinearRegression.OLS:
preprocessors = LinearRegressionLearner.preprocessors
elif self.reg_type == OWLinearRegression.Ridge:
preprocessors = RidgeRegressionLearner.preprocessors
else:
preprocessors = LassoRegressionLearner.preprocessors
else:
preprocessors = list(self.preprocessors)
preprocessors.append(RemoveNaNClasses())
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)
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)
def apply(self):
learner = self.learner
predictor = None
if self.data is not None:
if self.learner is None:
learner = LinearRegressionLearner(
preprocessors=self.preprocessors)
attributes = self.x_var_model[self.x_var_index]
class_var = self.y_var_model[self.y_var_index]
data_table = Table(Domain([attributes], class_vars=[class_var]),
self.data)
degree = int(self.polynomialexpansion)
learner = PolynomialLearner(learner, degree=degree)
learner.name = self.learner_name
predictor = learner(data_table)
x = data_table.X.ravel()
y = data_table.Y.ravel()
linspace = np.linspace(min(x), max(x), 1000).reshape(-1, 1)
values = predictor(linspace, predictor.Value)
self.plot_scatter_points(x, y)
self.plot_regression_line(linspace.ravel(), values.ravel())
x_label = self.x_var_model[self.x_var_index]
axis = self.plot.getAxis("bottom")
axis.setLabel(x_label)
y_label = self.y_var_model[self.y_var_index]
axis = self.plot.getAxis("left")
axis.setLabel(y_label)
self.set_range(x, y)
self.send("Learner", learner)
self.send("Predictor", predictor)
def apply(self):
learner = self.learner
predictor = None
if self.data is not None:
if self.learner is None:
learner = LinearRegressionLearner(preprocessors=self.preprocessors)
attributes = self.x_var_model[self.x_var_index]
class_var = self.y_var_model[self.y_var_index]
data_table = Table(Domain([attributes], class_vars=[class_var]), self.data)
degree = int(self.polynomialexpansion)
learner = PolynomialLearner(learner, degree=degree)
learner.name = self.learner_name
predictor = learner(data_table)
x = data_table.X.ravel()
y = data_table.Y.ravel()
linspace = np.linspace(min(x), max(x), 1000).reshape(-1,1)
values = predictor(linspace, predictor.Value)
self.plot_scatter_points(x, y)
self.plot_regression_line(linspace.ravel(), values.ravel())
x_label = self.x_var_model[self.x_var_index]
axis = self.plot.getAxis("bottom")
axis.setLabel(x_label)
y_label = self.y_var_model[self.y_var_index]
axis = self.plot.getAxis("left")
axis.setLabel(y_label)
self.set_range(x, y)
self.send("Learner", learner)
self.send("Predictor", predictor)
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]
args = {"preprocessors": self.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)
learner.name = self.learner_name
predictor = 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
self.send("Learner", learner)
self.send("Predictor", predictor)
def commit(self):
alpha = self.alphas[self.alpha_index]
preprocessors = self.preprocessors
if self.data is not None and np.isnan(self.data.Y).any():
self.warning(0, "Missing values of target variable(s)")
if not self.preprocessors:
if self.reg_type == OWLinearRegression.OLS:
preprocessors = LinearRegressionLearner.preprocessors
elif self.reg_type == OWLinearRegression.Ridge:
preprocessors = RidgeRegressionLearner.preprocessors
else:
preprocessors = LassoRegressionLearner.preprocessors
else:
preprocessors = list(self.preprocessors)
preprocessors.append(RemoveNaNClasses())
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)
learner.name = self.learner_name
predictor = 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
self.send("Linear Regression", learner)
self.send("Model", predictor)
def commit(self):
alpha = self.alphas[self.alpha_index]
preprocessors = self.preprocessors
if self.data is not None and np.isnan(self.data.Y).any():
self.warning(0, "Missing values of target variable(s)")
if not self.preprocessors:
if self.reg_type == OWLinearRegression.OLS:
preprocessors = LinearRegressionLearner.preprocessors
elif self.reg_type == OWLinearRegression.Ridge:
preprocessors = RidgeRegressionLearner.preprocessors
else:
preprocessors = LassoRegressionLearner.preprocessors
else:
preprocessors = list(self.preprocessors)
preprocessors.append(RemoveNaNClasses())
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)
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)
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)
def commit(self):
alpha = self.alphas[self.alpha_index]
args = {"preprocessors": self.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)
learner.name = self.learner_name
predictor = 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
self.send("Learner", learner)
self.send("Predictor", predictor)
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)
def apply(self):
degree = int(self.polynomialexpansion)
learner = self.LEARNER(preprocessors=self.preprocessors,
degree=degree,
learner=LinearRegressionLearner()
if self.learner is None else self.learner)
learner.name = self.learner_name
predictor = None
self.Error.all_none.clear()
if self.data is not None:
attributes = self.x_var_model[self.x_var_index]
class_var = self.y_var_model[self.y_var_index]
data_table = Table(Domain([attributes], class_vars=[class_var]),
self.data)
# all lines has nan
if sum(
math.isnan(line[0]) or math.isnan(line.get_class())
for line in data_table) == len(data_table):
self.Error.all_none()
self.clear_plot()
return
predictor = learner(data_table)
preprocessed_data = data_table
for preprocessor in learner.active_preprocessors:
preprocessed_data = preprocessor(preprocessed_data)
x = preprocessed_data.X.ravel()
y = preprocessed_data.Y.ravel()
linspace = np.linspace(np.nanmin(x), np.nanmax(x),
1000).reshape(-1, 1)
values = predictor(linspace, predictor.Value)
# calculate prediction for x from data
predicted = TestOnTrainingData(preprocessed_data, [learner])
self.rmse = round(RMSE(predicted)[0], 6)
self.mae = round(MAE(predicted)[0], 6)
# plot error bars
self.plot_error_bars(x, predicted.actual,
predicted.predicted.ravel())
# plot data points
self.plot_scatter_points(x, y)
# plot regression line
self.plot_regression_line(linspace.ravel(), values.ravel())
x_label = self.x_var_model[self.x_var_index]
axis = self.plot.getAxis("bottom")
axis.setLabel(x_label)
y_label = self.y_var_model[self.y_var_index]
axis = self.plot.getAxis("left")
axis.setLabel(y_label)
self.set_range(x, y)
self.send("Learner", learner)
self.send("Predictor", predictor)
# Send model coefficents
model = None
if predictor is not None:
model = predictor.model
if hasattr(model, "model"):
model = model.model
elif hasattr(model, "skl_model"):
model = model.skl_model
if model is not None and hasattr(model, "coef_"):
domain = Domain([ContinuousVariable("coef", number_of_decimals=7)],
metas=[StringVariable("name")])
coefs = [model.intercept_ + model.coef_[0]] + list(model.coef_[1:])
names = ["1", x_label] + \
["{}^{}".format(x_label, i) for i in range(2, degree + 1)]
coef_table = Table(domain, list(zip(coefs, names)))
self.send("Coefficients", coef_table)
else:
self.send("Coefficients", None)
self.send_data()
