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 = self.LEARNER(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 = self.LEARNER(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)
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)
if __name__ == "__main__":
import sys
from AnyQt.QtWidgets import QApplication
a = QApplication(sys.argv)
ow = OWUnivariateRegression()
learner = RidgeRegressionLearner(alpha=1.0)
polylearner = PolynomialLearner(learner, degree=2)
d = Table('iris')
ow.set_data(d)
ow.set_learner(learner)
ow.show()
a.exec_()
ow.saveSettings()
