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from itertools import chain
from AnyQt.QtCore import Qt
from AnyQt.QtWidgets import QLayout, QSizePolicy
from Orange.data import Table, Domain, ContinuousVariable, StringVariable
from Orange.regression.linear import (
LassoRegressionLearner, LinearRegressionLearner,
RidgeRegressionLearner, ElasticNetLearner
)
from Orange.widgets import settings, gui
from Orange.widgets.utils.owlearnerwidget import OWBaseLearner
from Orange.widgets.utils.widgetpreview import WidgetPreview
from Orange.widgets.widget import Output
class OWLinearRegression(OWBaseLearner):
name = "Linear Regression"
description = "A linear regression algorithm with optional L1 (LASSO), " \
"L2 (ridge) or L1L2 (elastic net) regularization."
icon = "icons/LinearRegression.svg"
replaces = [
"Orange.widgets.regression.owlinearregression.OWLinearRegression",
]
priority = 60
keywords = "linear regression, ridge, lasso, elastic net"
LEARNER = LinearRegressionLearner
class Outputs(OWBaseLearner.Outputs):
coefficients = Output("Coefficients", Table, explicit=True)
#: Types
REGULARIZATION_TYPES = ["No regularization", "Ridge regression (L2)",
"Lasso regression (L1)", "Elastic net regression"]
OLS, Ridge, Lasso, Elastic = 0, 1, 2, 3
ridge = settings.Setting(False)
reg_type = settings.Setting(OLS)
alpha_index = settings.Setting(0)
l2_ratio = settings.Setting(0.5)
fit_intercept = settings.Setting(True)
autosend = settings.Setting(True)
alphas = list(chain([x / 10000 for x in range(1, 10)],
[x / 1000 for x in range(1, 20)],
[x / 100 for x in range(2, 20)],
[x / 10 for x in range(2, 9)],
range(1, 20),
range(20, 100, 5),
range(100, 1001, 100)))
def add_main_layout(self):
# this is part of init, pylint: disable=attribute-defined-outside-init
box = gui.hBox(self.controlArea, "Parameters")
gui.checkBox(box, self, "fit_intercept",
"Fit intercept (unchecking it fixes it to zero)",
callback=self._intercept_changed)
box = gui.hBox(self.controlArea, "Regularization")
gui.radioButtons(box, self, "reg_type",
btnLabels=self.REGULARIZATION_TYPES,
callback=self._reg_type_changed)
self.alpha_box = box2 = gui.vBox(box, margin=10)
gui.widgetLabel(box2, "Regularization strength:")
gui.hSlider(
box2, self, "alpha_index",
minValue=0, maxValue=len(self.alphas) - 1,
callback=self._alpha_changed, createLabel=False)
box3 = gui.hBox(box2)
box3.layout().setAlignment(Qt.AlignCenter)
self.alpha_label = gui.widgetLabel(box3, "")
self._set_alpha_label()
box4 = gui.vBox(box2, margin=0)
gui.widgetLabel(box4, "Elastic net mixing:")
box5 = gui.hBox(box4)
gui.widgetLabel(box5, "L1")
self.l2_ratio_slider = gui.hSlider(
box5, self, "l2_ratio", minValue=0.01, maxValue=0.99,
intOnly=False, createLabel=False, width=120,
step=0.01, callback=self._l2_ratio_changed)
gui.widgetLabel(box5, "L2")
self.l2_ratio_label = gui.widgetLabel(
box4, "",
sizePolicy=(QSizePolicy.MinimumExpanding, QSizePolicy.Fixed))
self.l2_ratio_label.setAlignment(Qt.AlignCenter)
box5 = gui.hBox(self.controlArea)
box5.layout().setAlignment(Qt.AlignCenter)
self._set_l2_ratio_label()
self.layout().setSizeConstraint(QLayout.SetFixedSize)
self.controls.alpha_index.setEnabled(self.reg_type != self.OLS)
self.l2_ratio_slider.setEnabled(self.reg_type == self.Elastic)
def _intercept_changed(self):
self.apply()
def _reg_type_changed(self):
self.controls.alpha_index.setEnabled(self.reg_type != self.OLS)
self.l2_ratio_slider.setEnabled(self.reg_type == self.Elastic)
self.apply()
def _set_alpha_label(self):
self.alpha_label.setText("Alpha: {}".format(self.alphas[self.alpha_index]))
def _alpha_changed(self):
self._set_alpha_label()
self.apply()
def _set_l2_ratio_label(self):
self.l2_ratio_label.setText(
"{:.{}f} : {:.{}f}".format(1 - self.l2_ratio, 2, self.l2_ratio, 2))
def _l2_ratio_changed(self):
self._set_l2_ratio_label()
self.apply()
def create_learner(self):
alpha = self.alphas[self.alpha_index]
preprocessors = self.preprocessors
args = dict(preprocessors=preprocessors,
fit_intercept=self.fit_intercept)
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 update_model(self):
super().update_model()
coef_table = None
if self.model is not None:
domain = Domain(
[ContinuousVariable("coef")], metas=[StringVariable("name")])
coefs = list(self.model.coefficients)
names = [attr.name for attr in self.model.domain.attributes]
if self.fit_intercept:
coefs.insert(0, self.model.intercept)
names.insert(0, "intercept")
coef_table = Table.from_list(domain, list(zip(coefs, names)))
coef_table.name = "coefficients"
self.Outputs.coefficients.send(coef_table)
def get_learner_parameters(self):
regularization = "No Regularization"
if self.reg_type == OWLinearRegression.Ridge:
regularization = ("Ridge Regression (L2) with α={}"
.format(self.alphas[self.alpha_index]))
elif self.reg_type == OWLinearRegression.Lasso:
regularization = ("Lasso Regression (L1) with α={}"
.format(self.alphas[self.alpha_index]))
elif self.reg_type == OWLinearRegression.Elastic:
regularization = ("Elastic Net Regression with α={}"
" and L1:L2 ratio of {}:{}"
.format(self.alphas[self.alpha_index],
self.l2_ratio,
1 - self.l2_ratio))
return (
("Regularization", regularization),
("Fit intercept", ["No", "Yes"][self.fit_intercept])
)
if __name__ == "__main__": # pragma: no cover
WidgetPreview(OWLinearRegression).run(Table("housing"))
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