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[ENH] Add an option to set intercept in linear regression to 0 #4958

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merged 3 commits into from
Sep 18, 2020
Merged

[ENH] Add an option to set intercept in linear regression to 0 #4958

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80fcac0
OWLinearRegression: Add option to set intercept to 0
janezd Aug 27, 2020
2f4c44f
Orange.regression.linear: Lint
janezd Aug 27, 2020
cb5a551
Polynomial Learner: Add argument include_bias
janezd Aug 27, 2020
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10 changes: 9 additions & 1 deletion Orange/regression/linear.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,8 +28,10 @@ def score(self, data):
class LinearRegressionLearner(SklLearner, _FeatureScorerMixin):
__wraps__ = skl_linear_model.LinearRegression

def __init__(self, preprocessors=None):
# Arguments are needed for signatures, pylint: disable=unused-argument
def __init__(self, preprocessors=None, fit_intercept=True):
super().__init__(preprocessors=preprocessors)
self.params = vars()

def fit(self, X, Y, W=None):
model = super().fit(X, Y, W)
Expand All @@ -39,6 +41,7 @@ def fit(self, X, Y, W=None):
class RidgeRegressionLearner(LinearRegressionLearner):
__wraps__ = skl_linear_model.Ridge

# Arguments are needed for signatures, pylint: disable=unused-argument
def __init__(self, alpha=1.0, fit_intercept=True,
normalize=False, copy_X=True, max_iter=None,
tol=0.001, solver='auto', preprocessors=None):
Expand All @@ -49,6 +52,7 @@ def __init__(self, alpha=1.0, fit_intercept=True,
class LassoRegressionLearner(LinearRegressionLearner):
__wraps__ = skl_linear_model.Lasso

# Arguments are needed for signatures, pylint: disable=unused-argument
def __init__(self, alpha=1.0, fit_intercept=True, normalize=False,
precompute=False, copy_X=True, max_iter=1000,
tol=0.0001, warm_start=False, positive=False,
Expand All @@ -60,6 +64,7 @@ def __init__(self, alpha=1.0, fit_intercept=True, normalize=False,
class ElasticNetLearner(LinearRegressionLearner):
__wraps__ = skl_linear_model.ElasticNet

# Arguments are needed for signatures, pylint: disable=unused-argument
def __init__(self, alpha=1.0, l1_ratio=0.5, fit_intercept=True,
normalize=False, precompute=False, max_iter=1000,
copy_X=True, tol=0.0001, warm_start=False, positive=False,
Expand All @@ -71,6 +76,7 @@ def __init__(self, alpha=1.0, l1_ratio=0.5, fit_intercept=True,
class ElasticNetCVLearner(LinearRegressionLearner):
__wraps__ = skl_linear_model.ElasticNetCV

# Arguments are needed for signatures, pylint: disable=unused-argument
def __init__(self, l1_ratio=0.5, eps=0.001, n_alphas=100, alphas=None,
fit_intercept=True, normalize=False, precompute='auto',
max_iter=1000, tol=0.0001, cv=5, copy_X=True,
Expand All @@ -83,6 +89,7 @@ class SGDRegressionLearner(LinearRegressionLearner):
__wraps__ = skl_linear_model.SGDRegressor
preprocessors = SklLearner.preprocessors + [Normalize()]

# Arguments are needed for signatures, pylint: disable=unused-argument
def __init__(self, loss='squared_loss', penalty='l2', alpha=0.0001,
l1_ratio=0.15, fit_intercept=True, max_iter=5, tol=1e-3,
shuffle=True, epsilon=0.1, n_jobs=1, random_state=None,
Expand Down Expand Up @@ -140,6 +147,7 @@ def __str__(self):

class PolynomialModel(Model):
def __init__(self, model, polyfeatures):
super().__init__()
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This change is suggested by lint but otherwise unrelated to the issue.

self.model = model
self.polyfeatures = polyfeatures

Expand Down
25 changes: 20 additions & 5 deletions Orange/widgets/model/owlinearregression.py
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Original file line number Diff line number Diff line change
Expand Up @@ -39,6 +39,7 @@ class Outputs(OWBaseLearner.Outputs):
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)],
Expand All @@ -51,6 +52,11 @@ class Outputs(OWBaseLearner.Outputs):

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,
Expand Down Expand Up @@ -93,6 +99,9 @@ def add_main_layout(self):
def handleNewSignals(self):
self.apply()

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)
Expand All @@ -116,7 +125,8 @@ def _l2_ratio_changed(self):
def create_learner(self):
alpha = self.alphas[self.alpha_index]
preprocessors = self.preprocessors
args = {"preprocessors": 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:
Expand All @@ -134,9 +144,11 @@ def update_model(self):
if self.model is not None:
domain = Domain(
[ContinuousVariable("coef")], metas=[StringVariable("name")])
coefs = [self.model.intercept] + list(self.model.coefficients)
names = ["intercept"] + \
[attr.name for attr in self.model.domain.attributes]
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)
Expand All @@ -155,7 +167,10 @@ def get_learner_parameters(self):
.format(self.alphas[self.alpha_index],
self.l2_ratio,
1 - self.l2_ratio))
return (("Regularization", regularization), )
return (
("Regularization", regularization),
("Fit intercept", ["No", "Yes"][self.fit_intercept])
)


if __name__ == "__main__": # pragma: no cover
Expand Down