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import numpy as np
import sklearn.linear_model as skl_linear_model
import sklearn.preprocessing as skl_preprocessing
from Orange.data import Variable, ContinuousVariable
from Orange.preprocess import Normalize
from Orange.preprocess.score import LearnerScorer
from Orange.regression import Learner, Model, SklLearner, SklModel
__all__ = ["LinearRegressionLearner", "RidgeRegressionLearner",
"LassoRegressionLearner", "SGDRegressionLearner",
"ElasticNetLearner", "ElasticNetCVLearner",
"PolynomialLearner"]
class _FeatureScorerMixin(LearnerScorer):
feature_type = Variable
class_type = ContinuousVariable
def score(self, data):
data = Normalize()(data)
model = self(data)
return np.abs(model.coefficients), model.domain.attributes
class LinearRegressionLearner(SklLearner, _FeatureScorerMixin):
__wraps__ = skl_linear_model.LinearRegression
supports_weights = True
# 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)
return LinearModel(model.skl_model)
class RidgeRegressionLearner(LinearRegressionLearner):
__wraps__ = skl_linear_model.Ridge
supports_weights = True
# Arguments are needed for signatures, pylint: disable=unused-argument
def __init__(self, alpha=1.0, fit_intercept=True, copy_X=True,
max_iter=None, tol=0.001, solver='auto', preprocessors=None):
super().__init__(preprocessors=preprocessors)
self.params = vars()
class LassoRegressionLearner(LinearRegressionLearner):
__wraps__ = skl_linear_model.Lasso
supports_weights = True
# Arguments are needed for signatures, pylint: disable=unused-argument
def __init__(self, alpha=1.0, fit_intercept=True, precompute=False,
copy_X=True, max_iter=1000, tol=0.0001, warm_start=False,
positive=False, preprocessors=None):
super().__init__(preprocessors=preprocessors)
self.params = vars()
class ElasticNetLearner(LinearRegressionLearner):
__wraps__ = skl_linear_model.ElasticNet
supports_weights = True
# Arguments are needed for signatures, pylint: disable=unused-argument
def __init__(self, alpha=1.0, l1_ratio=0.5, fit_intercept=True,
precompute=False, max_iter=1000, copy_X=True, tol=0.0001,
warm_start=False, positive=False, preprocessors=None):
super().__init__(preprocessors=preprocessors)
self.params = vars()
class ElasticNetCVLearner(LinearRegressionLearner):
__wraps__ = skl_linear_model.ElasticNetCV
supports_weights = True
# 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, precompute='auto', max_iter=1000,
tol=0.0001, cv=5, copy_X=True, verbose=0, n_jobs=1,
positive=False, preprocessors=None):
super().__init__(preprocessors=preprocessors)
self.params = vars()
class SGDRegressionLearner(LinearRegressionLearner):
__wraps__ = skl_linear_model.SGDRegressor
preprocessors = SklLearner.preprocessors + [Normalize()]
supports_weights = True
# Arguments are needed for signatures, pylint: disable=unused-argument
def __init__(self, loss='squared_error', 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,
learning_rate='invscaling', eta0=0.01, power_t=0.25,
class_weight=None, warm_start=False, average=False,
preprocessors=None):
super().__init__(preprocessors=preprocessors)
self.params = vars()
class PolynomialLearner(Learner):
"""Generate polynomial features and learn a prediction model
Parameters
----------
learner : LearnerRegression
learner to be fitted on the transformed features
degree : int
degree of used polynomial
preprocessors : List[Preprocessor]
preprocessors to be applied on the data before learning
"""
name = 'poly learner'
preprocessors = SklLearner.preprocessors
def __init__(self, learner=LinearRegressionLearner(), degree=2,
preprocessors=None, include_bias=True):
super().__init__(preprocessors=preprocessors)
self.degree = degree
self.learner = learner
self.include_bias = include_bias
def fit(self, X, Y, W=None):
polyfeatures = skl_preprocessing.PolynomialFeatures(
self.degree, include_bias=self.include_bias)
X = polyfeatures.fit_transform(X)
clf = self.learner
if W is None or not self.supports_weights:
model = clf.fit(X, Y, None)
else:
model = clf.fit(X, Y, sample_weight=W.reshape(-1))
return PolynomialModel(model, polyfeatures)
class LinearModel(SklModel):
@property
def intercept(self):
return self.skl_model.intercept_
@property
def coefficients(self):
return self.skl_model.coef_
def __str__(self):
return 'LinearModel {}'.format(self.skl_model)
class PolynomialModel(Model):
def __init__(self, model, polyfeatures):
super().__init__()
self.model = model
self.polyfeatures = polyfeatures
def predict(self, X):
X = self.polyfeatures.fit_transform(X)
return self.model.predict(X)
def __str__(self):
return 'PolynomialModel {}'.format(self.model)
PolynomialLearner.__returns__ = PolynomialModel
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