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import numpy as np
import sklearn.linear_model as skl_linear_model
from Orange.classification import SklLearner, SklModel
from Orange.preprocess import Normalize
from Orange.preprocess.score import LearnerScorer
from Orange.data import Variable, DiscreteVariable
__all__ = ["LogisticRegressionLearner"]
class _FeatureScorerMixin(LearnerScorer):
feature_type = Variable
class_type = DiscreteVariable
def score(self, data):
data = Normalize()(data)
model = self(data)
return np.abs(model.coefficients), model.domain.attributes
class LogisticRegressionClassifier(SklModel):
@property
def intercept(self):
return self.skl_model.intercept_
@property
def coefficients(self):
return self.skl_model.coef_
class LogisticRegressionLearner(SklLearner, _FeatureScorerMixin):
__wraps__ = skl_linear_model.LogisticRegression
__returns__ = LogisticRegressionClassifier
preprocessors = SklLearner.preprocessors
supports_weights = True
def __init__(self, penalty="l2", dual=False, tol=0.0001, C=1.0,
fit_intercept=True, intercept_scaling=1, class_weight=None,
random_state=None, solver="auto", max_iter=100,
verbose=0, n_jobs=1, preprocessors=None):
super().__init__(preprocessors=preprocessors)
self.params = vars()
def _initialize_wrapped(self):
params = self.params.copy()
# The default scikit-learn solver `lbfgs` (v0.22) does not support the
# l1 penalty.
solver, penalty = params.pop("solver"), params.get("penalty")
if solver == "auto":
if penalty == "l1":
solver = "saga"
else:
solver = "lbfgs"
params["solver"] = solver
return self.__wraps__(**params)
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