Source code for

.. module:: CClassifierLogistic
   :synopsis: Logistic Regression (aka logit, MaxEnt) classifier

.. moduleauthor:: Battista Biggio <>
.. moduleauthor:: Ambra Demontis <>

from sklearn.linear_model import LogisticRegression

from secml.array import CArray
from import CClassifierLinearMixin, CClassifierSkLearn
from import CLossLogistic
from import CRegularizerL2

from import \

[docs]class CClassifierLogistic(CClassifierLinearMixin, CClassifierSkLearn, CClassifierGradientLogisticMixin): """Logistic Regression (aka logit, MaxEnt) classifier. Parameters ---------- C : float, optional Penalty parameter C of the error term. Default 1.0. max_iter : int, optional Maximum number of iterations taken for the solvers to converge. Default 100. random_state : int, RandomState or None, optional The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. Default None. preprocess : CPreProcess or str or None, optional Features preprocess to be applied to input data. Can be a CPreProcess subclass or a string with the type of the desired preprocessor. If None, input data is used as is. Attributes ---------- class_type : 'logistic' """ __class_type = 'logistic' _loss = CLossLogistic() _reg = CRegularizerL2() def __init__(self, C=1.0, max_iter=100, random_state=None, preprocess=None): sklearn_model = LogisticRegression( penalty='l2', dual=False, tol=0.0001, C=C, fit_intercept=True, intercept_scaling=1.0, class_weight=None, solver='liblinear', random_state=random_state, max_iter=max_iter, multi_class='ovr', verbose=0, warm_start=False) CClassifierSkLearn.__init__(self, sklearn_model, preprocess=preprocess) @property def w(self): if self.is_fitted(): return CArray(self._sklearn_model.coef_).ravel() else: return None @property def b(self): if self.is_fitted(): return CArray(self._sklearn_model.intercept_[0])[0] else: return None def _check_input(self, x, y=None): """Check if y contains only two classes.""" x, y = CClassifierSkLearn._check_input(self, x, y) if y is not None and y.unique().size != 2: raise ValueError("The data (x,y) has more than two classes.") return x, y