Source code for

.. module:: CClassifierRidge
   :synopsis: Ridge classifier

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

from sklearn.linear_model import RidgeClassifier

from secml.array import CArray
from import CClassifierLinearMixin, CClassifierSkLearn
from import CClassifierGradientRidgeMixin
from import CLossSquare
from import CRegularizerL2

[docs]class CClassifierRidge(CClassifierLinearMixin, CClassifierSkLearn, CClassifierGradientRidgeMixin): """Ridge Classifier. Parameters ---------- alpha : float, optional Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization. Default 1.0. max_iter : int, optional Maximum number of iterations for conjugate gradient solver. Default 1e5. class_weight : {dict, 'balanced', None}, optional Set the parameter C of class i to `class_weight[i] * C`. If not given (default), all classes are supposed to have weight one. The 'balanced' mode uses the values of labels to automatically adjust weights inversely proportional to class frequencies as `n_samples / (n_classes * np.bincount(y))`. tol : float, optional Precision of the solution. Default 1e-4. fit_intercept : bool, optional If True (default), the intercept is calculated, else no intercept will be used in calculations (e.g. data is expected to be already centered). 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 : 'ridge' """ __class_type = 'ridge' _loss = CLossSquare() _reg = CRegularizerL2() def __init__(self, alpha=1.0, max_iter=1e5, class_weight=None, tol=1e-4, fit_intercept=True, preprocess=None): # create instance of sklearn model sklearn_model = RidgeClassifier(alpha=alpha, fit_intercept=fit_intercept, tol=tol, max_iter=max_iter, class_weight=class_weight, solver='auto') # Calling the superclass init CClassifierSkLearn.__init__(self, sklearn_model=sklearn_model, preprocess=preprocess) @property def C(self): """Constant that multiplies the regularization term. Equal to 1 / alpha. """ return 1.0 / self.alpha @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] if \ self.fit_intercept else 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