secml.ml.classifiers.gradients¶

secml.ml.classifiers.gradients.mixin_classifier_gradient module¶

class secml.ml.classifiers.gradients.mixin_classifier_gradient.CClassifierGradientMixin[source]¶

Bases: object

Abstract Mixin class that defines basic methods for classifier gradients.

Methods

`grad_f_params`(self, x, y)	Derivative of the decision function w.r.t.
`grad_f_x`(self, x, y, \\kwargs)	Derivative of the classifier decision function w.r.t.
`grad_loss_params`(self, x, y[, loss])	Derivative of a given loss w.r.t.
`grad_tr_params`(self, x, y)	Derivative of the classifier training objective function w.r.t.
`hessian_tr_params`(self, x, y)	Hessian of the training objective w.r.t.

grad_f_params(self, x, y)[source]¶

Derivative of the decision function w.r.t. the classifier parameters.

Parameters

xCArray: Features of the dataset on which the training objective is computed
yint: Index of the class wrt the gradient must be computed.

grad_f_x(self, x, y, **kwargs)[source]¶

Derivative of the classifier decision function w.r.t. an input sample

Parameters

xCArray: features of the dataset on which the decision function is computed
yCArray: The label of the class wrt the function should be calculated.
kwargs: Optional arguments for the gradient method. See specific classifier for a full description.

Returns

gradientCArray: Gradient of the classifier’s output wrt input. Vector-like array.

grad_loss_params(self, x, y, loss=None)[source]¶

Derivative of a given loss w.r.t. the classifier parameters.

Parameters

xCArray: Features of the dataset on which the loss is computed
yCArray: Dataset labels
loss: None (default) or CLoss: If the loss is equal to None (default) the classifier loss is used to compute the derivative.

grad_tr_params(self, x, y)[source]¶

Derivative of the classifier training objective function w.r.t. the classifier parameters.

Parameters

xCArray: Features of the dataset on which the training objective is computed.
yCArray: Dataset labels.

hessian_tr_params(self, x, y)[source]¶

Hessian of the training objective w.r.t. the classifier parameters.

Parameters

xCArray: Features of the dataset on which the training objective is computed.
yCArray: Dataset labels.

secml.ml.classifiers.gradients.mixin_classifier_gradient_kde module¶

class secml.ml.classifiers.gradients.mixin_classifier_gradient_kde.CClassifierGradientKDEMixin[source]¶

Bases: secml.ml.classifiers.gradients.mixin_classifier_gradient.CClassifierGradientMixin

Mixin class for CClassifierKDE gradients.

Methods

`grad_f_params`(self, x, y)	Derivative of the decision function w.r.t.
`grad_f_x`(self, x, y, \\kwargs)	Derivative of the classifier decision function w.r.t.
`grad_loss_params`(self, x, y[, loss])	Derivative of a given loss w.r.t.
`grad_tr_params`(self, x, y)	Derivative of the classifier training objective function w.r.t.
`hessian_tr_params`(self, x, y)	Hessian of the training objective w.r.t.

secml.ml.classifiers.gradients.mixin_classifier_gradient_linear module¶

class secml.ml.classifiers.gradients.mixin_classifier_gradient_linear.CClassifierGradientLinearMixin[source]¶

Bases: secml.ml.classifiers.gradients.mixin_classifier_gradient.CClassifierGradientMixin

Mixin class for CClassifierLinear gradients.

Methods

`grad_f_params`(self, x[, y])	Derivative of the decision function w.r.t.
`grad_f_x`(self[, x, y])	Computes the gradient of the classifier’s output wrt input.
`grad_loss_params`(self, x, y[, loss])	Derivative of the classifier loss w.r.t.
`grad_tr_params`(self, x, y)	Derivative of the classifier training objective w.r.t. the classifier
`hessian_tr_params`(self, x, y)	Hessian of the training objective w.r.t.

grad_f_params(self, x, y=1)[source]¶

Derivative of the decision function w.r.t. the classifier parameters.

Parameters

xCArray: Features of the dataset on which the training objective is computed.
yint: Index of the class wrt the gradient must be computed.

grad_f_x(self, x=None, y=1, **kwargs)[source]¶

Computes the gradient of the classifier’s output wrt input.

Parameters

xCArray: The gradient is computed in the neighborhood of x.
yint, optional: Index of the class wrt the gradient must be computed. Default 1.
**kwargs: Optional parameters for the function that computes the gradient of the decision function. See the description of each classifier for a complete list of optional parameters.

Returns

gradientCArray: Gradient of the classifier’s output wrt input. Vector-like array.

grad_loss_params(self, x, y, loss=None)[source]¶

Derivative of the classifier loss w.r.t. the classifier parameters.

d_loss / d_params = d_loss / d_f * d_f / d_params

Parameters

xCArray: Features of the dataset on which the loss is computed.
yCArray: Dataset labels.
loss: None (default) or CLoss: If the loss is equal to None (default) the classifier loss is used to compute the derivative.

grad_tr_params(self, x, y)[source]¶

Derivative of the classifier training objective w.r.t. the classifier: parameters.

If the loss is equal to None (default) the classifier loss is used to compute the derivative.

d_train_obj / d_params = d_loss / d_f * d_f / d_params + d_reg / d_params

Parameters

xCArray: Features of the dataset on which the loss is computed.
yCArray: Dataset labels.

secml.ml.classifiers.gradients.mixin_classifier_gradient_logistic module¶

class secml.ml.classifiers.gradients.mixin_classifier_gradient_logistic.CClassifierGradientLogisticMixin[source]¶

Bases: secml.ml.classifiers.gradients.mixin_classifier_gradient_linear.CClassifierGradientLinearMixin

Mixin class for CClassifierLogistic gradients.

Methods

`grad_f_params`(self, x[, y])	Derivative of the decision function w.r.t.
`grad_f_x`(self[, x, y])	Computes the gradient of the classifier’s output wrt input.
`grad_loss_params`(self, x, y[, loss])	Derivative of the classifier loss w.r.t.
`grad_tr_params`(self, x, y)	Derivative of the classifier training objective w.r.t. the classifier
`hessian_tr_params`(self, x, y)	Hessian of the training objective w.r.t.

hessian_tr_params(self, x, y)[source]¶

Hessian of the training objective w.r.t. the classifier parameters.

Parameters

xCArray: Features of the dataset on which the training objective is computed.
yCArray: Dataset labels.

secml.ml.classifiers.gradients.mixin_classifier_gradient_ridge module¶

class secml.ml.classifiers.gradients.mixin_classifier_gradient_ridge.CClassifierGradientRidgeMixin[source]¶

Bases: secml.ml.classifiers.gradients.mixin_classifier_gradient_linear.CClassifierGradientLinearMixin

Mixin class for CClassifierRidge gradients.

Methods

`grad_f_params`(self, x[, y])	Derivative of the decision function w.r.t.
`grad_f_x`(self[, x, y])	Computes the gradient of the classifier’s output wrt input.
`grad_loss_params`(self, x, y[, loss])	Derivative of the classifier loss w.r.t.
`grad_tr_params`(self, x, y)	Derivative of the classifier training objective w.r.t. the classifier
`hessian_tr_params`(self, x[, y])	Hessian of the training objective w.r.t.

hessian_tr_params(self, x, y=None)[source]¶

Hessian of the training objective w.r.t. the classifier parameters.

Parameters

xCArray: Features of the dataset on which the training objective is computed.
yCArray: Dataset labels.

secml.ml.classifiers.gradients.mixin_classifier_gradient_sgd module¶

class secml.ml.classifiers.gradients.mixin_classifier_gradient_sgd.CClassifierGradientSGDMixin[source]¶

Bases: secml.ml.classifiers.gradients.mixin_classifier_gradient_linear.CClassifierGradientLinearMixin

Mixin class for CClassifierSGD gradients.

Methods

`grad_f_params`(self, x[, y])	Derivative of the decision function w.r.t.
`grad_f_x`(self[, x, y])	Computes the gradient of the classifier’s output wrt input.
`grad_loss_params`(self, x, y[, loss])	Derivative of the classifier loss w.r.t.
`grad_tr_params`(self, x, y)	Derivative of the classifier training objective function w.r.t.
`hessian_tr_params`(self, x, y)	Hessian of the training objective w.r.t.

grad_tr_params(self, x, y)[source]¶

Derivative of the classifier training objective function w.r.t. the classifier parameters

Parameters

xCArray: Features of the dataset on which the training objective is computed.
yCArray: dataset labels

secml.ml.classifiers.gradients.mixin_classifier_gradient_svm module¶

class secml.ml.classifiers.gradients.mixin_classifier_gradient_svm.CClassifierGradientSVMMixin[source]¶

Bases: secml.ml.classifiers.gradients.mixin_classifier_gradient_linear.CClassifierGradientLinearMixin

Mixin class for CClassifierSVM gradients.

Methods

`grad_f_params`(self, x[, y])	Derivative of the decision function w.r.t.
`grad_f_x`(self[, x, y])	Computes the gradient of the classifier’s output wrt input.
`grad_loss_params`(self, x, y[, loss])	Derivative of the loss w.r.t.
`grad_tr_params`(self, x, y)	Derivative of the classifier training objective w.r.t.
`hessian_tr_params`(self[, x, y])	Hessian of the training objective w.r.t.

grad_f_params(self, x, y=1)[source]¶

Derivative of the decision function w.r.t. the classifier parameters.

Parameters

xCArray: Features of the dataset on which the training objective is computed.
yint: Index of the class wrt the gradient must be computed.

grad_loss_params(self, x, y, loss=None)[source]¶

Derivative of the loss w.r.t. the classifier parameters

dL / d_params = dL / df * df / d_params

Parameters

xCArray: Features of the dataset on which the loss is computed.
yCArray: Labels of the training samples.
loss: None (default) or CLoss: If the loss is equal to None (default) the classifier loss is used to compute the derivative.

grad_tr_params(self, x, y)[source]¶

Derivative of the classifier training objective w.r.t. the classifier parameters.

dL / d_params = dL / df * df / d_params + dReg / d_params

Parameters

xCArray: Features of the dataset on which the loss is computed.
yCArray: Features of the training samples

hessian_tr_params(self, x=None, y=None)[source]¶: Hessian of the training objective w.r.t. the classifier parameters.