secml.ml.classifiers.reject¶

CClassifierReject¶

class secml.ml.classifiers.reject.c_classifier_reject.CClassifierReject(preprocess=None)[source]¶

Bases: secml.ml.classifiers.c_classifier.CClassifier

Abstract class that defines basic methods for Classifiers with reject.

A classifier assign a label (class) to new patterns using the informations learned from training set.

This interface implements a set of generic methods for training and classification that can be used for every algorithms. However, all of them can be reimplemented if specific routines are needed.

Parameters

preprocessstr or CNormalizer: Features preprocess to applied to input data. Can be a CNormalizer subclass or a string with the desired preprocess type. If None, input data is used as is.

Attributes

class_type: Defines class type.
classes: Return the list of classes on which training has been performed.
logger: Logger for current object.
n_classes: Number of classes of training dataset.
n_features: Number of features (before preprocessing).
verbose: Verbosity level of logger output.

Methods

`copy`(self)	Returns a shallow copy of current class.
`create`([class_item])	This method creates an instance of a class with given type.
`decision_function`(self, x[, y])	Computes the decision function for each pattern in x.
`deepcopy`(self)	Returns a deep copy of current class.
`estimate_parameters`(self, dataset, …[, …])	Estimate parameter that give better result respect a chose metric.
`fit`(self, dataset[, n_jobs])	Trains the classifier.
`get_class_from_type`(class_type)	Return the class associated with input type.
`get_params`(self)	Returns the dictionary of class parameters.
`get_subclasses`()	Get all the subclasses of the calling class.
`is_fitted`(self)	Return True if the classifier is trained (fitted).
`is_linear`(self)	True for linear classifiers, False otherwise.
`list_class_types`()	This method lists all types of available subclasses of calling one.
`load`(path)	Loads class from pickle object.
`predict`(self, x[, return_decision_function, …])	Perform classification of each pattern in x.
`save`(self, path)	Save class object using pickle.
`set`(self, param_name, param_value[, copy])	Set a parameter that has a specific name to a specific value.
`set_params`(self, params_dict[, copy])	Set all parameters passed as a dictionary {key: value}.
`timed`([msg])	Timer decorator.

abstract predict(self, x, return_decision_function=False, n_jobs=1)[source]¶

Perform classification of each pattern in x.

If a preprocess has been specified, input is normalized before classification.

Parameters

xCArray: Array with new patterns to classify, 2-Dimensional of shape (n_patterns, n_features).
return_decision_functionbool, optional: Whether to return the decision_function value along with predictions. Default False.
n_jobsint, optional: Number of parallel workers to use for classification. Default 1. Cannot be higher than processor’s number of cores.

Returns

labelsCArray: Flat dense array of shape (n_patterns,) with the label assigned to each test pattern. The classification label is the label of the class associated with the highest score. The rejected samples have label -1.
scoresCArray, optional: Array of shape (n_patterns, n_classes) with classification score of each test pattern with respect to each training class. Will be returned only if return_decision_function is True.

CClassifierRejectThreshold¶

class secml.ml.classifiers.reject.c_classifier_reject_threshold.CClassifierRejectThreshold(clf, threshold, preprocess=None)[source]¶

Bases: secml.ml.classifiers.reject.c_classifier_reject.CClassifierReject, secml.ml.classifiers.reject.mixin_classifier_gradient_reject_threshold.CClassifierGradientRejectThresholdMixin

Abstract class that defines basic methods for Classifiers with reject: based on a certain threshold.

A classifier assign a label (class) to new patterns using the informations learned from training set.

The samples for which the higher score is under a certain threshold are rejected by the classifier.

Parameters

clfCClassifier: Classifier to which we would like to apply a reject threshold.
thresholdfloat: Rejection threshold.
preprocessCPreProcess 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: Defines class type.
classes: Return the list of classes on which training has been performed.
clf: Returns the inner classifier.
logger: Logger for current object.
n_classes: Number of classes of training dataset.
n_features: Number of features (before preprocessing).
threshold: Returns the rejection threshold.
verbose: Verbosity level of logger output.

Methods

`copy`(self)	Returns a shallow copy of current class.
`create`([class_item])	This method creates an instance of a class with given type.
`decision_function`(self, x, y)	Computes the decision function for each pattern in x.
`deepcopy`(self)	Returns a deep copy of current class.
`estimate_parameters`(self, dataset, …[, …])	Estimate parameter that give better result respect a chose metric.
`fit`(self, dataset[, n_jobs])	Trains the classifier.
`get_class_from_type`(class_type)	Return the class associated with input type.
`get_params`(self)	Returns the dictionary of class parameters.
`get_subclasses`()	Get all the subclasses of the calling class.
`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.
`is_fitted`(self)	Return True if the classifier is trained (fitted).
`is_linear`(self)	True for linear classifiers, False otherwise.
`list_class_types`()	This method lists all types of available subclasses of calling one.
`load`(path)	Loads class from pickle object.
`predict`(self, x[, return_decision_function, …])	Perform classification of each pattern in x.
`save`(self, path)	Save class object using pickle.
`set`(self, param_name, param_value[, copy])	Set a parameter that has a specific name to a specific value.
`set_params`(self, params_dict[, copy])	Set all parameters passed as a dictionary {key: value}.
`timed`([msg])	Timer decorator.

property classes¶: Return the list of classes on which training has been performed.

property clf¶: Returns the inner classifier.

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

Computes the decision function for each pattern in x.

The discriminant function of the reject class is a vector with all its values equal to :math:` heta`, being :math:` heta` the reject threshold.

If a preprocess has been specified, input is normalized before computing the decision function.

Parameters

xCArray: Array with new patterns to classify, 2-Dimensional of shape (n_patterns, n_features).
yint: Index of the class wrt the gradient must be computed, -1 to compute it w.r.t. the reject class

Returns

scoreCArray: Value of the decision function for each test pattern. Dense flat array of shape (n_patterns,).

fit(self, dataset, n_jobs=1)[source]¶

Trains the classifier.

If a preprocess has been specified, input is normalized before training.

Parameters

datasetCDataset: Training set. Must be a CDataset instance with patterns data and corresponding labels.
n_jobsint, optional: Number of parallel workers to use for training the classifier. Default 1. Cannot be higher than processor’s number of cores.

Returns

trained_clsCClassifier: Instance of the classifier trained using input dataset.

property n_classes¶: Number of classes of training dataset.

predict(self, x, return_decision_function=False, n_jobs=<no value>)[source]¶

Perform classification of each pattern in x.

The score matrix of this classifier is equal to the predicted outputs plus a column (corresponding to the reject class) with all its values equal to $\theta$ , being $\theta$ the reject threshold.

The predicted class is therefore:

$c = \operatorname*{argmin}_k f_k(x)$

where $c$ correspond to the rejection class (i.e., $c=-1$ ) only when the maximum taken over the other classes (excluding the reject one) is not greater than the reject threshold $\theta$ .

If a preprocess has been specified, input is normalized before classification.

Parameters

xCArray: Array with new patterns to classify, 2-Dimensional of shape (n_patterns, n_features).
return_decision_functionbool, optional: Whether to return the decision_function value along with predictions. Default False.
n_jobsint, optional: Number of parallel workers to use for classification. Default _NoValue. Cannot be higher than processor’s number of cores.

Returns

labelsCArray: Flat dense array of shape (n_patterns,) with the label assigned to each test pattern. The classification label is the label of the class associated with the highest score. The samples for which the label is equal -1 are the ones rejected by the classifier
scoresCArray, optional: Array of shape (n_patterns, n_classes) with classification score of each test pattern with respect to each training class. Will be returned only if return_decision_function is True.

property threshold¶: Returns the rejection threshold.