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).
preprocess: Inner preprocessor (if any).
verbose: Verbosity level of logger output.

Methods

`backward`(self[, w])	Returns the preprocessor gradient wrt data.
`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.
`forward`(self, x[, caching])	Forward pass on input x.
`get_class_from_type`(class_type)	Return the class associated with input type.
`get_params`(self)	Returns the dictionary of class parameters.
`get_state`(self)	Returns the object state dictionary.
`get_subclasses`()	Get all the subclasses of the calling class.
`grad_f_x`(self, x, y)	Computes the gradient of the classifier’s decision function wrt x.
`gradient`(self, x[, w])	Compute gradient at x by doing a backward pass.
`is_fitted`(self)	Return True if the classifier is trained (fitted).
`list_class_types`()	This method lists all types of available subclasses of calling one.
`load`(path)	Loads object from file.
`load_state`(self, path)	Sets the object state from file.
`predict`(self, x[, return_decision_function, …])	Perform classification of each pattern in x.
`save`(self, path)	Save class object to file.
`save_state`(self, path)	Store the object state to file.
`set`(self, param_name, param_value[, copy])	Set a parameter of the class.
`set_params`(self, params_dict[, copy])	Set all parameters passed as a dictionary {key: value}.
`set_state`(self, state_dict[, copy])	Sets the object state using input dictionary.
`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

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. The classifier can also be already fitted. In this case, if a preprocessor was used during fitting, the same preprocessor must be passed to the outer classifier.
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, plus the rejection class.
n_features: Number of features (before preprocessing).
preprocess: Inner preprocessor (if any).
threshold: Returns the rejection threshold.
verbose: Verbosity level of logger output.

Methods

`backward`(self[, w])	Returns the preprocessor gradient wrt data.
`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.
`forward`(self, x[, caching])	Forward pass on input x.
`get_class_from_type`(class_type)	Return the class associated with input type.
`get_params`(self)	Returns the dictionary of class parameters.
`get_state`(self)	Returns the object state dictionary.
`get_subclasses`()	Get all the subclasses of the calling class.
`grad_f_x`(self, x, y)	Computes the gradient of the classifier’s decision function wrt x.
`gradient`(self, x[, w])	Compute gradient at x by doing a backward pass.
`is_fitted`(self)	Return True if the classifier is trained (fitted).
`list_class_types`()	This method lists all types of available subclasses of calling one.
`load`(path)	Loads object from file.
`load_state`(self, path)	Sets the object state from file.
`predict`(self, x[, return_decision_function, …])	Perform classification of each pattern in x.
`save`(self, path)	Save class object to file.
`save_state`(self, path)	Store the object state to file.
`set`(self, param_name, param_value[, copy])	Set a parameter of the class.
`set_params`(self, params_dict[, copy])	Set all parameters passed as a dictionary {key: value}.
`set_state`(self, state_dict[, copy])	Sets the object state using input dictionary.
`timed`([msg])	Timer decorator.

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

property clf¶: Returns the inner classifier.

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, plus the rejection class.

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.