secml.ml.classifiers¶
CClassifier¶
-
class
secml.ml.classifiers.c_classifier.
CClassifier
(preprocess=None)[source]¶ Bases:
secml.core.c_creator.CCreator
Abstract class that defines basic methods for Classifiers.
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
- 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.
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.
-
property
classes
¶ Return the list of classes on which training has been performed.
-
decision_function
(self, x, y=None)[source]¶ Computes the decision function for each pattern in x.
If a preprocess has been specified, input is normalized before computing the decision function.
Note
The actual decision function should be implemented inside
_decision_function
method.- Parameters
- xCArray
Array with new patterns to classify, 2-Dimensional of shape (n_patterns, n_features).
- yint or None, optional
The label of the class wrt the function should be calculated. If None, return the output for all classes.
- Returns
- scoreCArray
Value of the decision function for each test pattern. Dense flat array of shape (n_samples,) if y is not None, otherwise a (n_samples, n_classes) array.
-
estimate_parameters
(self, dataset, parameters, splitter, metric, pick='first', perf_evaluator='xval', n_jobs=1)[source]¶ Estimate parameter that give better result respect a chose metric.
- Parameters
- datasetCDataset
Dataset to be used for evaluating parameters.
- parametersdict
Dictionary with each entry as {parameter: list of values to test}. Example: {‘C’: [1, 10, 100], ‘gamma’: list(10.0 ** CArray.arange(-4, 4))}
- splitterCDataSplitter or str
Object to use for splitting the dataset into train and validation. A splitter type can be passed as string, in this case all default parameters will be used. For data splitters, num_folds is set to 3 by default. See CDataSplitter docs for more informations.
- metricCMetric or str
Object with the metric to use while evaluating the performance. A metric type can be passed as string, in this case all default parameters will be used. See CMetric docs for more informations.
- pick{‘first’, ‘last’, ‘random’}, optional
Defines which of the best parameters set pick. Usually, ‘first’ correspond to the smallest parameters while ‘last’ correspond to the biggest. The order is consistent to the parameters dict passed as input.
- perf_evaluatorCPerfEvaluator or str, optional
Performance Evaluator to use. Default ‘xval’.
- n_jobsint, optional
Number of parallel workers to use for performance evaluation. Default 1. Cannot be higher than processor’s number of cores.
- Returns
- best_parametersdict
Dictionary of best parameters found through performance evaluation.
-
fit
(self, dataset, n_jobs=1)[source]¶ Trains the classifier.
If a preprocess has been specified, input is normalized before training.
For multiclass case see .CClassifierMulticlass.
- Parameters
- datasetCDataset
Training set. Must be a
CDataset
instance with patterns data and corresponding labels.- n_jobsint
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.
-
is_fitted
(self)[source]¶ Return True if the classifier is trained (fitted).
- Returns
- bool
True or False depending on the result of the call to check_is_fitted.
-
property
n_classes
¶ Number of classes of training dataset.
-
property
n_features
¶ Number of features (before preprocessing).
-
predict
(self, x, return_decision_function=False)[source]¶ Perform classification of each pattern in x.
If 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.
- 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.
- 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.
CClassifierLinear¶
-
class
secml.ml.classifiers.c_classifier_linear.
CClassifierLinear
(preprocess=None)[source]¶ Bases:
secml.ml.classifiers.c_classifier.CClassifier
Abstract class that defines basic methods for linear classifiers.
A linear classifier assign a label (class) to new patterns computing the inner product between the patterns and a vector of weights for each training set feature.
This interface implements a set of generic methods for training and classification that can be used for every linear model.
- Parameters
- 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
b
Bias calculated from training data.
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.
w
Vector with each feature’s weight (dense or sparse).
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 linear 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)Return True as the classifier is linear.
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
b
¶ Bias calculated from training data.
-
fit
(self, dataset, n_jobs=1)[source]¶ Trains the linear classifier.
If a preprocess has been specified, input is normalized before training.
Training on 2nd class is avoided to speed up classification.
- Parameters
- datasetCDataset
Binary (2-classes) training set. Must be a
CDataset
instance with patterns data and corresponding labels.- n_jobsint
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
w
¶ Vector with each feature’s weight (dense or sparse).
CClassifierSkLearn¶
-
class
secml.ml.classifiers.sklearn.c_classifier_sklearn.
CClassifierSkLearn
(sklearn_model, preprocess=None)[source]¶ Bases:
secml.ml.classifiers.c_classifier.CClassifier
Generic wrapper for SkLearn classifiers.
- 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 and SkLearn model 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.
CClassifierDecisionTree¶
-
class
secml.ml.classifiers.sklearn.c_classifier_decision_tree.
CClassifierDecisionTree
(criterion='gini', splitter='best', max_depth=None, min_samples_split=2, preprocess=None)[source]¶ Bases:
secml.ml.classifiers.sklearn.c_classifier_sklearn.CClassifierSkLearn
Decision Tree Classifier.
- Parameters
- 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
‘dec-tree’Defines class type.
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 and SkLearn model 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.
CClassifierKNN¶
-
class
secml.ml.classifiers.sklearn.c_classifier_knn.
CClassifierKNN
(n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, preprocess=None)[source]¶ Bases:
secml.ml.classifiers.sklearn.c_classifier_sklearn.CClassifierSkLearn
K Neighbors Classifiers.
- Parameters
- 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
‘knn’Defines class type.
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 and SkLearn model 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.
kneighbors
(self, x[, num_samples])Find the training samples nearest to x
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.
-
kneighbors
(self, x, num_samples=None)[source]¶ Find the training samples nearest to x
- Parameters
- xCArray
The query point or points.
- num_samples: int or None
Number of neighbors to get. if None, use n_neighbors
- Returns
- distCArray
Array representing the lengths to points
- index_point: CArray
Indices of the nearest points in the training set
- tr_dataset.X: CArray
Training samples
CClassifierLogistic¶
-
class
secml.ml.classifiers.sklearn.c_classifier_logistic.
CClassifierLogistic
(C=1.0, max_iter=100, random_seed=None, preprocess=None)[source]¶ Bases:
secml.ml.classifiers.c_classifier_linear.CClassifierLinear
,secml.ml.classifiers.gradients.mixin_classifier_gradient_logistic.CClassifierGradientLogisticMixin
Logistic Regression (aka logit, MaxEnt) classifier.
- Parameters
- 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
C
Penalty parameter C of the error term.
b
Bias calculated from training data.
class_type
Defines class type.
classes
Return the list of classes on which training has been performed.
logger
Logger for current object.
- max_iter
n_classes
Number of classes of training dataset.
n_features
Number of features (before preprocessing).
- random_seed
verbose
Verbosity level of logger output.
w
Vector with each feature’s weight (dense or sparse).
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 linear 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])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.
is_fitted
(self)Return True if the classifier is trained (fitted).
is_linear
(self)Return True as the classifier is linear.
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
C
¶ Penalty parameter C of the error term.
-
property
max_iter
¶
-
property
random_seed
¶
CClassifierNearestCentroid¶
-
class
secml.ml.classifiers.sklearn.c_classifier_nearest_centroid.
CClassifierNearestCentroid
(metric='euclidean', shrink_threshold=None, preprocess=None)[source]¶ Bases:
secml.ml.classifiers.sklearn.c_classifier_sklearn.CClassifierSkLearn
CClassifierNearestCentroid.
- Parameters
- 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
‘nrst-centroid’Defines class type.
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 and SkLearn model 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.
-
property
centroids
¶
-
property
metric
¶
CClassifierRandomForest¶
-
class
secml.ml.classifiers.sklearn.c_classifier_random_forest.
CClassifierRandomForest
(n_estimators=10, criterion='gini', max_depth=None, min_samples_split=2, random_state=None, preprocess=None)[source]¶ Bases:
secml.ml.classifiers.sklearn.c_classifier_sklearn.CClassifierSkLearn
Random Forest classifier.
- Parameters
- 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
‘random-forest’Defines class type.
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 and SkLearn model 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.
CClassifierRidge¶
-
class
secml.ml.classifiers.sklearn.c_classifier_ridge.
CClassifierRidge
(alpha=1.0, kernel=None, max_iter=100000.0, class_weight=None, tol=0.0001, fit_intercept=True, preprocess=None)[source]¶ Bases:
secml.ml.classifiers.c_classifier_linear.CClassifierLinear
,secml.ml.classifiers.gradients.mixin_classifier_gradient_ridge.CClassifierGradientRidgeMixin
Ridge Classifier.
- Parameters
- 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
‘ridge’Defines class type.
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 linear 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])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.
is_fitted
(self)Return True if the classifier is trained (fitted).
is_kernel_linear
(self)Return True if the kernel is None or linear.
is_linear
(self)Return True if the classifier is linear.
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
C
¶ Constant that multiplies the regularization term.
Equal to 1 / alpha.
-
property
alpha
¶ Returns the Constant that multiplies the regularization term.
-
property
class_weight
¶ Weight of each training class.
-
property
kernel
¶ Kernel function.
-
property
n_tr_samples
¶ Returns the number of training samples.
CClassifierSGD¶
-
class
secml.ml.classifiers.sklearn.c_classifier_sgd.
CClassifierSGD
(loss, regularizer, kernel=None, alpha=0.01, fit_intercept=True, max_iter=1000, tol=-inf, shuffle=True, learning_rate='optimal', eta0=10.0, power_t=0.5, class_weight=None, warm_start=False, average=False, random_state=None, preprocess=None)[source]¶ Bases:
secml.ml.classifiers.c_classifier_linear.CClassifierLinear
,secml.ml.classifiers.gradients.mixin_classifier_gradient_sgd.CClassifierGradientSGDMixin
Stochastic Gradient Descent Classifier.
- Parameters
- 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
‘sgd’Defines class type.
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 linear 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])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.
is_fitted
(self)Return True if the classifier is trained (fitted).
is_kernel_linear
(self)Return True if the kernel is None or linear.
is_linear
(self)Return True if the classifier is linear.
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
C
¶ Constant that multiplies the regularization term.
Equal to 1 / alpha.
-
property
alpha
¶ Returns the Constant that multiplies the regularization term.
-
property
average
¶ When set to True, computes the averaged SGD weights. If set to an int greater than 1, averaging will begin once the total number of samples seen reaches average. So average=10 will begin averaging after seeing 10 samples.
-
property
class_weight
¶ Weight of each training class.
-
property
eta0
¶ The initial learning rate for the invscaling learning rate. Default is 10.0 (corresponding to sqrt(1.0/sqrt(alpha)), with alpha=0.0001).
-
property
kernel
¶ Kernel function.
-
property
loss
¶ Returns the loss function used by classifier.
-
property
n_tr_samples
¶ Returns the number of training samples.
-
property
power_t
¶ The exponent for inverse scaling learning rate.
-
property
regularizer
¶ Returns the regularizer function used by classifier.
CClassifierSVM¶
-
class
secml.ml.classifiers.sklearn.c_classifier_svm.
CClassifierSVM
(kernel=None, C=1.0, class_weight=None, preprocess=None, grad_sampling=1.0, store_dual_vars=None)[source]¶ Bases:
secml.ml.classifiers.c_classifier_linear.CClassifierLinear
,secml.ml.classifiers.gradients.mixin_classifier_gradient_svm.CClassifierGradientSVMMixin
Support Vector Machine (SVM) classifier.
- Parameters
- kernelNone or CKernel subclass, optional
Instance of a CKernel subclass to be used for computing similarity between patterns. If None (default), a linear SVM will be created.
- Cfloat, optional
Penalty parameter C of the error term. Default 1.0.
- 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)).
- 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.
- grad_samplingfloat
Percentage in (0.0, 1.0] of the alpha weights to be considered when computing the classifier gradient.
See also
CKernel
Pairwise kernels and metrics.
CClassifierLinear
Common interface for linear classifiers.
Notes
Current implementation relies on
sklearn.svm.SVC
for the training step.- Attributes
class_type
‘svm’Defines class type.
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])Fit the SVM 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])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.
is_fitted
(self)Return True if the classifier is trained (fitted).
is_kernel_linear
(self)Return True if the kernel is None or linear.
is_linear
(self)Return True if the classifier is linear.
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}.
sv_margin
(self[, tol])Margin Support Vectors.
sv_margin_idx
(self[, tol])Indices of Margin Support Vectors.
sv_margin_y
(self[, tol])Margin Support Vectors class (-1/+1).
timed
([msg])Timer decorator.
-
property
C
¶ Penalty parameter C of the error term.
-
property
alpha
¶ Signed coefficients of the SVs in the decision function.
-
property
class_weight
¶ Weight of each training class.
-
fit
(self, dataset, n_jobs=1)[source]¶ Fit the SVM classifier.
We use
sklearn.svm.SVC
for weights and Support Vectors computation. The routine will set alpha, sv, sv_idx and b parameters. For linear SVM (i.e. if kernel is None) we also store the ‘w’ flat vector with each feature’s weight.If a preprocess has been specified, input is normalized before computing the decision function.
- Parameters
- datasetCDataset
Binary (2-classes) 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_clsCClassifierSVM
Instance of the SVM classifier trained using input dataset.
-
property
grad_sampling
¶ Percentage of samples for approximate gradient.
-
property
kernel
¶ Kernel function (None if a linear classifier).
-
property
n_sv
¶ Return the number of support vectors.
In the 1st and in the 2nd column is stored the number of SVs for the negative and positive class respectively.
-
property
store_dual_vars
¶ Controls the store of dual space variables (SVs and alphas).
By default is None and dual variables are stored only if kernel is not None. If set to True, dual variables are stored even if kernel is None (linear SVM). If kernel is not None, cannot be set to False.
-
property
sv
¶ Support Vectors.
-
property
sv_idx
¶ Indices of Support Vectors within the training dataset.
-
sv_margin
(self, tol=1e-06)[source]¶ Margin Support Vectors.
- Parameters
- tolfloat
Alpha value threshold for considering a Support Vector on the margin.
- Returns
- CArray or None
Margin support vector, 2D CArray. If no margin support vector are found, return None.
- indicesCArray or None
Flat array with the indices of the margin support vectors. If no margin support vector are found, return None.
CClassifierPyTorch¶
-
class
secml.ml.classifiers.pytorch.c_classifier_pytorch.
CClassifierPyTorch
(model, loss=None, optimizer=None, input_shape=None, random_state=None, preprocess=None, softmax_outputs=False, epochs=10, batch_size=1, n_jobs=1)[source]¶ Bases:
secml.ml.classifiers.c_classifier_dnn.CClassifierDNN
,secml.ml.classifiers.gradients.mixin_classifier_gradient_pytorch.CClassifierGradientPyTorchMixin
CClassifierPyTorch, wrapper for PyTorch models.
- Parameters
- model:
torch.nn.Module object to use as classifier
- loss:
loss object from torch.nn
- optimizer:
optimizer object from torch.optim
- random_state: int or None, optional
random state to use for initializing the model weights. Default value is None.
- preprocess:
preprocessing module.
- softmax_outputs: bool, optional
if set to True, a softmax function will be applied to the return value of the decision function. Note: some implementation adds the softmax function to the network class as last layer or last forward function, or even in the loss function (see torch.nn.CrossEntropyLoss). Be aware that the softmax may have already been applied. Default value is False.
- epochs: int
number of epochs for training the neural network. Default value is 10.
- batch_size: int
size of the batches to use for loading the data. Default value is 1.
- n_jobs: int
number of workers to use for data loading and processing. Default value is 1.
- Attributes
class_type
‘pytorch-clf’Defines class type.
Methods
check_softmax
(self)Checks if a softmax layer has been defined in the network.
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_layer_output
(self, x[, layer_names])Returns the output of the desired net layer as CArray.
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, w, layer])Computes the gradient of the classifier’s output wrt input.
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.
hook_layer_output
(self[, layer_names])Creates handlers for the hooks that store the layer outputs.
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.
load_model
(self, filename[, classes])Restores the model and optimizer’s parameters.
n_jobs
(self)Returns the number of workers being used for loading and processing the data.
predict
(self, x[, return_decision_function])Perform classification of each pattern in x.
save
(self, path)Save class object using pickle.
save_model
(self, filename)Stores the model and optimizer’s parameters.
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.
get_layer_shape
-
property
batch_size
¶ Returns the batch size used for the dataset loader.
-
check_softmax
(self)[source]¶ Checks if a softmax layer has been defined in the network.
- Returns
- Boolean value stating if a softmax layer has been
- defined.
-
property
epochs
¶ Returns the number of epochs for which the model will be trained.
-
get_layer_output
(self, x, layer_names=None)[source]¶ Returns the output of the desired net layer as CArray.
- Parameters
- xCArray
Input data.
- layer_namesstr, list or None, optional
Name of the layer(s) to hook for getting the outputs. If None, the output of the last layer will be returned.
- Returns
- CArray or dict
Output of the desired layers, dictionary if more than one layer is requested.
-
get_params
(self)[source]¶ Returns the dictionary of class parameters.
A parameter is a PUBLIC or READ/WRITE attribute.
-
hook_layer_output
(self, layer_names=None)[source]¶ Creates handlers for the hooks that store the layer outputs.
- Parameters
- layer_nameslist or str, optional
List of layer names to hook. Cleans previously defined hooks to prevent multiple hook creations.
-
property
layer_shapes
¶ Returns a dictionary containing the shapes of the output of each layer of the model.
-
property
layers
¶ Returns the layers of the model, if possible.
-
load_model
(self, filename, classes=None)[source]¶ Restores the model and optimizer’s parameters. Notes: the model class and optimizer should be defined before loading the params.
- Parameters
- filenamestr
path where to find the stored model
- classeslist, tuple or None, optional
This parameter is used only if the model was stored with native PyTorch. Class labels (sorted) for matching classes to indexes in the loaded model. If classes is None, the classes will be assigned new indexes from 0 to n_classes.
-
property
loss
¶ Returns the loss function used by classifier.
-
property
optimizer
¶ Returns the optimizer used by classifier.
CModelCleverhans¶
-
class
secml.ml.classifiers.tf.c_model_cleverhans.
CModelCleverhans
(clf, out_dims=None)[source]¶ Bases:
cleverhans.model.Model
Receive our library classifier and convert it into a cleverhans model.
- Parameters
- clfCClassifier
SecML classifier, should be already trained.
- out_dimsint or None
The expected number of classes.
Notes
The Tesorflow model will be created in the current Tensorflow default graph.
- Attributes
- f_eval
- grad_eval
Methods
__call__
(self, \*args, \*\*kw)Call self as a function.
fprop
(self, x, \*\*kwargs)- Parameters
-
property
f_eval
¶
-
fprop
(self, x, **kwargs)[source]¶ - Parameters
- xnp.ndarray
Input samples.
- **kwargsdict
Any other argument for function.
- Returns
- dict
-
property
grad_eval
¶
-
secml.ml.classifiers.tf.c_model_cleverhans.
getrandbits
()¶
clf_utils¶
-
secml.ml.classifiers.clf_utils.
check_binary_labels
(labels)[source]¶ Check if input labels are binary {0, +1}.
- Parameters
- labelsCArray or int
Binary labels to be converted. As of PRALib convention, binary labels are {0, +1}.
- Raises
- ValueError
If input labels are not binary.
-
secml.ml.classifiers.clf_utils.
convert_binary_labels
(labels)[source]¶ Convert input binary labels to {-1, +1}.
- Parameters
- labelsCArray or int
Binary labels in {0, +1} to be converted to {-1, +1}.
- Returns
- converted_labelsCArray or int
Binary labels converted to {-1, +1}.
Examples
>>> from secml.ml.classifiers.clf_utils import convert_binary_labels >>> from secml.array import CArray
>>> print(convert_binary_labels(0)) -1
>>> print(convert_binary_labels(CArray([0,1,1,1,0,0]))) CArray([-1 1 1 1 -1 -1])