secml.ml.features.reduction

CReducer

class secml.ml.features.reduction.c_reducer.CReducer(preprocess=None)

Bases: secml.ml.features.c_preprocess.CPreProcess

Interface for feature dimensionality reduction algorithms.

Attributes

class_type

Defines class type.

logger

Logger for current object.

preprocess

Inner preprocessor (if any).

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.
create_chain(class_items, kwargs_list)	Creates a chain of preprocessors.
deepcopy(self)	Returns a deep copy of current class.
fit(self, x[, y])	Fit transformation algorithm.
fit_transform(self, x[, y])	Fit preprocessor using data and then transform data.
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.
gradient(self, x[, w])	Returns the preprocessor gradient wrt data.
list_class_types()	This method lists all types of available subclasses of calling one.
load(path)	Loads class from pickle object.
revert(self, x)	Revert data to original form.
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.
transform(self, x)	Apply the transformation algorithm on data.

CLDA

class secml.ml.features.reduction.c_reducer_lda.CLDA(n_components=None, preprocess=None)

Bases: secml.ml.features.reduction.c_reducer.CReducer

Linear Discriminant Analysis (LDA).

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‘lda’

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.
create_chain(class_items, kwargs_list)	Creates a chain of preprocessors.
deepcopy(self)	Returns a deep copy of current class.
fit(self, x[, y])	Fit transformation algorithm.
fit_transform(self, x[, y])	Fit preprocessor using data and then transform data.
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.
gradient(self, x[, w])	Returns the preprocessor gradient wrt data.
list_class_types()	This method lists all types of available subclasses of calling one.
load(path)	Loads class from pickle object.
revert(self, x)	Revert data to original form.
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.
transform(self, x)	Apply the transformation algorithm on data.

property classes

Unique targets used for training.

property eigenvec

Eigenvectors estimated from the training data. Is a matrix of shape: n_eigenvectors * n_features.

property mean

Per-feature empirical mean, estimated from the training data.

CPCA

class secml.ml.features.reduction.c_reducer_pca.CPCA(n_components=None, preprocess=None)

Bases: secml.ml.features.reduction.c_reducer.CReducer

Principal Component Analysis (PCA).

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‘pca’

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.
create_chain(class_items, kwargs_list)	Creates a chain of preprocessors.
deepcopy(self)	Returns a deep copy of current class.
fit(self, x[, y])	Fit transformation algorithm.
fit_transform(self, x[, y])	Fit preprocessor using data and then transform data.
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.
gradient(self, x[, w])	Returns the preprocessor gradient wrt data.
list_class_types()	This method lists all types of available subclasses of calling one.
load(path)	Loads class from pickle object.
revert(self, x)	Revert data to original form.
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.
transform(self, x)	Apply the transformation algorithm on data.

property components

Eigenvectors of inverse training array.

property eigenval

Eigenvalues estimated from the training data.

property eigenvec

Eigenvectors estimated from the training data.

property explained_variance

Variance explained by each of the selected components.

property explained_variance_ratio

Percentage of variance explained by each of the selected components.

If n_components is None, then all components are stored and the sum of explained variances is equal to 1.0

property mean

Per-feature empirical mean, estimated from the training data.