secml.ml.features.reduction¶
CReducer¶
-
class
secml.ml.features.reduction.c_reducer.
CReducer
(preprocess=None)[source]¶ 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.
inverse_transform
(self, x)Revert data to original form.
list_class_types
()This method lists all types of available subclasses of calling one.
load
(path)Loads class from pickle object.
revert
(self, x)Deprecated since version 0.9.
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)[source]¶ 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.
inverse_transform
(self, x)Revert data to original form.
list_class_types
()This method lists all types of available subclasses of calling one.
load
(path)Loads class from pickle object.
revert
(self, x)Deprecated since version 0.9.
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)[source]¶ 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.
inverse_transform
(self, x)Revert data to original form.
list_class_types
()This method lists all types of available subclasses of calling one.
load
(path)Loads class from pickle object.
revert
(self, x)Deprecated since version 0.9.
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.