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.
- n_jobs
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.
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 the preprocessor.
fit_forward
(self, x[, y, caching])Fit estimator using data and then execute forward on the data.
fit_transform
(self, x[, y])Fit preprocessor using data and then transform data.
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 hyperparameters.
get_state
(self)Returns the object state dictionary.
get_subclasses
()Get all the subclasses of the calling class.
gradient
(self, x[, w])Compute gradient at x by doing a backward pass.
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 object from file.
load_state
(self, path)Sets the object state from file.
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.
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
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.
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 the preprocessor.
fit_forward
(self, x[, y, caching])Fit estimator using data and then execute forward on the data.
fit_transform
(self, x[, y])Fit preprocessor using data and then transform data.
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 hyperparameters.
get_state
(self)Returns the object state dictionary.
get_subclasses
()Get all the subclasses of the calling class.
gradient
(self, x[, w])Compute gradient at x by doing a backward pass.
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 object from file.
load_state
(self, path)Sets the object state from file.
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.
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
lda
¶ Trained sklearn LDA transformer.
-
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
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.
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 the preprocessor.
fit_forward
(self, x[, y, caching])Fit estimator using data and then execute forward on the data.
fit_transform
(self, x[, y])Fit preprocessor using data and then transform data.
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 hyperparameters.
get_state
(self)Returns the object state dictionary.
get_subclasses
()Get all the subclasses of the calling class.
gradient
(self, x[, w])Compute gradient at x by doing a backward pass.
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 object from file.
load_state
(self, path)Sets the object state from file.
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.
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.