A Python library for Secure and Explainable Machine Learning
See full release here: https://gitlab.com/secml/secml/-/releases/v0.14.1
COptimizerPGDLS and COptimizerPGDLS not working properly if the classifier's gradient has multiple components with the same (max) value.CConstraintL1 crashing when projecting sparse data using default center value (scalar 0).CConstraintL1 projection caused by type casting.discrete from COptimizerPGDLS and COptimizerPGDExp.CAttackEvasion, COptimizer, CLineSearch, and corresponding subclasses.See full release here: https://gitlab.com/secml/secml/-/releases/v0.14
adv.attacks.evasion.foolbox with a wrapper for Foolbox.secml is now tested for compatibility with Python 3.8.CArray.secml is now tested for compatibility with Python 3.8.scipy >= 1.3.2, scikit-learn >= 0.22, matplotlib >= 3.pytorch extra component now installs: torch >= 1.4, torchvision >= 0.5.cleverhans extra component is now available on Python < 3.8 only, due to tensorflow 1 compatibility.adv.attacks.evasion.foolbox with a wrapper for Foolbox.shape parameter to the following CArray methods: get_data, tondarray, tocsr, tocoo, tocsc, todia, todok, tolil, tolist. The reshaping operation is performed after casting the array to the desired output data format.CMetricFNRatFPR, CMetricTHatFPR, CMetricTPRatTH, CMetricFNRatTH.CArray. If the number of dimensions of input data is higher than 2, the data is reshaped to 2 dims, and the original shape is stored in the new attribute input_shape.CDataLoaderMNIST now downloads the files from our model-zoo mirror (https://gitlab.com/secml/secml-zoo/-/tree/datasets/MNIST).CDataLoaderTorchDataset in a "pytorch" subfolder of SECML_DS_DIR to avoid naming collisions.CAttackPoisoning when y_target != None due to missing broadcasting to expected shape.StandardScaler, CScalerNorm, CScalerMinMax arguments using keywords to fix scikit futurewarning in version 0.23 or later.ValueError: k exceeds matrix dimensions not raised by scipy v1.5 if a k outside the array dimensions is used to extract a diagonal.frameon from CFigure.savefig as it is deprecated in matplotlib >= 3.1.papertype from CFigure.savefig as it is deprecated in matplotlib >= 3.3.MNIST typo in notebook 10.See full release here: https://gitlab.com/secml/secml/-/releases/v0.13
CAttackEvasionPGDExp.CClassifierDNR implementing Deep Neural Rejection (DNR). See Sotgiu et al. “Deep neural rejection against adversarial examples”, EURASIP J. on Info. Security (2020).CClassifierMulticlassOVO implementing One-vs-One multiclass classification scheme.CModule to support trainable modules via fit and fit_forward functions.attack_params.<param_name> as an input argument for the constructor of CSecEval.ml.scalers with a different implementation of ml.features.normalization classes directly based Scikit-Learn's scalers. Included classes are: CScalerMinMax, CScalerStd, CScalerNorm.CArray to specific scipy.sparse array formats: tocoo, tocsc, todia, todok, tolil.CAttackPoisoning now exposes: x0, xc, yc, objective_function and objective_function_gradient.n_jobs is now a init parameter of CModule and subclasses and not passed via fit anymore.CClassifierSVM native support to OVA multiclass scheme, without replicating the kernel in each one-vs-all classifier._clear_cache mechanism to CModule and classes that require caching data in the forward pass before backward (e.g., exponential kernels do that to avoid re-computing the kernel matrix in the backward pass).forward method for CClassifierMulticlassOVA and CClassifierMulticlassOVO.CAttack interface (now only requires implementing run as required by CSecEval).CAttackEvasionPGDLS). Now gradient is called once rather than twice to compute the gradient of the objective function.CSecEval will now check that the param_name input argument can be found in the attack class used in the evaluation.COptimizerPGD now exits optimization if constraint radius is 0. COptimizerPGD , COptimizerPGDLS and COptimizerPGDExp will now raise a warning if the 0-radius constraint is defined outside the given bounds.CClassifierSVM now uses n_jobs parameter for parallel execution of training in case of multiclass datasets.scipy.sparse .hstack and .vstack instead of a custom implementation in CSparse.concatenate.scipy.sparse .argmin and .argmax instead of a custom implementation in CSparse.argmin and CSparse.argmax.CClassifierSVM.store_dual_vars and kernel.setter from CClassifierSVM. Now a linear SVM is trained in the primal (w,b) if kernel=None, otherwise it is trained in the dual (alpha and b), on the precomputed training kernel matrix.fit interface from fit(ds) to fit(x,y) to be consistent across normalizers and classifiers.gradient(x, w) from CKernelRBF, CKernelLaplacian, CKernelEuclidean, CClassifierDNN, CNormalizerUnitNorm. The protected property grad_requires_forward now specifies if gradient has to compute an explicit forward pass or only propagate the input x through the pre-processing chain before calling backward.surrogate_data parameter from CAttackPoisoning and renamed it to double_init_ds in CAttackEvasion subclasses.CClassifierRejectThreshold now returns wrapped classifier classes plus the reject class (-1).COptimizerPGD which was missing index i.CAttackEvasionPGDLS to fix a crash when the class index of data points is greater or equal than the number of alternative data points.CClassifierPyTorch.backward not working properly due to a miscalculation of the number of input features of the model when a CNormalizeDNN is used as preprocessor.CClassifierRejectThreshold which can be bypassed by using the clf attribute setter, now removed.CCreator.set not allowing to set writable attributes of level-0 readable-only attributes.CCreator.get_params not returning level-0 not-writable attributes having one or more writable attributes.CFigure on Windows systems.model_zoo.load_model improperly building download urls depending on the system default url separator.CSparse to ensure they properly work independently from the sparse array format: save, load, __pow__, round, nan_to_num, logical_and, unique, bincount, prod, all, any, min, max.CArray.tocsr() now always returns a scipy.sparse.csr_matrix array as expected.discrete and surrogate_classifier parameter from CAttack.kernel is now removed from CClassifierSGD, CClassifierRidge and CClassifierLogistic classifiers.numpydoc to < 1.1 to avoid compatibility issues of the newest version.CClassifierRejectThreshold.predict method.random_state not set for CClassifierDecisionTree in notebook 4.