v0.14.1 (22/04/2021)

  • This version brings fixes for a few issues with the optimizers and related classes, along with improvements to documentation for all attacks, optimizers, and related classes.

Fixed (3 changes)

  • #923 Fixed COptimizerPGDLS and COptimizerPGDLS not working properly if the classifier’s gradient has multiple components with the same (max) value.

  • #919 Fixed CConstraintL1 crashing when projecting sparse data using default center value (scalar 0).

  • #920 Fixed inconsistent results between dense and sparse data for CConstraintL1 projection caused by type casting.

Removed & Deprecated (1 change)

  • #922 Removed unnecessary parameter discrete from COptimizerPGDLS and COptimizerPGDExp.

Documentation (2 changes)

  • #100017 Improved documentation of CAttackEvasion, COptimizer, CLineSearch, and corresponding subclasses.

  • #918 Installing the latest stable version of RobustBench instead of the master version.

v0.14 (23/03/2021)

  • #795 Added new package adv.attacks.evasion.foolbox with a wrapper for Foolbox.

  • #623 secml is now tested for compatibility with Python 3.8.

  • #861 N-Dimensional input is now accepted by CArray.

  • #853 Added new notebook tutorial with an application on Android Malware Detection.

  • #859 Add a new tutorial notebook containing example usage and attack against RobustBench models.

  • #845 Static Application Security Testing (SAST) using bandit is now executed during testing process.

Requirements (5 changes)

  • #623 secml is now tested for compatibility with Python 3.8.

  • #623 The following dependencies are now required: scipy >= 1.3.2, scikit-learn >= 0.22, matplotlib >= 3.

  • #623 The pytorch extra component now installs: torch >= 1.4, torchvision >= 0.5.

  • #623 The cleverhans extra component is now available on Python < 3.8 only, due to tensorflow 1 compatibility.

  • #822 Dropped official support of Python 3.5, which reached End Of Life on 13 Sep 2020. SecML may still be usable in the near future on Python 3.5 but we stopped running dedicated tests on this interpreter.

Added (3 changes)

  • #795 Added new package adv.attacks.evasion.foolbox with a wrapper for Foolbox.

  • #880 Added new 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.

  • #855 Added new ROC-related performance metrics: CMetricFNRatFPR, CMetricTHatFPR, CMetricTPRatTH, CMetricFNRatTH.

Improved (3 changes)

  • #861 N-Dimensional input is now accepted by 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.

  • #910 The MNIST dataset loader CDataLoaderMNIST now downloads the files from our model-zoo mirror (

  • #886 Torch datasets now stored by CDataLoaderTorchDataset in a “pytorch” subfolder of SECML_DS_DIR to avoid naming collisions.

Fixed (8 changes)

  • #897 Fixed crash in CAttackPoisoning when y_target != None due to missing broadcasting to expected shape.

  • #873 Use equality instead of identity to compare literals (fixing related SyntaxWarning in Python 3.8).

  • #867 Now calling StandardScaler, CScalerNorm, CScalerMinMax arguments using keywords to fix scikit futurewarning in version 0.23 or later.

  • #870 Filtering “DeprecationWarning: tostring() is deprecated. Use tobytes() instead.” raised by tensorflow 1.15 if numpy 1.19 is installed.

  • #868 Correctly escaping latex commands in docstrings to avoid “DeprecationWarning: invalid escape sequence s”.

  • #871 Fixed ValueError: k exceeds matrix dimensions not raised by scipy v1.5 if a k outside the array dimensions is used to extract a diagonal.

  • #872 Fixed scipy 1.5 not always keeping the dtype of the original array during getitem (especially if the result is an empty array).

  • #888 Filter warning raised by torchvision mnist loader first time you download.

Removed & Deprecated (2 changes)

  • #875 Removed parameter frameon from CFigure.savefig as it is deprecated in matplotlib >= 3.1.

  • #875 Removed parameter papertype from CFigure.savefig as it is deprecated in matplotlib >= 3.3.

Documentation (10 changes)

  • #853 Added new notebook tutorial with an application on Android Malware Detection.

  • #859 Add a new tutorial notebook containing example usage and attack against RobustBench models.

  • #898 Added “Open in Colab” button to all tutorial notebooks.

  • #899 Added “Edit on Gitlab” button to doc pages.

  • #900 Moved notebook 11 “Evasion Attacks on ImageNet (Computer Vision)” to “Applications” section.

  • #905 Changed image used by notebook 8, as the previous one is no more available.

  • #903 Updated roadmap page in documentation.

  • #890 Fixed multiple typos and improved language in the README.

  • #878 Updated intersphinx mapping for numpy’s documentation.

  • #850 Fixed MNIST typo in notebook 10.

v0.13 (24/07/2020)

  • #814 Added new evasion attack CAttackEvasionPGDExp.

  • #780 Added new classifier CClassifierDNR implementing Deep Neural Rejection (DNR). See Sotgiu et al. “Deep neural rejection against adversarial examples”, EURASIP J. on Info. Security (2020).

  • #47 Added new classifier CClassifierMulticlassOVO implementing One-vs-One multiclass classification scheme.

  • #765 Extended CModule to support trainable modules via fit and fit_forward functions.

  • #800 Security evaluation can now be run using Cleverhans attacks. The name of the parameter to check should be specified as attack_params.<param_name> as an input argument for the constructor of CSecEval.

  • #839 Experimental support of Windows operating system (version 7 or later).

Requirements (1 change)

  • #768 Removed temporary pin of Pillow to v6 which used to break torch and torchvision packages.

Added (4 changes)

  • #100007 Added new experimental package ml.scalers with a different implementation of ml.features.normalization classes directly based Scikit-Learn’s scalers. Included classes are: CScalerMinMax, CScalerStd, CScalerNorm.

  • #770 Added new methods to convert a CArray to specific scipy.sparse array formats: tocoo, tocsc, todia, todok, tolil.

  • #812 CAttackPoisoning now exposes: x0, xc, yc, objective_function and objective_function_gradient.

  • #776 n_jobs is now a init parameter of CModule and subclasses and not passed via fit anymore.

Improved (12 changes)

  • #817 Added CClassifierSVM native support to OVA multiclass scheme, without replicating the kernel in each one-vs-all classifier.

  • #574 Added _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).

  • #820 Add parallel execution of forward method for CClassifierMulticlassOVA and CClassifierMulticlassOVO.

  • #815 Simplified CAttack interface (now only requires implementing run as required by CSecEval).

  • #574 Modified kernel and classifier interfaces to allow their use as preprocessing modules.

  • #775 Improved efficiency in gradient computation of SVMs, by back-propagating the alpha values to the kernel.

  • #773 Improved efficiency in the computation of gradients of evasion attacks (CAttackEvasionPGDLS). Now gradient is called once rather than twice to compute the gradient of the objective function.

  • #801 CSecEval will now check that the param_name input argument can be found in the attack class used in the evaluation.

  • #695 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.

  • #828 CClassifierSVM now uses n_jobs parameter for parallel execution of training in case of multiclass datasets.

  • #767 Using scipy.sparse .hstack and .vstack instead of a custom implementation in CSparse.concatenate.

  • #772 Using scipy.sparse .argmin and .argmax instead of a custom implementation in CSparse.argmin and CSparse.argmax.

Changed (6 changes)

  • #817 Kernel is now used as preprocess in CClassifierSVM.

  • #817 Removed 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.

  • #765 Unified fit interface from fit(ds) to fit(x,y) to be consistent across normalizers and classifiers.

  • #574 Removed redundant definitions of 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.

  • #823 Removed surrogate_data parameter from CAttackPoisoning and renamed it to double_init_ds in CAttackEvasion subclasses.

  • #829 CClassifierRejectThreshold now returns wrapped classifier classes plus the reject class (-1).

Fixed (10 changes)

  • #816 Fixed stop condition of COptimizerPGD which was missing index i.

  • #825 Infer the number of attacked classifier classes directly from it (instead of inferring it from surrogate data) in CAttackEvasionPGDLS to fix a crash when the class index of data points is greater or equal than the number of alternative data points.

  • #810 Fixed 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.

  • #803 Fixed checks on the inner classifier in CClassifierRejectThreshold which can be bypassed by using the clf attribute setter, now removed.

  • #818 Fixed CCreator.set not allowing to set writable attributes of level-0 readable-only attributes.

  • #819 Fixed CCreator.get_params not returning level-0 not-writable attributes having one or more writable attributes.

  • #785 Fixed constant override of matplotlib backend in CFigure on Windows systems.

  • #783 Fixed model_zoo.load_model improperly building download urls depending on the system default url separator.

  • #771 Fixed the following methods of 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.

  • #769 CArray.tocsr() now always returns a scipy.sparse.csr_matrix array as expected.

Removed & Deprecated (2 changes)

  • #540 Removed discrete and surrogate_classifier parameter from CAttack.

  • #777 Deprecated attribute kernel is now removed from CClassifierSGD, CClassifierRidge and CClassifierLogistic classifiers.

Documentation (10 changes)

  • #839 Windows is now displayed as a supported Operating System in README and setup.

  • #806 Documented pytorch extra component installation requirements under Windows.

  • #834 Temporarily pinned numpydoc to < 1.1 to avoid compatibility issues of the newest version.

  • #807 Documentation is now built using Sphinx theme v0.5 or higher.

  • #830 Fixed links to repository pages by adding a dash after project name.

  • #758 Added a direct link to the repository in README.

  • #788 Notebooks now include a warning about the required extra components (if any).

  • #787 Fixed argmin -> argmax typo in docstring of CClassifierRejectThreshold.predict method.

  • #789 Fixed notebook 4 not correctly generating a separate dataset for training the target classifiers.

  • #791 Fixed random_state not set for CClassifierDecisionTree in notebook 4.

v0.12 (11/03/2020)

  • #726 Refactored kernel package (now Kernel classes are now inherited from CModule, which enables computing gradients more efficiently. This will enable us to use kernels as preprocessors in future releases.

  • #755 Package has been moved to secml.model_zoo.

  • #721 Dictionary with model zoo definitions is now dynamically downloaded and updated from our repository located at The package model_zoo.models containing python scripts defining models structure is now removed and the scripts will be downloaded from the same repository upon request.

Added (7 changes)

  • #660 CClassifierPyTorch now accepts as input a PyTorch learning rate scheduler via the optimizer_scheduler parameter.

  • #678 Added new parameter return_optimizer to CClassifierPyTorch.get_state which allows getting the state of the classifier without including the state of the optimizer (and of the new optimizer_scheduler).

  • Added random_state parameter to CPSKMedians.

  • Added the parameter minlength to the bincount method of CArray.

  • Added new CNormalizerTFIDF which implements a term frequency–inverse document frequency features normalizer.

  • #666 Added new utils.download_utils.dl_file_gitlab function which allows downloading a file from a repository, including branch and access token setting.

  • #722 Added new optional parameter headers to utils.download_utils.dl_file function which allows specifying additional headers for the download request.

Improved (8 changes)

  • #664 The following CClassifierPyTorch parameters can now be modified after instancing the class: optimizer, epochs, batch_size. This will make some procedures easier, like fine-tuning a pre-trained network.

  • #712 download_utils.dl_file() will now use the filename stored in response’s headers if available. The previous behavior (get the last part of the download url) will be used as a fallback.

  • #748 CNormalizerUnitNorm re-factored by adding gradient computation.

  • #706 Rewrite CKernelRBF gradient when passing w to speed up computations by avoiding broadcasting.

  • #730 CClassifierPyTorch has been modified to clean cached outputs and save memory when caching such data is not required.

  • Internally optimized variables can be stored inside the attack class and fetched when needed.

  • Accurate evaluation of objective function for some cleverhans attacks (CW, Elastic Net).

  • #666 Model zoo downloader ml.model_zoo.load_model function will now try to download the version of a requested model corresponding to the version of secml. If not found, the latest ‘master’ version of the model will be downloaded instead.

Changed (3 changes)

  • #664 When passing pre-trained models to CClassifierDNN and subclasses the new pretrained parameter should now be set to True. Optionally, an array of classes on which the model has been pre-trained can be passed via the new pretrained_classes parameter. If pretrained_classes is left None, the number of classes will be inferred from the size of the last DNN layer as before.

  • CConstraintL2.project(x) projects now x onto a hypersphere of radius radius-tol, with tol=1e-6. This conservative projection ensures that x is projected always inside the hypersphere, overcoming projection violations due to numerical rounding errors.

  • CModule.gradient is not calling forward anymore, but only prepares data for backward. The forward step is not required, indeed, for modules that implement analytical gradients rather than autodiff.

Fixed (10 changes)

  • #677 Fixed CClassifierPyTorch.get_state crashing when optimizer is not defined.

  • #134 Fixed passage of n_jobs parameter to CDataLoaderPyTorch in CClassifierPyTorch where 2 processes are being used by the loader even if n_jobs is set to 1. The default value for parameter num_workers in CDataLoaderPyTorch is now correctly 0.

  • #749 Fixed CArray.argmin and .argmax returning float types when applied to sparse arrays of float dtype.

  • Gradient is now correctly computed in CClassifierPytorch even if softmax_outputs are active.

  • #707 Fixed initial value of the objective function being computed before starting point projection in COptimizerPGDLS.

  • #667 Fixed download_utils.dl_file() not removing url parameters from the name of the stored file.

  • #715 download_utils.dl_file() now correctly manage the absence of the ‘content-length’ header from response.

  • Inverted sign of computed kernel similarity (to have a distance measure).

  • #710 Random seed in CClassifierPyTorch is now correctly applied also when running on the CuDNN backend.

  • #639: Objective function parameter (objective_function) in CAttackEvasionCleverhans is now correctly populated for ElasticNetMethod and SPSA attacks.

Removed & Deprecated (5 changes)

  • #748 CNormalizerUnitNorm.inverse_transform has been removed (it only worked if one inverted x after transforming it, but not if other transforms were applied in between).

  • Removed the parameters n_feats and n_classes from the interface of CAttackEvasionCleverhans.

  • #744 Deprecate kernel parameter from CClassifierSGD and CClassifierRidge and removed deprecated parameter kernel='linear' from notebook 01-Training.ipynb.

  • #643 Removed deprecated parameter random_seed from CClassifierLogistic. Use random_state instead.

  • #643 Removed deprecated method is_linear from CClassifier, CNormalizer, and related subclasses.

Documentation (5 changes)

  • #756 Fixed format of output arrays reported in CArray.__mul__ and .__truediv__ methods.

  • #681 Fixed few typos in CExplainerIntegratedGradients.

  • #674 Added CClassifierDNN to the documentation.

  • #711 Added a “How to cite SecML” section in README.

  • #703 Updated copyright notice in README.

v0.11.2 (07/01/2020)

  • This version brings fixes for a few reported issues with CAttack and subclasses, along with the new Developers and Contributors guide.

Requirements (1 change)

  • #700 Temporarily pinned Pillow to v6 to avoid breaking torch and torchvision packages.

Fixed (7 changes)

  • #698 Fixed CAttackEvasionCleverhans definition of class_type.

  • #662 The number of function and gradient evaluations made during double initialization in CAttackEvasionPGDLS are now correctly considered by .f_eval and .grad_eval properties.

  • #699 Fixed batch processing in CClassifierPyTorch not working properly if the number of samples to be classified is not a multiple of batch_size.

  • #691 Function and gradient evaluation counts in CAttackEvasionCleverhans returned by .f_eval and .grad_eval properties now only consider the last optimized sample, consistently with other CAttack subclasses.

  • #701 Default value of double_init parameter in CAttackEvasionPGDLS set to True as originally intended.

  • #684 The solution returned by COptimizerPGD is now always the best one found during the minimization process.

  • #697 Fixed unittests failing under numpy v1.18 due to a change in the errors raised by genfromtxt.

Documentation (2 changes)

  • #671 Added Developers and Contributors guide.

  • #694 Added a new notebook tutorial on advanced evasion attacks using Deep Neural Networks and ImageNet dataset.

v0.11.1 (18/12/2019)

  • Fixed compatibility issues with recently released scikit-learn v0.22 and scipy v1.4.

Fixed (3 changes)

  • #687 Fixed reshaping of sparse arrays to vector-like when using Scipy v1.4.

  • #686 Replaced deprecated import of interp function from scipy namespace instead of numpy namespace.

  • #668 Fixed unittests failing under scikit-learn v0.22 due to a change in their class output.

v0.11 (02/12/2019)

  • #653 Added new package, which provides a zoo of pre-trained SecML models. The list of available models will be greatly expanded in the future. See for more details.

  • #629 Greatly improved the performance of the grad_f_x method for CClassifier and CPreProcess classes, especially when nested via preprocess attribute.

  • #613 Support for Python 2.7 is dropped. Python version 3.5, 3.6, or 3.7 is now required.

Requirements (2 changes)

  • #633 The following dependencies are now required: numpy >= 1.17, scipy >= 1.3.1, scikit-learn >= 0.21 matplotlib = 3.

  • #622 Removed dependency on six library.

Added (5 changes)

  • #539 Added new core interface to get and set the state of an object instance: set_state, get_state, save_state, load_state. The state of an object is a simple human-readable Python dictionary object which stores the data necessary to restore an instance to a specific status. Please not that to guarantee the exact match between the original object instance and the restored one, the standard save/load interface should be used.

  • #647 Added new function core.attr_utils.get_protected which returns a protected attribute from a class (if exists).

  • #629 CClassifier and CPreProcess classes now provide a gradient method, which computes the gradient by doing a forward and a backward pass on the classifier or preprocessor function chain, accepting an optional pre-multiplier w.

  • #539 Added new accessible attributes to multiple classes: CNormalizerMinMax .m .q; CReducerLDA .lda; CClassifierKNN .tr; CClassifierRidge .tr; CClassifierSGD .tr; CClassifierPyTorch .trained.

  • #640 Added random_state parameter to CClassifierDecisionTree.

Improved (6 changes)

  • #631 Data objects are now stored using protocol 4 by This protocol adds support for very large objects, pickling more kinds of objects, and some data format optimizations.

  • #639 Objective function parameter (objective_function) in CAttackEvasionCleverhans is now correctly populated for the following attacks: CarliniWagnerL2, FastGradientMethod, ProjectedGradientDescent, LBFGS, MomentumIterativeMethod, MadryEtAl, BasicIterativeMethod.

  • #638 The sequence of modifications to the attack point (x_seq parameter) is now correctly populated in CAttackEvasionCleverhans.

  • #595 A pre-trained classifier can now be passed to CClassifierRejectThreshold to avoid running fit twice.

  • #627 Slight improvement of CKernel.gradient() method performance by removing unnecessary calls.

  • #630 Sparse data can now be used in CKernelHistIntersect.

Changed (2 changes)

  • #616 Renamed CModelCleverhans to _CModelCleverhans as this class is not supposed to be explicitly used.

  • #111 Default value of the parameter tol changed from -inf to None in CClassifierSGD. This change should not alter the classifier behavior when using the default parameters.

Fixed (8 changes)

  • #611 Fixed CDataloaderMNIST crashing depending on the desired number of samples and digits to load.

  • #652 Number of gradient computations returned by CAttackEvasionCleverhans.grad_eval is now accurate.

  • #650 Fixed CAttackEvasionCleverhans.f_eval wrongly returns the number of gradient evaluations.

  • #637 Fixed checks on y_taget in CAttackEvasionCleverhans which compared the 0 label to untargeted case (y_true = None).

  • #648 Function core.attr_utils.is_public now correctly return False for properties.

  • #649 Fixed wrong use of core.attr_utils.is_public in CCreator and CDatasetHeader.

  • #655 Fixed CClassifierRejectThreshold.n_classes not taking into account the rejected class (label -1).

  • #636 Fixed a TypeError raised by CFigure.clabel() when using matplotlib 3.

Removed & Deprecated (4 changes)

  • #628 Method is_linear of CClassifier and CNormalizer subclasses is now deprecated.

  • #641 Parameter random_seed of CClassifierLogistic is now deprecated. Use random_state instead.

  • #603 Removed deprecated class CNormalizerMeanSTD.

  • #603 Removed deprecated parameter batch_size from CKernel and subclasses.

Documentation (4 changes)

  • #625 Reorganized notebooks tutorials into different categories: Machine Learning, Adversarial Machine Learning, and Explainable Machine Learning.

  • #615 Added a tutorial notebook on the use of Cleverhans library wrapper.

  • #607 Settings module secml.settings is now correctly displayed in the docs.

  • #626 Added missing reference to CPlotMetric class in docs.

v0.10 (29/10/2019)

  • #535 Added new package secml.explanation, which provides different methods for explaining machine learning models. See documentation and examples for more information.

  • #584 [beta] Added CAttackEvasionCleverhans to support adversarial attacks from CleverHans, a Python library to benchmark vulnerability of machine learning systems to adversarial examples.

Requirements (1 change)

  • #580 PyTorch version 1.3 is now supported.

Added (4 changes)

  • #565 Added new abstract interface CClassifierDNN from which new classes implementing Deep Neural Networks can inherit.

  • #555 Added CNormalizerDNN, which allows using a CClassifierDNN as a preprocessor.

  • #593 Added CDataLoaderTorchDataset, which allows converting a torchvision dataset into a CDataset.

  • #598 Added gradient method for CKernelHistIntersection.

Improved (6 changes)

  • #562 Extended support of CClassifierPyTorch to nested PyTorch modules.

  • #594 CClassifierPyTorch.load_model() is now able to also load models trained with PyTorch (without using our wrapper). New parameter classes added to the method to match classes to indexes in the loaded model.

  • #579 Left side single row/column broadcast is now supported for sparse vs sparse CArray operations.

  • #582 Improved performance of CNormalizerMeanStd when multiple channels are defined.

  • #576 Vastly improved the performance of kernels by removing loops over samples in many classes and refactoring main routines.

  • #562 Improved grad_f_x computation at a specific layer in CClassifierPyTorch.

Changed (4 changes)

  • #578 CClassifierPyTorch now inherits from CClassifierDNN. The following changed accordingly: parameter torch_model renamed to model; property layer_shapes is now defined; method save_checkpoint removed.

  • #562 Parameter layer of CClassifierPyTorch.get_layer_output() is now renamed layer_names as a list of layers names is supported (a single layer name is still supported as input). A dictionary is returned if multiple layers are requested. See the documentation for more information.

  • #533 Double initialization in CAttackEvasionPGDLS will now be executed regardless of the classifier type (linear or nonlinear) if the double_init parameter of .run() method is set to True.

  • #591 It is now not required to call the fit method of CNormalizerMeanSTD if fixed mean/std values are used.

Fixed (4 changes)

  • #561 Fixed CConstraintBox not always applied correctly for float data.

  • #577 Fixed CClassifierPyTorch.decision_function applying preprocess twice.

  • #581 Fixed gradient computation of CKernelChebyshevDistance.

  • #599 Kernels using distances are now based on negative distances (to correctly represent similarity measures). Affected classes are: CKernelChebyshevDistance, CKernelEuclidean.

Removed & Deprecated (5 changes)

  • #561 Removed parameter precision from CConstraint.is_violated().

  • #575 Parameter batch_size of CKernel is now deprecated.

  • #597 Removed unused parameter gamma from CKernelChebyshevDistance.

  • #596 Removed CKernelHamming.

  • #602 Renamed CNormalizerMeanSTD to CNormalizerMeanStd. The old class has been deprecated and will be removed in a future version.

Documentation (5 changes)

  • #538 Added a notebook tutorial on the use of Explainable ML methods provided by the secml.explanation package.

  • #573 Improved visualization of attack results in 07-ImageNet tutorial.

  • #610 Fixed spacing between parameter and parameter type in the docs.

  • #605 Fixed documentation of classes requiring extra components not being displayed.

  • #608 Added acknowledgments to README.

v0.9 (11/10/2019)

  • #536 Added CClassifierPytorch to support Neural Networks (NNs) through PyTorch deep learning platform.

Improved (1 change)

  • #556 CFigure.imshow now supports PIL images as input.

Changed (1 change)

  • #532 Method CPreProcess.revert() is now renamed .inverse_transform().

Fixed (1 change)

  • #554 Fixed CClassifierSkLearn.predict() not working when using pretrained sklearn models.

Documentation (2 changes)

  • #559 Deprecated functions and classes are now correctly visualized in the documentation.

  • #560 Updated development roadmap accordingly to 0.10, 0.11 and 0.12 releases.

Deprecations (3 changes)

  • #532 Method CPreProcess.revert() is deprecated. Use .inverse_transform() instead.

  • #552 CClassifierKDE is now deprecated. Use CClassifierSkLearn with sklearn.neighbors.KernelDensity instead.

  • #553 CClassifierMCSLinear is now deprecated. Use CClassifierSkLearn with sklearn.ensemble.BaggingClassifier instead.

v0.8.1 (05/09/2019)

This version does not contain any significant change.

Documentation (2 changes)

  • #523 Fixed documentation not compiling under Sphinx v2.2.

  • #529 Updated roadmap accordingly for v0.9 release.

v0.8 (06/08/2019)

  • First public release!