Metadata-Version: 2.1
Name: secml
Version: 0.11.1rc2
Summary: A library for Secure and Explainable Machine Learning
Home-page: https://secml.gitlab.io
Maintainer: Marco Melis
Maintainer-email: marco.melis@unica.it
License: Apache License 2.0
Download-URL: https://pypi.python.org/pypi/secml#files
Project-URL: Bug Tracker, https://gitlab.com/secml/secml/issues
Project-URL: Source Code, https://gitlab.com/secml/secml
Description: # SecML: A library for Secure and Explainable Machine Learning
        
        SecML is an open-source Python library for the **security evaluation** of
        Machine Learning (ML) algorithms.
        
        It comes with a set of powerful features:
        - **Wide range of supported ML algorithms.** All supervised learning algorithms
          supported by `scikit-learn` are available, as well as Neural Networks (NNs)
          through [PyTorch](https://pytorch.org/) deep learning platform.
        - **Built-in attack algorithms.** Evasion and poisoning attacks based on a
          custom-developed fast solver. In addition, we provide connectors to other 
          third-party Adversarial Machine Learning libraries.
        - **Dense/Sparse data support.** We provide full, transparent support for both
          dense (through `numpy` library) and sparse data (through `scipy` library)
          in a single data structure.
        - **Visualize your results.** We provide visualization and plotting framework,
          based on the widely-known library [matplotlib](https://matplotlib.org/).
        - **Explain your results.** Explainable ML methods to interpret model decisions
          via influential features and prototypes.
        - **Model Zoo.** Use our pre-trained models to save time and easily replicate 
          scientific results.
        - **Multi-processing.** Do you want to save time further? We provide full
          compatibility with all the multi-processing features of `scikit-learn` and
          `pytorch`, along with built-in support of the [joblib](
          https://joblib.readthedocs.io/) library.
        - **Extensible.** Easily create new components, like ML models or attack 
          algorithms, by extending the provided abstract interfaces.
        
        ### SecML is currently in development.
        If you encounter any bug, please report them using the 
        [GitLab issue tracker](https://gitlab.com/secml/secml/issues).  
        Please see our [ROADMAP](https://secml.gitlab.io/roadmap.html) for an overview 
        of the future development directions.
        
        [![Status Alpha](https://img.shields.io/badge/status-alpha-yellow.svg)](.)
        [![Python 3.5 | 3.6 | 3.7](https://img.shields.io/badge/python-3.5%20%7C%203.6%20%7C%203.7-brightgreen.svg)](.)
        [![Platform Linux | MacOS ](https://img.shields.io/badge/platform-linux%20%7C%20macos-lightgrey.svg)](.)
        [![Apache License 2.0](https://img.shields.io/badge/license-Apache--2.0-blue.svg)](https://www.apache.org/licenses/LICENSE-2.0)
        
        
        ## Installation Guide
        
        We recommend instaling SecML in a specific environment along with its dependencies.
        
        Common frameworks to create and manage envs are [virtualenv](https://virtualenv.pypa.io) 
        and [conda](https://conda.io). Both alternatives provide convenient user guides on 
        how to properly setup the envs, so this guide will not cover the configuration 
        procedure.
        
        ### Operating System requirements
        
        SecML can run under Python >= 3.5 with no additional configuration steps 
        required, as all its dependencies are available as wheel packages for 
        the primary macOS versions and Linux distributions.
        
        However, to support additional advanced features more packages can be necessary
        depending on the Operating System used:
        
         - Linux (Ubuntu >= 16.04 or equivalent dist)
           - `python3-tk`, for running MatplotLib Tk-based backends;
           - NVIDIA<sup>®</sup> CUDA<sup>®</sup> Toolkit for running `tf-gpu`
             [extra component](#extra-components).
             See the [TensorFlow Guide](https://www.tensorflow.org/install/gpu).
              
         - macOS (macOS >= 10.12 Sierra)
           - Nothing to note.
        
        
        ### Installation process
        
        Before starting the installation process try to obtain the latest version
        of the `pip` manager by calling: `pip install -U pip`
        
        The setup process is managed by the Python package `setuptools`.
        Be sure to obtain the latest version by calling: `pip install -U setuptools`
        
        Once the environment is set up, SecML can installed and run by multiple means:
        
         1. Install from official PyPI repository:
            - `pip install secml`
            
         2. Install from wheel/zip package (https://pypi.python.org/pypi/secml#files):
            - `pip install <package-file>`
        
        In all cases, the setup process will try to install the correct dependencies.
        In case something goes wrong during the install process, try to install
        the dependencies **first** by calling: `pip install -r requirements.txt`
        
        SecML should now be importable in python via: `import secml`.
        
        To update a current installation using any of the previous methods, 
        add the `-U` parameter after the `pip install` directive.
        Please see our [Update Guides](https://secml.gitlab.io/update.html) for specific
        upgrade intructions depending on the source and target version.
        
        
        ## Extra Components
        
        SecML comes with a set of extras components that can be installed if desired.
        
        To specify the extra components to install, add the section `[extras]` while
        calling `pip install`. `extras` will be a comma-separated list of components 
        you want to install. Example:
         - `pip install secml[extra1,extra2]`
        
        All the installation procedures via `pip` described above allow definition
        of the `[extras]` section.
        
        ### Available extra components
         - `pytorch` : Neural Networks (NNs) through [PyTorch](https://pytorch.org/) deep learning platform.  
           Will install: `torch >= 1.1`, `torchvision >= 0.2.2`
         - `cleverhans` : Wrapper of [CleverHans](https://github.com/tensorflow/cleverhans), 
           a Python library to benchmark vulnerability of machine learning systems
           to adversarial examples. Will install: `tensorflow >= 1.14.*, < 2`, `cleverhans`
         - `tf-gpu` : Shortcut for installing `TensorFlow` package with GPU support.  
           Will install: `tensorflow-gpu >= 1.14.*, < 2`
        
        
        ## Usage Guide
        
        SecML is based on [numpy](http://www.numpy.org/), [scipy](https://www.scipy.org/), 
        [scikit-learn](https://scikit-learn.org/) and [pytorch](https://pytorch.org/), 
        widely-used packages for scientific computing and machine learning with Python.
        
        As a result, most of the interfaces of the library should be pretty familiar 
        to frequent users of those packages.
        
        The primary data class is the `secml.array.CArray`, multi-dimensional
        (currently limited to 2 dimensions) array structure which embeds both dense
        and sparse data accepting as input `numpy.ndarray` and `scipy.sparse.csr_matrix`
        (more sparse formats will be supported soon). This structure is the standard
        input and output of all other classes in the library.
        
        The `secml.ml` package contains all the Machine Learning algorithms and
        support classes, including classifiers, loss and regularizer functions,
        kernels and performance evaluation functions. Also, a zoo of pre-trained 
        models is provided by the `secml.ml.model_zoo` package.
        
        The `secml.adv` package contains evasion and poisoning attacks based on a
        custom-developed solver, along with classes to easily perform security
        evaluation of Machine Learning algorithms.
        
        The `secml.explanation` package contains different explainable 
        Machine Learning methods that allow interpreting classifiers decisions 
        by analyzing the relevant components such as features or training prototypes.
        
        The `secml.figure` package contains a visualization and plotting framework
        based on [matplotlib](https://matplotlib.org/).
        
        
        ## Contributors
        
        **Your contribution is foundamental!**
        
        If you want to help the development of SecML, just set up the project locally
        by the following means:
        
         1. _(devs only)_ Install from local GitLab repository:
            - Clone the project repository in a directory of your choice
            - Run installation as: `pip install .`
            
         2. _(devs only)_ Install from remote GitLab repository. In this case, given
            `{repourl}` in the format, es., `gitlab.com/secml/secml`:
            - `pip install git+ssh://git@{repourl}.git[@branch]#egg=secml`
            A specific branch to install can be specified using `[@branch]` parameter.
            If omitted, the default branch will be installed.
            
        Contributions can be sent in the form of a merge request via our 
        [GitLab issue tracker](https://gitlab.com/secml/secml/issues).
            
        SecML can also be added as a dependency for other libraries/project.
        Just add `secml` or the full repository path command 
        `git+ssh://git@{repourl}.git[@branch]#egg=secml` to the `requirements.txt` file.
        
        #### Editable Installation (development mode)
        
        For SecML developers or users want to use the latest `dev` version
        of the library, `pip` provides a convenient option which is called: **editable mode**.
        
        By calling `pip install` with the `-e` option or `python setup.py develop`,
        only a reference to the project files is "installed" in the active
        environment. In this way, project files can be edited/updated and the
        new versions will be automatically executed by the Python interpreter.
        
        Two common scenarios are listed below:
        
         1. Editable install from a previously cloned local repository
            - Navigate to the repository directory
            - Run `python setup.py develop`
            
         2. Editable install from remote repository
            - Run `pip install -e git+ssh://git@{repourl}.git[@branch]#egg=secml`
            - Project will be cloned automatically in `<venv path>/src/secml`
            - The new repository can then be updated using standard `git` commands
        
        Editable installs are also available while using SecML as a
        dependency of other libraries/projects
        (see [Installation Guide](#installation-guide) for more information).
        
        
        ## Authors
        This library is maintained by 
        [PRALab - Pattern Recognition and Applications Lab](https://pralab.diee.unica.it).
        
        List of contributors:
         - Marco Melis (maintainer) [1]
         - Ambra Demontis [1]
         - Maura Pintor [1], [2]
         - Battista Biggio [1], [2]
        
        [1] Department of Electrical and Electronic Engineering, University of Cagliari, Italy  
        [2] Pluribus One, Italy
        
        
        ## Credits
        - `numpy` Travis E, Oliphant. "A guide to NumPy", USA: Trelgol Publishing, 2006.
        - `scipy` Travis E. Oliphant. "Python for Scientific Computing", Computing in 
          Science & Engineering, 9, 10-20, 2007.
        - `scikit-learn` [Pedregosa et al., "Scikit-learn: Machine Learning in Python", 
          JMLR 12, pp. 2825-2830, 2011.](http://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html)
        - `matplotlib` [J. D. Hunter, "Matplotlib: A 2D Graphics Environment", 
          Computing in Science & Engineering, vol. 9, no. 3, pp. 90-95, 2007.](
          https://doi.org/10.1109/MCSE.2007.55)
        - `pytorch` Paszke, Adam, et al. "Automatic differentiation in pytorch.", NIPS-W, 2017.
        - `cleverhans` [Papernot, Nicolas, et al. "Technical Report on the CleverHans v2.1.0 
          Adversarial Examples Library." arXiv preprint arXiv:1610.00768 (2018).](
          https://arxiv.org/abs/1610.00768)
        
        
        ## Acknowledgements
        SecML has been partially developed with the support of European Union’s 
        [ALOHA project](https://www.aloha-h2020.eu/) Horizon 2020 Research and 
        Innovation programme, grant agreement No. 780788.
        
        
        ## Copyright
        SecML has been developed by [PRALab - Pattern Recognition and Applications lab](
        https://pralab.diee.unica.it) and [Pluribus One s.r.l.](https://www.pluribus-one.it/) 
        under [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). Copyright 2019.
Platform: Linux
Platform: Mac OS-X
Platform: Unix
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: Implementation :: PyPy
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Requires-Python: >=3.5.*, <4
Description-Content-Type: text/markdown
Provides-Extra: unittests
Provides-Extra: tf-gpu
Provides-Extra: pytorch
Provides-Extra: cleverhans
