Metadata-Version: 2.1
Name: suod
Version: 0.0.0
Summary: An Acceleration System for Large Scale Unsupervised Anomaly Detection
Home-page: https://github.com/yzhao062/suod
Author: Yue Zhao
Author-email: zhaoy@cmu.edu
License: UNKNOWN
Download-URL: https://github.com/yzhao062/combo/archive/master.zip
Description: SUOD: An Acceleration System for Large Scale Unsupervised Anomaly Detection
        ===========================================================================
        
        **Deployment & Documentation & Stats**
        
        .. image:: https://img.shields.io/pypi/v/suod.svg?color=brightgreen
           :target: https://pypi.org/project/suod/
           :alt: PyPI version
        
        ----
        
        
        **SUOD** (Toward **S**\calable **U**\nsupervised **O**\utlier **D**\etection) is an **acceleration framework for large scale unsupervised outlier detector training and prediction**.
        Notably, anomaly detection is often formulated as an unsupervised problem since the ground truth is expensive to acquire in practice.
        As a result, analysts often build many diversified models and further combine them (sometimes with rule-based models)---this has become a standard process in many industries to 
        offset the challenges of the data imbalance and unsupervised nature. However, **building a large number of unsupervised models are very costly or even infeasible on high-dimensional, large datasets**.
        
        SUOD is therefore proposed to alleviate, if not fully fix, this problem.
        The focus of SUOD is **to accelerate the training and prediction when a large number of anomaly detectors are presented**.
        
        
        ----
        
        
        If you use SUOD in a scientific publication, we would appreciate citations to the following paper::
        
            @inproceedings{zhao2020suod,
              author  = {Zhao, Yue and Ding, Xueying and Yang, Jianing and Haoping Bai},
              title   = {{SUOD}: Toward Scalable Unsupervised Outlier Detection},
              journal = {Workshops at the Thirty-Fourth AAAI Conference on Artificial Intelligence},
              year    = {2020}
            }
        
        ::
        
            Yue Zhao, Xueying Ding, Jianing Yang, Haoping Bai, "Toward Scalable Unsupervised Outlier Detection". Workshops at the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020.
        
        
        A preliminary version of paper can be accessed `here <https://www.andrew.cmu.edu/user/yuezhao2/papers/20-preprint-suod.pdf>`_. The revised and extended version will be submitted to `KDD 2020 (ADS track) <https://www.kdd.org/kdd2020/>`_
        
        [`Preprint <https://www.andrew.cmu.edu/user/yuezhao2/papers/20-preprint-suod.pdf>`_], [`slides <https://www.andrew.cmu.edu/user/yuezhao2/misc/10715-SUOD-Toward-Scalable-Unsupervised-Outlier-Detection.pdf>`_], [`AICS <http://aics.site/AICS2020/>`_]
        
        ------------
        
        Reproduction Instructions
        ^^^^^^^^^^^^^^^^^^^^^^^^^
        
        All three modules can be **executed separately** and the demo codes are in /AAAI_Workshop/{M1_RP, M2_BPS, and M3_PSA}.
        For instance, you could navigate to /M1_RP/demo_random_projection.py. Demo codes all start with "demo_*.py".
        
        **A full example may be found in demo_full.py under the root directory.**
        
        **Production level code will be released soon---it will support PyPI installation with full documentation and example!**
        
        ------------
        
        
        Installation
        ^^^^^^^^^^^^
        
        It is recommended to use **pip** for installation. Please make sure
        **the latest version** is installed, as suod is updated frequently:
        
        .. code-block:: bash
        
           pip install suod            # normal install
           pip install --upgrade suod  # or update if needed
           pip install --pre suod      # or include pre-release version for new features
        
        Alternatively, you could clone and run setup.py file:
        
        .. code-block:: bash
        
           git clone https://github.com/yzhao062/suod.git
           cd suod
           pip install .
        
        
        **Required Dependencies**\ :
        
        
        * Python 3.5, 3.6, or 3.7
        * joblib
        * matplotlib (**optional for running examples**)
        * numpy>=1.13
        * numba>=0.35
        * pyod
        * scipy>=0.19.1
        * scikit_learn>=0.19.1
        
        
        **Note on Python 2**\ :
        The maintenance of Python 2.7 will be stopped by January 1, 2020 (see `official announcement <https://github.com/python/devguide/pull/344>`_).
        To be consistent with the Python change and suod's dependent libraries, e.g., scikit-learn,
        **suod only supports Python 3.5+** and we encourage you to use
        Python 3.5 or newer for the latest functions and bug fixes. More information can
        be found at `Moving to require Python 3 <https://python3statement.org/>`_.
        
        
        ----
        
        
        **More to come...**
        Last updated on Dec 23, 2019.
        
        Feel free to star for the future update :)
        
        ----
        
        References
        ----------
        
        .. [#Johnson1984Extensions] Johnson, W.B. and Lindenstrauss, J., 1984. Extensions of Lipschitz mappings into a Hilbert space. *Contemporary mathematics*, 26(189-206), p.1.
        
Keywords: ensemble learning,anomaly detection,outlier ensembles,data mining,machine learning,python
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/x-rst
