Metadata-Version: 2.1
Name: suod
Version: 0.0.7
Summary: A Scalable Framework for Unsupervised Outlier Detection (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/suod/archive/master.zip
Description: SUOD: Accelerating Large-scare Unsupervised Heterogeneous Outlier Detection
        ===========================================================================
        
        **Deployment & Documentation & Stats**
        
        .. image:: https://img.shields.io/pypi/v/suod.svg?color=brightgreen
           :target: https://pypi.org/project/suod/
           :alt: PyPI version
        
        
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           :target: https://suod.readthedocs.io/en/latest/?badge=latest
           :alt: Documentation Status
        
        
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           :target: https://github.com/yzhao062/suod/stargazers
           :alt: GitHub stars
        
        
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           :target: https://github.com/yzhao062/suod/network
           :alt: GitHub forks
        
        
        .. image:: https://pepy.tech/badge/suod
           :target: https://pepy.tech/project/suod
           :alt: Downloads
        
        
        .. image:: https://pepy.tech/badge/suod/month
           :target: https://pepy.tech/project/suod
           :alt: Downloads
        
        
        ----
        
        
        **Build Status & Coverage & Maintainability & License**
        
        
        .. image:: https://travis-ci.org/yzhao062/suod.svg?branch=master
           :target: https://travis-ci.org/yzhao062/suod
           :alt: Build Status
        
        
        .. image:: https://circleci.com/gh/yzhao062/SUOD.svg?style=svg
           :target: https://circleci.com/gh/yzhao062/SUOD
           :alt: Circle CI
        
        
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           :target: https://ci.appveyor.com/project/yzhao062/combo/branch/master
           :alt: Appveyor
        
        
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           :target: https://coveralls.io/github/yzhao062/SUOD
           :alt: Coverage Status
        
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           :alt: License
        
        
        ----
        
        **Background**: Outlier detection (OD) is a key data mining task for identifying abnormal objects from general samples with numerous high-stake applications including fraud detection and intrusion detection.
        Due to the lack of ground truth labels, practitioners often have to build a large number of unsupervised models that are heterogeneous (i.e., different algorithms and hyperparameters) for further combination and analysis with ensemble learning, rather than relying on a single model.
        However, **this yields severe scalability issues on high-dimensional, large datasets**.
        
        **SUOD** (**S**\calable **U**\nsupervised **O**\utlier **D**\etection) is an **acceleration framework for large-scale unsupervised heterogeneous outlier detector training and prediction**.
        It focuses on three complementary aspects to accelerate (dimensionality reduction for high-dimensional data, model approximation for complex models, and execution efficiency improvement for taskload imbalance within distributed systems), while controlling detection performance degradation.
        
        Since its inception in Sep 2019, SUOD has been successfully used in various academic researches and industry applications with more than 700,000 downloads,
        including PyOD [#Zhao2019PyOD]_ and `IQVIA <https://www.iqvia.com/>`_ medical claim analysis.
        
        
        .. image:: https://raw.githubusercontent.com/yzhao062/SUOD/master/figs/system_overview.png
           :target: https://raw.githubusercontent.com/yzhao062/SUOD/master/figs/system_overview.png
           :alt: SUOD System
        
        SUOD is featured for:
        
        * **Unified APIs, detailed documentation, and examples** for the easy use.
        * **Optimized performance with JIT and parallelization** when possible, using `numba <https://github.com/numba/numba>`_ and `joblib <https://github.com/joblib/joblib>`_.
        * **Fully compatible with the models in PyOD**.
        * **Customizable modules and flexible design**: each module may be turned on/off or totally replaced by custom functions.
        
        Roadmap:
        
        * Provide more choices of distributed schedulers (adapted for SUOD), e.g., batch sampling, Sparrow (SOSP'13), Pigeon (SoCC'19) etc.
        * Enable the flexibility of selecting data projection methods.
        
        ----
        
        **API Demo**\ :
        
        
        .. code-block:: python
        
        
            from suod.models.base import SUOD
        
            # initialize a set of base outlier detectors to train and predict on
            base_estimators = [
                LOF(n_neighbors=5, contamination=contamination),
                LOF(n_neighbors=15, contamination=contamination),
                LOF(n_neighbors=25, contamination=contamination),
                HBOS(contamination=contamination),
                PCA(contamination=contamination),
                OCSVM(contamination=contamination),
                KNN(n_neighbors=5, contamination=contamination),
                KNN(n_neighbors=15, contamination=contamination),
                KNN(n_neighbors=25, contamination=contamination)]
        
            # initialize a SUOD model with all features turned on
            model = SUOD(base_estimators=base_estimators, n_jobs=6,  # number of workers
                         rp_flag_global=True,  # global flag for random projection
                         bps_flag=True,  # global flag for balanced parallel scheduling
                         approx_flag_global=False,  # global flag for model approximation
                         contamination=contamination)
        
            model.fit(X_train)  # fit all models with X
            model.approximate(X_train)  # conduct model approximation if it is enabled
            predicted_labels = model.predict(X_test)  # predict labels
            predicted_scores = model.decision_function(X_test)  # predict scores
            predicted_probs = model.predict_proba(X_test)  # predict outlying probability
        
        ----
        
        `The corresponding paper <https://www.andrew.cmu.edu/user/yuezhao2/papers/21-mlsys-suod.pdf>`_ is published in Conference on Machine Learning Systems (MLSys).
        See https://mlsys.org/ for more information.
        
        
        If you use SUOD in a scientific publication, we would appreciate citations to the following paper::
        
        
            @inproceedings{zhao2021suod,
              title={SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection},
              author={Zhao, Yue and Hu, Xiyang and Cheng, Cheng and Wang, Cong and Wan, Changlin and Wang, Wen and Yang, Jianing and Bai, Haoping and Li, Zheng and Xiao, Cao and others},
              journal={Proceedings of Machine Learning and Systems},
              year={2021}
            }
        
        ::
        
            Zhao, Y., Hu, X., Cheng, C., Wang, C., Wan, C., Wang, W., Yang, J., Bai, H., Li, Z., Xiao, C. and Wang, Y., 2021. SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection. Proceedings of Machine Learning and Systems (MLSys).
        
        
        **Table of Contents**\ :
        
        
        * `Installation <#installation>`_
        * `API Cheatsheet & Reference <#api-cheatsheet--reference>`_
        * `Examples <#examples>`_
        * `Model Save & Load <#model-save--load>`_
        
        
        ------------
        
        
        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
        * numpy>=1.13
        * pandas (**optional for building the cost forecast model**)
        * 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/>`_.
        
        
        ------------
        
        
        API Cheatsheet & Reference
        ^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        Full API Reference: (https://suod.readthedocs.io/en/latest/api.html).
        
        * **fit(X, y)**\ : Fit estimator. y is optional for unsupervised methods.
        * **approximate(X)**\ : Use supervised models to approximate unsupervised base detectors. Fit should be invoked first.
        * **predict(X)**\ : Predict on a particular sample once the estimator is fitted.
        * **predict_proba(X)**\ : Predict the probability of a sample belonging to each class once the estimator is fitted.
        
        
        Examples
        ^^^^^^^^
        
        All three modules can be **executed separately** and the demo codes are in /examples/module_examples/{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".
        
        **The examples for the full framework** can be found under /examples folder; run "demo_base.py" for
        a simplified example. Run "demo_full.py" for a full example.
        
        It is noted the best performance may be achieved with multiple cores available.
        
        ------------
        
        
        Model Save & Load
        ^^^^^^^^^^^^^^^^^
        
        SUOD takes a similar approach of sklearn regarding model persistence.
        See `model persistence <https://scikit-learn.org/stable/modules/model_persistence.html>`_ for clarification.
        
        In short, we recommend to use joblib or pickle for saving and loading SUOD models.
        See `"examples/demo_model_save_load.py" <https://github.com/yzhao062/suod/blob/master/examples/demo_model_save_load.py>`_ for an example.
        In short, it is simple as below:
        
        .. code-block:: python
        
            from joblib import dump, load
        
            # save the fitted model
            dump(model, 'model.joblib')
            # load the model
            model = load('model.joblib')
        
        
        
        **More to come...**
        Last updated on Jan 14th, 2021.
        
        Feel free to star and watch 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.
        
        .. [#Zhao2019PyOD] Zhao, Y., Nasrullah, Z. and Li, Z., 2019. PyOD: A Python Toolbox for Scalable Outlier Detection. *Journal of Machine Learning Research*, 20, pp.1-7.
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
