pytod 0.0.3
Tensor-based outlier detection. A general GPU-accelerated framework.
Keywords: pytorch,tensor operation,outlier detection,acceleration,data mining,machine learning,python

(Py)TOD: GPU-accelerated Outlier Detection via Tensor Operations
================================================================


**Deployment & Documentation & Stats & License**

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   :alt: PyPI version


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   :alt: GitHub stars


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   :alt: GitHub forks


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-----


**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.

We propose **TOD**, a system for efficient and scalable outlier detection (OD) on distributed multi-GPU machines.
A key idea behind TOD is *decomposing OD applications into basic tensor algebra operations for GPU acceleration*.

**Authors**: TOD is developed by the same author(s) of the popular PyOD and PyGOD. Specifically, `Yue Zhao <https://www.andrew.cmu.edu/user/yuezhao2/>`_,
`Prof. George Chen <http://www.andrew.cmu.edu/user/georgech/>`_, and `Prof. Zhihao Jia <https://cs.cmu.edu/~zhihaoj2>`_.
**The code is being cleaned up and released. Please watch and star!**

**Citing TOD**\ : Check out `the design paper <https://www.andrew.cmu.edu/user/yuezhao2/papers/22-preprint-tod.pdf>`_.
If you use TOD in a scientific publication, we would appreciate
citations to the following paper::


    @article{zhao2021tod,
      title={TOD: GPU-accelerated Outlier Detection via Tensor Operations},
      author={Zhao, Yue and Chen, George H and Jia, Zhihao},
      journal={arXiv preprint arXiv:2110.14007},
      year={2021}
    }

or::

    Zhao, Y., Chen, G.H. and Jia, Z., 2021. TOD: GPU-accelerated Outlier Detection via Tensor Operations. arXiv preprint arXiv:2110.14007.



----


One Reason to Use It:
^^^^^^^^^^^^^^^^^^^^^

On average, **TOD is 11 times faster than PyOD** on a diverse group of OD algorithms!

If you need another reason: it can handle much larger datasets---more than **a million sample** OD within an hour!

**GPU-accelerated Outlier Detection with 5 Lines of Code**\ :


.. code-block:: python


    # train the COPOD detector
    from pytod.models.knn import KNN
    clf = KNN() # default GPU device is used
    clf.fit(X_train)

    # get outlier scores
    y_train_scores = clf.decision_scores_  # raw outlier scores on the train data
    y_test_scores = clf.decision_function(X_test)  # predict raw outlier scores on test



**TOD is featured for**:

* **Unified APIs, detailed documentation, and examples** for the easy use (under construction)
* **More than 5 different OD algorithms** and more are being added
* **The support of multi-GPU acceleration**
* **Advanced techniques** including *provable quantization* and *automatic batching*


**Table of Contents**\ :


* `Installation <#installation>`_
* `Implemented Algorithms <#implemented-algorithms>`_
* `A Motivating Example PyOD vs. PyTOD <#a-motivating-example-pyod-vs-pytod>`_
* `Paper Reproducibility <#paper-reproducibility>`_
* `Programming Model Interface <#programming-model-interface>`_
* `End-to-end Performance Comparison with PyOD <#end-to-end-performance-comparison-with-pyod>`_

----

Installation
^^^^^^^^^^^^

It is recommended to use **pip** for installation. Please make sure
**the latest version** is installed, as PyTOD is updated frequently:

.. code-block:: bash

   pip install pytod            # normal install
   pip install --upgrade pytod  # or update if needed

Alternatively, you could clone and run setup.py file:

.. code-block:: bash

   git clone https://github.com/yzhao062/pytod.git
   cd pyod
   pip install .

**Required Dependencies**\ :


* Python 3.6+
* numpy>=1.13
* torch>=1.7 (**it is safer if you install by yourself**)
* scipy>=0.19.1
* scikit_learn>=0.20.0
* pyod (**for comparison**)

----


Implemented Algorithms
^^^^^^^^^^^^^^^^^^^^^^

PyTOD toolkit consists of three major functional groups (to be cleaned up):

**(i) Individual Detection Algorithms** :

===================  ==================  ======================================================================================================  =====  ========================================
Type                 Abbr                Algorithm                                                                                               Year   Ref
===================  ==================  ======================================================================================================  =====  ========================================
Linear Model         PCA                 Principal Component Analysis (the sum of weighted projected distances to the eigenvector hyperplanes)   2003   [#Shyu2003A]_
Proximity-Based      LOF                 Local Outlier Factor                                                                                    2000   [#Breunig2000LOF]_
Proximity-Based      COF                 Connectivity-Based Outlier Factor                                                                       2002   [#Tang2002Enhancing]_
Proximity-Based      HBOS                Histogram-based Outlier Score                                                                           2012   [#Goldstein2012Histogram]_
Proximity-Based      kNN                 k Nearest Neighbors (use the distance to the kth nearest neighbor as the outlier score)                 2000   [#Ramaswamy2000Efficient]_
Proximity-Based      AvgKNN              Average kNN (use the average distance to k nearest neighbors as the outlier score)                      2002   [#Angiulli2002Fast]_
Proximity-Based      MedKNN              Median kNN (use the median distance to k nearest neighbors as the outlier score)                        2002   [#Angiulli2002Fast]_
Probabilistic        ABOD                Angle-Based Outlier Detection                                                                           2008   [#Kriegel2008Angle]_
Probabilistic        COPOD               COPOD: Copula-Based Outlier Detection                                                                   2020   [#Li2020COPOD]_
Probabilistic        FastABOD            Fast Angle-Based Outlier Detection using approximation                                                  2008   [#Kriegel2008Angle]_
===================  ==================  ======================================================================================================  =====  ========================================


**Code is being released**. Watch and star for the latest news!


----


A Motivating Example PyOD vs. PyTOD!
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

`kNN example <https://github.com/yzhao062/pytod/blob/main/examples/knn_example.py>`_
shows that how fast and how easy PyTOD is. Take the famous kNN outlier detection as an example:

#. Initialize a kNN detector, fit the model, and make the prediction.

   .. code-block:: python

       from pytod.models.knn import KNN   # kNN detector

       # train kNN detector
       clf_name = 'KNN'
       clf = KNN()
       clf.fit(X_train)


   .. code-block:: python

       # if GPU is not available, use CPU instead
       clf = KNN(device='cpu')
       clf.fit(X_train)

#. Get the prediction results

   .. code-block:: python

       # get the prediction label and outlier scores of the training data
       y_train_pred = clf.labels_  # binary labels (0: inliers, 1: outliers)
       y_train_scores = clf.decision_scores_  # raw outlier scores

#. On a simple laptop, let us see how fast it is in comparison to PyOD for 30,000 samples with 20 features

   .. code-block:: python

      KNN-PyOD ROC:1.0, precision @ rank n:1.0
      Execution time 11.26 seconds

   .. code-block:: python

      KNN-PyTOD-GPU ROC:1.0, precision @ rank n:1.0
      Execution time 2.82 seconds

   .. code-block:: python

      KNN-PyTOD-CPU ROC:1.0, precision @ rank n:1.0
      Execution time 3.36 seconds

It is easy to see, PyTOD shows both better efficiency than PyOD.

----

Paper Reproducibility
^^^^^^^^^^^^^^^^^^^^^

**Datasets**: OD benchmark datasets are available at `datasets folder <https://github.com/yzhao062/pytod/tree/main/reproducibility/datasets/ODDS>`_.

**Scripts for reproducibility is available in** `reproducibility folder <https://github.com/yzhao062/pytod/tree/main/reproducibility>`_.

Cleanup is on the way!

----

Programming Model Interface
^^^^^^^^^^^^^^^^^^^^^^^^^^^

Complex OD algorithms can be abstracted into common tensor operators.

.. image:: https://raw.githubusercontent.com/yzhao062/pytod/master/figs/abstraction.png
   :target: https://raw.githubusercontent.com/yzhao062/pytod/master/figs/abstraction.png


For instance, ABOD and COPOD can be assembled by the basic tensor operators.

.. image:: https://raw.githubusercontent.com/yzhao062/pytod/master/figs/abstraction_example.png
   :target: https://raw.githubusercontent.com/yzhao062/pytod/master/figs/abstraction_example.png


----

End-to-end Performance Comparison with PyOD
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Overall, it is much (on avg. 11 times) faster than PyOD takes way less run time.

.. image:: https://raw.githubusercontent.com/yzhao062/pytod/master/figs/run_time.png
   :target: https://raw.githubusercontent.com/yzhao062/pytod/master/figs/run_time.png


----

Reference
^^^^^^^^^


.. [#Aggarwal2015Outlier] Aggarwal, C.C., 2015. Outlier analysis. In Data mining (pp. 237-263). Springer, Cham.

.. [#Aggarwal2015Theoretical] Aggarwal, C.C. and Sathe, S., 2015. Theoretical foundations and algorithms for outlier ensembles.\ *ACM SIGKDD Explorations Newsletter*\ , 17(1), pp.24-47.

.. [#Aggarwal2017Outlier] Aggarwal, C.C. and Sathe, S., 2017. Outlier ensembles: An introduction. Springer.

.. [#Almardeny2020A] Almardeny, Y., Boujnah, N. and Cleary, F., 2020. A Novel Outlier Detection Method for Multivariate Data. *IEEE Transactions on Knowledge and Data Engineering*.

.. [#Angiulli2002Fast] Angiulli, F. and Pizzuti, C., 2002, August. Fast outlier detection in high dimensional spaces. In *European Conference on Principles of Data Mining and Knowledge Discovery* pp. 15-27.

.. [#Arning1996A] Arning, A., Agrawal, R. and Raghavan, P., 1996, August. A Linear Method for Deviation Detection in Large Databases. In *KDD* (Vol. 1141, No. 50, pp. 972-981).

.. [#Breunig2000LOF] Breunig, M.M., Kriegel, H.P., Ng, R.T. and Sander, J., 2000, May. LOF: identifying density-based local outliers. *ACM Sigmod Record*\ , 29(2), pp. 93-104.

.. [#Burgess2018Understanding] Burgess, Christopher P., et al. "Understanding disentangling in beta-VAE." arXiv preprint arXiv:1804.03599 (2018).

.. [#Goldstein2012Histogram] Goldstein, M. and Dengel, A., 2012. Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm. In *KI-2012: Poster and Demo Track*\ , pp.59-63.

.. [#Gopalan2019PIDForest] Gopalan, P., Sharan, V. and Wieder, U., 2019. PIDForest: Anomaly Detection via Partial Identification. In Advances in Neural Information Processing Systems, pp. 15783-15793.

.. [#Hardin2004Outlier] Hardin, J. and Rocke, D.M., 2004. Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator. *Computational Statistics & Data Analysis*\ , 44(4), pp.625-638.

.. [#He2003Discovering] He, Z., Xu, X. and Deng, S., 2003. Discovering cluster-based local outliers. *Pattern Recognition Letters*\ , 24(9-10), pp.1641-1650.

.. [#Iglewicz1993How] Iglewicz, B. and Hoaglin, D.C., 1993. How to detect and handle outliers (Vol. 16). Asq Press.

.. [#Janssens2012Stochastic] Janssens, J.H.M., Huszár, F., Postma, E.O. and van den Herik, H.J., 2012. Stochastic outlier selection. Technical report TiCC TR 2012-001, Tilburg University, Tilburg Center for Cognition and Communication, Tilburg, The Netherlands.

.. [#Kingma2013Auto] Kingma, D.P. and Welling, M., 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114.

.. [#Kriegel2008Angle] Kriegel, H.P. and Zimek, A., 2008, August. Angle-based outlier detection in high-dimensional data. In *KDD '08*\ , pp. 444-452. ACM.

.. [#Kriegel2009Outlier] Kriegel, H.P., Kröger, P., Schubert, E. and Zimek, A., 2009, April. Outlier detection in axis-parallel subspaces of high dimensional data. In *Pacific-Asia Conference on Knowledge Discovery and Data Mining*\ , pp. 831-838. Springer, Berlin, Heidelberg.

.. [#Lazarevic2005Feature] Lazarevic, A. and Kumar, V., 2005, August. Feature bagging for outlier detection. In *KDD '05*. 2005.

.. [#Li2019MADGAN] Li, D., Chen, D., Jin, B., Shi, L., Goh, J. and Ng, S.K., 2019, September. MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks. In *International Conference on Artificial Neural Networks* (pp. 703-716). Springer, Cham.

.. [#Li2020COPOD] Li, Z., Zhao, Y., Botta, N., Ionescu, C. and Hu, X. COPOD: Copula-Based Outlier Detection. *IEEE International Conference on Data Mining (ICDM)*, 2020.

.. [#Liu2008Isolation] Liu, F.T., Ting, K.M. and Zhou, Z.H., 2008, December. Isolation forest. In *International Conference on Data Mining*\ , pp. 413-422. IEEE.

.. [#Liu2019Generative] Liu, Y., Li, Z., Zhou, C., Jiang, Y., Sun, J., Wang, M. and He, X., 2019. Generative adversarial active learning for unsupervised outlier detection. *IEEE Transactions on Knowledge and Data Engineering*.

.. [#Papadimitriou2003LOCI] Papadimitriou, S., Kitagawa, H., Gibbons, P.B. and Faloutsos, C., 2003, March. LOCI: Fast outlier detection using the local correlation integral. In *ICDE '03*, pp. 315-326. IEEE.

.. [#Pevny2016Loda] Pevný, T., 2016. Loda: Lightweight on-line detector of anomalies. *Machine Learning*, 102(2), pp.275-304.

.. [#Ramaswamy2000Efficient] Ramaswamy, S., Rastogi, R. and Shim, K., 2000, May. Efficient algorithms for mining outliers from large data sets. *ACM Sigmod Record*\ , 29(2), pp. 427-438.

.. [#Rousseeuw1999A] Rousseeuw, P.J. and Driessen, K.V., 1999. A fast algorithm for the minimum covariance determinant estimator. *Technometrics*\ , 41(3), pp.212-223.

.. [#Ruff2018Deep] Ruff, L., Vandermeulen, R., Goernitz, N., Deecke, L., Siddiqui, S.A., Binder, A., Müller, E. and Kloft, M., 2018, July. Deep one-class classification. In *International conference on machine learning* (pp. 4393-4402). PMLR.

.. [#Scholkopf2001Estimating] Scholkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J. and Williamson, R.C., 2001. Estimating the support of a high-dimensional distribution. *Neural Computation*, 13(7), pp.1443-1471.

.. [#Shyu2003A] Shyu, M.L., Chen, S.C., Sarinnapakorn, K. and Chang, L., 2003. A novel anomaly detection scheme based on principal component classifier. *MIAMI UNIV CORAL GABLES FL DEPT OF ELECTRICAL AND COMPUTER ENGINEERING*.

.. [#Tang2002Enhancing] Tang, J., Chen, Z., Fu, A.W.C. and Cheung, D.W., 2002, May. Enhancing effectiveness of outlier detections for low density patterns. In *Pacific-Asia Conference on Knowledge Discovery and Data Mining*, pp. 535-548. Springer, Berlin, Heidelberg.

.. [#Wang2020adVAE] Wang, X., Du, Y., Lin, S., Cui, P., Shen, Y. and Yang, Y., 2019. adVAE: A self-adversarial variational autoencoder with Gaussian anomaly prior knowledge for anomaly detection. *Knowledge-Based Systems*.

.. [#Zhao2018XGBOD] Zhao, Y. and Hryniewicki, M.K. XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning. *IEEE International Joint Conference on Neural Networks*\ , 2018.

.. [#Zhao2019LSCP] Zhao, Y., Nasrullah, Z., Hryniewicki, M.K. and Li, Z., 2019, May. LSCP: Locally selective combination in parallel outlier ensembles. In *Proceedings of the 2019 SIAM International Conference on Data Mining (SDM)*, pp. 585-593. Society for Industrial and Applied Mathematics.

.. [#Zhao2021SUOD] Zhao, Y., Hu, X., Cheng, C., Wang, C., Wan, C., Wang, W., Yang, J., Bai, H., Li, Z., Xiao, C., Wang, Y., Qiao, Z., Sun, J. and Akoglu, L. (2021). SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection. *Conference on Machine Learning and Systems (MLSys)*.