Metadata-Version: 2.1
Name: pyod
Version: 0.7.8
Summary: A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)
Home-page: https://github.com/yzhao062/pyod
Author: Yue Zhao
Author-email: zhaoy@cmu.edu
License: UNKNOWN
Download-URL: https://github.com/yzhao062/pyod/archive/master.zip
Description: Python Outlier Detection (PyOD)
        ===============================
        
        **Deployment & Documentation & Stats**
        
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        -----
        
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        -----
        
        PyOD is a comprehensive and scalable **Python toolkit** for **detecting outlying objects** in 
        multivariate data. This exciting yet challenging field is commonly referred as 
        `Outlier Detection <https://en.wikipedia.org/wiki/Anomaly_detection>`_
        or `Anomaly Detection <https://en.wikipedia.org/wiki/Anomaly_detection>`_.
        Since 2017, PyOD has been successfully used in various academic researches and
        commercial products [#Gopalan2019PIDForest]_ [#Li2019MADGAN]_ [#Wang2020adVAE]_ [#Zhao2019LSCP]_.
        It is also well acknowledged by the machine learning community with various dedicated posts/tutorials, including
        `Analytics Vidhya <https://www.analyticsvidhya.com/blog/2019/02/outlier-detection-python-pyod/>`_,
        `KDnuggets <https://www.kdnuggets.com/2019/02/outlier-detection-methods-cheat-sheet.html>`_,
        `Towards Data Science <https://towardsdatascience.com/anomaly-detection-for-dummies-15f148e559c1>`_,
        `Computer Vision News <https://rsipvision.com/ComputerVisionNews-2019March/18/>`_, and
        `awesome-machine-learning <https://github.com/josephmisiti/awesome-machine-learning#python-general-purpose>`_.
        
        
        PyOD is featured for:
        
        * **Unified APIs, detailed documentation, and interactive examples** across various algorithms.
        * **Advanced models**\ , including **Neural Networks/Deep Learning** and **Outlier Ensembles**.
        * **Optimized performance with JIT and parallelization** when possible, using `numba <https://github.com/numba/numba>`_ and `joblib <https://github.com/joblib/joblib>`_.
        * **Compatible with both Python 2 & 3**.
        
        
        **Note on Python 2.7**\ :
        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 PyOD's dependent libraries, e.g., scikit-learn, we will
        stop supporting Python 2.7 in the near future (dates are still to be decided). 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 Demo**\ :
        
        
           .. code-block:: python
        
        
               # train the KNN detector
               from pyod.models.knn import KNN
               clf = KNN()
               clf.fit(X_train)
        
               # get outlier scores
               y_train_scores = clf.decision_scores_  # raw outlier scores
               y_test_scores = clf.decision_function(X_test)  # outlier scores
        
        
        **Citing PyOD**\ :
        
        `PyOD paper <http://www.jmlr.org/papers/volume20/19-011/19-011.pdf>`_ is published in
        `JMLR <http://www.jmlr.org/>`_ (machine learning open-source software track).
        If you use PyOD in a scientific publication, we would appreciate
        citations to the following paper::
        
            @article{zhao2019pyod,
              author  = {Zhao, Yue and Nasrullah, Zain and Li, Zheng},
              title   = {PyOD: A Python Toolbox for Scalable Outlier Detection},
              journal = {Journal of Machine Learning Research},
              year    = {2019},
              volume  = {20},
              number  = {96},
              pages   = {1-7},
              url     = {http://jmlr.org/papers/v20/19-011.html}
            }
        
        or::
        
            Zhao, Y., Nasrullah, Z. and Li, Z., 2019. PyOD: A Python Toolbox for Scalable Outlier Detection. Journal of machine learning research (JMLR), 20(96), pp.1-7.
        
        
        **Key Links and Resources**\ :
        
        
        * `View the latest codes on Github <https://github.com/yzhao062/pyod>`_
        * `Execute Interactive Jupyter Notebooks <https://mybinder.org/v2/gh/yzhao062/pyod/master>`_
        * `Anomaly Detection Resources <https://github.com/yzhao062/anomaly-detection-resources>`_
        
        
        **Table of Contents**\ :
        
        
        * `Installation <#installation>`_
        * `API Cheatsheet & Reference <#api-cheatsheet--reference>`_
        * `Implemented Algorithms <#implemented-algorithms>`_
        * `Algorithm Benchmark <#algorithm-benchmark>`_
        * `Quick Start for Outlier Detection <#quick-start-for-outlier-detection>`_
        * `Quick Start for Combining Outlier Scores from Various Base Detectors <#quick-start-for-combining-outlier-scores-from-various-base-detectors>`_
        * `How to Contribute <#how-to-contribute>`_
        * `Inclusion Criteria <#inclusion-criteria>`_
        
        
        ----
        
        
        Installation
        ^^^^^^^^^^^^
        
        It is recommended to use **pip** for installation. Please make sure
        **the latest version** is installed, as PyOD is updated frequently:
        
        .. code-block:: bash
        
           pip install pyod            # normal install
           pip install --upgrade pyod  # or update if needed
           pip install --pre pyod      # 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/pyod.git
           cd pyod
           pip install .
        
        
        **Note on Python 2.7**\ :
        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 PyOD's dependent libraries, e.g., scikit-learn, we will
        stop supporting Python 2.7 in the near future (dates are still to be decided). 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/>`_.
        
        
        **Required Dependencies**\ :
        
        
        * Python 2.7, 3.5, 3.6, or 3.7
        * combo>=0.0.8
        * numpy>=1.13
        * numba>=0.35
        * scipy>=0.19.1
        * scikit_learn>=0.19.1
        
        **Optional Dependencies (see details below)**\ :
        
        
        * keras (optional, required for AutoEncoder)
        * matplotlib (optional, required for running examples)
        * pandas (optional, required for running benchmark)
        * tensorflow (optional, required for AutoEncoder, other backend works)
        * xgboost (optional, required for XGBOD)
        
        **Warning 1**\ :
        PyOD has multiple neural network based models, e.g., AutoEncoders, which are
        implemented in Keras. However, PyOD does **NOT** install **keras** and/or
        **tensorflow** for you. This reduces the risk of interfering with your local copies.
        If you want to use neural-net based models, please make sure Keras and a backend library, e.g., TensorFlow, are installed.
        Instructions are provided: `neural-net FAQ <https://github.com/yzhao062/pyod/wiki/Setting-up-Keras-and-Tensorflow-for-Neural-net-Based-models>`_.
        Similarly, models depending on **xgboost**, e.g., XGBOD, would **NOT** enforce xgboost installation by default.
        
        **Warning 2**\ :
        Running examples needs **matplotlib**, which may throw errors in conda
        virtual environment on mac OS. See reasons and solutions `mac_matplotlib <https://github.com/yzhao062/pyod/issues/6>`_.
        
        **Warning 3**\ :
        PyOD contains multiple models that also exist in scikit-learn. However, these two
        libraries' API is not exactly the same--it is recommended to use only one of them
        for consistency but not mix the results. Refer `Differences between sckit-learn and PyOD <https://pyod.readthedocs.io/en/latest/issues.html>`_
        for more information.
        
        
        ----
        
        
        API Cheatsheet & Reference
        ^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        Full API Reference: (https://pyod.readthedocs.io/en/latest/pyod.html). API cheatsheet for all detectors:
        
        
        * **fit(X)**\ : Fit detector.
        * **decision_function(X)**\ : Predict raw anomaly score of X using the fitted detector.
        * **predict(X)**\ : Predict if a particular sample is an outlier or not using the fitted detector.
        * **predict_proba(X)**\ : Predict the probability of a sample being outlier using the fitted detector.
        * **fit_predict(X)**\ : **[Deprecated in V0.6.9]** Fit detector first and then predict whether a particular sample is an outlier or not.
        * **fit_predict_score(X, y)**\ : **[Deprecated in V0.6.9]** Fit the detector, predict on samples, and evaluate the model by predefined metrics, e.g., ROC.
        
        
        Key Attributes of a fitted model:
        
        
        * **decision_scores_**\ : The outlier scores of the training data. The higher, the more abnormal.
          Outliers tend to have higher scores.
        * **labels_**\ : The binary labels of the training data. 0 stands for inliers and 1 for outliers/anomalies.
        
        
        **Note** \ : fit_predict() and fit_predict_score() are deprecated in V0.6.9 due
        to consistency issue and will be removed in V0.8.0. To get the binary labels
        of the training data X_train, one should call clf.fit(X_train) and use
        clf.labels\_, instead of calling clf.predict(X_train).
        
        
        ----
        
        Implemented Algorithms
        ^^^^^^^^^^^^^^^^^^^^^^
        
        PyOD toolkit consists of three major functional groups:
        
        **(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]_
        Linear Model         MCD               Minimum Covariance Determinant (use the mahalanobis distances as the outlier scores)                    1999   [#Hardin2004Outlier]_ [#Rousseeuw1999A]_
        Linear Model         OCSVM             One-Class Support Vector Machines                                                                       2001   [#Scholkopf2001Estimating]_
        Linear Model         LMDD              Deviation-based Outlier Detection (LMDD)                                                                1996   [#Arning1996A]_
        Proximity-Based      LOF               Local Outlier Factor                                                                                    2000   [#Breunig2000LOF]_
        Proximity-Based      COF               Connectivity-Based Outlier Factor                                                                       2002   [#Tang2002Enhancing]_
        Proximity-Based      CBLOF             Clustering-Based Local Outlier Factor                                                                   2003   [#He2003Discovering]_
        Proximity-Based      LOCI              LOCI: Fast outlier detection using the local correlation integral                                       2003   [#Papadimitriou2003LOCI]_
        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]_
        Proximity-Based      SOD               Subspace Outlier Detection                                                                              2009   [#Kriegel2009Outlier]_
        Probabilistic        ABOD              Angle-Based Outlier Detection                                                                           2008   [#Kriegel2008Angle]_
        Probabilistic        FastABOD          Fast Angle-Based Outlier Detection using approximation                                                  2008   [#Kriegel2008Angle]_
        Probabilistic        SOS               Stochastic Outlier Selection                                                                            2012   [#Janssens2012Stochastic]_
        Outlier Ensembles    IForest           Isolation Forest                                                                                        2008   [#Liu2008Isolation]_
        Outlier Ensembles                      Feature Bagging                                                                                         2005   [#Lazarevic2005Feature]_
        Outlier Ensembles    LSCP              LSCP: Locally Selective Combination of Parallel Outlier Ensembles                                       2019   [#Zhao2019LSCP]_
        Outlier Ensembles    XGBOD             Extreme Boosting Based Outlier Detection **(Supervised)**                                               2018   [#Zhao2018XGBOD]_
        Outlier Ensembles    LODA              Lightweight On-line Detector of Anomalies                                                               2016   [#Pevny2016Loda]_
        Neural Networks      AutoEncoder       Fully connected AutoEncoder (use reconstruction error as the outlier score)                                    [#Aggarwal2015Outlier]_ [Ch.3]
        Neural Networks      VAE               Variational AutoEncoder (use reconstruction error as the outlier score)                                 2013   [#Kingma2013Auto]_
        Neural Networks      SO_GAAL           Single-Objective Generative Adversarial Active Learning                                                 2019   [#Liu2019Generative]_
        Neural Networks      MO_GAAL           Multiple-Objective Generative Adversarial Active Learning                                               2019   [#Liu2019Generative]_
        ===================  ================  ======================================================================================================  =====  ========================================
        
        
        **(ii) Outlier Ensembles & Outlier Detector Combination Frameworks**:
        
        ===================  ================  =====================================================================================================  =====  ========================================
        Type                 Abbr              Algorithm                                                                                              Year   Ref
        ===================  ================  =====================================================================================================  =====  ========================================
        Outlier Ensembles                      Feature Bagging                                                                                        2005   [#Lazarevic2005Feature]_
        Outlier Ensembles    LSCP              LSCP: Locally Selective Combination of Parallel Outlier Ensembles                                      2019   [#Zhao2019LSCP]_
        Outlier Ensembles    XGBOD             Extreme Boosting Based Outlier Detection **(Supervised)**                                              2018   [#Zhao2018XGBOD]_
        Outlier Ensembles    LODA              Lightweight On-line Detector of Anomalies                                                              2016   [#Pevny2016Loda]_
        Combination          Average           Simple combination by averaging the scores                                                             2015   [#Aggarwal2015Theoretical]_
        Combination          Weighted Average  Simple combination by averaging the scores with detector weights                                       2015   [#Aggarwal2015Theoretical]_
        Combination          Maximization      Simple combination by taking the maximum scores                                                        2015   [#Aggarwal2015Theoretical]_
        Combination          AOM               Average of Maximum                                                                                     2015   [#Aggarwal2015Theoretical]_
        Combination          MOA               Maximization of Average                                                                                2015   [#Aggarwal2015Theoretical]_
        Combination          Median            Simple combination by taking the median of the scores                                                  2015   [#Aggarwal2015Theoretical]_
        Combination          majority Vote     Simple combination by taking the majority vote of the labels (weights can be used)                     2015   [#Aggarwal2015Theoretical]_
        ===================  ================  =====================================================================================================  =====  ========================================
        
        
        **(iii) Utility Functions**:
        
        ===================  ======================  =====================================================================================================================================================  ======================================================================================================================================
        Type                 Name                    Function                                                                                                                                               Documentation
        ===================  ======================  =====================================================================================================================================================  ======================================================================================================================================
        Data                 generate_data           Synthesized data generation; normal data is generated by a multivariate Gaussian and outliers are generated by a uniform distribution                  `generate_data <https://pyod.readthedocs.io/en/latest/pyod.utils.html#module-pyod.utils.data.generate_data>`_
        Data                 generate_data_clusters  Synthesized data generation in clusters; more complex data patterns can be created with multiple clusters                                              `generate_data_clusters <https://pyod.readthedocs.io/en/latest/pyod.utils.html#pyod.utils.data.generate_data_clusters>`_
        Stat                 wpearsonr               Calculate the weighted Pearson correlation of two samples                                                                                              `wpearsonr <https://pyod.readthedocs.io/en/latest/pyod.utils.html#module-pyod.utils.stat_models.wpearsonr>`_
        Utility              get_label_n             Turn raw outlier scores into binary labels by assign 1 to top n outlier scores                                                                         `get_label_n <https://pyod.readthedocs.io/en/latest/pyod.utils.html#module-pyod.utils.utility.get_label_n>`_
        Utility              precision_n_scores      calculate precision @ rank n                                                                                                                           `precision_n_scores <https://pyod.readthedocs.io/en/latest/pyod.utils.html#module-pyod.utils.utility.precision_n_scores>`_
        ===================  ======================  =====================================================================================================================================================  ======================================================================================================================================
        
        ----
        
        
        Algorithm Benchmark
        ^^^^^^^^^^^^^^^^^^^
        
        **The comparison among of implemented models** is made available below
        (\ `Figure <https://raw.githubusercontent.com/yzhao062/pyod/master/examples/ALL.png>`_\ ,
        `compare_all_models.py <https://github.com/yzhao062/pyod/blob/master/examples/compare_all_models.py>`_\ ,
        `Interactive Jupyter Notebooks <https://mybinder.org/v2/gh/yzhao062/pyod/master>`_\ ).
        For Jupyter Notebooks, please navigate to **"/notebooks/Compare All Models.ipynb"**.
        
        
        .. image:: https://raw.githubusercontent.com/yzhao062/pyod/master/examples/ALL.png
           :target: https://raw.githubusercontent.com/yzhao062/pyod/master/examples/ALL.png
           :alt: Comparision_of_All
        
        A benchmark is supplied for select algorithms to provide an overview of the implemented models.
        In total, 17 benchmark datasets are used for comparison, which
        can be downloaded at `ODDS <http://odds.cs.stonybrook.edu/#table1>`_.
        
        For each dataset, it is first split into 60% for training and 40% for testing.
        All experiments are repeated 10 times independently with random splits.
        The mean of 10 trials is regarded as the final result. Three evaluation metrics
        are provided:
        
        - The area under receiver operating characteristic (ROC) curve
        - Precision @ rank n (P@N)
        - Execution time
        
        Check the latest `benchmark <https://pyod.readthedocs.io/en/latest/benchmark.html>`_. You could replicate this process by running
        `benchmark.py <https://github.com/yzhao062/pyod/blob/master/notebooks/benchmark.py>`_.
        
        
        ----
        
        
        Quick Start for Outlier Detection
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        PyOD has been well acknowledged by the machine learning community with a few featured posts and tutorials.
        
        **Analytics Vidhya**: `An Awesome Tutorial to Learn Outlier Detection in Python using PyOD Library <https://www.analyticsvidhya.com/blog/2019/02/outlier-detection-python-pyod/>`_
        
        **KDnuggets**: `Intuitive Visualization of Outlier Detection Methods <https://www.kdnuggets.com/2019/02/outlier-detection-methods-cheat-sheet.html>`_, `An Overview of Outlier Detection Methods from PyOD <https://www.kdnuggets.com/2019/06/overview-outlier-detection-methods-pyod.html>`_
        
        **Towards Data Science**: `Anomaly Detection for Dummies <https://towardsdatascience.com/anomaly-detection-for-dummies-15f148e559c1>`_
        
        **Computer Vision News (March 2019)**: `Python Open Source Toolbox for Outlier Detection <https://rsipvision.com/ComputerVisionNews-2019March/18/>`_
        
        `"examples/knn_example.py" <https://github.com/yzhao062/pyod/blob/master/examples/knn_example.py>`_
        demonstrates the basic API of using kNN detector. **It is noted that the API across all other algorithms are consistent/similar**.
        
        More detailed instructions for running examples can be found in `examples directory <https://github.com/yzhao062/pyod/blob/master/examples>`_.
        
        
        #. Initialize a kNN detector, fit the model, and make the prediction.
        
           .. code-block:: python
        
        
               from pyod.models.knn import KNN   # kNN detector
        
               # train kNN detector
               clf_name = 'KNN'
               clf = KNN()
               clf.fit(X_train)
        
               # 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
        
               # get the prediction on the test data
               y_test_pred = clf.predict(X_test)  # outlier labels (0 or 1)
               y_test_scores = clf.decision_function(X_test)  # outlier scores
        
        #. Evaluate the prediction by ROC and Precision @ Rank n (p@n).
        
           .. code-block:: python
        
        
               # evaluate and print the results
               print("\nOn Training Data:")
               evaluate_print(clf_name, y_train, y_train_scores)
               print("\nOn Test Data:")
               evaluate_print(clf_name, y_test, y_test_scores)
        
        
        #. See a sample output & visualization.
        
        
           .. code-block:: python
        
        
               On Training Data:
               KNN ROC:1.0, precision @ rank n:1.0
        
               On Test Data:
               KNN ROC:0.9989, precision @ rank n:0.9
        
           .. code-block:: python
        
        
               visualize(clf_name, X_train, y_train, X_test, y_test, y_train_pred,
                   y_test_pred, show_figure=True, save_figure=False)
        
        Visualization (\ `knn_figure <https://raw.githubusercontent.com/yzhao062/pyod/master/examples/KNN.png>`_\ ):
        
        .. image:: https://raw.githubusercontent.com/yzhao062/pyod/master/examples/KNN.png
           :target: https://raw.githubusercontent.com/yzhao062/pyod/master/examples/KNN.png
           :alt: kNN example figure
        
        
        ----
        
        Quick Start for Combining Outlier Scores from Various Base Detectors
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        Outlier detection often suffers from model instability due to its unsupervised
        nature. Thus, it is recommended to combine various detector outputs, e.g., by averaging,
        to improve its robustness. Detector combination is a subfield of outlier ensembles;
        refer [#Aggarwal2017Outlier]_ for more information.
        
        
        Four score combination mechanisms are shown in this demo:
        
        
        #. **Average**: average scores of all detectors.
        #. **maximization**: maximum score across all detectors.
        #. **Average of Maximum (AOM)**: divide base detectors into subgroups and take the maximum score for each subgroup. The final score is the average of all subgroup scores.
        #. **Maximum of Average (MOA)**: divide base detectors into subgroups and take the average score for each subgroup. The final score is the maximum of all subgroup scores.
        
        
        "examples/comb_example.py" illustrates the API for combining the output of multiple base detectors
        (\ `comb_example.py <https://github.com/yzhao062/pyod/blob/master/examples/comb_example.py>`_\ ,
        `Jupyter Notebooks <https://mybinder.org/v2/gh/yzhao062/pyod/master>`_\ ). For Jupyter Notebooks,
        please navigate to **"/notebooks/Model Combination.ipynb"**
        
        
        #. Import models and generate sample data.
        
           .. code-block:: python
        
               from pyod.models.knn import KNN
               from pyod.models.combination import aom, moa, average, maximization
               from pyod.utils.data import generate_data
        
               X, y = generate_data(train_only=True)  # load data
        
        #. First initialize 20 kNN outlier detectors with different k (10 to 200), and get the outlier scores.
        
           .. code-block:: python
        
               # initialize 20 base detectors for combination
               k_list = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140,
                           150, 160, 170, 180, 190, 200]
        
               train_scores = np.zeros([X_train.shape[0], n_clf])
               test_scores = np.zeros([X_test.shape[0], n_clf])
        
               for i in range(n_clf):
                   k = k_list[i]
        
                   clf = KNN(n_neighbors=k, method='largest')
                   clf.fit(X_train_norm)
        
                   train_scores[:, i] = clf.decision_scores_
                   test_scores[:, i] = clf.decision_function(X_test_norm)
        
        #. Then the output scores are standardized into zero mean and unit variance before combination.
           This step is crucial to adjust the detector outputs to the same scale.
        
        
           .. code-block:: python
        
               from pyod.utils.utility import standardizer
               train_scores_norm, test_scores_norm = standardizer(train_scores, test_scores)
        
        #. Then four different combination algorithms are applied as described above.
        
           .. code-block:: python
        
               comb_by_average = average(test_scores_norm)
               comb_by_maximization = maximization(test_scores_norm)
               comb_by_aom = aom(test_scores_norm, 5) # 5 groups
               comb_by_moa = moa(test_scores_norm, 5)) # 5 groups
        
        #. Finally, all four combination methods are evaluated with ROC and Precision @ Rank n.
        
           .. code-block:: bash
        
               Combining 20 kNN detectors
               Combination by Average ROC:0.9194, precision @ rank n:0.4531
               Combination by Maximization ROC:0.9198, precision @ rank n:0.4688
               Combination by AOM ROC:0.9257, precision @ rank n:0.4844
               Combination by MOA ROC:0.9263, precision @ rank n:0.4688
        
        ----
        
        How to Contribute
        ^^^^^^^^^^^^^^^^^
        
        You are welcome to contribute to this exciting project:
        
        
        * Please first check Issue lists for "help wanted" tag and comment the one
          you are interested. We will assign the issue to you.
        
        * Fork the master branch and add your improvement/modification/fix.
        
        * Create a pull request to **development branch** and follow the pull request template `PR template <https://github.com/yzhao062/pyod/blob/master/PULL_REQUEST_TEMPLATE.md>`_
        
        * Automatic tests will be triggered. Make sure all tests are passed. Please make sure all added modules are accompanied with proper test functions.
        
        
        To make sure the code has the same style and standard, please refer to abod.py, hbos.py, or feature_bagging.py for example.
        
        You are also welcome to share your ideas by opening an issue or dropping me an email at zhaoy@cmu.edu :)
        
        
        Inclusion Criteria
        ^^^^^^^^^^^^^^^^^^
        
        Similarly to `scikit-learn <https://scikit-learn.org/stable/faq.html#what-are-the-inclusion-criteria-for-new-algorithms>`_,
        We mainly consider well-established algorithms for inclusion.
        A rule of thumb is at least two years since publication, 50+ citations, and usefulness.
        
        However, we encourage the author(s) of newly proposed models to share and add your implementation into PyOD
        for boosting ML accessibility and reproducibility.
        This exception only applies if you could commit to the maintenance of your model for at least two year period.
        
        
        ----
        
        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.
        
        .. [#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.
        
        .. [#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.
        
        .. [#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.
        
        .. [#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.
        
        .. [#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.
        
Keywords: outlier detection,anomaly detection,outlier ensembles,data mining,neural networks
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
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 :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/x-rst
