Metadata-Version: 1.0
Name: kenchi
Version: 0.9.0
Summary: A set of python modules for anomaly detection
Home-page: http://kenchi.readthedocs.io
Author: Kon
Author-email: kon.y.ohr.n@gmail.com
License: MIT
Description-Content-Type: UNKNOWN
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        kenchi
        ======
        
        This is a set of python modules for anomaly detection.
        
        Dependencies
        ------------
        
        -  Python (>=3.6)
        -  matplotlib (>=2.1.1)
        -  networkx (>=2.0)
        -  numpy (>=1.14.0)
        -  scikit-learn (>=0.19.1)
        -  scipy (>=1.0.0)
        
        Installation
        ------------
        
        You can install via ``pip``
        
        ::
        
            pip install kenchi
        
        or ``conda``.
        
        ::
        
            conda install -c y_ohr_n kenchi
        
        Anomaly detection methods
        -------------------------
        
        #. FastABOD [#kriegel08]_
        #. MiniBatchKMeans
        #. KNN [#angiulli02]_, [#ramaswamy00]_
        #. OneTimeSampling [#sugiyama13]_
        #. LOF [#breunig00]_
        #. IForest [#liu08]_
        #. PCA
        #. GMM
        #. HBOS [#goldstein12]_
        #. KDE
        #. SparseStructureLearning [#ide09]_
        
        Examples
        --------
        
        .. code:: python
        
            import matplotlib.pyplot as plt
            from kenchi.datasets import load_wdbc
            from kenchi.outlier_detection import *
        
            # Load the breast cancer wisconsin dataset
            X, y      = load_wdbc(random_state=0)
        
            f, ax     = plt.subplots()
            detectors = [
                FastABOD(),
                MiniBatchKMeans(random_state=0),
                LOF(),
                KNN(),
                IForest(random_state=0),
                PCA(),
                KDE()
            ]
        
            for det in detectors:
                # Fit the model, and plot the ROC curve
                det.fit(X).plot_roc_curve(X=None, y=y, ax=ax)
        
            plt.show()
        
        .. image:: https://raw.githubusercontent.com/Y-oHr-N/kenchi/master/docs/images/plot_roc_curve.png
        
        License
        -------
        
        BSD 3-Clause "New" or "Revised" License
        
        Copyright (c) 2018, Kon
        
        References
        ----------
        
        .. [#angiulli02] Angiulli, F., and Pizzuti, C.,
            `"Fast outlier detection in high dimensional spaces," <https://doi.org/10.1007/3-540-45681-3_2>`_
            In Proceedings of PKDD'02, pp. 15-27, 2002.
        
        .. [#breunig00] Breunig, M. M., Kriegel, H.-P., Ng, R. T., and Sander, J.,
            `"LOF: identifying density-based local outliers," <https://doi.org/10.1145/335191.335388>`_
            In ACM sigmod record, pp. 93-104, 2000.
        
        .. [#goldstein12] Goldstein, M., and Dengel, A.,
            "Histogram-based outlier score (HBOS): A fast unsupervised anomaly detection algorithm,"
            KI'12: Poster and Demo Track, pp. 59-63, 2012.
        
        .. [#ide09] Ide, T., Lozano, C., Abe N., and Liu, Y.,
            `"Proximity-based anomaly detection using sparse structure learning," <https://doi.org/10.1137/1.9781611972795.9>`_
            In Proceedings of SDM'09, pp. 97-108, 2009.
        
        .. [#kriegel11] Kriegel, H.-P., Kroger, P., Schubert E., and Zimek, A.,
            `"Interpreting and unifying outlier scores," <https://doi.org/10.1137/1.9781611972818.2>`_
            In Proceedings of SDM'11, pp. 13-24, 2011.
        
        .. [#kriegel08] Kriegel, H.-P., Schubert M., and Zimek, A.,
            `"Angle-based outlier detection in high-dimensional data," <https://doi.org/10.1145/1401890.1401946>`_
            In Proceedings of SIGKDD'08, pp. 444-452, 2008.
        
        .. [#liu08] Liu, F. T., Ting K. M., and Zhou, Z.-H.,
            `"Isolation forest," <https://doi.org/10.1145/2133360.2133363>`_
            In Proceedings of ICDM'08, pp. 413-422, 2008.
        
        .. [#ramaswamy00] Ramaswamy, S., Rastogi R., and Shim, K.,
            `"Efficient algorithms for mining outliers from large data sets," <https://doi.org/10.1145/335191.335437>`_
            In Proceedings of SIGMOD'00, pp. 427-438, 2000.
        
        .. [#sugiyama13] Sugiyama M., and Borgwardt, K.,
            "Rapid distance-based outlier detection via sampling,"
            Advances in NIPS'13, pp. 467-475, 2013.
        
Platform: UNKNOWN
