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"description": ".. image:: https://img.shields.io/pypi/v/kenchi.svg\n :target: https://pypi.org/project/kenchi\n\n.. image:: https://img.shields.io/pypi/pyversions/kenchi.svg\n :target: https://pypi.org/project/kenchi\n\n.. image:: https://img.shields.io/pypi/l/kenchi.svg\n :target: https://github.com/HazureChi/kenchi/blob/master/LICENSE\n\n.. image:: https://img.shields.io/conda/v/Y_oHr_N/kenchi.svg\n :target: https://anaconda.org/Y_oHr_N/kenchi\n\n.. image:: https://img.shields.io/conda/pn/Y_oHr_N/kenchi.svg\n :target: https://anaconda.org/Y_oHr_N/kenchi\n\n.. image:: https://img.shields.io/readthedocs/kenchi/stable.svg\n :target: http://kenchi.rtfd.io\n\n.. image:: https://img.shields.io/travis/HazureChi/kenchi/master.svg\n :target: https://travis-ci.org/HazureChi/kenchi\n\n.. image:: https://img.shields.io/appveyor/ci/Y-oHr-N/kenchi/master.svg\n :target: https://ci.appveyor.com/project/Y-oHr-N/kenchi/branch/master\n\n.. image:: https://img.shields.io/coveralls/github/HazureChi/kenchi/master.svg\n :target: https://coveralls.io/github/HazureChi/kenchi?branch=master\n\n.. image:: https://img.shields.io/codeclimate/maintainability/HazureChi/kenchi.svg\n :target: https://codeclimate.com/github/HazureChi/kenchi\n\n.. image:: https://mybinder.org/badge.svg\n :target: https://mybinder.org/v2/gh/HazureChi/kenchi/master?urlpath=lab\n\nkenchi\n======\n\nThis is a scikit-learn compatible library for anomaly detection.\n\nDependencies\n------------\n\n- Required dependencies\n #. `numpy>=1.13.3 `_ (BSD 3-Clause License)\n #. `scikit-learn>=0.20.0 `_ (BSD 3-Clause License)\n #. `scipy>=0.19.1 `_ (BSD 3-Clause License)\n- Optional dependencies\n #. `matplotlib>=2.1.2 `_ (PSF-based License)\n #. `networkx>=2.2 `_ (BSD 3-Clause License)\n\nInstallation\n------------\n\nYou can install via ``pip``\n\n::\n\n pip install kenchi\n\nor ``conda``.\n\n::\n\n conda install -c y_ohr_n kenchi\n\nAlgorithms\n----------\n\n- Outlier detection\n #. FastABOD [#kriegel08]_\n #. LOF [#breunig00]_ (scikit-learn wrapper)\n #. KNN [#angiulli02]_, [#ramaswamy00]_\n #. OneTimeSampling [#sugiyama13]_\n #. HBOS [#goldstein12]_\n- Novelty detection\n #. OCSVM [#scholkopf01]_ (scikit-learn wrapper)\n #. MiniBatchKMeans\n #. IForest [#liu08]_ (scikit-learn wrapper)\n #. PCA\n #. GMM (scikit-learn wrapper)\n #. KDE [#parzen62]_ (scikit-learn wrapper)\n #. SparseStructureLearning [#ide09]_\n\nExamples\n--------\n\n.. code:: python\n\n import matplotlib.pyplot as plt\n import numpy as np\n from kenchi.datasets import load_pima\n from kenchi.outlier_detection import *\n from kenchi.pipeline import make_pipeline\n from sklearn.model_selection import train_test_split\n from sklearn.preprocessing import StandardScaler\n\n np.random.seed(0)\n\n scaler = StandardScaler()\n\n detectors = [\n FastABOD(novelty=True, n_jobs=-1), OCSVM(),\n MiniBatchKMeans(), LOF(novelty=True, n_jobs=-1),\n KNN(novelty=True, n_jobs=-1), IForest(n_jobs=-1),\n PCA(), KDE()\n ]\n\n # Load the Pima Indians diabetes dataset.\n X, y = load_pima(return_X_y=True)\n X_train, X_test, _, y_test = train_test_split(X, y)\n\n # Get the current Axes instance\n ax = plt.gca()\n\n for det in detectors:\n # Fit the model according to the given training data\n pipeline = make_pipeline(scaler, det).fit(X_train)\n\n # Plot the Receiver Operating Characteristic (ROC) curve\n pipeline.plot_roc_curve(X_test, y_test, ax=ax)\n\n # Display the figure\n plt.show()\n\n.. figure:: https://raw.githubusercontent.com/HazureChi/kenchi/master/docs/images/readme.png\n :align: center\n\nReferences\n----------\n\n.. [#angiulli02] Angiulli, F., and Pizzuti, C.,\n `\"Fast outlier detection in high dimensional spaces,\" `_\n In Proceedings of PKDD, pp. 15-27, 2002.\n\n.. [#breunig00] Breunig, M. M., Kriegel, H.-P., Ng, R. T., and Sander, J.,\n `\"LOF: identifying density-based local outliers,\" `_\n In Proceedings of SIGMOD, pp. 93-104, 2000.\n\n.. [#dua17] Dua, D., and Karra Taniskidou, E.,\n `\"UCI Machine Learning Repository,\" `_\n 2017.\n\n.. [#goix16] Goix, N.,\n `\"How to evaluate the quality of unsupervised anomaly detection algorithms?\" `_\n In ICML Anomaly Detection Workshop, 2016.\n\n.. [#goldstein12] Goldstein, M., and Dengel, A.,\n \"Histogram-based outlier score (HBOS): A fast unsupervised anomaly detection algorithm,\"\n KI: Poster and Demo Track, pp. 59-63, 2012.\n\n.. [#ide09] Ide, T., Lozano, C., Abe, N., and Liu, Y.,\n `\"Proximity-based anomaly detection using sparse structure learning,\" `_\n In Proceedings of SDM, pp. 97-108, 2009.\n\n.. [#kriegel11] Kriegel, H.-P., Kroger, P., Schubert, E., and Zimek, A.,\n `\"Interpreting and unifying outlier scores,\" `_\n In Proceedings of SDM, pp. 13-24, 2011.\n\n.. [#kriegel08] Kriegel, H.-P., Schubert, M., and Zimek, A.,\n `\"Angle-based outlier detection in high-dimensional data,\" `_\n In Proceedings of SIGKDD, pp. 444-452, 2008.\n\n.. [#lee03] Lee, W. S, and Liu, B.,\n \"Learning with positive and unlabeled examples using weighted Logistic Regression,\"\n In Proceedings of ICML, pp. 448-455, 2003.\n\n.. [#liu08] Liu, F. T., Ting, K. M., and Zhou, Z.-H.,\n `\"Isolation forest,\" `_\n In Proceedings of ICDM, pp. 413-422, 2008.\n\n.. [#parzen62] Parzen, E.,\n `\"On estimation of a probability density function and mode,\" `_\n Ann. Math. Statist., 33(3), pp. 1065-1076, 1962.\n\n.. [#ramaswamy00] Ramaswamy, S., Rastogi, R., and Shim, K.,\n `\"Efficient algorithms for mining outliers from large data sets,\" `_\n In Proceedings of SIGMOD, pp. 427-438, 2000.\n\n.. [#scholkopf01] Scholkopf, B., Platt, J. C., Shawe-Taylor, J. C., Smola, A. J., and Williamson, R. C.,\n `\"Estimating the Support of a High-Dimensional Distribution,\" `_\n Neural Computation, 13(7), pp. 1443-1471, 2001.\n\n.. [#sugiyama13] Sugiyama, M., and Borgwardt, K.,\n `\"Rapid distance-based outlier detection via sampling,\" `_\n Advances in NIPS, pp. 467-475, 2013.\n\n\n",
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