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        "author": "Masafumi Abeta",
        "author_email": "ground0state@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3.8"
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        "description": "pyanom\n======\n\n|image0| |image1|\n\nThis library is Python projects for anomaly detection. This contains\nthese techniques.\n\n-  Kullback-Leibler desity estimation\n-  Singular spectrum analysis\n-  Graphical lasso\n-  CUMSUM anomaly detection\n-  Hoteling T2\n-  Directional data anomaly detection\n\nREQUIREMENTS\n------------\n\n-  numpy\n-  pandas\n-  scikit-learn\n-  scipy\n\nINSTALLATION\n------------\n\n.. code:: bash\n\n   pip install pyanom\n\nUSAGE\n-----\n\nKullback-Leibler desity estimation\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n.. code:: python\n\n   import numpy as np\n   from pyanom.density_ratio_estimation import KLDensityRatioEstimator\n\n   X_normal = np.loadtxt(\"./data/normal_data.csv\", delimiter=\",\")\n   X_error = np.loadtxt(\"./data/error_data.csv\", delimiter=\",\")\n\n   model = KLDensityRatioEstimator(\n      band_width=h, lr=0.001, max_iter=100000)\n   model.fit(X_normal, X_error)\n   anomaly_score = model.score(X_normal, X_error)\n\nSingular spectrum analysis\n~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n.. code:: python\n\n   import numpy as np\n   from pyanom.subspace_methods import SSA\n\n   y_error = np.loadtxt(\"./data/timeseries_error2.csv\", delimiter=\",\")\n\n   model = SSA(window_size=50, trajectory_n=25, trajectory_pattern=3, test_n=25, test_pattern=2, lag=25)\n   model.fit(y_error)\n   anomaly_score = model.score()\n\nGraphical lasso\n~~~~~~~~~~~~~~~\n\n.. code:: python\n\n   import numpy as np\n   from pyanom.structure_learning import GraphicalLasso\n\n   X_normal = np.loadtxt(\"./data/normal_data.csv\", delimiter=\",\")\n   X_error = np.loadtxt(\"./data/error_data.csv\", delimiter=\",\")\n\n   model = GraphicalLasso(rho=0.1)\n   model.fit(X_normal)\n   anomaly_score = model.score(X_error)\n\nDirect learning sparse changes\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n.. code:: python\n\n   from pyanom.structure_learning import DirectLearningSparseChanges\n\n   model = DirectLearningSparseChanges(\n      lambda1=0.1, lambda2=0.3, max_iter=10000)\n   model.fit(X_normal, X_error)\n   pmatrix_diff = model.get_sparse_changes()\n\nCUSUM anomaly detection\n~~~~~~~~~~~~~~~~~~~~~~~\n\n.. code:: python\n\n   import numpy as np\n   from pyanom.outlier_detection import CAD\n\n   y_normal = np.loadtxt(\n      \"./data/timeseries_normal.csv\", delimiter=\",\").reshape(-1, 1)\n   y_error = np.loadtxt(\n      \"./data/timeseries_error.csv\", delimiter=\",\").reshape(-1, 1)\n\n   model = CAD(threshold=1.0)\n   model.fit(y_normal)\n   anomaly_score = model.score(y_error)\n\nHoteling T2\n~~~~~~~~~~~\n\n.. code:: python\n\n   import numpy as np\n   from pyanom.outlier_detection import HotelingT2\n\n   X_normal = np.loadtxt(\"./data/normal_data.csv\", delimiter=\",\")\n   X_error = np.loadtxt(\"./data/error_data.csv\", delimiter=\",\")\n\n   model = HotelingT2()\n   model.fit(X_normal)\n   anomaly_score = model.score(X_error)\n\nDirectional data anomaly DirectionalDataAnomalyDetection\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n.. code:: python\n\n   import numpy as np\n   from pyanom.outlier_detection import AD3\n\n   X_normal = np.loadtxt(\n      \"./data/normal_direction_data.csv\", delimiter=\",\")\n   X_error = np.loadtxt(\"./data/error_direction_data.csv\", delimiter=\",\")\n\n   model = AD3()\n   model.fit(X_normal, normalize=True)\n   anomaly_score = model.score(X_error)\n\n.. |image0| image:: https://img.shields.io/badge/python-3.6%7C3.7%7C3.8-green?style=plastic\n.. |image1| image:: https://img.shields.io/badge/dynamic/json.svg?label=version&colorB=5f9ea0&query=$.version&uri=https://raw.githubusercontent.com/ground0state/pyanom/master/package.json&style=plastic\n\n\n\n",
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