Metadata-Version: 2.1
Name: pyanom
Version: 0.0.1b1
Summary: Anomaly detection package
Home-page: https://github.com/ground0state/pyanom
Author: Masafumi Abeta
Author-email: ground0state@gmail.com
License: MIT
Description: pyanom
        ======
        
        |image0| |image1|
        
        This library is Python projects for anomaly detection. This contains
        these techniques.
        
        -  Kullback-Leibler desity estimation
        -  Singular spectrum analysis
        -  Graphical lasso
        -  CUMSUM anomaly detection
        -  Hoteling T2
        -  Directional data anomaly detection
        
        REQUIREMENTS
        ------------
        
        -  numpy
        -  pandas
        
        INSTALLATION
        ------------
        
        .. code:: bash
        
           pip install pyanom
        
        USAGE
        -----
        
        Kullback-Leibler desity estimation
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        .. code:: python
        
           import numpy as np
           from pyanom.density_ratio_estimation import KLDensityRatioEstimation
        
           X_normal = np.loadtxt("../input/normal_data.csv", delimiter=",")
           X_error = np.loadtxt("../input/error_data.csv", delimiter=",")
        
           model = KLDensityRatioEstimation(
               band_width=0.1, learning_rate=0.1, num_iterations=100)
           model.fit(X_normal, X_error)
           anomaly_score = model.predict(X_normal, X_error)
        
        Singular spectrum analysis
        ~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        .. code:: python
        
           import numpy as np
           from pyanom.subspace_methods import SSA
        
           y_error = np.loadtxt("../input/timeseries_error2.csv", delimiter=",")
        
           model = SSA()
           model.fit(y_error, window_size=50, trajectory_n=25, trajectory_pattern=3, test_n=25, test_pattern=2, lag=25)
           anomaly_score = model.score()
        
        Graphical lasso
        ~~~~~~~~~~~~~~~
        
        .. code:: python
        
           import numpy as np
           from pyanom.structure_learning import GraphicalLasso
        
           X_normal = np.loadtxt("../input/normal_data.csv", delimiter=",")
           X_error = np.loadtxt("../input/error_data.csv", delimiter=",")
        
           model = GraphicalLasso()
           model.fit(X_normal, rho=0.01, normalize=True)
           anomaly_score = model.outlier_analysis_score(X_error)
        
        CUSUM anomaly detection
        ~~~~~~~~~~~~~~~~~~~~~~~
        
        .. code:: python
        
           import numpy as np
           from pyanom.outlier_detection import CAD
        
           y_normal = np.loadtxt(
               "../input/timeseries_normal.csv", delimiter=",").reshape(-1, 1)
           y_error = np.loadtxt(
               "../input/timeseries_error.csv", delimiter=",").reshape(-1, 1)
        
           model = CAD()
           model.fit(y_normal, threshold=1)
           anomaly_score = model.score(y_error)
        
        Hoteling T2
        ~~~~~~~~~~~
        
        .. code:: python
        
           import numpy as np
           from pyanom.outlier_detection import HotelingT2
        
           X_normal = np.loadtxt("../input/normal_data.csv", delimiter=",")
           X_error = np.loadtxt("../input/error_data.csv", delimiter=",")
        
           model = HotelingT2()
           model.fit(X_normal)
           anomaly_score = model.score(X_error)
        
        Directional data anomaly DirectionalDataAnomalyDetection
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        .. code:: python
        
           import numpy as np
           from pyanom.outlier_detection import DirectionalDataAnomalyDetection
        
           X_normal = np.loadtxt(
               "../input/normal_direction_data.csv", delimiter=",")
           X_error = np.loadtxt("../input/error_direction_data.csv", delimiter=",")
        
           model = DirectionalDataAnomalyDetection()
           model.fit(X_normal, normalize=True))
           anomaly_score = model.score(X_error)
        
        .. |image0| image:: https://img.shields.io/badge/python-3.6%7C3.7%7C3.8-green?style=plastic
        .. |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
        
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Development Status :: 4 - Beta
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/x-rst
