Metadata-Version: 2.0
Name: kenchi
Version: 0.8.0
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
Platform: UNKNOWN
Requires-Dist: matplotlib (>=2.1.1)
Requires-Dist: networkx (>=2.1)
Requires-Dist: numpy (>=1.14.0)
Requires-Dist: pandas (>=0.22.0)
Requires-Dist: scikit-learn (>=0.19.1)
Requires-Dist: scipy (>=1.0.0)
Requires-Dist: nose (>=1.3.7)

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kenchi
======

This is a set of python modules for anomaly detection.

Requirements
------------

-  Python (>=3.6)
-  matplotlib (>=2.1.1)
-  networkx (>=2.0)
-  numpy (>=1.14.0)
-  pandas (>=0.22.0)
-  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

Usage
-----

.. code:: python

    import matplotlib.pyplot as plt
    from kenchi.datasets import load_breast_cancer
    from kenchi.outlier_detection import *

    f, ax = plt.subplots()

    # Load the breast cancer wisconsin dataset
    X, y  = load_breast_cancer(random_state=0)

    for det in [FastABOD(), KNN(), MiniBatchKMeans(), PCA(), KDE()]:
        # Fit the model, and plot the ROC curve
        det.fit(X).plot_roc_curve(X, y, ax=ax)

    plt.show()

.. image:: https://raw.githubusercontent.com/Y-oHr-N/kenchi/master/docs/images/plot_roc_curve.png

License
-------

The MIT License (MIT)

Copyright (c) 2017 Kon


