kenchi 0.2.0

A set of python modules for anomaly detection

kenchi

This is a set of python modules for anomaly detection.

Requirements

• Python (>=3.5)

• numpy (>=1.11.2)

• scipy (>=0.18.1)

• scikit-learn (>=0.18.0)

Installation

You can install via pip.

pip install kenchi

Usage

>>> import numpy as np
>>> from kenchi import GaussianDetector
>>> train_size = 1000
>>> test_size  = 100
>>> n_outliers = 10
>>> n_features = 10
>>> rnd        = np.random.RandomState(0)
>>> mean       = np.zeros(n_features)
>>> cov        = np.eye(n_features)
>>> X_train    = rnd.multivariate_normal(mean, cov, train_size)
>>> X_test     = np.concatenate((
...     rnd.multivariate_normal(mean, cov, test_size - n_outliers),
...     rnd.uniform(-10.0, 10.0, size=(n_outliers, n_features))
... ))
>>> det        = GaussianDetector(use_method_of_moments=True).fit(X_train)
>>> det.predict(X_test)
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)

License

The MIT License (MIT)

Copyright (c) 2017 Kon