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Description:

inne 0.2.0

iNNE
iNNE - Isolation-based anomaly detection using nearest-neighbor ensembles.
Based on the paper:

Tharindu R., et al. Isolation-based anomaly detection using nearest-neighbor ensembles. Computational Intelligence (2018)

Matlab code of iNNE:

https://github.com/zhuye88/iNNE

Introduction to the paper:

https://www.jianshu.com/p/379a5898beb6

Abstract of the paper:

The first successful isolation-based anomaly detector, ie, iForest, uses
trees as a means to perform isolation. Although it has been shown to
have advantages over existing anomaly detectors, we have identified 4
weaknesses, ie, its inability to detect local anomalies, anomalies with
a high percentage of irrelevant attributes, anomalies that are masked by
axis-parallel clusters, and anomalies in multimodal data sets. To
overcome these weaknesses, this paper shows that an alternative
isolation mechanism is required and thus presents iNNE or isolation
using Nearest Neighbor Ensemble. Although relying on nearest neighbors,
iNNE runs significantly faster than the existing nearest neighbor-based
methods such as the local outlier factor, especially in data sets having
thousands of dimensions or millions of instances. This is because the
proposed method has linear time complexity and constant space
complexity.

Documentation, including tutorials, are available on ReadTheDocs at
https://inne.readthedocs.io.

Installing
PyPI install, presuming you have an up to date pip.
pip install inne
For a manual install of the latest code directly from GitHub:
pip install git+https://github.com/xhan97/inne.git
Alternatively download the package, install requirements, and manually run the installer:
wget https://codeload.github.com/xhan97/inne/zip/refs/heads/master
unzip inne-master.zip
rm inne-master.zip
cd inne-master

pip install -r requirements.txt

python setup.py install

How to use iNNE
The inne package inherits from sklearn classes, and thus drops in neatly
next to other sklearn with an identical calling API. Similarly it
supports input in a variety of formats: an array (or pandas dataframe) of shape (num_samples x num_features).
from inne import IsolationNNE
from sklearn.datasets import make_blobs

data, _ = make_blobs(1000)

clf = IsolationNNE(n_estimators=200, max_samples=16)
clf.fit(data)
anomaly_labels = clf.predict(data)

Running the Tests
The package tests can be run after installation using the command:
pip install pytest
or, if pytest is installed:
pytest inne/tests
If one or more of the tests fail, please report a bug at https://github.com/xhan97/inne/issues

Python Version
Python 3 is recommend the better option if it is available to you.

Citing
If you have used this codebase in a scientific publication and wish to
cite it, please use the following publication (Bibtex format):
@article{bandaragoda2018isolation,
title={Isolation-based anomaly detection using nearest-neighbor ensembles},
author={Bandaragoda, Tharindu R and Ting, Kai Ming and Albrecht, David and Liu, Fei Tony and Zhu, Ye and Wells, Jonathan R},
journal={Computational Intelligence},
volume={34},
number={4},
pages={968-998},
year={2018},
publisher={Wiley Online Library} }

How to contribute
Thanks for your interest in contributing to inne. A guide is shown in inne’s Documentation.

License
BSD license