inne 0.0.1

A Isolation based anomaly detection algorithms.

iNNE

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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.

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Installing

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PyPI install, presuming you have an up to date pip.

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

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How to use iNNE

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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)

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Running the Tests

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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

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Python Version

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

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Citing

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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} }

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How to contribute

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

License

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BSD license