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Extended Isolation Forest for anomaly detection

Project description

<a href=”https://github.com/sahandha/eif/releases/tag/v1.0.2”> <img src=”https://img.shields.io/badge/release-v1.0.2-blue.svg” alt=”latest release” /></a><a href=”https://pypi.org/project/eif/1.0.2/”><img src=”https://img.shields.io/badge/pypi-v1.0.2-orange.svg” alt=”pypi version”/></a> # Extended Isolation Forest

This is a simple package implementation for the Extended Isolation Forest method. It is an improvement on the original algorithm Isolation Forest which is described (among other places) in this [paper](icdm08b.pdf) for detecting anomalies and outliers from a data point distribution. The original code can be found at [https://github.com/mgckind/iso_forest](https://github.com/mgckind/iso_forest)

For an N dimensional data set, Extended Isolation Forest has N levels of extension, with 0 being identical to the case of standard Isolation Forest, and N-1 being the fully extended version.

## Installation

pip install eif

or directly from the repository

pip install git+https://github.com/sahandha/eif.git

## Requirements

  • numpy

No extra requirements are needed. In addition, it also contains means to draw the trees created using the [igraph](http://igraph.org/) library. See the example for tree visualizations

## Use

See these notebooks for examples on how to use it

  • [Basics](Notebooks/IsolationForest.ipynb)
  • [3D Example](Notebooks/general_3D_examples.ipynb)
  • [Tree visualizations](Notebooks/TreeVisualization.ipynb)

## Release

### v1.0.2 #### 2018-OCT-01 - Added documentation, examples and software paper

### v1.0.1 #### 2018-AUG-08 - Bugfix for multidimensional data

### v1.0.0 #### 2018-JUL-15 - Initial Release

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