Skip to main content

Isolation Forest for anomaly detection

Project description

# iso_forest

This is a simple package implementation for the isolation forest method described (among other places) in this [paper](icdm08b.pdf) for detecting anomalies and outliers from a data point distribution.

## Extended isolation forest

For an extended version of this algorithm that produces more precise scoring maps please visit this repository

[https://github.com/sahandha/eif](https://github.com/sahandha/eif)/

## Installation

pip install iso_forest

or directly from the Github repository

pip install git+https://github.com/mgckind/iso_forest.git

It supports python2 and python3

## Requirements

  • numpy

No extra requirements are needed for the algorithm.

In addition, it also contains means to draw the trees created using the [igraph](http://igraph.org/) library.

## Use Examples

See these 2 notebooks examples on how to use it

  • [basics](demo_iforest.ipynb)
  • [tree visualization and anomaly PDFs](demo_vis_pdf.ipynb)

## Releases

### v1.0.3

  • Initial Release

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for iso-forest, version 1.0.3
Filename, size File type Python version Upload date Hashes
Filename, size iso_forest-1.0.3-py2.py3-none-any.whl (4.9 kB) File type Wheel Python version 2.7 Upload date Hashes View hashes
Filename, size iso_forest-1.0.3.tar.gz (3.4 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page