https://github.com/jmbhughes/crcf

Combination Robust Cut Forests: Merging Isolation Forests and Robust Random Cut Forests
https://github.com/jmbhughes/crcf

anomaly-detection isolation-forest machine-learning robust-random-cut-forest trees

Last synced: 2 months ago
JSON representation

Combination Robust Cut Forests: Merging Isolation Forests and Robust Random Cut Forests

• Host: GitHub
• URL: https://github.com/jmbhughes/crcf
• Owner: jmbhughes
• License: mit
• Created: 2018-12-03T03:40:43.000Z (over 6 years ago)
• Default Branch: main
• Last Pushed: 2023-08-09T03:59:52.000Z (almost 2 years ago)
• Last Synced: 2025-04-16T03:09:36.188Z (2 months ago)
• Topics: anomaly-detection, isolation-forest, machine-learning, robust-random-cut-forest, trees
• Language: Python
• Homepage: https://jmbhughes.github.io/crcf/
• Size: 2.51 MB
• Stars: 14
• Watchers: 2
• Forks: 3
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Combination Robust Cut Forests

[![CodeFactor](https://www.codefactor.io/repository/github/jmbhughes/crcf/badge)](https://www.codefactor.io/repository/github/jmbhughes/crcf)

[![PyPI version](https://badge.fury.io/py/crcf.svg)](https://badge.fury.io/py/crcf)

[![codecov](https://codecov.io/gh/jmbhughes/crcf/branch/main/graph/badge.svg?token=YBZERHDU75)](https://codecov.io/gh/jmbhughes/crcf)

Isolation Forests **[Liu+2008]** and Robust Random Cut Trees **[Guha+2016]** are very similar in many ways, 

as outlined in the [supporting overview](overview.pdf). Most notably, they are extremes

of the same outlier scoring function: 

$$\theta \textrm{Depth} + (1 - \theta) \textrm{[Co]Disp}$$ 

The combination robust cut forest allows you to combine both scores by using an theta other than 0 or 1. 

# Install

You can install with through `pip install crcf`. Alternatively, you can download the repository and run 

`python3 setup.py install` or `pip3 install .` Please note that this package uses features from Python 3.7+

and is not compatible with earlier Python versions. 

# Tasks

- [X] complete basic implementation

- [X] provide clear documentation and usage instructions

- [ ] ensure interface allows for fitting and scoring on multiple points at the same time

- [ ] implement a better saving method than pickling

- [ ] use random tests with hypothesis

- [ ] implement tree down in cython

- [ ] accelerate forests with multi-threading

- [ ] incorporate categorical variable support, including categorical rules

- [ ] complete the write-up document with a benchmarking of performance

# References

- **[Liu+2008]**: [Liu, Fei Tony, Kai Ming Ting, and Zhi-Hua Zhou. 

"Isolation forest." In 2008 Eighth IEEE International Conference on Data Mining, 

pp. 413-422. IEEE, 2008.](https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/icdm08b.pdf?q=isolation-forest)

- **[Guha+2016]**: [Guha, Sudipto, Nina Mishra, Gourav Roy, and Okke Schrijvers. 

"Robust random cut forest based anomaly detection on streams." 

In International conference on machine learning, pp. 2712-2721. 2016.](http://proceedings.mlr.press/v48/guha16.pdf)