Skip to main content

Combination Robust Cut Forests

Project description

Combination Robust Cut Forests

CodeFactor PyPI version

Isolation Forests [Liu+2008] and Robust Random Cut Trees [Guha+2016] are very similar in many ways, as outlined in the supporting overview. 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.

The tests can be run from pytest with python3 setup.py test.

Tasks

  • complete basic implementation
  • provide clear documentation and usage instructions
  • 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

Project details


Download files

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

Source Distribution

crcf-0.0.3.tar.gz (10.6 kB view hashes)

Uploaded Source

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page