This repository provides an Python implementation of the "Generalized Isolation Forest" (GIF) algorithm for unsupervised detection of outliers in data. GIF has originally been proposed in:
Buschjäger, S., Honysz, PJ. & Morik, K. Randomized outlier detection with trees. International Journal of Data Science and Analytics (2020). https://doi.org/10.1007/s41060-020-00238-w
More information on this package, including a quick start guide, examples and how to use this within C++, is given here.
We provide Linux wheel packages for various Python versions, which can be installed like this:
pip install genif
Windows or macOS builds are currently not provided. Please resort to installation from source, if you are either using Windows or macOS.
- GCC >= 5.4.0 (older versions or other compilers such as Clang or ICC may work, but have not been tested yet.)
- CMake >= 3.5.1
- OpenMP
- Recursively clone this repository by issueing
git clone --recurse-submodules git@github.com:philippjh/genif.git - Change your working directory to the root of the repository. Run
pip3 install . - The Python package manager will now build and install the package.
Part of the work on this paper has been supported by Deutsche Forschungsgemeinschaft (DFG) within the Collaborative Research Center SFB 876 "Providing Information by Resource-Constrained Analysis", project A1, http://sfb876.tu-dortmund.de and by the German Competence Center for Machine Learning Rhine Ruhr (ML2R, https://www.ml2r.de, 01IS18038A), funded by the German Federal Ministry for Education and Research.