Skip to main content

Machine Learning interface for High Energy Physics Phenomenology

Project description

PhenoAI is a machine learning interface for applications in High Energy Physics Phenomenology. It allows the user to use trained machine learning algorithms from the PhenoAI algorithm library (see link below) via a consistent interface. Trained algorithms can be stored in a folder with PhenoAI structure using the maker module of the package and shared, making it possible to communicate full-dimensional results so that one does not have to flee to models with lower dimensionality or to project out informative dimensions of the full problem.

Algorithm library

The current version of the package allows the user to use algorithms trained by scikit-learn and keras. These algorithms have to be created by the user, or have to be loaded from an external source like the algorithm library: (work in progress).


Documentation, a quick start guide and a range of examples can be found on the official PhenoAI website:


[coming soon]

To Do

  • Add support for ROOT trained algorithms

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 phenoai, version 0.1.3
Filename, size File type Python version Upload date Hashes
Filename, size phenoai-0.1.3.tar.gz (51.2 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page