Machine Learning for High Energy Physics
Project description
hep_ml provides specific machine learning tools for purposes of high energy physics (written in python).
Main points
uniform classifiers - the classifiers with low correlation of predictions and mass (or some other variable(s))
uBoost optimized implementation inside
UGradientBoosting (with different losses, specially FlatnessLoss is very interesting)
measures of uniformity (see hep_ml.metrics)
advanced losses for classification, regression and ranking for UGradientBoosting (see hep_ml.losses).
hep_ml.nnet - theano-based flexible neural networks
hep_ml.reweight - reweighting multidimensional distributions (multi here means 2, 3, 5 and more dimensions - see GBReweighter!)
sklearn-compatibility of estimators.
Installation
pip install hep_ml
To use latest version, clone it and install with pip:
git clone https://github.com/arogozhnikov/hep_ml.git
cd hep_ml
sudo pip install .
Links
License
Apache 2.0, library is open-source.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for hep_ml-0.4.0-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | af0e0f3fb526dd65d6e764606f10e4d31d21e6b1c8ff00fe67346def65b1a4e0 |
|
MD5 | d40be35777ec6de0f324b0f3a469a0ba |
|
BLAKE2b-256 | 9b55f2dd34b0bd88a4d67bc5b1032b33a5ce3cbde4941f9b81c090bb662af14b |