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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 .

License

Apache 2.0, library is open-source.

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