hep_ml provides specific machine learning tools for purposes of high energy physics.

• uniform classifiers - the classifiers with low correlation of predictions and mass (or some other variable, or even set of variables)
  • uBoost optimized implementation inside
  • UGradientBoosting (with different losses, specially FlatnessLoss is of high interest)
• 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!)
• hep_ml.splot - minimalistic sPlot-ting
• hep_ml.speedup - building models for fast classification (Bonsai BDT)
• sklearn-compatibility of estimators.

Basic installation:

pip install hep_ml

If you're new to python and never used pip, first install scikit-learn with these instructions.

To use latest development version, clone it and install with pip:

git clone https://github.com/arogozhnikov/hep_ml.git
cd hep_ml
pip install .

Local testing:

nosetests tests/

• documentation
• notebooks, code examples
  • you may need to install ROOT and root_numpy to run those
• repository
• issue tracker

Libraries you'll require to make your life easier and HEPpier.

• IPython Notebook — web-shell for python
• scikit-learn — general-purpose library for machine learning in python
• numpy — 'MATLAB in python', vector operation in python. Use it you need to perform any number crunching.
• theano — optimized vector analytical math engine in python
• ROOT — main data format in high energy physics
• root_numpy — python library to deal with ROOT files (without pain)

Apache 2.0, hep_ml is an open-source library.

Linux, Mac OS X and Windows are supported.

hep_ml supports both python 2 and python 3.