hep_ml provides specific machine learning tools for purposes of high energy physics (written in python).

• 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.

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 .

• documentation
• notebook examples
• repository
• issue tracker
• old repository

Libraries you'll require to make your life easier.

• IPython Notebook — web-shell for python
• scikit-learn — general-purpose library for machine learning in python
• REP — python wrappers around different machine learning libraries (including TMVA) + goodies, required to plot learning curves and reports after classification. Required to execute howtos from this repository
• 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, library is open-source.