hep_ml

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.

Plain and simple:

pip install hep_ml

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

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.

arogozhnikov/hep_ml