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.
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
git clone https://github.com/arogozhnikov/hep_ml.git
pip install .
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)
hep_ml is an open-source library.
Linux, Mac OS X and Windows are supported.
hep_ml supports both python 2 and python 3.