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!)
hep_ml.splot - minimalistic sPlot-ting
hep_ml.speedup - building models for fast classification (Bonsai BDT)
sklearn-compatibility of estimators.
Installation
Basic installation:
pip install hep_ml
If you’re new to python and don’t 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
sudo pip install .
Links
License
Apache 2.0, library is open-source.
Platforms
Linux, Mac OS X and Windows are supported.
Project details
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