A package for machine learning studies with HEP
When installing make sure to initialise and update the submodule:
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git submodule init -
git submodule update
Start by turning your root trees into numpy arrays! Then carry out some python based data analysis.
Setup with anaconda:
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Install anaconda v4 (I couldn't get it to work with v5) e.g. using
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bash /nfs/dust/cms/user/elwoodad/Anaconda2-4.4.0-Linux-x86_64.sh -
choose whether you want anaconda added to bashrc, overwriting system default packages (i didn't)
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point the anacondaSetup.sh file to your install (see mine as example)
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Make a conda environment with all the software you'll need (hepML)
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When running on the naf you can do:
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conda env create -f environment.yml -
Or you can do a generic install of all the relevant tools (e.g. on maxwell max-display):
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conda create -n hepML -c nlesc root root_numpy keras pandas seaborn scikit-learn tensorflow tensorflow-gpu pydot -
Only include tensorflow-gpu if a graphics card is available, otherwise there can be errors
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NOTE: I've had problems on some systems getting root to install, so you can optionally leave out root and root_numpy if there is an error and install them separately
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root and numpy are installed using instructions in https://nlesc.gitbooks.io/cern-root-conda-recipes/content/index.html , but the environment file should do everything
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Activate your environment and you're good to go
source activate hepML
An example script showing off some of the basic features is available:
python exampleScript.py
A more sophisticated script that produced the results in https://arxiv.org/abs/1806.00322 is available:
python run.py