Skip to main content

Machine Learning for High Energy Physics

Project description

hep_ml

hep_ml provides specific machine learning tools for purposes of high energy physics.

travis status appveyor status PyPI version Documentation DOI

hep_ml, python library for high energy physics

Main points

  • 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 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 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

Related projects

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)

License

Apache 2.0, hep_ml is an open-source library.

Platforms

Linux, Mac OS X and Windows are supported.

hep_ml supports both python 2 and python 3.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
hep_ml-0.6.0-py2.py3-none-any.whl (50.8 kB) Copy SHA256 hash SHA256 Wheel py2.py3
hep_ml-0.6.0.tar.gz (43.8 kB) Copy SHA256 hash SHA256 Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page