hep_ml
hep_ml provides specific machine learning tools for purposes of high energy physics.
Main features
- 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.
Installation
Plain and simple:
pip install hep_ml
If you're new to python and never used pip
, first install scikit-learn with these instructions.
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.