hep_ml documentation

hep_ml is machine learning library filled with methods used in high energy physics. Classifiers and transformers from hep_ml are sklearn-compatible.

Installation

To install hep_ml, use pip. Type in terminal:

pip install hep_ml

Installation for developers

After cloning repository type in bash:

cd hep_ml
pip install -e . -r requirements.txt

Contents:

• hep_ml documentation
• Gradient boosting
• Losses for Gradient Boosting
  • Examples
• uBoost
  • Examples
• Metric functions
  • Examples
• Neural networks
  • Examples
  • Interface
  • Recommended networks
  • Custom networks
  • Loss functions
  • Trainers
• Preprocessing data
  • Examples
• Reweighting algorithms
  • Examples
• Fast predictions
  • Example
• sPlot
  • Example
• Code Examples

Links

• documentation

• notebook examples

• repository

• issue tracker

Related projects

Libraries you’ll require to make your life easier.

• IPython Notebook — web-shell for python

• scikit-learn — general-purpose library for machine learning in python

• REP — python wrappers around different machine learning libraries (including TMVA) + goodies, required to plot learning curves reports after classification

• 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)