Metadata-Version: 2.1
Name: hep-ml-lab
Version: 0.1.0
Summary: An end-to-end framework for applying machine learning to high-energy physics research.
Author: Star9daisy
Requires-Python: >=3.8,<3.12
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Dist: matplotlib (>=3.7.1,<4.0.0)
Requires-Dist: numpy (>=1.24.3,<2.0.0)
Requires-Dist: pyyaml (>=6.0,<7.0)
Requires-Dist: rich (>=13.4.2,<14.0.0)
Requires-Dist: scikit-learn (>=1.2.2,<2.0.0)
Requires-Dist: tensorflow (>=2.11.0,<3.0.0)
Description-Content-Type: text/markdown

# HEP ML Lab (HML)
[![codecov](https://codecov.io/gh/Star9daisy/hml/branch/main/graph/badge.svg?token=6VWJi5ct6c)](https://codecov.io/gh/Star9daisy/hml)

❗ This framework is currently undergoing **rapid iteration**. Any comments and suggestions are welcome.

## Introduction

HEP ML Lab (HML) is an end-to-end framework for applying machine learning (ML) to high energy physics (HEP)
research. It provides a set of interfaces for data generation, model training and evaluation. It is designed to
be modular and extensible so that you can easily customize it for your own research.

## Module overview

Here is a brief overview of the modules in HML:

- `hml.generators`: API of Madgraph5 for simulating colliding events;
- `hml.theories`: Particle physics models;
- `hml.observables`: General observables in jet physics;
- `hml.representations`: Different data structure used to represent an event;
- `hml.datasets`: Existing datasets and helper classes for creating new datasets;
- `hml.methods`: Cuts, trees and networks for classification;
- `hml.metrics`: Metrics used in classical signal vs background analysis;

