https://github.com/arogozhnikov/hep_ml

Machine Learning for High Energy Physics.

Science Score: 59.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
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.zenodo.json file
Found .zenodo.json file
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DOI references
Found 3 DOI reference(s) in README
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Academic publication links
Links to: zenodo.org
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Committers with academic emails
4 of 13 committers (30.8%) from academic institutions
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Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (17.0%) to scientific vocabulary

Keywords

boosting-algorithms high-energy-physics machine-learning neural-networks python reweighting-algorithms scikit-learn splot

Keywords from Contributors

hep

Last synced: 5 months ago · JSON representation

Repository

Machine Learning for High Energy Physics.

Basic Info

Host: GitHub
Owner: arogozhnikov
License: other
Language: Jupyter Notebook
Default Branch: master
Homepage: https://arogozhnikov.github.io/hep_ml/
Size: 92.5 MB

Statistics

Stars: 192
Watchers: 16
Forks: 66
Open Issues: 24
Releases: 12

Topics

boosting-algorithms high-energy-physics machine-learning neural-networks python reweighting-algorithms scikit-learn splot

Created almost 11 years ago · Last pushed 6 months ago

Metadata Files

Readme Changelog License

README.md

hep_ml

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

hep_ml, python library for high energy physics

Notes

Jul 2025: 🎉 hepml v0.8.0: code and examples modernized (courtesy of Jonas Eschle), now `hepml` works with python 3.9-3.13.

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.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:

bash pip install hep_ml

If you're new to python and never used pip, first install scikit-learn with these instructions.

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 all current python versions (python >= 3.9). Old versions (hep_ml<=0.7) supported python 2.

Owner

Name: Alex Rogozhnikov
Login: arogozhnikov
Kind: user
Location: San Francisco
Company: Aperture Science

Website: https://arogozhnikov.github.io
Repositories: 9
Profile: https://github.com/arogozhnikov

ML + Science, einops, scientific tools

GitHub Events

Total

Create event: 6
Issues event: 4
Release event: 1
Watch event: 11
Delete event: 3
Issue comment event: 16
Push event: 8
Gollum event: 1
Pull request event: 12
Fork event: 2

Last Year

Create event: 6
Issues event: 4
Release event: 1
Watch event: 11
Delete event: 3
Issue comment event: 16
Push event: 8
Gollum event: 1
Pull request event: 12
Fork event: 2

Committers

Last synced: 9 months ago

All Time

Total Commits: 344
Total Committers: 13
Avg Commits per committer: 26.462
Development Distribution Score (DDS): 0.07

Past Year

Commits: 2
Committers: 2
Avg Commits per committer: 1.0
Development Distribution Score (DDS): 0.5

Top Committers

Name	Email	Commits
Alexey	i**m@g**m	320
Andrey Ustyuzhanin	a**u@g**m	5
tata-antares	t**s@y**u	5
Jonas Eschle	j**e@c**h	3
Richard Lane	r**e@b**k	2
Michael K. Wilkinson	m**n@g**m	2
connesy	c****y	1
Konstantin Schubert	s**n@g**m	1
Konstantin Gizdov	k**v@g**m	1
Ayush Thada	i**a@g**m	1
Alex Pearce	a**x@a**e	1
Kerim Guseinov	k**v@c**h	1
Ainsleigh	a**l@c**h	1

Committer Domains (Top 20 + Academic)

cern.ch: 3 alexpearce.me: 1 bristol.ac.uk: 1 yandex.ru: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 66
Total pull requests: 40
Average time to close issues: 2 months
Average time to close pull requests: 22 days
Total issue authors: 29
Total pull request authors: 14
Average comments per issue: 2.09
Average comments per pull request: 0.48
Merged pull requests: 32
Bot issues: 0
Bot pull requests: 1

Past Year

Issues: 5
Pull requests: 15
Average time to close issues: about 1 month
Average time to close pull requests: about 9 hours
Issue authors: 5
Pull request authors: 4
Average comments per issue: 3.8
Average comments per pull request: 0.4
Merged pull requests: 10
Bot issues: 0
Bot pull requests: 1

View more stats

Top Authors

Issue Authors

arogozhnikov (26)
marthaisabelhilton (4)
acampove (3)
jonas-eschle (3)
bifani (3)
adendek (2)
gmarceca (2)
alexpearce (2)
jcob95 (1)
Emix26 (1)
RDMoise (1)
PietroBernardiIT (1)
MaggaP (1)
vrivesmolina (1)
ehhov (1)

Pull Request Authors

arogozhnikov (10)
jonas-eschle (10)
anaderi (5)
tlikhomanenko (4)
ehhov (2)
goi42 (2)
kgizdov (1)
ahill187 (1)
itsayushthada (1)
alexpearce (1)
KonstantinSchubert (1)
richard-lane (1)
connesy (1)
dependabot[bot] (1)

Top Labels

Issue Labels

question (20) enhancement (7) bug (2) wontfix (1)

Pull Request Labels

dependencies (1) github_actions (1)

Packages

Total packages: 4
Total downloads:
- pypi 1,405 last-month
Total docker downloads: 272

Total dependent packages: 1
(may contain duplicates)
Total dependent repositories: 50
(may contain duplicates)
Total versions: 32
Total maintainers: 2

pypi.org: hep-ml

Machine Learning for High Energy Physics

Homepage: https://github.com/arogozhnikov/hep_ml
Documentation: https://hep-ml.readthedocs.io/
License: Apache 2.0
Latest release: 0.8.0
published 8 months ago

Versions: 12
Dependent Packages: 1
Dependent Repositories: 49
Downloads: 1,405 Last month
Docker Downloads: 272

Rankings

Dependent repos count: 2.1%

Docker downloads count: 2.3%

Stargazers count: 5.3%

Forks count: 5.4%

Average: 5.5%

Downloads: 7.9%

Dependent packages count: 10.1%

Maintainers (1)

alex.rogozhnikov

Last synced: 6 months ago

proxy.golang.org: github.com/arogozhnikov/hep_ml

Documentation: https://pkg.go.dev/github.com/arogozhnikov/hep_ml#section-documentation
License: other
Latest release: v0.8.0
published 8 months ago

Versions: 13
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Forks count: 3.1%

Stargazers count: 3.8%

Average: 6.5%

Dependent packages count: 8.4%

Dependent repos count: 10.6%

Last synced: 6 months ago

spack.io: py-hep-ml

Machine Learning for High Energy Physics

Homepage: https://github.com/arogozhnikov/hep_ml
License: []
Latest release: 0.7.1
published over 3 years ago

Versions: 2
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent repos count: 0.0%

Forks count: 13.8%

Stargazers count: 15.2%

Average: 21.6%

Dependent packages count: 57.3%

Maintainers (1)

adamjstewart

Last synced: 6 months ago

conda-forge.org: hep_ml

Homepage: https://github.com/arogozhnikov/hep_ml
License: Apache-2.0
Latest release: 0.7.1
published about 4 years ago

Versions: 5
Dependent Packages: 0
Dependent Repositories: 1

Rankings

Forks count: 23.5%

Dependent repos count: 24.4%

Stargazers count: 28.3%

Average: 31.9%

Dependent packages count: 51.6%

Last synced: 6 months ago

Dependencies

requirements.txt pypi

ipython >=3.0
matplotlib >=1.4
numpy >=1.9
pandas >=0.14.0
root_numpy >=3.3.0
scikit-learn >=1
scipy >=0.15.0
six *
sphinx_rtd_theme *
theano >=1.0.2

setup.py pypi

numpy *
pandas *
scikit-learn *
scipy *
six *
theano *

.github/workflows/publish_to_pypi.yml actions

actions/checkout v2 composite
actions/setup-python v2 composite

.github/workflows/run_tests.yml actions

actions/checkout v2 composite
actions/setup-python v2 composite

https://github.com/arogozhnikov/hep_ml

Science Score: 59.0%

Keywords

Keywords from Contributors

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

hep_ml

Notes

Main features

Installation

Links

Related projects

License

Platforms

Owner

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

pypi.org: hep-ml

Rankings

Maintainers (1)

proxy.golang.org: github.com/arogozhnikov/hep_ml

Rankings

spack.io: py-hep-ml

Rankings

Maintainers (1)

conda-forge.org: hep_ml

Rankings

Dependencies