# hep_ml
**hep_ml** provides specific machine learning tools for purposes of high energy physics.
<!--- [](https://travis-ci.org/arogozhnikov/hep_ml) --->
[](https://github.com/arogozhnikov/hep_ml/actions/workflows/run_tests.yml)
[](https://badge.fury.io/py/hep-ml)
[](https://arogozhnikov.github.io/hep_ml/)
[](https://doi.org/10.5281/zenodo.1247379)
### 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 <br />
(_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](http://scikit-learn.org/stable/install.html).
### Links
* [documentation](https://arogozhnikov.github.io/hep_ml/)
* [notebooks, code examples](https://github.com/arogozhnikov/hep_ml/tree/master/notebooks)
- you may need to install `ROOT` and `root_numpy` to run those
* [repository](https://github.com/arogozhnikov/hep_ml)
* [issue tracker](https://github.com/arogozhnikov/hep_ml/issues)
### Related projects
Libraries you'll require to make your life easier and HEPpier.
* [IPython Notebook](http://ipython.org/notebook.html) — web-shell for python
* [scikit-learn](http://scikit-learn.org/) — general-purpose library for machine learning in python
* [numpy](http://www.numpy.org/) — 'MATLAB in python', vector operation in python.
Use it you need to perform any number crunching.
* [theano](http://deeplearning.net/software/theano/) — optimized vector analytical math engine in python
* [ROOT](https://root.cern.ch/) — main data format in high energy physics
* [root_numpy](http://rootpy.github.io/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.
Raw data
{
"_id": null,
"home_page": "https://github.com/arogozhnikov/hep_ml",
"name": "hep-ml",
"maintainer": null,
"docs_url": null,
"requires_python": null,
"maintainer_email": null,
"keywords": "machine learning, supervised learning, uncorrelated methods of machine learning, high energy physics, particle physics",
"author": "Alex Rogozhnikov",
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/57/a7/70928abfe68691040e8bee05ec3c8cc0c83f70cb48f12b9632cdb639f5cf/hep_ml-0.7.3.tar.gz",
"platform": null,
"description": "# hep_ml\n\n**hep_ml** provides specific machine learning tools for purposes of high energy physics.\n\n<!--- [](https://travis-ci.org/arogozhnikov/hep_ml) --->\n[](https://github.com/arogozhnikov/hep_ml/actions/workflows/run_tests.yml)\n[](https://badge.fury.io/py/hep-ml)\n[](https://arogozhnikov.github.io/hep_ml/)\n[](https://doi.org/10.5281/zenodo.1247379)\n\n\n\n\n\n\n### Main features\n\n* uniform classifiers - the classifiers with low correlation of predictions and mass (or some other variable, or even set of variables)\n * __uBoost__ optimized implementation inside\n * __UGradientBoosting__ (with different losses, specially __FlatnessLoss__ is of high interest)\n* measures of uniformity (see **hep_ml.metrics**)\n* advanced losses for classification, regression and ranking for __UGradientBoosting__ (see **hep_ml.losses**). \n* **hep_ml.nnet** - theano-based flexible neural networks \n* **hep_ml.reweight** - reweighting multidimensional distributions <br />\n (_multi_ here means 2, 3, 5 and more dimensions - see GBReweighter!)\n* **hep_ml.splot** - minimalistic sPlot-ting \n* **hep_ml.speedup** - building models for fast classification (Bonsai BDT)\n* **sklearn**-compatibility of estimators.\n\n### Installation\n\nPlain and simple:\n\n```bash\npip install hep_ml\n```\n\nIf you're new to python and never used `pip`, first install scikit-learn [with these instructions](http://scikit-learn.org/stable/install.html).\n\n### Links\n\n* [documentation](https://arogozhnikov.github.io/hep_ml/)\n* [notebooks, code examples](https://github.com/arogozhnikov/hep_ml/tree/master/notebooks)\n - you may need to install `ROOT` and `root_numpy` to run those \n* [repository](https://github.com/arogozhnikov/hep_ml)\n* [issue tracker](https://github.com/arogozhnikov/hep_ml/issues)\n\n### Related projects \nLibraries you'll require to make your life easier and HEPpier.\n\n* [IPython Notebook](http://ipython.org/notebook.html) — web-shell for python\n* [scikit-learn](http://scikit-learn.org/) — general-purpose library for machine learning in python\n* [numpy](http://www.numpy.org/) — 'MATLAB in python', vector operation in python. \n Use it you need to perform any number crunching. \n* [theano](http://deeplearning.net/software/theano/) — optimized vector analytical math engine in python\n* [ROOT](https://root.cern.ch/) — main data format in high energy physics \n* [root_numpy](http://rootpy.github.io/root_numpy/) — python library to deal with ROOT files (without pain)\n\n\n### License\nApache 2.0, `hep_ml` is an open-source library.\n\n### Platforms \nLinux, Mac OS X and Windows are supported.\n\n**hep_ml** supports both python 2 and python 3.\n",
"bugtrack_url": null,
"license": "Apache 2.0",
"summary": "Machine Learning for High Energy Physics",
"version": "0.7.3",
"project_urls": {
"Homepage": "https://github.com/arogozhnikov/hep_ml"
},
"split_keywords": [
"machine learning",
" supervised learning",
" uncorrelated methods of machine learning",
" high energy physics",
" particle physics"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "aec7a8f736b66166b3b394a2fe172ee9c3bcefb2fabd500e136df96cafcee30d",
"md5": "6844cd0b1ab889f6a9e2e15aed8d2cbd",
"sha256": "629a2685f92ef50e258d17f7e01be5d3f1629641d63a854ec19b20e46b357acd"
},
"downloads": -1,
"filename": "hep_ml-0.7.3-py3-none-any.whl",
"has_sig": false,
"md5_digest": "6844cd0b1ab889f6a9e2e15aed8d2cbd",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 55301,
"upload_time": "2024-10-16T21:52:17",
"upload_time_iso_8601": "2024-10-16T21:52:17.788490Z",
"url": "https://files.pythonhosted.org/packages/ae/c7/a8f736b66166b3b394a2fe172ee9c3bcefb2fabd500e136df96cafcee30d/hep_ml-0.7.3-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "57a770928abfe68691040e8bee05ec3c8cc0c83f70cb48f12b9632cdb639f5cf",
"md5": "b8d0bc45a7a79c55f637343a0494abc1",
"sha256": "606bb1d7724a71dbecc67998ea46cb304f93a17e1b777c199abb3f7f481ebc6b"
},
"downloads": -1,
"filename": "hep_ml-0.7.3.tar.gz",
"has_sig": false,
"md5_digest": "b8d0bc45a7a79c55f637343a0494abc1",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 60351,
"upload_time": "2024-10-16T21:52:19",
"upload_time_iso_8601": "2024-10-16T21:52:19.351716Z",
"url": "https://files.pythonhosted.org/packages/57/a7/70928abfe68691040e8bee05ec3c8cc0c83f70cb48f12b9632cdb639f5cf/hep_ml-0.7.3.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-10-16 21:52:19",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "arogozhnikov",
"github_project": "hep_ml",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [
{
"name": "numpy",
"specs": [
[
">=",
"1.9"
]
]
},
{
"name": "scipy",
"specs": [
[
">=",
"0.15.0"
]
]
},
{
"name": "matplotlib",
"specs": [
[
">=",
"1.4"
]
]
},
{
"name": "pandas",
"specs": [
[
">=",
"0.14.0"
]
]
},
{
"name": "ipython",
"specs": [
[
">=",
"3.0"
]
]
},
{
"name": "root_numpy",
"specs": [
[
">=",
"3.3.0"
]
]
},
{
"name": "scikit-learn",
"specs": [
[
">=",
"1"
]
]
},
{
"name": "theano",
"specs": [
[
">=",
"1.0.2"
]
]
},
{
"name": "six",
"specs": []
},
{
"name": "sphinx_rtd_theme",
"specs": []
}
],
"lcname": "hep-ml"
}
JSON
