{"id":13477264,"url":"https://github.com/arogozhnikov/hep_ml","last_synced_at":"2025-05-15T17:05:08.093Z","repository":{"id":31539537,"uuid":"35104122","full_name":"arogozhnikov/hep_ml","owner":"arogozhnikov","description":"Machine Learning for High Energy Physics.","archived":false,"fork":false,"pushed_at":"2024-10-16T21:51:37.000Z","size":96568,"stargazers_count":186,"open_issues_count":22,"forks_count":65,"subscribers_count":16,"default_branch":"master","last_synced_at":"2025-05-08T04:05:58.732Z","etag":null,"topics":["boosting-algorithms","high-energy-physics","machine-learning","neural-networks","python","reweighting-algorithms","scikit-learn","splot"],"latest_commit_sha":null,"homepage":"https://arogozhnikov.github.io/hep_ml/","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/arogozhnikov.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2015-05-05T14:17:36.000Z","updated_at":"2025-04-23T12:48:30.000Z","dependencies_parsed_at":"2024-01-13T11:13:05.589Z","dependency_job_id":"1f56cd5e-b3f4-446a-a3f3-a0cd03d6a277","html_url":"https://github.com/arogozhnikov/hep_ml","commit_stats":{"total_commits":340,"total_committers":12,"mean_commits":"28.333333333333332","dds":"0.13235294117647056","last_synced_commit":"a14a82332715d81b7ab180eff5c625e001736f19"},"previous_names":[],"tags_count":13,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arogozhnikov%2Fhep_ml","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arogozhnikov%2Fhep_ml/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arogozhnikov%2Fhep_ml/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arogozhnikov%2Fhep_ml/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/arogozhnikov","download_url":"https://codeload.github.com/arogozhnikov/hep_ml/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254384987,"owners_count":22062422,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["boosting-algorithms","high-energy-physics","machine-learning","neural-networks","python","reweighting-algorithms","scikit-learn","splot"],"created_at":"2024-07-31T16:01:40.367Z","updated_at":"2025-05-15T17:05:03.085Z","avatar_url":"https://github.com/arogozhnikov.png","language":"Jupyter Notebook","funding_links":[],"categories":["Jupyter Notebook"],"sub_categories":[],"readme":"# hep_ml\n\n**hep_ml** provides specific machine learning tools for purposes of high energy physics.\n\n\u003c!--- [![travis status](https://travis-ci.org/arogozhnikov/hep_ml.svg?branch=master)](https://travis-ci.org/arogozhnikov/hep_ml) ---\u003e\n[![Run tests](https://github.com/arogozhnikov/hep_ml/actions/workflows/run_tests.yml/badge.svg)](https://github.com/arogozhnikov/hep_ml/actions/workflows/run_tests.yml)\n[![PyPI version](https://badge.fury.io/py/hep-ml.svg)](https://badge.fury.io/py/hep-ml)\n[![Documentation](https://img.shields.io/badge/documentation-link-blue.svg)](https://arogozhnikov.github.io/hep_ml/)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1247379.svg)](https://doi.org/10.5281/zenodo.1247379)\n\n\n\n![hep_ml, python library for high energy physics](https://github.com/arogozhnikov/hep_ml/blob/data/data_to_download/hep_ml_image.png)\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 \u003cbr /\u003e\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) \u0026mdash; web-shell for python\n* [scikit-learn](http://scikit-learn.org/)  \u0026mdash; general-purpose library for machine learning in python\n* [numpy](http://www.numpy.org/)  \u0026mdash; 'MATLAB in python', vector operation in python. \n    Use it you need to perform any number crunching. \n* [theano](http://deeplearning.net/software/theano/)  \u0026mdash; optimized vector analytical math engine in python\n* [ROOT](https://root.cern.ch/)  \u0026mdash; main data format in high energy physics \n* [root_numpy](http://rootpy.github.io/root_numpy/)  \u0026mdash; 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","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farogozhnikov%2Fhep_ml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Farogozhnikov%2Fhep_ml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farogozhnikov%2Fhep_ml/lists"}