{"data":{"slug":"automl-robo","name":"RoBO","tagline":"A Robust Bayesian Optimization framework","github_url":"https://github.com/automl/RoBO","owner":"automl","repo":"RoBO","owner_avatar_url":"https://avatars.githubusercontent.com/u/6469053?v=4","primary_language":"Python","stars":492,"forks":129,"topics":[],"archived":false,"github_pushed_at":"2019-04-30T08:47:29+00:00","maintenance_label":"Dormant","url":"https://www.graphcanon.com/tools/automl-robo","markdown_url":"https://www.graphcanon.com/tools/automl-robo.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/automl-robo","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=automl-robo","description":"RoBO: a Robust Bayesian Optimization framework","homepage_url":null,"license":"BSD-3-Clause","open_issues":25,"watchers":36,"ai_summary":"RoBO is a Python-based framework for conducting robust Bayesian Optimization, making use of Gaussian processes and random forests libraries for optimization tasks.","readme_excerpt":"RoBO - a Robust Bayesian Optimization framework.\n================================================\n\nMaster Branch\n------------------\n\n\n\n\n\nInstallation\n------------\n\nRoBO uses the Gaussian processes library [george](https://github.com/automl/george.git) and the random forests library [pyrfr](https://github.com/automl/random_forest_run). In order to use these libraries make sure that libeigen and swig are installed:\n\n```\nsudo apt-get install libeigen3-dev swig \n```\n\nDownload RoBO and then change into the new directory:\n\n```\ngit clone https://github.com/automl/RoBO\ncd RoBO/\n```\n\nInstall the required dependencies.\n```\nfor req in $(cat requirements.txt); do pip install $req; done\n```\n\nFinally install RoBO by:\n\n```\npython setup.py install\n```\n\n\n\nDocumentation\n-------------\nYou can find the documentation for RoBO here http://automl.github.io/RoBO/\n\n\nCiting RoBO\n-----------\n\nTo cite RoBO please reference our BayesOpt paper:\n```\n@INPROCEEDINGS{klein-bayesopt17,\nauthor    = {A. Klein and S. Falkner and N. Mansur and F. Hutter},\ntitle     = {RoBO: A Flexible and Robust Bayesian Optimization Framework in Python},\nbooktitle = {NIPS 2017 Bayesian Optimization Workshop},\nyear      = {2017},\nmonth     = dec,\n}\n```","github_created_at":"2015-03-27T09:02:20+00:00","created_at":"2026-07-11T23:36:33.383621+00:00","updated_at":"2026-08-04T12:00:51.421503+00:00","categories":[{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"bayesian-optimization","name":"bayesian-optimization"},{"slug":"gaussian-processes","name":"gaussian processes"},{"slug":"python","name":"python"},{"slug":"random-forests","name":"random forests"}],"trust":{"provenance":{"is_fork":false,"github_id":32976730,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-04T12:00:50.624Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":2653,"last_release_at":null},"security_summary":{"status":"findings","scanner":"osv@v1","low_count":15,"high_count":0,"last_scan_at":"2026-07-11T23:36:36.156Z","medium_count":0,"scan_profile":"deps","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-04T12:00:51.091Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-08-04T12:00:51.091Z"},"license_spdx":{"value":"BSD-3-Clause","source":"github.license","observed_at":"2026-08-04T12:00:51.091Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["For tasks requiring robust handling of noisy data in Bayesian Optimization","When needing integration with libraries like [george] and [pyrfr]"],"when_not_to_use":["Avoid if your project strictly requires open-source licenses other than BSD-3-Clause","Not suitable for users not comfortable installing external dependencies manually"],"source":"enrich:decision_facts","observed_at":"2026-07-12T13:32:08.197Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"RoBO is a Python framework for robust Bayesian optimization using Gaussian processes and random forests."}]}}