{"id":25853105,"url":"https://github.com/mlojek/optilab","last_synced_at":"2026-03-15T19:31:01.562Z","repository":{"id":260110265,"uuid":"825768445","full_name":"mlojek/optilab","owner":"mlojek","description":"Python framework for black-box optimization.","archived":false,"fork":false,"pushed_at":"2026-03-14T19:56:46.000Z","size":438,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-03-14T20:03:53.588Z","etag":null,"topics":["blackbox-optimization","cd","cec","ci","cmaes","gecco","metamodels","python","surrogate-models"],"latest_commit_sha":null,"homepage":"https://optilab.readthedocs.io","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mlojek.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,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2024-07-08T13:08:28.000Z","updated_at":"2026-03-09T15:31:40.000Z","dependencies_parsed_at":null,"dependency_job_id":"49e8a49d-c6f4-4558-8f13-3c92bede97e8","html_url":"https://github.com/mlojek/optilab","commit_stats":null,"previous_names":["mlojek/sofes","mlojek/optilab"],"tags_count":14,"template":false,"template_full_name":null,"purl":"pkg:github/mlojek/optilab","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mlojek%2Foptilab","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mlojek%2Foptilab/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mlojek%2Foptilab/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mlojek%2Foptilab/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mlojek","download_url":"https://codeload.github.com/mlojek/optilab/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mlojek%2Foptilab/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30550245,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-15T15:03:43.933Z","status":"ssl_error","status_checked_at":"2026-03-15T15:03:37.630Z","response_time":61,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["blackbox-optimization","cd","cec","ci","cmaes","gecco","metamodels","python","surrogate-models"],"created_at":"2025-03-01T14:32:21.388Z","updated_at":"2026-03-15T19:31:01.539Z","avatar_url":"https://github.com/mlojek.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Optilab\n[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)\n![Docker Pulls](https://img.shields.io/docker/pulls/mlojek/optilab?logo=Docker\u0026label=Dockerhub%20pulls)\n![Read the Docs](https://img.shields.io/readthedocs/optilab)\n\nOptilab is a lightweight and flexible python framework for testing black-box optimization.\n\n## Features\n- ✅ Intuitive interface to quickly prototype and run optimizers and metamodels.\n- 📚 High quality documentation.\n- 📈 Objective functions, optimizers, plotting and data handling.\n- ⋙ CLI functionality to easily summarize results of previous experiments.\n- 🚀 Multiprocessing for faster computation.\n\n## How to install\nOptilab has been tested to work on python versions 3.11 and above. To install it from PyPI, run:\n```\npip install optilab\n```\nYou can also install from source by cloning this repo and running:\n```\nmake install\n```\n\n## Try the demos\nLearn how to use optilab and fit it to your needs with demo notebooks in `demo` directory.\n\n## CLI tool\nOptilab comes with a powerful CLI tool to easily summarize your experiments. It allows for plotting the results and performing statistical testing to check for statistical significance in optimization results.\n```\nusage: optilab [-h] [--aggregate_pvalues] [--aggregate_stats] [--entries ENTRIES [ENTRIES ...]]\n               [--hide_outliers] [--hide_plots] [--no_save] [--raw_values]\n               [--save_path SAVE_PATH] [--siginificance SIGINIFICANCE] [--test_evals] [--test_y]\n               pickle_path\n\nOptilab CLI utility.\n\npositional arguments:\n  pickle_path           Path to pickle file or directory with optimization runs.\n\noptions:\n  -h, --help            show this help message and exit\n  --aggregate_pvalues   Aggregate pvalues of stat tests against run 0 in each pickle file into\n                        one table.\n  --aggregate_stats     Aggregate median and iqr for all processed runs into one table.\n  --entries ENTRIES [ENTRIES ...]\n                        Space separated list of indexes of entries to include in analysis.\n  --hide_outliers       If specified, outliers will not be shown in the box plot.\n  --hide_plots          Hide plots when running the script.\n  --no_save             If specified, no artifacts will be saved.\n  --raw_values          If specified, y values below tolerance are not substituted by tolerance\n                        value.\n  --save_path SAVE_PATH\n                        Path to directory to save the artifacts. Default is the user's working\n                        directory.\n  --significance SIGNIFICANCE\n                        Statistical significance of the U tests. Default value is 0.05.\n  --test_evals          Perform Mann-Whitney U test on eval values.\n  --test_y              Perform Mann-Whitney U test on y values.\n```\n\n## Docker\nThis project comes with a docker container. You can pull it from dockerhub:\n```\ndocker pull mlojek/optilab\n```\nOr build it yourself:\n```\nmake docker\n```","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmlojek%2Foptilab","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmlojek%2Foptilab","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmlojek%2Foptilab/lists"}