{"id":14970829,"url":"https://github.com/simonblanke/search-data-explorer","last_synced_at":"2025-06-12T20:34:07.720Z","repository":{"id":57449395,"uuid":"335906823","full_name":"SimonBlanke/search-data-explorer","owner":"SimonBlanke","description":"Visualize search-data from your gradient-free-optimization run.","archived":false,"fork":false,"pushed_at":"2024-04-03T06:50:38.000Z","size":1226,"stargazers_count":3,"open_issues_count":0,"forks_count":1,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-06-05T12:48:49.448Z","etag":null,"topics":["dashboard","data-exploration","data-science","matplotlib","pandas","plotly","python","statistics","streamlit","tabular-data","visualization"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/SimonBlanke.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":"2021-02-04T09:43:08.000Z","updated_at":"2024-09-05T12:38:25.000Z","dependencies_parsed_at":"2024-01-16T12:46:14.305Z","dependency_job_id":"4d051fab-4162-40f8-a08b-55012d402395","html_url":"https://github.com/SimonBlanke/search-data-explorer","commit_stats":{"total_commits":101,"total_committers":2,"mean_commits":50.5,"dds":0.04950495049504955,"last_synced_commit":"51b4ee97ea1e3259849b80490f841d1cf5016232"},"previous_names":["simonblanke/optimization-dashboard","simonblanke/tabular-data-explorer"],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/SimonBlanke/search-data-explorer","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SimonBlanke%2Fsearch-data-explorer","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SimonBlanke%2Fsearch-data-explorer/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SimonBlanke%2Fsearch-data-explorer/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SimonBlanke%2Fsearch-data-explorer/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SimonBlanke","download_url":"https://codeload.github.com/SimonBlanke/search-data-explorer/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SimonBlanke%2Fsearch-data-explorer/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":259522456,"owners_count":22870469,"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":["dashboard","data-exploration","data-science","matplotlib","pandas","plotly","python","statistics","streamlit","tabular-data","visualization"],"created_at":"2024-09-24T13:44:12.501Z","updated_at":"2025-06-12T20:34:07.684Z","avatar_url":"https://github.com/SimonBlanke.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cH1 align=\"center\"\u003e\n    Search Data Explorer\n\u003c/H1\u003e\n\n\u003cbr\u003e\n\n\u003cH2 align=\"center\"\u003e\n    Visualize optimization search-data via plotly in a streamlit dashboard\n\u003c/H2\u003e\n\nThe Search-Data-Explorer is a simple application specialized to visualize search-data generated from [Gradient-Free-Optimizers](https://github.com/SimonBlanke/Gradient-Free-Optimizers) or [Hyperactive](https://github.com/SimonBlanke/Hyperactive). It is designed as an easy-to-use tool to gain insights into multi-dimensional data, as commonly found in optimization.\n\nI created this package, because I needed a convenient tool to visually analyse search-data during the development of gradient-free-optimization algorithms. My goal for this package is to help users get insight into the search-data and its corresponding objective-function and search-space. Building on this insight could help improve the selection of the search-space, compare models in the objective-function or explain the behaviour of the optimization algorithm.\n\n\n\u003cbr\u003e\n\n## Disclaimer\n\nThis project is in an early development stage and is only tested manually. If you encounter bugs or have suggestions for improvements, then please open an issue.\n\n\n\u003cbr\u003e\n\n## Installation\n\n```console\npip install search-data-explorer\n```\n\n\u003cbr\u003e\n\n## How to use\n\nThe Search Data Explorer has a very simple API, that can be explained by the examples below or just execute the command \"`search-data-explorer` [file]\" to open the Search Data Explorer without executing a python script.\n\n\n\u003cbr\u003e\n\n### search-data requirements\n\nThe Search Data Explorer is used by loading the search-data with a few lines of code. The search data that is loaded from file must follow the pattern below. The columns can have any name but must contain the `score`, which is always included in search-data from [Gradient-Free-Optimizers](https://github.com/SimonBlanke/Gradient-Free-Optimizers) or [Hyperactive](https://github.com/SimonBlanke/Hyperactive).\n\n\u003ctable class=\"table\"\u003e\n\u003cthead class=\"table-head\"\u003e\n    \u003ctr class=\"row\"\u003e\n    \u003ctd class=\"cell\"\u003efirst column name\u003c/td\u003e\n    \u003ctd class=\"cell\"\u003eanother column name\u003c/td\u003e\n    \u003ctd class=\"cell\"\u003e...\u003c/td\u003e\n    \u003ctd class=\"cell\"\u003escore\u003c/td\u003e\n    \u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody class=\"table-body\"\u003e\n    \u003ctr class=\"row\"\u003e\n    \u003ctd class=\"cell\"\u003e0.756\u003c/td\u003e\n    \u003ctd class=\"cell\"\u003e0.1\u003c/td\u003e\n    \u003ctd class=\"cell\"\u003e0.2\u003c/td\u003e\n    \u003ctd class=\"cell\"\u003e-3\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr class=\"row\"\u003e\n    \u003ctd class=\"cell\"\u003e0.823\u003c/td\u003e\n    \u003ctd class=\"cell\"\u003e0.3\u003c/td\u003e\n    \u003ctd class=\"cell\"\u003e0.1\u003c/td\u003e\n    \u003ctd class=\"cell\"\u003e-10\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr class=\"row\"\u003e\n    \u003ctd class=\"cell\"\u003e...\u003c/td\u003e\n    \u003ctd class=\"cell\"\u003e...\u003c/td\u003e\n    \u003ctd class=\"cell\"\u003e...\u003c/td\u003e\n    \u003ctd class=\"cell\"\u003e...\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr class=\"row\"\u003e\n    \u003ctd class=\"cell\"\u003e...\u003c/td\u003e\n    \u003ctd class=\"cell\"\u003e...\u003c/td\u003e\n    \u003ctd class=\"cell\"\u003e...\u003c/td\u003e\n    \u003ctd class=\"cell\"\u003e...\u003c/td\u003e\n    \u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\n\n\n\u003cbr\u003e\n\n## Examples\n\n\n\u003cbr\u003e\n\n### Load search-data by passing dataframe\n\nYou can pass the search-data directly, if you do not want to save your search-data to disk and just explore it one time after the optimization has finished.\n\n```python\nimport numpy as np\nfrom gradient_free_optimizers import RandomSearchOptimizer\n\nfrom search_data_explorer import SearchDataExplorer\n\n\ndef parabola_function(para):\n    loss = para[\"x\"] * para[\"x\"] + para[\"y\"] * para[\"y\"] + para[\"y\"] * para[\"y\"]\n    return -loss\n\n\nsearch_space = {\n    \"x\": np.arange(-10, 10, 0.1),\n    \"y\": np.arange(-10, 10, 0.1),\n    \"z\": np.arange(-10, 10, 0.1),\n}\n\n# generate search-data for this example with gradient-free-optimizers\n\nopt = RandomSearchOptimizer(search_space)\nopt.search(parabola_function, n_iter=1000)\n\nsearch_data = opt.search_data\n\n\n# Open Search-Data-Explorer\n\nsde = SearchDataExplorer()\nsde.open(search_data)  # pass search-data\n```\n\n\n\u003cbr\u003e\n\n### Load search-data by passing path to file\n\nIf you already have a search-data file on disk you can pass the path to the file to the search-data-explorer.\n\n```python\nimport numpy as np\nfrom gradient_free_optimizers import RandomSearchOptimizer\n\nfrom search_data_explorer import SearchDataExplorer\n\n\ndef parabola_function(para):\n    loss = para[\"x\"] * para[\"x\"] + para[\"y\"] * para[\"y\"] + para[\"y\"] * para[\"y\"]\n    return -loss\n\n\nsearch_space = {\n    \"x\": np.arange(-10, 10, 0.1),\n    \"y\": np.arange(-10, 10, 0.1),\n    \"z\": np.arange(-10, 10, 0.1),\n}\n\n# generate search-data for this example with gradient-free-optimizers\n\nopt = RandomSearchOptimizer(search_space)\nopt.search(parabola_function, n_iter=1000)\n\nsearch_data = opt.search_data\nsearch_data.to_csv(\"search_data.csv\", index=False)\n\n\n# Open Search-Data-Explorer\n\nsde = SearchDataExplorer()\nsde.open(\"model1.csv\")  # pass path to file on disk\n```\n\n\n\u003cbr\u003e\n\n### Load search-data by browsing for file\n\nYou can just open the search-data-explorer without passing a file or path. In this case you can browse for the file via a menu inside the search-data-explorer.\n\n```python\nimport numpy as np\nfrom gradient_free_optimizers import RandomSearchOptimizer\n\nfrom search_data_explorer import SearchDataExplorer\n\n\ndef parabola_function(para):\n    loss = para[\"x\"] * para[\"x\"] + para[\"y\"] * para[\"y\"] + para[\"y\"] * para[\"y\"]\n    return -loss\n\n\nsearch_space = {\n    \"x\": np.arange(-10, 10, 0.1),\n    \"y\": np.arange(-10, 10, 0.1),\n    \"z\": np.arange(-10, 10, 0.1),\n}\n\n# generate search-data for this example with gradient-free-optimizers\n\nopt = RandomSearchOptimizer(search_space)\nopt.search(parabola_function, n_iter=1000)\n\nsearch_data = opt.search_data\nsearch_data.to_csv(\"search_data.csv\", index=False)\n\n\n# Open Search-Data-Explorer\n\nsde = SearchDataExplorer()\nsde.open()  # start without passing anything and use the file explorer within the search-data-explorer\n```\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsimonblanke%2Fsearch-data-explorer","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsimonblanke%2Fsearch-data-explorer","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsimonblanke%2Fsearch-data-explorer/lists"}