{"id":15288141,"url":"https://github.com/dunnkers/fseval","last_synced_at":"2025-04-13T06:32:12.118Z","repository":{"id":36950431,"uuid":"274001213","full_name":"dunnkers/fseval","owner":"dunnkers","description":"Benchmarking framework for Feature Selection and Feature Ranking algorithms 🚀","archived":false,"fork":false,"pushed_at":"2023-04-12T09:57:56.000Z","size":24710,"stargazers_count":18,"open_issues_count":0,"forks_count":6,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-04-12T04:18:50.730Z","etag":null,"topics":["automl","benchmarking","benchmarking-framework","benchmarks","feature-rankers","feature-ranking","feature-selection","hydra","machine-learning","python","scikit-learn","wandb"],"latest_commit_sha":null,"homepage":"https://dunnkers.com/fseval","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/dunnkers.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2020-06-21T23:46:25.000Z","updated_at":"2024-03-29T03:02:05.000Z","dependencies_parsed_at":"2025-04-12T04:28:53.277Z","dependency_job_id":null,"html_url":"https://github.com/dunnkers/fseval","commit_stats":{"total_commits":409,"total_committers":6,"mean_commits":68.16666666666667,"dds":"0.044009779951100225","last_synced_commit":"e7ca7c4e9d8d81a07b4033f32550744168a45a3e"},"previous_names":[],"tags_count":16,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dunnkers%2Ffseval","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dunnkers%2Ffseval/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dunnkers%2Ffseval/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dunnkers%2Ffseval/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dunnkers","download_url":"https://codeload.github.com/dunnkers/fseval/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248618277,"owners_count":21134200,"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":["automl","benchmarking","benchmarking-framework","benchmarks","feature-rankers","feature-ranking","feature-selection","hydra","machine-learning","python","scikit-learn","wandb"],"created_at":"2024-09-30T15:44:22.121Z","updated_at":"2025-04-13T06:32:07.060Z","avatar_url":"https://github.com/dunnkers.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# fseval\n\n[![build status](https://github.com/dunnkers/fseval/actions/workflows/python-app.yml/badge.svg)](https://github.com/dunnkers/fseval/actions/workflows/python-app.yml)\n[![pypi badge](https://img.shields.io/pypi/v/fseval.svg?maxAge=3600)](https://pypi.org/project/fseval/)\n[![Black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n[![Downloads](https://pepy.tech/badge/fseval/month)](https://pepy.tech/project/fseval) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/fseval)\n[![codecov](https://codecov.io/gh/dunnkers/fseval/branch/master/graph/badge.svg?token=R5ZXH8UPCI)](https://codecov.io/gh/dunnkers/fseval)\n[![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/dunnkers/fseval.svg?logo=lgtm\u0026logoWidth=18)](https://lgtm.com/projects/g/dunnkers/fseval/context:python) ![PyPI - License](https://img.shields.io/pypi/l/fseval)\n[![DOI](https://zenodo.org/badge/274001213.svg)](https://zenodo.org/badge/latestdoi/274001213)\n[![Open in Remote - Containers](https://img.shields.io/static/v1?label=Remote%20-%20Containers\u0026message=Open\u0026color=blue\u0026logo=visualstudiocode)](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/dunnkers/fseval)\n[![DOI](https://joss.theoj.org/papers/10.21105/joss.04611/status.svg)](https://doi.org/10.21105/joss.04611)\n\nBenchmarking framework for Feature Selection and Feature Ranking algorithms 🚀\n\n## Demo\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Bsuxxuw0-mEsYRSnNbmvD_wNUAkOPiQa?usp=sharing)\n\n## Install\n\n1. Installation through [PyPi](https://pypi.org/project/fseval/)\n    \u003csup\u003e\u003csub\u003e\n    ⭐️ RECOMMENDED OPTION\n    \u003c/sub\u003e\u003c/sup\u003e\n\n    ```shell\n    pip install fseval\n    ```\n\n\n2. Installation from source \n\n    ```shell\n    git clone https://github.com/dunnkers/fseval.git\n    cd fseval\n    pip install -r requirements.txt\n    pip install .\n    ```\n\nYou can now import fseval `import fseval` in your Python code, or use the `fseval` command in your terminal. For an example, run `fseval --help`. For more information, see the documentation link below ⌄. \n\n## Documentation\n\n[![docs preview](./website/static/img/docs-preview.png)](https://dunnkers.com/fseval)\n\nSee the [documentation](https://dunnkers.com/fseval).\n\n## About\nBuilt at the University of Groningen and published in **The Journal of Open Source Software** (JOSS):\n- https://joss.theoj.org/papers/10.21105/joss.04611\n\n\u003cdetails\u003e\n\u003csummary\u003e\nProject has some early roots in another project, which is a feature selection algorithm called FeatBoost (see full citation below).\n\u003c/summary\u003e\n\n\nA. Alsahaf, N. Petkov, V. Shenoy, G. Azzopardi, \"A framework for feature selection through boosting\", Expert Systems with Applications,\nVolume 187, 2022, 115895, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2021.115895.\n    \nThe open source Python code of FeatBoost is available in https://github.com/amjams/FeatBoost.\n\u003c/details\u003e\n   \n\n---\n\n\u003cp align=\"center\"\u003e2023 — \u003ca href=\"https://dunnkers.com/\"\u003eJeroen Overschie\u003c/a\u003e\u003c/p\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdunnkers%2Ffseval","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdunnkers%2Ffseval","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdunnkers%2Ffseval/lists"}