{"id":24370607,"url":"https://github.com/cbmira01/featureranking","last_synced_at":"2026-05-08T13:04:45.998Z","repository":{"id":202484814,"uuid":"375047100","full_name":"cbmira01/FeatureRanking","owner":"cbmira01","description":"A data science technique implemented in OpenCL.","archived":false,"fork":false,"pushed_at":"2022-04-14T20:03:47.000Z","size":400,"stargazers_count":0,"open_issues_count":9,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-01-19T04:36:00.250Z","etag":null,"topics":["data-science","entropy","feature-reduction","machine-learning-algorithms","numpy","opencl","pyopencl","python","windows-10"],"latest_commit_sha":null,"homepage":"","language":"Python","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/cbmira01.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2021-06-08T14:52:44.000Z","updated_at":"2022-04-08T14:16:46.000Z","dependencies_parsed_at":"2024-05-10T23:00:21.694Z","dependency_job_id":null,"html_url":"https://github.com/cbmira01/FeatureRanking","commit_stats":null,"previous_names":["cbmira01/featureranking"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cbmira01%2FFeatureRanking","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cbmira01%2FFeatureRanking/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cbmira01%2FFeatureRanking/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cbmira01%2FFeatureRanking/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/cbmira01","download_url":"https://codeload.github.com/cbmira01/FeatureRanking/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243205154,"owners_count":20253419,"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":["data-science","entropy","feature-reduction","machine-learning-algorithms","numpy","opencl","pyopencl","python","windows-10"],"created_at":"2025-01-19T04:36:07.189Z","updated_at":"2026-05-08T13:04:40.949Z","avatar_url":"https://github.com/cbmira01.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# Feature Ranking\n\nThis Python project implements a compute-intensive data mining task on \nworkstation devices capable of parallel computation. A particular data science \nalgorithm for feature ranking is implemented in the OpenCL framework and \ncompared to an unaccelerated version written in Python (NumPy).\n\n## How to run this project\n\n### Requirements\n\nThis project was built and tested on Windows 10 64-bit, with up-to-date \nCPU and GPU drivers. You'll need at least one OpenCL device discoverable on \nyour workstation, a \"recent\" version of Anaconda (eg, 2021.05), a Git \nclient like [Git Bash](https://git-scm.com) available at the Anaconda command \nline, and a local directory on your workstation where your code repositories\nare housed.\n\n### Setup from scratch\n\n- Open the Anaconda CMD prompt into the Conda (base) environment.\n\n- Change to your preferred local repository base directory: \n    \u003e cd C:\\\\Users\\\\your-user-name\\\\...\\\\your-local-repos\u003e\n\n- Clone the FeatureRanking project GitHub repository: \n    \u003e git clone https://github.com/cbmira01/FeatureRanking\n\n- Change to the FeatureRanking project root directory: \n    \u003e cd .\\FeatureRanking\n\n- Create the Conda environment for the project: \n    \u003e conda env create --file environment.yml\n\n- After packages have loaded, activate the new Conda environment:\n    \u003e conda activate feature_ranking\n\n### Test the installation\n\n- To run Python programs, change to the .\\src folder: \n    \u003e cd .\\src\n\n- The quick-demo program will dump a list of available OpenCL devices\n  and run a very small OpenCL workload on each one: \n    \u003e python quick_demo.py\n\n- A few unit tests can be run:\n    \u003e python run_tests.py\n\n### After a successful installation\n\n- Run the main program: \n    \u003e python feature_ranking.py\n\n- More information on setup is available in the project [Wiki](https://github.com/cbmira01/FeatureRanking/wiki).\n\n## More information\n\nMore information about feature ranking, OpenCL, the experimental approach of\nthis project, and results are described in the project [Wiki](https://github.com/cbmira01/FeatureRanking/wiki).\n\n## Credits\n\nCredits, attributions, and works consulted are summarized in the project [Wiki](https://github.com/cbmira01/FeatureRanking/wiki).\n\n## Notes for Code Louisville project grading\n\nThis project fullfilled requirements for the Summer 2021 Code Louisville Python session.\n\n- Implements a “master loop” console application where the user can repeatedly \nenter commands/perform actions, as seen in\n    \u003e ./src/feature_ranking.py\n\n- Creates and uses a dictionary or list, as seen in  \n    \u003e ./src/prep_data.py\n\n- Reads data from external JSON and CSV files, as seen in\n    \u003e ./src/prep_data.py\n\n- Function calls that return values are used throughout, for example\n    \u003e ./src/fr_opencl.py\n    \u003e ./src/feature_ranking.py\n\n- Implements a \"stretch\" goal: setup and testing of the OpenCL framework.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcbmira01%2Ffeatureranking","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcbmira01%2Ffeatureranking","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcbmira01%2Ffeatureranking/lists"}