{"id":13937475,"url":"https://github.com/machinalis/featureforge","last_synced_at":"2025-04-05T00:09:27.646Z","repository":{"id":14213692,"uuid":"16920465","full_name":"machinalis/featureforge","owner":"machinalis","description":"A set of tools for creating and testing machine learning features, with a scikit-learn compatible API","archived":false,"fork":false,"pushed_at":"2017-12-26T17:08:12.000Z","size":188,"stargazers_count":383,"open_issues_count":11,"forks_count":77,"subscribers_count":33,"default_branch":"develop","last_synced_at":"2025-03-28T23:08:43.447Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"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/machinalis.png","metadata":{"files":{"readme":"README.rst","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}},"created_at":"2014-02-17T17:21:12.000Z","updated_at":"2025-03-05T22:15:48.000Z","dependencies_parsed_at":"2022-07-08T04:01:47.010Z","dependency_job_id":null,"html_url":"https://github.com/machinalis/featureforge","commit_stats":null,"previous_names":[],"tags_count":7,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/machinalis%2Ffeatureforge","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/machinalis%2Ffeatureforge/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/machinalis%2Ffeatureforge/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/machinalis%2Ffeatureforge/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/machinalis","download_url":"https://codeload.github.com/machinalis/featureforge/tar.gz/refs/heads/develop","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247266564,"owners_count":20910836,"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":[],"created_at":"2024-08-07T23:03:36.819Z","updated_at":"2025-04-05T00:09:27.630Z","avatar_url":"https://github.com/machinalis.png","language":"Python","readme":"Feature Forge\n=============\n\nThis library provides a set of tools that can be useful in many machine\nlearning applications (classification, clustering, regression, etc.), and\nparticularly helpful if you use scikit-learn (although this can work if\nyou have a different algorithm).\n\nMost machine learning problems involve an step of feature definition and\npreprocessing. Feature Forge helps you with:\n\n* Defining and documenting features\n* Testing your features against specified cases and against randomly generated\n  cases (stress-testing). This helps you making your application more robust\n  against invalid/misformatted input data. This also helps you checking that\n  low-relevance results when doing feature analysis is actually because the\n  feature is bad, and not because there's a slight bug in your feature code.\n* Evaluating your features on a data set, producing a feature evaluation\n  matrix. The evaluator has a robust mode that allows you some tolerance both\n  for invalid data and buggy features.\n* Experimentation: running, registering, classifying and reproducing\n  experiments for determining best settings for your problems.\n\nInstallation\n------------\n\nJust ``pip install featureforge``.\n\nDocumentation\n-------------\n\nDocumentation is available at http://feature-forge.readthedocs.org/en/latest/\n\nContact information\n-------------------\n\nFeature Forge is copyright 2014 Machinalis (http://www.machinalis.com/). Its primary\nauthors are:\n\n* Javier Mansilla \u003cjmansilla@machinalis.com\u003e (jmansilla at github)\n* Daniel Moisset \u003cdmoisset@machinalis.com\u003e (dmoisset at github)\n* Rafael Carrascosa \u003crcarrascosa@machinalis.com\u003e (rafacarrascosa at github)\n\nAny contributions or suggestions are welcome, the official channel for this is\nsubmitting github pull requests or issues.\n\nChangelog\n---------\n0.1.7:\n    - StatsManager api change (order of arguments swapped)\n    - For experimentation, enabled a way of booking experiments forever.\n\n0.1.6:\n    - Bug fixes related to sparse matrices.\n    - Small documentation improvements.\n    - Reduced default logging verbosity.\n\n0.1.5:\n    - Using sparse numpy matrices by default.\n\n0.1.4:\n    - Discarded the need of using forked version of Schema library.\n\n0.1.3:\n    - Added support for running and generating stats for experiments\n\n0.1.2:\n    - Fixing installer dependencies\n\n0.1.1:\n    - Added support for python 3\n    - Added support for bag-of-words features\n\n0.1:\n    - Initial release\n","funding_links":[],"categories":["Feature Engineering","Python"],"sub_categories":["General","General-Purpose Machine Learning"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmachinalis%2Ffeatureforge","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmachinalis%2Ffeatureforge","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmachinalis%2Ffeatureforge/lists"}