{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/alipy-active-learning-in-python","title":"ALiPy: Active Learning in Python","arxiv_id":"1901.03802","date":"2019-01-12","proceeding":null,"authors":["Ying-Peng Tang","Guo-Xiang Li","Sheng-Jun Huang"],"abstract":"Supervised machine learning methods usually require a large set of labeled\nexamples for model training. However, in many real applications, there are\nplentiful unlabeled data but limited labeled data; and the acquisition of\nlabels is costly. Active learning (AL) reduces the labeling cost by iteratively\nselecting the most valuable data to query their labels from the annotator. This\narticle introduces a Python toobox ALiPy for active learning. ALiPy provides a\nmodule based implementation of active learning framework, which allows users to\nconveniently evaluate, compare and analyze the performance of active learning\nmethods. In the toolbox, multiple options are available for each component of\nthe learning framework, including data process, active selection, label query,\nresults visualization, etc. In addition to the implementations of more than 20\nstate-of-the-art active learning algorithms, ALiPy also supports users to\neasily configure and implement their own approaches under different active\nlearning settings, such as AL for multi-label data, AL with noisy annotators,\nAL with different costs and so on. The toolbox is well-documented and\nopen-source on Github, and can be easily installed through PyPI.","url_abs":"http://arxiv.org/abs/1901.03802v1","url_pdf":"http://arxiv.org/pdf/1901.03802v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"alipy-active-learning-in-python","repo_url":"https://github.com/NUAA-AL/ALiPy","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"alipy-active-learning-in-python","repo_url":"https://github.com/ariapoy/alipy","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"alipy-active-learning-in-python","repo_url":"https://github.com/micka-charpak/ProjetAnalyse","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"active-learning","task_name":"Active Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}