{"id":18248016,"url":"https://github.com/hourout/linora","last_synced_at":"2025-04-04T15:31:57.121Z","repository":{"id":37663147,"uuid":"158527260","full_name":"Hourout/linora","owner":"Hourout","description":"Simple and efficient tools for data science.","archived":false,"fork":false,"pushed_at":"2024-05-17T08:21:24.000Z","size":3594,"stargazers_count":12,"open_issues_count":0,"forks_count":4,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-20T14:56:10.164Z","etag":null,"topics":["data-analysis","data-mining","data-science","hyperparameter-optimization","lightgbm","machine-learning","python","xgboost"],"latest_commit_sha":null,"homepage":"https://www.yuque.com/jinqing-ps0ax/linora/htibub","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Hourout.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,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-11-21T09:58:08.000Z","updated_at":"2024-05-17T08:21:27.000Z","dependencies_parsed_at":"2024-01-19T15:13:43.474Z","dependency_job_id":"9a4da2b9-96aa-48f4-9101-1f14be14b05e","html_url":"https://github.com/Hourout/linora","commit_stats":{"total_commits":1121,"total_committers":3,"mean_commits":373.6666666666667,"dds":0.00535236396074934,"last_synced_commit":"17dd0f2aaa61ac912ded526a49d022f8f8780313"},"previous_names":[],"tags_count":17,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hourout%2Flinora","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hourout%2Flinora/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hourout%2Flinora/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hourout%2Flinora/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Hourout","download_url":"https://codeload.github.com/Hourout/linora/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247202941,"owners_count":20900873,"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-analysis","data-mining","data-science","hyperparameter-optimization","lightgbm","machine-learning","python","xgboost"],"created_at":"2024-11-05T09:35:16.313Z","updated_at":"2025-04-04T15:31:52.104Z","avatar_url":"https://github.com/Hourout.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"![](https://github.com/Hourout/linora/blob/master/image/linora.png)\n\n\n![PyPI version](https://img.shields.io/pypi/pyversions/linora.svg)\n![Github license](https://img.shields.io/github/license/Hourout/linora.svg)\n[![PyPI](https://img.shields.io/pypi/v/linora.svg)](https://pypi.python.org/pypi/linora)\n![PyPI format](https://img.shields.io/pypi/format/linora.svg)\n![contributors](https://img.shields.io/github/contributors/Hourout/linora)\n![downloads](https://img.shields.io/pypi/dm/linora.svg)\n[![Documentation](https://img.shields.io/badge/docs-linora-blue.svg)](https://www.yuque.com/jinqing-ps0ax/linora/htibub) \n\nLinora is a simple and efficient data mining and data analysis tool that allows you to do related data mining tasks without using sklearn to the maximum extent. It is perfectly compatible with pandas and runs faster and saves memory compared to sklearn.\n\n\n| [API Document](https://www.yuque.com/jinqing-ps0ax/linora/htibub) | [中文介绍](https://github.com/Hourout/linora/blob/master/document/Chinese.md) |\n\n## Installation\n\nTo install this verson from [PyPI](https://pypi.org/project/linora/), type:\n\n```\npip install linora -U\n```\n\nTo get the newest one from this repo (note there may be frequent updates), type:\n\n```\npip install git+https://github.com/Hourout/linora.git\n```\n\n## Feature\n- metrics\n- metrics charts\n- feature columns module\n- feature selection module\n- image augmentation\n- text processing\n- model param search\n- sample\n- parallel\n- logger\n- config\n- progbar\n- schedulers\n\n## Example\n\n```python\nimport linora as la\n\n# plot ks curve\nlabel = [1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1]\nlabel_prob = [0.8, 0.4, 0.2, 0.5, 0.9, 0.2, 0.8, 0.6, 0.1, 0.3, 0.8, 0.3, 0.9, 0.2, 0.84, \n              0.2, 0.5, 0.23, 0.83, 0.71, 0.34, 0.3, 0.2, 0.7, 0.2, 0.8, 0.3, 0.59, 0.26, 0.16, 0.13, 0.8]\nla.chart.ks_curve(label, label_prob)\n```\n![](https://github.com/Hourout/linora/blob/master/image/ks_curve.png)\n\n## Contact\nPlease contact me if you have any related questions or improvements.\n\n[WeChat](https://github.com/Hourout/linora/blob/master/image/hourout_wechat.jpeg)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhourout%2Flinora","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhourout%2Flinora","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhourout%2Flinora/lists"}