{ "info": { "author": "JinQing Lee", "author_email": "hourout@163.com", "bugtrack_url": null, "classifiers": [ "Intended Audience :: Developers", "Intended Audience :: Education", "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Scientific/Engineering :: Visualization" ], "description": "![](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\nLinora is a simple and efficient tools for data mining and data analysis.\n\n\n\n| [API Document](https://github.com/Hourout/linora/blob/master/document/api.md) | [\u4e2d\u6587\u4ecb\u7ecd](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\n```\n\nTo get the newest one from this repo (note that we are in the alpha stage, so there may be frequent updates), type:\n\n```\npip install git+git://github.com/Hourout/linora.git\n```\n\n## Feature\n- metrics\n- metrics charts\n- feature columns\n- feature selection\n- image augmentation\n- text processing\n- model param search\n- sample splits\n\n## Example\n[more example](https://github.com/Hourout/linora/blob/master/example/readme.md)\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\n", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/Hourout/linora", "keywords": "hyperparameter-optimization,XGBoost,LightGBM,data-mining,data-analysis,machine-learning", "license": "Apache License Version 2.0", "maintainer": "", "maintainer_email": "", "name": "linora", "package_url": "https://pypi.org/project/linora/", 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