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"description": "# Lale\n\n[](https://github.com/IBM/lale/actions?query=workflow%3ATests+branch%3Amaster)\n[](https://lale.readthedocs.io/en/latest/?badge=latest)\n[](https://pypi.python.org/pypi/lale/)\n[](https://pycqa.github.io/isort/)\n[](https://github.com/psf/black)\n[](https://opensource.org/licenses/Apache-2.0)\n[](https://bestpractices.coreinfrastructure.org/projects/5863)\n
\n![\"logo\"](\"https://github.com/IBM/lale/raw/master/docs/img/lale_logo.jpg\")
\n\nREADME in other languages: \n[\u4e2d\u6587](https://github.com/IBM/lale/blob/master/docs/README-cn.md),\n[deutsch](https://github.com/IBM/lale/blob/master/docs/README-de.md),\n[fran\u00e7ais](https://github.com/IBM/lale/blob/master/docs/README-fr.md),\nor [contribute](https://github.com/IBM/lale/blob/master/CONTRIBUTING.md) your own.\n\nLale is a Python library for semi-automated data science.\nLale makes it easy to automatically select algorithms and tune\nhyperparameters of pipelines that are compatible with\n[scikit-learn](https://scikit-learn.org), in a type-safe fashion. If\nyou are a data scientist who wants to experiment with automated\nmachine learning, this library is for you!\nLale adds value beyond scikit-learn along three dimensions:\nautomation, correctness checks, and interoperability.\nFor *automation*, Lale provides a consistent high-level interface to\nexisting pipeline search tools including Hyperopt, GridSearchCV, and SMAC.\nFor *correctness checks*, Lale uses JSON Schema to catch mistakes when\nthere is a mismatch between hyperparameters and their type, or between\ndata and operators.\nAnd for *interoperability*, Lale has a growing library of transformers\nand estimators from popular libraries such as scikit-learn, XGBoost,\nPyTorch etc.\nLale can be installed just like any other Python package and can be\nedited with off-the-shelf Python tools such as Jupyter notebooks.\n\n* [Introductory guide](https://nbviewer.jupyter.org/github/IBM/lale/blob/master/examples/docs_guide_for_sklearn_users.ipynb) for scikit-learn users\n* [Installation instructions](https://github.com/IBM/lale/blob/master/docs/installation.rst)\n* Technical overview [slides](https://github.com/IBM/lale/blob/master/talks/2019-1105-lale.pdf), [notebook](https://nbviewer.jupyter.org/github/IBM/lale/blob/master/examples/talk_2019-1105-lale.ipynb), and [video](https://www.youtube.com/watch?v=R51ZDJ64X18&list=PLGVZCDnMOq0pwoOqsaA87cAoNM4MWr51M&index=35&t=0s)\n* IBM's [AutoAI SDK](http://wml-api-pyclient-v4.mybluemix.net/#autoai-beta-ibm-cloud-only) uses Lale, see demo [notebook](https://dataplatform.cloud.ibm.com/exchange/public/entry/view/8bddf7f7e5d004a009c643750b16d0c0)\n* Guide for wrapping [new operators](https://nbviewer.jupyter.org/github/IBM/lale/blob/master/examples/docs_new_operators.ipynb)\n* Guide for [contributing](https://github.com/IBM/lale/blob/master/CONTRIBUTING.md) to Lale\n* [FAQ](https://github.com/IBM/lale/blob/master/docs/faq.rst)\n* [Papers](https://github.com/IBM/lale/blob/master/docs/papers.rst)\n* Python [API documentation](https://lale.readthedocs.io/en/latest/)\n\nThe name Lale, pronounced *laleh*, comes from the Persian word for\ntulip. Similarly to popular machine-learning libraries such as\nscikit-learn, Lale is also just a Python library, not a new stand-alone\nprogramming language. It does not require users to install new tools\nnor learn new syntax.\n\nLale is distributed under the terms of the Apache 2.0 License, see\n[LICENSE.txt](https://github.com/IBM/lale/blob/master/LICENSE.txt).\nIt is currently in an **Alpha release**, without warranties of any\nkind.\n\n\n",
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