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https://github.com/IBM/lale

Library for Semi-Automated Data Science
https://github.com/IBM/lale

artificial-intelligence automated-machine-learning automl data-science dataquality hyperparameter-optimization hyperparameter-search hyperparameter-tuning ibm-research ibm-research-ai interoperability machine-learning pipeline-testing pipeline-tests python scikit-learn

Last synced: about 1 month ago
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Library for Semi-Automated Data Science

Host: GitHub
URL: https://github.com/IBM/lale
Owner: IBM
License: apache-2.0
Created: 2019-08-06T14:39:27.000Z (about 5 years ago)
Default Branch: master
Last Pushed: 2024-07-30T19:36:57.000Z (about 2 months ago)
Last Synced: 2024-07-30T23:56:39.733Z (about 2 months ago)
Topics: artificial-intelligence, automated-machine-learning, automl, data-science, dataquality, hyperparameter-optimization, hyperparameter-search, hyperparameter-tuning, ibm-research, ibm-research-ai, interoperability, machine-learning, pipeline-testing, pipeline-tests, python, scikit-learn
Language: Python
Homepage: https://lale.readthedocs.io
Size: 40.5 MB
Stars: 326
Watchers: 23
Forks: 82
Open Issues: 23
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.txt

Awesome Lists containing this project

README

        # Lale

[![Tests](https://github.com/IBM/lale/workflows/Tests/badge.svg?branch=master)](https://github.com/IBM/lale/actions?query=workflow%3ATests+branch%3Amaster)

[![Documentation Status](https://readthedocs.org/projects/lale/badge/?version=latest)](https://lale.readthedocs.io/en/latest/?badge=latest)

[![PyPI version shields.io](https://img.shields.io/pypi/v/lale?color=success)](https://pypi.python.org/pypi/lale/)

[![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)

[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

[![linting: pylint](https://img.shields.io/badge/linting-pylint-yellowgreen)](https://github.com/PyCQA/pylint)

[![security: bandit](https://img.shields.io/badge/security-bandit-yellow.svg)](https://github.com/PyCQA/bandit)

[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)

[![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/5863/badge)](https://bestpractices.coreinfrastructure.org/projects/5863)






README in other languages: 

[中文](https://github.com/IBM/lale/blob/master/docs/README-cn.md),

[deutsch](https://github.com/IBM/lale/blob/master/docs/README-de.md),

[français](https://github.com/IBM/lale/blob/master/docs/README-fr.md),

or [contribute](https://github.com/IBM/lale/blob/master/CONTRIBUTING.md) your own.

Lale is a Python library for semi-automated data science.

Lale makes it easy to automatically select algorithms and tune

hyperparameters of pipelines that are compatible with

[scikit-learn](https://scikit-learn.org), in a type-safe fashion.  If

you are a data scientist who wants to experiment with automated

machine learning, this library is for you!

Lale adds value beyond scikit-learn along three dimensions:

automation, correctness checks, and interoperability.

For *automation*, Lale provides a consistent high-level interface to

existing pipeline search tools including Hyperopt, GridSearchCV, and SMAC.

For *correctness checks*, Lale uses JSON Schema to catch mistakes when

there is a mismatch between hyperparameters and their type, or between

data and operators.

And for *interoperability*, Lale has a growing library of transformers

and estimators from popular libraries such as scikit-learn, XGBoost,

PyTorch etc.

Lale can be installed just like any other Python package and can be

edited with off-the-shelf Python tools such as Jupyter notebooks.

* [Introductory guide](https://github.com/IBM/lale/blob/master/examples/docs_guide_for_sklearn_users.ipynb) for scikit-learn users

* [Installation instructions](https://github.com/IBM/lale/blob/master/docs/installation.rst)

* Technical overview [slides](https://github.com/IBM/lale/blob/master/talks/2019-1105-lale.pdf), [notebook](https://github.com/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)

* 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)

* Guide for wrapping [new operators](https://github.com/IBM/lale/blob/master/examples/docs_new_operators.ipynb)

* Guide for [contributing](https://github.com/IBM/lale/blob/master/CONTRIBUTING.md) to Lale

* [FAQ](https://github.com/IBM/lale/blob/master/docs/faq.rst)

* [Papers](https://github.com/IBM/lale/blob/master/docs/papers.rst)

* Python [API documentation](https://lale.readthedocs.io/en/latest/)

The name Lale, pronounced *laleh*, comes from the Persian word for

tulip. Similarly to popular machine-learning libraries such as

scikit-learn, Lale is also just a Python library, not a new stand-alone

programming language. It does not require users to install new tools

nor learn new syntax.

Lale is distributed under the terms of the Apache 2.0 License, see

[LICENSE.txt](https://github.com/IBM/lale/blob/master/LICENSE.txt).

It is currently in an **Alpha release**, without warranties of any

kind.