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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, 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 for scikit-learn users
- Installation instructions
- Technical overview slides, notebook, and video
- IBM's AutoAI SDK uses Lale, see demo notebook
- Guide for wrapping new operators
- Guide for contributing to Lale
- FAQ
- Papers
- Python API documentation
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. It is currently in an Alpha release, without warranties of any kind.