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Description:

lale 0.8.4

Lale

README in other languages:
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or contribute 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, 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.