Metadata-Version: 2.1
Name: lale
Version: 0.3.0
Summary: Language for Automated Learning Exploration
Home-page: https://github.com/IBM/lale
Author: Avraham Shinnar, Guillaume Baudart, Kiran Kate, Martin Hirzel
License: UNKNOWN
Description: # Lale
        
        [![Build Status](https://travis-ci.com/IBM/lale.svg?branch=master)](https://travis-ci.com/IBM/lale)
        [![Documentation Status](https://readthedocs.org/projects/lale/badge/?version=latest)](https://lale.readthedocs.io/en/latest/?badge=latest)
        <br />
        <img src="https://github.com/IBM/lale/raw/master/docs/img/lale_logo.jpg" alt="logo" width="55px"/>
        
        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 GridSearchCV, SMAC, and
        Hyperopt.
        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.
        
        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.
        
        * Technical overview [slides](https://github.com/IBM/lale/blob/master/talks/2019-0529-lale.pdf)
        * [Installation instructions](https://github.com/IBM/lale/blob/master/docs/installation.rst)
        * Python [API documentation](https://lale.readthedocs.io/en/latest/)
        * Guide for wrapping [new operators](https://github.com/IBM/lale/blob/master/docs/new_operators.md)
        * arXiv [paper](https://arxiv.org/pdf/1906.03957.pdf)
        
        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.
        
        The following paper has a technical deep-dive:
        ```
        @Article{arxiv19-lale,
          author = "Hirzel, Martin and Kate, Kiran and Shinnar, Avraham and Roy, Subhrajit and Ram, Parikshit",
          title = "Type-Driven Automated Learning with {Lale}",
          journal = "CoRR",
          volume = "abs/1906.03957",
          year = 2019,
          month = may,
          url = "https://arxiv.org/abs/1906.03957" }
        ```
        
        Contributors are expected to submit a "Developer's Certificate of
        Origin", which can be found in [DCO1.1.txt](https://github.com/IBM/lale/blob/master/DCO1.1.txt).
        
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: full
Provides-Extra: test
