https://github.com/ibm/lale

Library for Semi-Automated Data Science

Science Score: 36.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
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.zenodo.json file
Found .zenodo.json file
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DOI references
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Academic publication links
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Committers with academic emails
1 of 28 committers (3.6%) from academic institutions
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Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (15.6%) to scientific vocabulary

Keywords

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

Keywords from Contributors

bias bias-correction bias-detection bias-finder bias-reduction codait discrimination fairness fairness-ai fairness-awareness-model

Last synced: 6 months ago · JSON representation

Repository

Library for Semi-Automated Data Science

Basic Info

Host: GitHub
Owner: IBM
License: apache-2.0
Language: Python
Default Branch: master
Homepage: https://lale.readthedocs.io
Size: 40.8 MB

Statistics

Stars: 342
Watchers: 21
Forks: 80
Open Issues: 24
Releases: 85

Topics

Created over 6 years ago · Last pushed 10 months ago

Metadata Files

Readme Contributing License

README.md

Lale

logo

README in other languages: 中文, deutsch, français, 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.

Owner

Name: International Business Machines
Login: IBM
Kind: organization
Email: awesome@ibm.com
Location: United States of America

Website: https://www.ibm.com/opensource/
Twitter: ibmdeveloper
Repositories: 3,152
Profile: https://github.com/IBM

GitHub Events

Total

Release event: 1
Watch event: 12
Issue comment event: 2
Push event: 16
Pull request event: 34
Fork event: 1
Create event: 1

Last Year

Release event: 1
Watch event: 12
Issue comment event: 2
Push event: 16
Pull request event: 34
Fork event: 1
Create event: 1

Committers

Last synced: 11 months ago

All Time

Total Commits: 1,489
Total Committers: 28
Avg Commits per committer: 53.179
Development Distribution Score (DDS): 0.641

Past Year

Commits: 20
Committers: 3
Avg Commits per committer: 6.667
Development Distribution Score (DDS): 0.45

Top Committers

Name	Email	Commits
Martin Hirzel	h**l@g**m	534
Avi Shinnar	s**r@u**m	421
kiran-kate	4****e	330
Guillaume Baudart	g**t@i**m	56
Louis Mandel	m****l	51
kakate@us.ibm.com	k**e@u**m	20
mfeffer	f**8@g**m	18
Chirag Sahni	s**g@y**n	10
Pari Ram	p**m@g**u	8
Daniel Ryszka	6****M	7
Thomas Parnell	t**a@z**m	5
Ingkarat Rak-amnouykit	i**w@h**m	5
szymonkucharczyk	7****k	4
Jason Tsay	j**y@i**m	3
vaisaxena	3****a	2
haodeqi	4****i	2
daniel-karl	5****l	2
Martin Hirzel	h**l@u**m	1
ksrinivs64	k****4	1
Vicky Vishal Sahu	v**u@g**m	1
Steve Martinelli	4****r	1
Souhit Dey	o**d@p**m	1
Rafalll-Maciasz	1****z	1
Panagiotis Hadjidoukas (Chatzidoukas)	p**o@g**m	1
MateuszSzymkowskiIBM	8****M	1
Marta Tomzik	1****k	1
BO SONG	1****g	1
Ali Reza Yahyapour	l**y@g**m	1

Committer Domains (Top 20 + Academic)

us.ibm.com: 3 ibm.com: 2 zurich.ibm.com: 1 gatech.edu: 1 yahoo.in: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 12
Total pull requests: 177
Average time to close issues: over 1 year
Average time to close pull requests: 9 days
Total issue authors: 7
Total pull request authors: 10
Average comments per issue: 1.75
Average comments per pull request: 0.09
Merged pull requests: 163
Bot issues: 0
Bot pull requests: 2

Past Year

Issues: 1
Pull requests: 32
Average time to close issues: N/A
Average time to close pull requests: 9 days
Issue authors: 1
Pull request authors: 3
Average comments per issue: 5.0
Average comments per pull request: 0.03
Merged pull requests: 28
Bot issues: 0
Bot pull requests: 1

View more stats

Top Authors

Issue Authors

kiran-kate (4)
rootsmusic (3)
tdoublep (2)
hirzel (2)
josk0 (1)
ghost (1)
mfeffer (1)

Pull Request Authors

shinnar (161)
hirzel (59)
kiran-kate (8)
Rafalll-Maciasz (3)
mfeffer (2)
marta-tomzik (2)
dependabot[bot] (2)
DanielRyszkaIBM (2)
renovate[bot] (2)
TrellixVulnTeam (1)

Top Labels

Issue Labels

help wanted (1)

Pull Request Labels

dependencies (2)

Packages

Total packages: 1
Total downloads:
- pypi 4,527 last-month
Total docker downloads: 28

Total dependent packages: 2
Total dependent repositories: 9
Total versions: 88
Total maintainers: 3

pypi.org: lale

Library for Semi-Automated Data Science

Homepage: https://github.com/IBM/lale
Documentation: https://lale.readthedocs.io/
License: Apache License 2.0
Latest release: 0.9.0
published 11 months ago

Versions: 88
Dependent Packages: 2
Dependent Repositories: 9
Downloads: 4,527 Last month
Docker Downloads: 28

Rankings

Dependent packages count: 2.1%

Stargazers count: 3.6%

Average: 4.1%

Forks count: 4.9%

Dependent repos count: 4.9%

Downloads: 5.0%

Maintainers (3)

csplavi hirzel kiran-kate

Last synced: 6 months ago

Dependencies

.github/workflows/build.yml actions

actions/cache v3 composite
actions/checkout v3 composite
actions/setup-python v4 composite
pre-commit/action v3.0.0 composite
pypa/gh-action-pypi-publish release/v1 composite

.github/workflows/release.yml actions

actions/checkout v2 composite
actions/setup-python v2 composite
pypa/gh-action-pypi-publish master composite
pypa/gh-action-pypi-publish release/v1 composite

Dockerfile docker

python ${python_version}-stretch build

docs/requirements.txt pypi

aif360 >=0.4.0
astunparse *
black ==19.10b0
cvxpy >=1.0,<=1.1.7
decorator *
docutils <0.17
graphviz *
h5py *
hyperopt *
imbalanced-learn *
jsonschema *
jsonsubschema *
liac-arff >=2.4.0
lightgbm *
m2r2 *
numba ==0.49.0
numpy *
pandas *
scikit-learn >=0.20.3
scipy *
smac <=0.10.0
sphinx >=5.0.0
sphinx_rtd_theme >=0.5.2
sphinxcontrib-svg2pdfconverter *
sphinxcontrib.apidoc *
typing-extensions *
xgboost <=1.3.3