CCA-Zoo
CCA-Zoo: A collection of Regularized, Deep Learning based, Kernel, and Probabilistic CCA methods in a scikit-learn style framework - Published in JOSS (2021)
Science Score: 100.0%
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✓CITATION.cff file
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✓DOI references
Found 10 DOI reference(s) in README and JOSS metadata -
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✓JOSS paper metadata
Published in Journal of Open Source Software
Keywords
Scientific Fields
Repository
Canonical Correlation Analysis Zoo: A collection of Regularized, Deep Learning based, Kernel, and Probabilistic methods in a scikit-learn style framework
Basic Info
- Host: GitHub
- Owner: jameschapman19
- License: mit
- Language: Python
- Default Branch: main
- Homepage: https://cca-zoo.readthedocs.io/en/latest/
- Size: 7.74 MB
Statistics
- Stars: 211
- Watchers: 2
- Forks: 44
- Open Issues: 32
- Releases: 118
Topics
Metadata Files
README.md
# CCA-Zoo
**Unlock the hidden relationships in multiview data.**
[](https://doi.org/10.5281/zenodo.4382739)
[](https://codecov.io/gh/jameschapman19/cca_zoo)
[](https://cca-zoo.readthedocs.io/en/latest/?badge=latest)
[](https://pypi.org/project/cca-zoo/)
[](https://pypi.org/project/cca-zoo/)
[](https://doi.org/10.21105/joss.03823)
Introduction
In today's data-driven world, revealing hidden relationships across multiview datasets is critical. CCA-Zoo is your go-to library, featuring a robust selection of linear, kernel, and deep canonical correlation analysis methods.
Designed to be user-friendly, CCA-Zoo is inspired by the ease of use in scikit-learn and mvlearn. It provides a seamless programming experience with familiar fit, transform, and fit_transform methods.
📖 Table of Contents
🚀 Quick Start
Installation
Whether you're a pip enthusiast or a poetry aficionado, installing CCA-Zoo is a breeze:
```bash pip install cca-zoo
For additional features
pip install cca-zoo[probabilistic, visualisation, deep] ```
For Poetry users:
```bash poetry add cca-zoo
For extra features
poetry add cca-zoo[probabilistic, visualisation, deep] ```
Note that deep requires torch and lightning which may be better installed separately following the PyTorch installation guide.
probabilistic requires numpyro which may be better installed separately following the NumPyro installation guide.
visualisation requires matplotlib and seaborn
Plug into the Machine Learning Ecosystem
CCA-Zoo is designed to be compatible with the machine learning ecosystem. It is built on top of scikit-learn, tensorly, torch, pytorch-lightning, and numpyro.
🏎️ Performance Highlights
CCA-Zoo shines when it comes to high-dimensional data analysis. It significantly outperforms scikit-learn, particularly as dimensionality increases. For comprehensive benchmarks, see our script and the graph below.
📚 Detailed Documentation
Embark on a journey through multiview correlations with our comprehensive guide.
🙏 How to Cite
Your support means a lot to us! If CCA-Zoo has been beneficial for your research, there are two ways to show your appreciation:
- Star our GitHub repository.
- Cite our research paper in your publications.
For citing our work, please use the following BibTeX entry:
bibtex
@software{Chapman_CCA-Zoo_2023,
author = {Chapman, James and Wang, Hao-Ting and Wells, Lennie and Wiesner, Johannes},
doi = {10.5281/zenodo.4382739},
month = aug,
title = {{CCA-Zoo}},
url = {https://github.com/jameschapman19/cca_zoo},
version = {2.3.0},
year = {2023}
}
Or check out our JOSS paper:
📜 Chapman et al., (2021). CCA-Zoo: A collection of Regularized, Deep Learning based, Kernel, and Probabilistic CCA methods in a scikit-learn style framework. Journal of Open Source Software, 6(68), 3823, Link.
👩💻 Contribute
Every idea, every line of code adds value. Check out our contribution guide and help CCA-Zoo soar to new heights!
🙌 Acknowledgments
Special thanks to the pioneers whose work has shaped this field. Explore their work:
- Regularised CCA/PLS: MATLAB
- Sparse PLS: MATLAB SPLS
- DCCA/DCCAE: Keras DCCA, Torch DCCA
Owner
- Name: James Chapman
- Login: jameschapman19
- Kind: user
- Location: London
- Company: UCL
- Website: https://jameschapman19.github.io
- Twitter: chapmajw
- Repositories: 38
- Profile: https://github.com/jameschapman19
Studying for a PhD in Machine Learning and Neuroimaging at University College London (UCL)
JOSS Publication
CCA-Zoo: A collection of Regularized, Deep Learning based, Kernel, and Probabilistic CCA methods in a scikit-learn style framework
Authors
Centre for Medical Image Computing, University College London, London, UK
Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Université de Montréal, Montréal, QC, Canada, Centre de Recherche de l'Hôpital du Sacré Coeur de Montréal, Université de Montréal, Montréal, QC, Canada
Editor
Elizabeth DuPre
Tags
Multiview Machine LearningCitation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - family-names: "Chapman" given-names: "James" orcid: "https://orcid.org/0000-0002-9364-8118" - family-names: "Wang" given-names: "Hao-Ting" orcid: "https://orcid.org/0000-0003-4078-2038" - family-names: "Wells" given-names: "Lennie" - family-names: "Wiesner" given-names: "Johannes" title: "CCA-Zoo" version: 2.3.0 doi: 10.5281/zenodo.4382739 date-released: 2023-08-22 url: "https://github.com/jameschapman19/cca_zoo"
GitHub Events
Total
- Issues event: 6
- Watch event: 17
- Issue comment event: 7
- Fork event: 4
Last Year
- Issues event: 6
- Watch event: 17
- Issue comment event: 7
- Fork event: 4
Committers
Last synced: 7 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| James Chapman | j****9@u****k | 1,766 |
| Hao-Ting Wang | h****w@g****m | 7 |
| JohannesWiesner | j****r@g****m | 3 |
| Lennie | 5****W | 3 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 78
- Total pull requests: 54
- Average time to close issues: about 2 months
- Average time to close pull requests: about 3 hours
- Total issue authors: 28
- Total pull request authors: 5
- Average comments per issue: 4.79
- Average comments per pull request: 0.46
- Merged pull requests: 50
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 8
- Pull requests: 0
- Average time to close issues: about 7 hours
- Average time to close pull requests: N/A
- Issue authors: 5
- Pull request authors: 0
- Average comments per issue: 0.13
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- JohannesWiesner (20)
- jameschapman19 (10)
- Umaruchain (6)
- BogyeomKim (5)
- LegrandNico (3)
- vignesh294 (3)
- Neel-132 (3)
- nienkevanunen (2)
- teakfi (2)
- AdirRahamim (2)
- baili119 (2)
- WantongLi123 (2)
- psycholinguistics2125 (1)
- amogh3892 (1)
- Wolongchicken (1)
Pull Request Authors
- jameschapman19 (45)
- W-L-W (4)
- JohannesWiesner (3)
- htwangtw (1)
- LegrandNico (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 1,030 last-month
- Total dependent packages: 0
- Total dependent repositories: 3
- Total versions: 177
- Total maintainers: 1
pypi.org: cca-zoo
Canonical Correlation Analysis Zoo: A collection of Regularized, Deep Learning based, Kernel, and Probabilistic methods in a scikit-learn style framework
- Homepage: https://github.com/jameschapman19/cca_zoo
- Documentation: https://cca-zoo.readthedocs.io/
- License: MIT
-
Latest release: 2.6.0
published almost 2 years ago
Rankings
Maintainers (1)
Dependencies
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