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https://github.com/jameschapman19/cca_zoo
Canonical Correlation Analysis Zoo: A collection of Regularized, Deep Learning based, Kernel, and Probabilistic methods in a scikit-learn style framework
https://github.com/jameschapman19/cca_zoo
canonical-correlation-analysis cca cca-zoo dcca deep kernel multiset-cca multiview pls pytorch tensor-cca
Last synced: 6 days ago
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Canonical Correlation Analysis Zoo: A collection of Regularized, Deep Learning based, Kernel, and Probabilistic methods in a scikit-learn style framework
- Host: GitHub
- URL: https://github.com/jameschapman19/cca_zoo
- Owner: jameschapman19
- License: mit
- Created: 2020-10-13T18:58:42.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2024-04-19T10:57:15.000Z (9 months ago)
- Last Synced: 2025-01-11T01:07:29.844Z (13 days ago)
- Topics: canonical-correlation-analysis, cca, cca-zoo, dcca, deep, kernel, multiset-cca, multiview, pls, pytorch, tensor-cca
- Language: Python
- Homepage: https://cca-zoo.readthedocs.io/en/latest/
- Size: 7.74 MB
- Stars: 197
- Watchers: 4
- Forks: 41
- Open Issues: 29
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
README
# 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](#-quick-start)
- [Performance Highlights](#-performance-highlights)
- [Detailed Documentation](#-detailed-documentation)
- [How to Cite](#-how-to-cite)
- [Contribute](#-contribute)
- [Acknowledgments](#-acknowledgments)
## 🚀 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](https://pytorch.org/get-started/locally/).
`probabilistic` requires `numpyro` which may be better installed separately following the [NumPyro installation guide](https://num.pyro.ai/en/stable/getting_started.html#installation).
`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](benchmark/cca_high_dimensions.py) and the graph below.
## 📚 Detailed Documentation
Embark on a journey through multiview correlations with our [comprehensive guide](https://cca-zoo.readthedocs.io/en/latest/).
## 🙏 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:
1. Star our GitHub repository.
2. 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](https://doi.org/10.21105/joss.03823).
## 👩💻 Contribute
Every idea, every line of code adds value. Check out our [contribution guide](https://cca-zoo.readthedocs.io/en/latest/developer_info/contribute.html) 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](https://github.com/anaston/PLS_CCA_framework)
- Sparse PLS: [MATLAB SPLS](https://github.com/jmmonteiro/spls)
- DCCA/DCCAE: [Keras DCCA](https://github.com/VahidooX), [Torch DCCA](https://github.com/Michaelvll/DeepCCA)