| Name | cca-zoo JSON |
| Version |
2.6.0
JSON |
| download |
| home_page | https://github.com/jameschapman19/cca_zoo |
| Summary | Canonical Correlation Analysis Zoo: A collection of Regularized, Deep Learning based, Kernel, and Probabilistic methods in a scikit-learn style framework |
| upload_time | 2024-04-19 10:57:42 |
| maintainer | None |
| docs_url | None |
| author | jameschapman |
| requires_python | <4.0.0,>=3.8 |
| license | MIT |
| keywords |
cca
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<div align="center">
<img src="docs/logos/cca-zoo-logo.svg" alt="drawing" width="200"/>
# 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)
</div>
## 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`.
<img src="docs/_static/CCA_Zoo_map.svg" alt="drawing" width="1000"/>
## 🏎️ 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)
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"description": "<div align=\"center\">\n\n<img src=\"docs/logos/cca-zoo-logo.svg\" alt=\"drawing\" width=\"200\"/>\n\n# CCA-Zoo\n\n**Unlock the hidden relationships in multiview data.**\n\n[](https://doi.org/10.5281/zenodo.4382739)\n[](https://codecov.io/gh/jameschapman19/cca_zoo)\n\n[](https://cca-zoo.readthedocs.io/en/latest/?badge=latest)\n[](https://pypi.org/project/cca-zoo/)\n[](https://pypi.org/project/cca-zoo/)\n[](https://doi.org/10.21105/joss.03823)\n\n\n</div>\n\n## Introduction\n\nIn 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.\n\nDesigned 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.\n\n## \ud83d\udcd6 Table of Contents\n\n- [Quick Start](#-quick-start)\n- [Performance Highlights](#-performance-highlights)\n- [Detailed Documentation](#-detailed-documentation)\n- [How to Cite](#-how-to-cite)\n- [Contribute](#-contribute)\n- [Acknowledgments](#-acknowledgments)\n\n## \ud83d\ude80 Quick Start\n\n### Installation\n\nWhether you're a `pip` enthusiast or a `poetry` aficionado, installing CCA-Zoo is a breeze:\n\n```bash\npip install cca-zoo\n# For additional features\npip install cca-zoo[probabilistic, visualisation, deep]\n```\n\nFor Poetry users:\n\n```bash\npoetry add cca-zoo\n# For extra features\npoetry add cca-zoo[probabilistic, visualisation, deep]\n```\n\nNote that `deep` requires `torch` and `lightning` which may be better installed separately following the [PyTorch installation guide](https://pytorch.org/get-started/locally/).\n\n`probabilistic` requires `numpyro` which may be better installed separately following the [NumPyro installation guide](https://num.pyro.ai/en/stable/getting_started.html#installation).\n\n`visualisation` requires `matplotlib` and `seaborn`\n\n## Plug into the Machine Learning Ecosystem\n\nCCA-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`.\n\n<img src=\"docs/_static/CCA_Zoo_map.svg\" alt=\"drawing\" width=\"1000\"/>\n\n## \ud83c\udfce\ufe0f Performance Highlights\nCCA-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.\n\n\n\n\n## \ud83d\udcda Detailed Documentation\n\nEmbark on a journey through multiview correlations with our [comprehensive guide](https://cca-zoo.readthedocs.io/en/latest/).\n\n## \ud83d\ude4f How to Cite\n\nYour support means a lot to us! If CCA-Zoo has been beneficial for your research, there are two ways to show your appreciation:\n\n1. Star our GitHub repository.\n2. Cite our research paper in your publications.\n\nFor citing our work, please use the following BibTeX entry:\n\n```bibtex\n@software{Chapman_CCA-Zoo_2023,\nauthor = {Chapman, James and Wang, Hao-Ting and Wells, Lennie and Wiesner, Johannes},\ndoi = {10.5281/zenodo.4382739},\nmonth = aug,\ntitle = {{CCA-Zoo}},\nurl = {https://github.com/jameschapman19/cca_zoo},\nversion = {2.3.0},\nyear = {2023}\n}\n```\n\nOr check out our JOSS paper:\n\n\ud83d\udcdc 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).\n\n## \ud83d\udc69\u200d\ud83d\udcbb Contribute\n\nEvery 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!\n\n## \ud83d\ude4c Acknowledgments\n\nSpecial thanks to the pioneers whose work has shaped this field. Explore their work:\n\n- Regularised CCA/PLS: [MATLAB](https://github.com/anaston/PLS_CCA_framework)\n- Sparse PLS: [MATLAB SPLS](https://github.com/jmmonteiro/spls)\n- DCCA/DCCAE: [Keras DCCA](https://github.com/VahidooX), [Torch DCCA](https://github.com/Michaelvll/DeepCCA)\n",
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