{"id":13688840,"url":"https://github.com/jameschapman19/cca_zoo","last_synced_at":"2025-04-05T00:10:28.748Z","repository":{"id":37773713,"uuid":"303801602","full_name":"jameschapman19/cca_zoo","owner":"jameschapman19","description":"Canonical Correlation Analysis Zoo: A collection of Regularized, Deep Learning based, Kernel, and Probabilistic methods in a scikit-learn style framework","archived":false,"fork":false,"pushed_at":"2024-04-19T10:57:15.000Z","size":8120,"stargazers_count":203,"open_issues_count":31,"forks_count":42,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-03-28T23:08:43.462Z","etag":null,"topics":["canonical-correlation-analysis","cca","cca-zoo","dcca","deep","kernel","multiset-cca","multiview","pls","pytorch","tensor-cca"],"latest_commit_sha":null,"homepage":"https://cca-zoo.readthedocs.io/en/latest/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jameschapman19.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-10-13T18:58:42.000Z","updated_at":"2025-02-19T12:21:47.000Z","dependencies_parsed_at":"2023-09-21T18:00:32.593Z","dependency_job_id":"a47b6908-2877-4d2d-97b6-7b9dd80e4f94","html_url":"https://github.com/jameschapman19/cca_zoo","commit_stats":{"total_commits":1214,"total_committers":5,"mean_commits":242.8,"dds":"0.11696869851729819","last_synced_commit":"93da5a71249244a0d0ccb38dff769d3aaa231028"},"previous_names":[],"tags_count":120,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jameschapman19%2Fcca_zoo","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jameschapman19%2Fcca_zoo/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jameschapman19%2Fcca_zoo/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jameschapman19%2Fcca_zoo/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jameschapman19","download_url":"https://codeload.github.com/jameschapman19/cca_zoo/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247266565,"owners_count":20910836,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["canonical-correlation-analysis","cca","cca-zoo","dcca","deep","kernel","multiset-cca","multiview","pls","pytorch","tensor-cca"],"created_at":"2024-08-02T15:01:24.521Z","updated_at":"2025-04-05T00:10:28.728Z","avatar_url":"https://github.com/jameschapman19.png","language":"Python","funding_links":[],"categories":["Python"],"sub_categories":[],"readme":"\u003cdiv align=\"center\"\u003e\n\n\u003cimg src=\"docs/logos/cca-zoo-logo.svg\" alt=\"drawing\" width=\"200\"/\u003e\n\n# CCA-Zoo\n\n**Unlock the hidden relationships in multiview data.**\n\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5748062.svg)](https://doi.org/10.5281/zenodo.4382739)\n[![codecov](https://codecov.io/gh/jameschapman19/cca_zoo/branch/main/graph/badge.svg?token=JHG9VUB0L8)](https://codecov.io/gh/jameschapman19/cca_zoo)\n![Build Status](https://github.com/jameschapman19/cca_zoo/actions/workflows/changes.yml/badge.svg)\n[![Documentation Status](https://readthedocs.org/projects/cca-zoo/badge/?version=latest)](https://cca-zoo.readthedocs.io/en/latest/?badge=latest)\n[![version](https://img.shields.io/pypi/v/cca-zoo)](https://pypi.org/project/cca-zoo/)\n[![downloads](https://img.shields.io/pypi/dm/cca-zoo)](https://pypi.org/project/cca-zoo/)\n[![DOI](https://joss.theoj.org/papers/10.21105/joss.03823/status.svg)](https://doi.org/10.21105/joss.03823)\n\n\n\u003c/div\u003e\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## 📖 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## 🚀 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\u003cimg src=\"docs/_static/CCA_Zoo_map.svg\" alt=\"drawing\" width=\"1000\"/\u003e\n\n## 🏎️ 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![Benchmark Plot CCA](benchmark/CCA_Speed_Benchmark.svg)\n![Benchmark Plot PLS](benchmark/PLS_Speed_Benchmark.svg)\n\n## 📚 Detailed Documentation\n\nEmbark on a journey through multiview correlations with our [comprehensive guide](https://cca-zoo.readthedocs.io/en/latest/).\n\n## 🙏 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📜 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## 👩‍💻 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## 🙌 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","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjameschapman19%2Fcca_zoo","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjameschapman19%2Fcca_zoo","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjameschapman19%2Fcca_zoo/lists"}