CCA-Zoo

CCA-Zoo

Multiview Canonical Correlation Analysis in Python

PyPI Python CI codecov DOI License: MIT uv Ruff Types: mypy strict

CCA-Zoo is a Python library of reference implementations of Canonical Correlation Analysis (CCA) algorithms from the literature, from classical CCA (Hotelling 1936) through sparse, kernel, deep, and probabilistic variants — each documented with the paper it comes from. It's also built to be used directly: every model follows the same scikit-learn estimator API (fit, transform, fit_transform, score), is fully typed (PEP 561), and is tested against known closed-form solutions where one exists.

Installation

uv add cca-zoo        # or: pip install cca-zoo

Install optional extras as needed:

uv add "cca-zoo[deep]"          # DCCA variants (requires PyTorch + Lightning)
uv add "cca-zoo[probabilistic]" # Probabilistic CCA (requires NumPyro + JAX)
uv add "cca-zoo[tree]"          # TreeCCA (requires XGBoost, optionally LightGBM)
uv add "cca-zoo[all]"           # Everything above

(substitute pip install for uv add if you're not using uv)

Quick start

from cca_zoo.datasets import JointData
from cca_zoo.linear import CCA

# Generate correlated two-view data from a linear latent variable model
data = JointData(
    n_views=2,
    n_samples=200,
    n_features=[50, 50],
    latent_dimensions=2,
    signal_to_noise=2.0,
    random_state=0,
)
train_views = data.sample()
test_views = data.sample()

# Fit CCA and evaluate
model = CCA(latent_dimensions=2).fit(train_views)
print(model.score(test_views))  # canonical correlations, shape (2,)

# Project views into the shared latent space
z1, z2 = model.transform(test_views)  # each shape (200, 2)

Available methods

cca_zoo.linear

Class Description Views
CCA Standard CCA (Hotelling 1936) 2
rCCA Regularised CCA / canonical ridge 2
PLS Partial Least Squares 2
MCCA Multiset CCA — pairwise sum objective ≥2
GCCA Generalised CCA — shared latent projection ≥2
TCCA Tensor CCA — higher-order cross-moment ≥2
PartialCCA CCA adjusted for confounding variables (Rao 1969) ≥2
GRCCA Group-regularised CCA (Tuzhilina, Tozzi & Hastie 2021) ≥2
CCAR3 CCA via reduced-rank regression, row-sparse in high dimensions (Donnat & Tuzhilina 2024) 2
CCA_EY Stochastic Eckart-Young CCA (unconstrained gradient descent) 2
PLS_EY Stochastic Eckart-Young PLS (unconstrained gradient descent) 2
MCCA_EY Multiview Eckart-Young CCA (unconstrained gradient descent) ≥2
SCCA_PMD Sparse CCA via PMD (Witten 2009) ≥2
SCCA_ADMM Sparse CCA via ADMM (Suo 2017) ≥2
SCCA_IPLS Sparse CCA via iterative PLS (Mai & Zhang 2019) ≥2
SCCA_Span SpanCCA (Asteris 2016) ≥2
ElasticCCA Elastic net regularised CCA (Waaijenborg 2008) ≥2
ParkhomenkoCCA Soft-threshold sparse CCA (Parkhomenko 2009) ≥2
PLS_ALS ALS variant of PLS (power iteration) ≥2

cca_zoo.nonparametric

Class Description
KCCA Kernel CCA
KGCCA Kernel Generalised CCA
KTCCA Kernel Tensor CCA

cca_zoo.tree (requires [tree])

Class Description Views
TreeCCA Gradient-boosted-tree CCA (Eckart-Young objective) ≥2

cca_zoo.deep (requires [deep])

Built on PyTorch Lightning — models are trained with a standard lightning.Trainer, not a fit() wrapper. See the deep learning guide.

Class Reference
DCCA Andrew et al. 2013 — pluggable objective
DCCA_EY Eigengame / Eckart-Young objective
DCCA_NOI Wang et al. 2015 — non-linear orthogonal iterations
DCCA_SDL Chang et al. 2018 — stochastic decorrelation loss
DCCAE Wang et al. 2015 — with autoencoder reconstruction
DVCCA Wang et al. 2016 — variational
DTCCA Wong et al. 2021 — deep tensor CCA
DMCCA Deep multiset CCA — pairwise-sum objective, ≥2 views
DGCCA Benton et al. 2019 — deep generalised CCA, ≥2 views
SplitAE Split autoencoder baseline
BarlowTwins Zbontar et al. 2021
VICReg Bardes et al. 2022

cca_zoo.probabilistic (requires [probabilistic])

Class Reference
ProbabilisticCCA Bach & Jordan 2005; Wang 2007 — MCMC via NumPyro

cca_zoo.model_selection

Class Description
GridSearchCV Cross-validated hyperparameter search for multiview models

Documentation

Full documentation, user guides, and API reference at: https://jameschapman19.github.io/cca_zoo/

See CHANGELOG.md for what's changed between releases.

Citing

If CCA-Zoo is useful in your research, please cite:

@article{Chapman2021,
  title   = {{CCA-Zoo}: A collection of Regularized, Deep Learning based, Kernel,
             and Probabilistic {CCA} methods in a scikit-learn style framework},
  author  = {Chapman, James and Wang, Hao-Ting and Wells, Lennie and Wiesner, Johannes},
  journal = {Journal of Open Source Software},
  volume  = {6},
  number  = {68},
  pages   = {3823},
  year    = {2021},
  doi     = {10.21105/joss.03823},
}

Contributing

Contributions are welcome. See docs/contributing.md for development setup, coding standards, and pull request guidelines.

Project links

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Key dates

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  • Released:
Newer release available (3.1.1)

1 maintainer

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Avatar for chapmajw from gravatar.com chapmajw

Credits

Author: James Chapman

GitHub Statistics

Data verified by PyPI on Aug 3, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License (MIT)

Requires

Python >=3.10

Provides Extra

all deep dev docs probabilistic tree

Tags

canonical correlation analysis cca multiview

Classifiers

Report project as malware

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