Metadata-Version: 2.4
Name: cca-zoo
Version: 3.0.0
Summary: Multiview CCA methods in a scikit-learn style framework
Project-URL: Homepage, https://github.com/jameschapman19/cca_zoo
Project-URL: Repository, https://github.com/jameschapman19/cca_zoo
Project-URL: Documentation, https://jameschapman19.github.io/cca_zoo/
Project-URL: Bug Tracker, https://github.com/jameschapman19/cca_zoo/issues
Author-email: James Chapman <james.chapman.19@ucl.ac.uk>
License: MIT
License-File: LICENSE
Keywords: canonical correlation analysis,cca,multiview
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.10
Requires-Dist: numpy>=1.26
Requires-Dist: scikit-learn>=1.4
Requires-Dist: scipy>=1.11
Requires-Dist: tensorly>=0.8
Provides-Extra: all
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Requires-Dist: jax>=0.4; extra == 'all'
Requires-Dist: lightning>=2.1; extra == 'all'
Requires-Dist: numpyro>=0.13; extra == 'all'
Requires-Dist: torch>=2.1; extra == 'all'
Provides-Extra: deep
Requires-Dist: lightning>=2.1; extra == 'deep'
Requires-Dist: torch>=2.1; extra == 'deep'
Provides-Extra: dev
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Requires-Dist: pytest-cov>=5; extra == 'dev'
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Requires-Dist: numpyro>=0.13; extra == 'probabilistic'
Description-Content-Type: text/markdown

<div align="center">
<img src="docs/logos/cca-zoo-logo.svg" alt="CCA-Zoo" width="180"/>

# CCA-Zoo

**Multiview Canonical Correlation Analysis in Python**

[![PyPI](https://img.shields.io/pypi/v/cca-zoo)](https://pypi.org/project/cca-zoo/)
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[![DOI](https://joss.theoj.org/papers/10.21105/joss.03823/status.svg)](https://doi.org/10.21105/joss.03823)
[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)

</div>

CCA-Zoo is a Python library implementing a wide range of **Canonical Correlation Analysis** (CCA) and related multiview learning methods. It follows the [scikit-learn](https://scikit-learn.org) estimator API: every model exposes `fit`, `transform`, `fit_transform`, and `score`.

---

## Installation

```bash
pip install cca-zoo
```

Install optional extras as needed:

```bash
pip install cca-zoo[deep]          # DCCA variants (requires PyTorch + Lightning)
pip install cca-zoo[probabilistic] # Probabilistic CCA (requires NumPyro + JAX)
pip install cca-zoo[all]           # Everything above
```

---

## Quick start

```python
import numpy as np
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 |
| `CCA_EY` | Stochastic Eckart-Young CCA (Riemannian GD) | 2 |
| `PLS_EY` | Stochastic Eckart-Young PLS (Riemannian GD) | 2 |
| `MCCA_EY` | Multiview Eckart-Young CCA (Riemannian GD) | ≥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.deep` *(requires `[deep]`)*

| 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 |
| `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 |

---

## Documentation

Full documentation, user guides, and API reference at:
**[https://jameschapman19.github.io/cca_zoo/](https://jameschapman19.github.io/cca_zoo/)**

---

## Citing

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

```bibtex
@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](docs/contributing.md) for development setup, coding standards, and pull request guidelines.
