Metadata-Version: 2.1
Name: cca-zoo
Version: 1.10.5
Summary: Canonical Correlation Analysis Zoo: A collection of Regularized, Deep Learning based, Kernel, and Probabilistic methods in a scikit-learn style framework
Home-page: https://github.com/jameschapman19/cca_zoo
Author: jameschapman
Author-email: james.chapman.19@ucl.ac.uk
License: MIT
Keywords: cca
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: scikit-learn (>=1.0)
Requires-Dist: mvlearn
Requires-Dist: scipy (>=1.7)
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: pandas
Requires-Dist: tensorly
Requires-Dist: joblib
Provides-Extra: all
Requires-Dist: torch (>=1.9.0) ; extra == 'all'
Requires-Dist: torchvision ; extra == 'all'
Requires-Dist: pytorch-lightning ; extra == 'all'
Requires-Dist: Pillow ; extra == 'all'
Requires-Dist: jax (~=0.2.20) ; extra == 'all'
Requires-Dist: numpyro ; extra == 'all'
Requires-Dist: arviz ; extra == 'all'
Provides-Extra: deep
Requires-Dist: torch (>=1.9.0) ; extra == 'deep'
Requires-Dist: torchvision ; extra == 'deep'
Requires-Dist: pytorch-lightning ; extra == 'deep'
Requires-Dist: Pillow ; extra == 'deep'
Provides-Extra: probabilistic
Requires-Dist: jax (~=0.2.20) ; extra == 'probabilistic'
Requires-Dist: numpyro ; extra == 'probabilistic'
Requires-Dist: arviz ; extra == 'probabilistic'

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# CCA zoo

`cca-zoo` is a collection of linear, kernel, and deep methods for canonical correlation analysis of multiview data. 
Where possible I have followed the `scikit-learn`/`mvlearn` APIs and models therefore have `fit`/`transform`/`fit_transform` methods as standard.

## Installation

Dependency of some implemented algorithms are heavy, such as `pytorch` and `numpyro`. 
We provide several options to accomandate the user's needs.
For full details of algoritms inclused, please refere to section [Implemented Methods](#implemented-methods)

Standard installation: 

```
pip install cca-zoo
```
For deep learning elements use:
```
pip install cca-zoo[deep]
```

For probabilistic elements use:
```
pip install cca-zoo[probabilistic]
```
## Documentation
Available at https://cca-zoo.readthedocs.io/en/latest/

## Citation:
If this repository was helpful to you please do give a star.

In case this work is used as part of research I attach a DOI bibtex entry:

```bibtex
@software{james_chapman_2021_4925892,
  author       = {James Chapman and
                  Hao-Ting Wang},
  title        = {jameschapman19/cca\_zoo:},
  month        = jun,
  year         = 2021,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.4382739},
  url          = {https://doi.org/10.5281/zenodo.4382739}
}
```

## Implemented Methods

### Standard Install
- CCA (Canonical Correlation Analysis): Solutions based on either alternating least squares or as the solution to genrralized eigenvalue problem
- PLS (Partial Least Squares)
- [rCCA (Ridge Regularized Canonical Correlation Analysis)](https://www.sciencedirect.com/science/article/abs/pii/0304407676900105?via%3Dihub)
- [GCCA (Generalized CCA)](https://academic.oup.com/biomet/article-abstract/58/3/433/233349?redirectedFrom=fulltext)
- MCCA (Multiset CCA)
- K(M)CCA (kernel Multiset CCA)
- [TCCA (Tensor CCA)](https://arxiv.org/pdf/1502.02330.pdf)
- [KTCCA (kernel Tensor CCA)](https://arxiv.org/pdf/1502.02330.pdf)
- [SCCA (Sparse CCA)](https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13043)
- [SPLS (Sparse PLS/Penalized Matrix Decomposition](https://web.stanford.edu/~hastie/Papers/PMD_Witten.pdf)
- [ElasticCCA (Penalized CCA)](https://pubmed.ncbi.nlm.nih.gov/19689958/)
- [SWCCA (Sparse Weighted CCA)](https://arxiv.org/abs/1710.04792v1#:~:text=However%2C%20classical%20and%20sparse%20CCA%20models%20consider%20the,where%20weights%20are%20used%20for%20regularizing%20different%20samples)
- [SpanCCA](http://akyrillidis.github.io/pubs/Conferences/cca.pdf)

### `[deep]` Install
- DCCA (Deep CCA)

  Using either Andrew's original [Tracenorm Objective](https://ttic.uchicago.edu/~klivescu/papers/andrew_icml2013.pdf) or Wang's [alternating least squares solution](https://arxiv.org/pdf/1510.02054v1.pdf)

- [DGCCA (Deep Generalized CCA)](https://www.aclweb.org/anthology/W19-4301.pdf)

  An alternative objective based on the linear GCCA solution. Can be extended to more than 2 views

- [DMCCA (Deep Multiset CCA)](https://arxiv.org/abs/1904.01775)

  An alternative objective based on the linear MCCA solution. Can be extended to more than 2 views

- [DTCCA (Deep Tensor CCA)](https://arxiv.org/pdf/2005.11914.pdf)
- [DCCAE (Deep Canonically Correlated Autoencoders)](http://proceedings.mlr.press/v37/wangb15.pdf)
- [DVCCA/DVCCA Private (Deep variational CCA)](https://arxiv.org/pdf/1610.03454.pdf)

### `[probabilistic]` Install
- [Variational Bayes CCA](https://ieeexplore.ieee.org/document/4182407)

## Contributions
A guide to contributions is available at https://cca-zoo.readthedocs.io/en/latest/developer_info/contribute.html

## Sources

I've added this section to give due credit to the repositories that helped me in addition to their copyright notices in
the code where relevant.

### Other Implementations of (regularised)CCA/PLS

[MATLAB implementation](https://github.com/anaston/PLS_CCA_framework)

### Implementation of Sparse PLS

MATLAB implementation of SPLS by [@jmmonteiro](https://github.com/jmmonteiro/spls)

### Other Implementations of DCCA/DCCAE

Keras implementation of DCCA from [@VahidooX's github page](https://github.com/VahidooX)

The following are the other implementations of DCCA in MATLAB and C++. These codes are written by the authors of the original paper:

[Torch implementation](https://github.com/Michaelvll/DeepCCA) of DCCA from @MichaelVll & @Arminarj

C++ implementation of DCCA from Galen Andrew's [website](https://homes.cs.washington.edu/~galen/)

MATLAB implementation of DCCA/DCCAE from Weiran Wang's [website](http://ttic.uchicago.edu/~wwang5/dccae.html)

MATLAB implementation of [TCCA](https://github.com/rciszek/mdr_tcca)

### Implementation of VAE

[Torch implementation of VAE](https://github.com/pytorch/examples/tree/master/vae)


