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|
%global _empty_manifest_terminate_build 0
Name: python-cca-zoo
Version: 1.17.7
Release: 1
Summary: Canonical Correlation Analysis Zoo: A collection of Regularized, Deep Learning based, Kernel, and Probabilistic methods in a scikit-learn style framework
License: MIT
URL: https://github.com/jameschapman19/cca_zoo
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/79/58/d137e5dfc61e77e2abf5d1211d5b30bbaa82635adc44fe58312498342ca2/cca_zoo-1.17.7.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-scipy
Requires: python3-scikit-learn
Requires: python3-scikit-prox
Requires: python3-pytest
Requires: python3-matplotlib
Requires: python3-pandas
Requires: python3-seaborn
Requires: python3-tensorly
Requires: python3-joblib
Requires: python3-mvlearn
Requires: python3-tqdm
Requires: python3-setuptools
Requires: python3-torch
Requires: python3-torchvision
Requires: python3-pytorch-lightning
Requires: python3-jax
Requires: python3-numpyro
Requires: python3-arviz
Requires: python3-torch
Requires: python3-torchvision
Requires: python3-pytorch-lightning
Requires: python3-jax
Requires: python3-numpyro
Requires: python3-arviz
%description
[](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)
# CCA-Zoo
`cca-zoo` is a collection of linear, kernel, and deep methods for canonical correlation analysis of multiview data.
Where possible it follows 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 accomodate the user's needs.
For full details of algorithms included, please refer 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:
CCA-Zoo is intended as research software. Citations and use of our software help us justify the effort which has gone
into, and will keep going into, maintaining and growing this project. Stars on the repo are also greatly appreciated :)
If you have used CCA-Zoo in your research, please consider citing 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, https://doi.org/10.21105/joss.03823
With bibtex entry:
```bibtex
@article{Chapman2021,
doi = {10.21105/joss.03823},
url = {https://doi.org/10.21105/joss.03823},
year = {2021},
publisher = {The Open Journal},
volume = {6},
number = {68},
pages = {3823},
author = {James Chapman and Hao-Ting Wang},
title = {CCA-Zoo: A collection of Regularized, Deep Learning based, Kernel, and Probabilistic CCA methods in a scikit-learn style framework},
journal = {Journal of Open Source Software}
}
```
## 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)
%package -n python3-cca-zoo
Summary: Canonical Correlation Analysis Zoo: A collection of Regularized, Deep Learning based, Kernel, and Probabilistic methods in a scikit-learn style framework
Provides: python-cca-zoo
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-cca-zoo
[](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)
# CCA-Zoo
`cca-zoo` is a collection of linear, kernel, and deep methods for canonical correlation analysis of multiview data.
Where possible it follows 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 accomodate the user's needs.
For full details of algorithms included, please refer 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:
CCA-Zoo is intended as research software. Citations and use of our software help us justify the effort which has gone
into, and will keep going into, maintaining and growing this project. Stars on the repo are also greatly appreciated :)
If you have used CCA-Zoo in your research, please consider citing 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, https://doi.org/10.21105/joss.03823
With bibtex entry:
```bibtex
@article{Chapman2021,
doi = {10.21105/joss.03823},
url = {https://doi.org/10.21105/joss.03823},
year = {2021},
publisher = {The Open Journal},
volume = {6},
number = {68},
pages = {3823},
author = {James Chapman and Hao-Ting Wang},
title = {CCA-Zoo: A collection of Regularized, Deep Learning based, Kernel, and Probabilistic CCA methods in a scikit-learn style framework},
journal = {Journal of Open Source Software}
}
```
## 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)
%package help
Summary: Development documents and examples for cca-zoo
Provides: python3-cca-zoo-doc
%description help
[](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)
# CCA-Zoo
`cca-zoo` is a collection of linear, kernel, and deep methods for canonical correlation analysis of multiview data.
Where possible it follows 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 accomodate the user's needs.
For full details of algorithms included, please refer 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:
CCA-Zoo is intended as research software. Citations and use of our software help us justify the effort which has gone
into, and will keep going into, maintaining and growing this project. Stars on the repo are also greatly appreciated :)
If you have used CCA-Zoo in your research, please consider citing 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, https://doi.org/10.21105/joss.03823
With bibtex entry:
```bibtex
@article{Chapman2021,
doi = {10.21105/joss.03823},
url = {https://doi.org/10.21105/joss.03823},
year = {2021},
publisher = {The Open Journal},
volume = {6},
number = {68},
pages = {3823},
author = {James Chapman and Hao-Ting Wang},
title = {CCA-Zoo: A collection of Regularized, Deep Learning based, Kernel, and Probabilistic CCA methods in a scikit-learn style framework},
journal = {Journal of Open Source Software}
}
```
## 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)
%prep
%autosetup -n cca-zoo-1.17.7
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-cca-zoo -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 1.17.7-1
- Package Spec generated
|
