Yet another vine copula package, using PyTorch.

• C/D/R-Vine full-simulation/ quantile-regression/ conditional-simulation, all in one package
  • Flexible simulation workflow for experienced users
• Vectorized tensor computation with GPU (device='cuda') support
• Shorter runtimes for higher dimension simulations
• Decoupled dataclasses and factory methods
• Pure Python library, inspired by and tested against pyvinecopulib on Windows, Linux, MacOS
• IO and visualization support

# inside the `./pyproject.toml` file;
numpy = "*"
python = "^3.10"
scipy = "*"
# optional to facilitate customization
torch = { version = "^2", optional = true }

For PyTorch with cuda support on Windows:

pip install torch --index-url https://download.pytorch.org/whl/cu121 --force-reinstall
# check cuda availability
python -c "import torch; print(torch.cuda.is_available())"

• By pip from PyPI:

pip install torchvinecopulib torch

• Or with full drawing and bivariate dependency metric support:

pip install torchvinecopulib torch matplotlib pot scikit-learn

• Or pip from ./dist/*.whl or ./dist/*.tar.gz in this repo. Need to use proper file name.

# inside project root folder
pip install ./dist/torchvinecopulib-2024.10.1-py3-none-any.whl
# or
pip install ./dist/torchvinecopulib-2024.10.1.tar.gz

After git clone https://github.com/TY-Cheng/torchvinecopulib.git, cd into the project root where pyproject.toml exists,

# inside project root folder
poetry lock && poetry install -E dev_cpu --with dev_cpu --sync
# or
poetry lock && poetry install -E dev_cuda --with dev_cuda --sync

Visit the ./examples/ folder for .ipynb Jupyter notebooks.

• Visit GitHub Pages website

• Or visit html-preview.github.io

• Or build by yourself (need Sphinx, theme furo and the GNU make)

# inside project root folder
sphinx-apidoc -o ./docs ./torchvinecopulib && cd ./docs && make html && cd ..

# inside project root folder
python -m pytest ./tests
# coverage report
coverage run -m pytest ./tests && coverage html

• more (non-parametric) bicop class in torch
• port to TensorFlow Probability for cuda-compatible Student's t cdf/ppf

We welcome contributions, whether it's a bug report, feature suggestion, code contribution, or documentation improvement.

• If you encounter any issues with the project or have ideas for new features, please open an issue on GitHub or privately email us. Make sure to include detailed information about the problem or feature request, including steps to reproduce for bugs.

1. Fork the repository and create a new branch from the main branch.
2. Make your changes and ensure they adhere to the project's coding style and conventions.
3. Write tests for any new functionality and ensure existing tests pass.
4. Commit your changes with clear and descriptive commit messages.
5. Push your changes to your fork and submit a pull request to the main branch of the original repository.

• Keep pull requests focused on addressing a single issue or feature.
• Include a clear and descriptive title and description for your pull request.
• Make sure all tests pass before submitting the pull request.
• If your pull request addresses an open issue, reference the issue number in the description using the syntax #issue_number.
• in-place ops can be slower
• torch.jit.script can be slower

Copyright (C) 2024- Tuoyuan Cheng, Kan Chen

This file is part of torchvinecopulib. torchvinecopulib is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

torchvinecopulib is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with torchvinecopulib. If not, see http://www.gnu.org/licenses/.