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https://github.com/4ment/torchtree

A probabilistic framework in PyTorch for phylogenetic models

https://github.com/4ment/torchtree

Science Score: 46.0%

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    Low similarity (17.9%) to scientific vocabulary

Keywords

phylogenetics pytorch variational-inference
Last synced: 7 months ago · JSON representation

Repository

A probabilistic framework in PyTorch for phylogenetic models

Basic Info
Statistics
  • Stars: 17
  • Watchers: 2
  • Forks: 3
  • Open Issues: 0
  • Releases: 2
Topics
phylogenetics pytorch variational-inference
Created about 6 years ago · Last pushed over 1 year ago
Metadata Files
Readme License

README.md

torchtree

Python package License: GPL v3 docs PyPI PyPI - Python Version

torchtree is a program designed for developing and inferring phylogenetic models. Implemented in Python, it leverages PyTorch for automatic differentiation. The suite of inference algorithms encompasses variational inference, Hamiltonian Monte Carlo, maximum a posteriori, and Markov chain Monte Carlo.

For a comprehensive assessment of torchtree's performance and use cases, please see our evaluation repository, torchtree-experiments, where torchtree was rigorously tested on various datasets and benchmarked for accuracy and speed.

Getting Started

Dependencies

### Installation Use an Anaconda environment (Optional) bash conda env create -f environment.yml conda activate torchtree

To install the latest stable version you can run bash pip install torchtree

To build torchtree from source you can run bash git clone https://github.com/4ment/torchtree pip install torchtree/

Check install bash torchtree --help

Documentation

For detailed information on how to use torchtree and its features, please refer to the official documentation and API reference. - Documentation - API Reference

Quick start

torchtree requires a JSON file containing models and algorithms. A configuration file can be generated using torchtree-cli, a command line-based tool. This two-step process allows the user to adjust values in the configuration file, such as hyperparameters.

torchtree-cli implements several subcommands, each corresponding to a different type of inference algorithm. A list of available subcommands can be obtained by running torchtree-cli --help.

The following subcommands are available:

  • advi: Automatic differentiation variational inference
  • hmc: Hamiltonian Monte Carlo
  • map: Maximum a posteriori
  • mcmc: Markov chain Monte Carlo

Each subcommand/algorithm requires a different set of arguments which can be obtained by running torchtree-cli <subcommand> --help.

torchtree-cli requires an alignment file in FASTA format and a tree file in either Newick or NEXUS format. While torchtree uses the DendroPy library to parse and manipulate phylogenetic trees, it is recommended to use a Newick file due to the numerous variations of the NEXUS format.

Let's explore a few examples of how to use these programs using an influenza A virus dataset containing 69 DNA sequences. The alignment and tree files are located in the data directory.

1 - Generating a configuration file

Some examples of models using variational inference:

Unrooted tree with GTR+W4 model

W4 refers to a site model with 4 rates categories coming from a discretized Weibull distribution. This is similar to the more commonly used discretized Gamma distribution site model.

bash torchtree-cli advi -i data/fluA.fa -t data/fluA.tree -m GTR -C 4 > fluA.json

Time tree with strict clock and constant coalescent model

bash torchtree-cli advi -i data/fluA.fa -t data/fluA.tree -m JC69 --clock strict --coalescent constant > fluA.json

2 - Running torchtree

This will generate sample.csv and sample.trees files containing parameter and tree samples drawn from the variational distribution bash torchtree fluA.json

torchtree plug-in

torchtree can be easily extended without modifying the code base thanks its modular implementation. Some examples of plug-ins:

A GitHub template is available to assist in the development of a plug-in, and it is highly recommended to use it. This template provides a structured starting point, ensuring consistency and compatibility with torchtree while streamlining the development process.

How to cite

If you use torchtree, please consider citing:

```

@misc{fourment2024torchtree, title={torchtree: flexible phylogenetic model development and inference using {PyTorch}}, author={Mathieu Fourment and Matthew Macaulay and Christiaan J Swanepoel and Xiang Ji and Marc A Suchard and Frederick A Matsen IV}, year={2024}, eprint={2406.18044}, archivePrefix={arXiv}, primaryClass={q-bio.PE}, url={https://arxiv.org/abs/2406.18044} } ```

License

Distributed under the GPLv3 License. See LICENSE for more information.

Acknowledgements

torchtree makes use of the following libraries and tools, which are under their own respective licenses:

Owner

  • Name: Mathieu Fourment
  • Login: 4ment
  • Kind: user
  • Location: Australia
  • Company: University of Technology Sydney

GitHub Events

Total
  • Issues event: 3
  • Watch event: 4
  • Issue comment event: 1
  • Push event: 16
  • Pull request event: 5
  • Fork event: 1
  • Create event: 2
Last Year
  • Issues event: 3
  • Watch event: 4
  • Issue comment event: 1
  • Push event: 16
  • Pull request event: 5
  • Fork event: 1
  • Create event: 2

Committers

Last synced: 8 months ago

All Time
  • Total Commits: 234
  • Total Committers: 2
  • Avg Commits per committer: 117.0
  • Development Distribution Score (DDS): 0.004
Past Year
  • Commits: 3
  • Committers: 1
  • Avg Commits per committer: 3.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Mathieu Fourment m****t@g****m 233
Matthew Macaulay m****y@u****u 1
Committer Domains (Top 20 + Academic)
uts.edu.au: 1

Issues and Pull Requests

Last synced: 8 months ago

All Time
  • Total issues: 3
  • Total pull requests: 8
  • Average time to close issues: 9 months
  • Average time to close pull requests: 3 months
  • Total issue authors: 2
  • Total pull request authors: 4
  • Average comments per issue: 0.33
  • Average comments per pull request: 0.5
  • Merged pull requests: 4
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 1
  • Pull requests: 2
  • Average time to close issues: about 2 hours
  • Average time to close pull requests: 9 days
  • Issue authors: 1
  • Pull request authors: 2
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.5
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
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Top Authors
Issue Authors
  • 4ment (2)
  • maremita (1)
Pull Request Authors
  • 4ment (3)
  • mattapow (3)
  • matsen (2)
  • maremita (2)
Top Labels
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bug (1) documentation (1)
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 30 last-month
  • Total dependent packages: 1
  • Total dependent repositories: 1
  • Total versions: 6
  • Total maintainers: 1
pypi.org: torchtree

Phylogenetic inference with pytorch

  • Versions: 6
  • Dependent Packages: 1
  • Dependent Repositories: 1
  • Downloads: 30 Last month
Rankings
Dependent packages count: 4.8%
Forks count: 19.2%
Stargazers count: 19.4%
Average: 19.5%
Dependent repos count: 21.6%
Downloads: 32.6%
Maintainers (1)
4ment
Last synced: 8 months ago

Dependencies

requirements.txt pypi
  • dendropy *
  • numpy >=1.7
  • torch >=1.9
.github/workflows/python-package.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
.github/workflows/publish_documentation.yml actions
  • JamesIves/github-pages-deploy-action 3.6.2 composite
  • actions/checkout v2.3.1 composite
  • actions/setup-python v1 composite
docs/requirements.txt pypi
  • dendropy *
  • numpy *
  • sphinx *
  • sphinx-autoapi *
  • sphinx-book-theme *
  • sphinxcontrib-bibtex *
  • torch *
pyproject.toml pypi
setup.py pypi
environment.yml conda
  • dendropy
  • numpy >=1.7
  • pip
  • python 3.7.*
  • pytorch >=1.9