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https://github.com/4ment/torchtree
A probabilistic framework in PyTorch for phylogenetic models
https://github.com/4ment/torchtree
phylogenetics pytorch variational-inference
Last synced: about 5 hours ago
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A probabilistic framework in PyTorch for phylogenetic models
- Host: GitHub
- URL: https://github.com/4ment/torchtree
- Owner: 4ment
- License: gpl-3.0
- Created: 2020-01-11T23:47:07.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2024-10-30T19:38:54.000Z (8 days ago)
- Last Synced: 2024-10-30T20:28:54.322Z (8 days ago)
- Topics: phylogenetics, pytorch, variational-inference
- Language: Jupyter Notebook
- Homepage: https://4ment.github.io/torchtree
- Size: 37.6 MB
- Stars: 13
- Watchers: 4
- Forks: 3
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# torchtree
[](https://github.com/4ment/torchtree/actions/workflows/python-package.yml)
[](https://www.gnu.org/licenses/gpl-3.0)
[](https://github.com/4ment/torchtree/actions/workflows/publish_documentation.yml)
torchtree is a program designed for inferring phylogenetic trees from molecular sequences. 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.
- [Getting Started](#getting-started)
- [Dependencies](#dependencies)
- [Installation](#installation)
- [Quick start](#quick-start)
- [Plug-ins](#torchtree-plug-in)
## Getting Started
### Dependencies
- [DendroPy]
- [PyTorch]
### 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
```
## 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.
### 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 external packages
- [torchtree-bito]
- [torchtree-physher]
- [torchtree-scipy]
- [torchtree-tensorflow]
## License
Distributed under the GPLv3 License. See [LICENSE](LICENSE) for more information.
## Acknowledgements
torchtree makes use of the following libraries and tools, which are under their own respective licenses:
- [PyTorch]
- [DendroPy]
[DendroPy]: https://github.com/jeetsukumaran/DendroPy
[PyTorch]: https://pytorch.org
[torchtree-bito]: https://github.com/4ment/torchtree-bito
[torchtree-physher]: https://github.com/4ment/torchtree-physher
[torchtree-scipy]: https://github.com/4ment/torchtree-scipy
[torchtree-tensorflow]: https://github.com/4ment/torchtree-tensorflow