phyddle docs

Note

This private beta version of phyddle is still under development. It is generally tested and stable with respect to a few use standard cases. Much of the documentation and some key features are still missing. Most phyddle development occurs on a 16-core Intel Macbook Pro laptop and a 64-core Ubuntu server, so there are also unknown portability/scalability issues to correct. Any feedback is appreciated! michael.landis@wustl.edu

phyddle is a pipeline-based toolkit for fiddling around with phylogenetic models and deep learning. phyddle can be installed as a Python package using pip and used for pipeline analyses using scripts provided through GitHub.

A standard phyddle analysis performs the following tasks for you:

_images/phyddle_pipeline.png
  • Pipeline configuration applies analysis settings provided through a config file and/or command line arguments.

  • Simulate simulates a large training dataset under the model to be Formatted (parallelized, partly compressed).

  • Format encodes the raw simulated data into tensor format for Training.

  • Train shuffles and splits training data, builds a network, then trains and saves the network with the data for Estimation.

  • Estimate produces model estimates for a new dataset with the trained network.

  • Plot generates figures that summarize the training data (Format), the network and its training (Train), and any estimates for new datasets (Estimate).

In addition, phyddle is distributed with example scripts to simulate phylogenetic training datasets using RevBayes, R, and MASTER.

To learn how to use phyddle, we recommend exploring the topics from top-to-bottom as listed on the left-hand side of this page. Visit the Quick start and Installation pages to get started.