A. halleri metal tolerance traits in a Bayesian Causal Network

Background

This analysis uses a GPU enabled and cloud scalable pytorch container base image. The pytorch image is modified by installing gcc graphviz graphviz-dev nano vim, and the python packages CausalNex as well as its associated dependencies and pygraphviz which is used to visualize the networks.

Analysis

• A description of the most current analysis can be found written as a Jupyter Notebook in GoogleColab here.

• NOTE: the Colab Notebook will not have the CPU power to run the structure learning algorithm, it is very computationally intensive (~4 hrs on a server with >250 cores).

• Optionally this could use CyVerse to host a notebook with the analysis data??

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Running Docker Container Image:

Example for running structure learning

Run the causalnex container image, in the detached state -d, by mounting your current working directory (assumes the docker user is running the process within this repo folder) as the folder /work inside the spinning container. Executing the script for structure learning

docker run -d --rm --gpus all -v $(pwd):/work -w /work rbartelme/pytorch-causalnex:0.0.2 python /work/scripts/learn-structure.py

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Experimental Notes:

• The experiment /outputs/Apr06/ had many nodes, with weak connections, no pickling of structure model
• On the other hand,in /outputs/May06/ switching from row-wise to column wise removal of NaN removes most of the dataset
• Rerunning with previous NaN removal behavior on May 07 2021