pyDarwin

Python solution for using several machine learning methods to search a candidate solution space for the optimal population models in NONMEM.

Visit pyDarwin Documentation to learn more.

System Requirements

• Windows 10
• Windows Server 2018, 2019
• CentOS8/RHEL8
• Ubuntu >= 18.04

Grid Computing Support

• Sun Grid Engine (SGE)

Installation Prerequisites

• Python >= 3.10
• NONMEM >= 7.4.3
• R >= 4.0.0 (optional)

Note: Requirements are Python and NONMEM installation with nmfe.bat available. R is required if using post-run R penalty function.

Installation

First, create a new virtual environment:

python -m venv .venv

This will create a virtual environment in the folder .venv

Next, use pip to install the pyDarwin package from the Certara managed PyPi repo:

Released Version

pip install pyDarwin-Certara --index-url https://certara.jfrog.io/artifactory/api/pypi/certara-pypi-release-public/simple --extra-index-url https://pypi.python.org/simple/

Development Version

pip install pyDarwin-Certara --pre --upgrade --force-reinstall --index-url https://certara.jfrog.io/artifactory/api/pypi/certara-pypi-develop-local/simple --extra-index-url https://pypi.python.org/simple/

Usage

python -m darwin.run_search <template_path> <tokens_path> <options_path>

To execute, call the run_search function from the darwin module and provide the following file paths as arguments:

1. Template file (e.g., template.txt) - basic shell for NONMEM control files
2. Tokens file (e.g., tokens.json) - json file describing the dimensions of the search space and the options in each dimension
3. Options file (e.g., options.json) - json file describing algorithm, run options, and post-run penalty code configurations.

Example

After cloning https://github.com/certara/pyDarwin from GitHub, navigate to one of the example folders e.g.,

cd .\pyDarwin\examples\user\Example1

Then execute:

python -m darwin.run_search template.txt tokens.json options.json

Note: Both absolute and relative file paths are supported.