Theory aware Machine Learning (TaML)

This repository contains code to incorporate imperfect theory into machine learning for improved prediction and explainability. Specifically, it focuses on the case study of the dimensions of a polymer chain in different solvent qualities. For machine learning methods, three methods are considered: Gaussian Process Regression with heteroscedastic noise, Gaussian Process Regression with homoscedastic noise and Random Forest.

This code is provided to supplement a manuscript to be published. For those wishing to incorporate key ideas from the manuscript including incorporating theory and using Gaussian Process Regression with heteroscedastic noise, we suggest starting with MethodComparision_GPR_HeteroscedasticNoise in the notebook folder. However, all the code needed to reproduce all of the figures is provided.

Running the code

All code Python based and can be used on any operating system.

First clone the code via

git clone https://github.com/usnistgov/TaML.git

Create a Python virtual enviroment

python3 -m TaML env

where TaML is the name of the virtual enviroment and env in the location of the virtual enviroment

Activate the virtual enviroment

source env/bin/activate

Install dependencies

python3 -m pip install -r requirements.txt

Notebooks

Included notebooks include DataVisualization for visualizing the input data used for machine learning, MethodComparision_GPR_HeteroscedasticNoise for comparing different methods for incorporating theory into machine learning using Gaussian Process Regression with heteroscedastic noise, MethodComparision_GPR_HomoscedasticNoise for comparing different methods for incorporating theory into machine learning using Gaussian Process Regression with homoscedastic noise, and ViewResults for plotting the relative performance of different methods for incorporating theory into machine learning for three different machine learning models.

For users interested in testing ideas, we recommend focusing on the MethodComparison_GPR_HeteroscedasticNoise notebook as it explores the different methods and takes into account the known uncertainities in the input data.

To run the Juypter notebooks, navigate to the notebook folder and run

jupyter notebook

Source code

The source code compares a variety of methods for incoporating theory into machine learning for three different machine learning models: Gaussian Process Regression with heteroscedastic noise, Gaussian Process Regression with homoscedastic noise and Random Forest. The output of the files can be plotted by modifying the notebook title ViewResults such that the data files are pulled from a local run as opposed to the stored data.

To run the source code, navigate to the src folder and run

python3 main.py

Contact

Debra J. Audus, PhD
Polymer Analytics Project
Materials Science and Engineering Division
Material Measurement Laboratory
National Institute of Standards and Technology

Email: [email protected]
GithubID: @debraaudus
Project website: https://www.nist.gov/programs-projects/polymer-analytics
Staff website: https://www.nist.gov/people/debra-audus

How to cite

Please check back later. This will be updated once the accompanying manuscipt is published.