mrf_example.ipynb contains a straight-forward example of automated active learning for a simple 1 input -> 1 output; using candidate nuclear fusion material behaviour as a theme.
mrf_example.ipynb uses helper functions defined in utils.py.
The Uncertainty Engine follows a graph-based paradigm, allowing flexible construction of complex machine learning workflows. To make this more accessible, the graph logic is wrapped into reusable functions in utils.py, presenting a more familiar functional interface for Python users.
These functions are used throughout the notebook, but you can explore utils.py directly to understand the underlying graph-based implementation.
- Clone this repo
- Set up a virtual environment (or similar) - refer to https://realpython.com/python-virtual-environments-a-primer/ if unsure)
- Install all project dependencies, by entering the following into your terminal:
pip install -r requirements.txt - Head to
mrf_example.ipynband enjoy!