IMoTE: Interactive MOdel Tree Explorer

IMoTE is an interactive app to fit, visualise and explore linear model trees for regression. Inspect the tree as an interactive graph, see the linear model and data behind every node, and trace a single data point through the tree to understand its prediction.

Overview

IMoTE supports two linear model tree algorithms out of the box:

• PILOT (PIecewise Linear Organic Tree): a fast linear model tree algorithm, using the fast-model-trees implementation.
• M5: the classic model tree algorithm, using m5py.

Trees fitted with other tools can be loaded by writing an adapter. A partykit (R) adapter is included as an example.

Installation

pip install imote

IMoTE needs Python 3.10 or newer.

Quick start

Start the app

imote

Then open the address shown in the terminal (by default http://127.0.0.1:8050). From the New Tree card you can fit PILOT or M5 on a built-in dataset or on your own CSV file.

IMoTE stores saved trees, uploaded datasets and generated plots in ~/.imote. Set the IMOTE_HOME environment variable to use another folder.

Documentation

The full documentation, with a guide to every part of the interface and how to write your own adapter, is at https://stan-uantwerp.github.io/IMoTE/.

Papers

• PILOT: Raymaekers, J., Rousseeuw, P. J., Verdonck, T., & Yao, R. (2024). Fast linear model trees by PILOT. Machine Learning, 113(9), 6561-6610. https://doi.org/10.1007/s10994-024-06590-3
• M5: Quinlan, J. R. (1992). Learning with continuous classes. In 5th Australian Joint Conference on Artificial Intelligence (Vol. 92, pp. 343-348).

Development

git clone https://github.com/STAN-UAntwerp/IMoTE
cd IMoTE
pip install -e ".[dev]"   # IMoTE in editable mode, plus pytest, build and twine
python -m imote.app       # start the app in Dash debug mode, it reloads on code changes

To work on the documentation: pip install -e ".[docs]", then mkdocs serve.

License

MIT, see LICENSE.