bebms 0.4.1

Bayesian Event-Based Model for Disease Subtype and Stage Inference

pysubebm

Installation

pip install bebms=

or git clone this project, and then

pip install -e .

Generate synthetic data

Git clone this repository, and at the root, run

bash gen.sh

The generated data will be found at [bebms/test/my_data](bebms/test/my_data/) as .csv files.

The parameters are pre-set and can be found at [bebms/data/params.json](bebms/data/params.json). You can modify the parameters by modifying the json file.

You can also change parameters in config.toml to adjust what data to generate.

Run bebms algorithm

To run bebms, after git cloning this repository, at the root, run

bash test.sh

You can check [bebms/test/test.py](bebms/test/test.py) to learn how to use the [run_bebms](bebms/run.py) function.

The results will be saved in the folder of [bebms/test/algo_results](bebms/test/algo_results/).

Compare with SuStaIn

You can also compare the results of bebms with those of SuStaIn.

First, you need to install packages required by SuSta

pip install git+https://github.com/noxtoby/awkde
pip install git+https://github.com/hongtaoh/ucl_kde_ebm
pip install git+https://github.com/hongtaoh/pySuStaIn

Then, at the root of this repository, run

bash test_sustain.sh

You can check details at [bebms/test/test_sustain.py](bebms/test/test_sustain.py).

The results will be saved in the folder of [bebms/test/sustain_results](bebms/test/sustain_results/).

Save comparison results

You can save the results of bebms along with those of SuStaIn by running at the root:

python3 save_csv.py

The results will be found at the root as all_results.csv.

Use your own data

You can use your own data. But make sure that your data follows the format as in data in [bebms/data/samples](bebms/data/samples/).

Changelogs

• 2025-08-21 (V 0.0.3)
  • Did the generate_data.py.
• 2025-08-22 (V 0.0.5)
  • Did the mh.py
  • Correct conjugate_priors implementation.
• 2025-08-23 (V 0.1.2)
  • Improved functions in utils.py.
• 2025-08-29 (V 0.1.3)
  • Didn't change much.
• 2025-08-30 (V 0.1.8)
  • Optimized compute_likelihood_and_posteriors such that we only calculate healthy participants' ln likelihood once every time.
  • Made sure subtype assignment accuracy does not apply to healthy participants at all.
  • Fixed a major bug in data generation. The very low subtype assignment might be due to this error.
  • Included both subtype accuracy in run.py.
• 2025-08-31 (V 0.2.5)
  • Resacle event times and disease stages for exp7-9 such that max(event_times) = max_stage -1, and max(disease_stages) = max_stage.
  • Changed the experiments and some of the implementation.
  • Forcing max(event_times) = max_stage -1, but not forcing disease stages.
• 2025-09-01 (V 0.2.9)
  • REMOVED THE Forcing max(event_times) = max_stage -1
  • Modified the run.py.
• 2025-09-02 (V 0.3.3.1)
  • Redid the staging and subtyping.
  • Integrated with labels and not.
• 2025-09-04 (V 0.3.3.2)
  • Made sure in staging with labels, the new_order indices starts from 1 instead of 0. This is because participant stages now start from 0.
• 2025-09-06 (V 0.3.5.6)
  • Added the plot function back.
• 2025-09-08 (V 0.3.5.8)
  • Added ml_subtype in output results.
  • Added all_logs to the output returned in run.py.
• 2025-09-21 (V 0.3.9)
  • Removed iteration >= burn_in when updating best_*.
• 2025-11-03 (V 0.4.1)
  • Changed the package name to bebms.
  • Edited README.