This repository contains the code for the ADEPT method found in this manuscript..
adept.py
has the code defining the ADEPT model.example.ipynb
gives an example of how to use theADEPT
class.evaluation.py
provides evaluation metrics.generate_bimodal_data.py
generates simulated data with two modes. See Section 4.1 of the manuscript for more information.generate_multimodal_sim_data.py
generates simulated data with four modes. See Section 4.2 of the manuscript for more information.generate_gbsg_data.py
splits the German Breast Cancer Study Group 2 data set for training, testing, and validation. See Section 5 of the manuscript for more information.generate_flchain_data.py
splits the assay of free light chain data set for training, testing, and validation. See Section 5 of the manuscript for more information.pipeline.py
is used to generate the results shown in the Sections 4 and 5 of the manuscript.plotting.py
contains code to make lots forexample.ipynb
More information about the Stroke data set used in Section 5 of the manuscript can be found here.
To cite this work use the following citation:
@InProceedings{pmlr-v238-hickey24a,
title = { Adaptive Discretization for Event PredicTion {(ADEPT)} },
author = {Hickey, Jimmy and Henao, Ricardo and Wojdyla, Daniel and Pencina, Michael and Engelhard, Matthew},
booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics},
pages = {1351--1359},
year = {2024},
editor = {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen},
volume = {238},
series = {Proceedings of Machine Learning Research},
month = {02--04 May},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v238/hickey24a/hickey24a.pdf},
url = {https://proceedings.mlr.press/v238/hickey24a.html}
}