AutoODE Redux

This project aims to improve upon the original AutoODE work that this originally forked from. We hope to improve deep learning models using data-driving priors and probabilistic losses on the known statistics.

Setup & installation

Install dependencies by installing and activating the environment:

conda env create -f environment.yml
conda activate ode

Or alternatively, if using pip:

pip install -r requirements.txt

Install this package in development mode (so you get edits without re-installing):

python setup.py develop

If you want to contribute, please install pre-commit to stay PEP8 compliant:

pre-commit install

To access the data you have to set up the git submodule for the COVID-19 repository:

git submodule init
git submodule update

TODO ITEMS:

• write a paper outline, envision the figures we want to produce/experiments we want to run
• write base classes and an API
• (w/ simulated data) write plotting routines (e.g. SEUR... vs time, gif of infected % spreading over US states heatmap, visualizing the MMD)
• a test suite (lol)
• a literature review especially w.r.t. parameters in compartmental models

Outstanding research questions we need to answer (this counts as work todo!):

• what exactly is the research question we are asking (e.g. making the best predictive model vs showcasing how to include demographic information)... leaning toward the latter
• will readers care about the MLE of the comparment model's parameters?
• how to we compare to previous models (what are the metrics of comparison)

Paper:

Rui Wang, Danielle Maddix, Christos Faloutsos, Yuyang Wang, Rose Yu Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems, Annual Conference on Learning for Dynamics and Control (L4DC), 2021

Abstract:

How can we learn a dynamical system to make forecasts, when some variables are unobserved? For instance, in COVID-19, we want to forecast the number of infected and death cases but we do not know the count of susceptible and exposed people. While mechanics compartment models are widely-used in epidemic modeling, data-driven models are emerging for disease forecasting. As a case study, we compare these two types of models for COVID-19 forecasting and notice that physics-based models significantly outperform deep learning models. We present a hybrid approach, AutoODE-COVID, which combines a novel compartmental model with automatic differentiation. Our method obtains a 57.4% reduction in mean absolute errors for 7-day ahead COVID-19 forecasting compared with the best deep learning competitor. To understand the inferior performance of deep learning, we investigate the generalization problem in forecasting. Through systematic experiments, we found that deep learning models fail to forecast under shifted distributions either in the data domain or the parameter domain. This calls attention to rethink generalization especially for learning dynamical systems.

Description

1. ode_nn/:

• DNN.py: Pytorch implementation of Seq2Seq, Auto-FC, Transformer, Neural ODE.
• Graph.py: Pytorch implementation of Graph Attention, Graph Convolution.
• AutoODE.py: Pytorch implementation of AutoODE(-COVID).
• train.py: data loaders, train epoch, validation epoch, test epoch functions.

3. Run_DSL.ipynb: train deep sequence models and graph neural nets.
4. Run_AutoODE.ipynb: train AutoODE-COVID.
5. Evaluation.ipynb: evaluation functions and prediction visualization

Requirement

• python 3.6
• pytorch 10.1
• matplotlib
• scipy
• numpy
• pandas
• dgl

Cite

@inproceedings{wang2020bridging,
title={Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems},
author={Rui Wang and Danielle Maddix and Christos Faloutsos and Yuyang Wang and Rose Yu},
journal={arXiv preprint arXiv:2011.10616},
year={2020}
}