Fig 1. (Top) Generative model of Neural Continuous-Discrete State Space Model. (Bottom) Amortized inference for auxiliary variables and continuous-discrete Bayesian inference for states.
This repository contains code for reproducing the experiments presented in the ICML 2023 paper Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series.
NCDSSM requires Python 3.8 or higher.
- Create a conda environment (optional, but recommended).
conda create --name ncdssm python=3.9
conda activate ncdssm- Clone this repo and install the package.
# Install package with all optional dependencies for running experiments
pip install --editable ".[exp]"- For bouncing ball, damped pendulum, climate and Pymunk datasets, generate/download them by running the scripts in
data/.
# Bouncing Ball dataset
python data/bouncing_ball.py
# Damped Pendulum dataset
python data/damped_pendulum.py
# USHCN Climate dataset
python data/climate.py
# Pymunk datasets
python data/box.py
python data/pong.py- For CMU MoCap dataset, download the preprocessed dataset (the file named
mocap35.mat) available at cagatayyildiz/ODE2VAE and place it indata/mocap/mocap35.mat.
Train models using train_pymunk.py --config /path/to/config for the Pymunk datasets and train_ts.py --config /path/to/config for all other datasets. The yaml config files can be found in configs/. The keys in the config files can also be passed as command line options to the training script as --key value.
# Train NCDSSM-NL on Damped Pendulum with 30% missing
python train_ts.py --config configs/pendulum_ncdssmnl.yaml --train_missing_p 0.3
# Train NCDSSM-LL on Bouncing Ball with 80% missing
python train_ts.py --config configs/ball_ncdssmll.yaml --train_missing_p 0.8
# Train NCDSSM-NL on CMU MoCap (setup 1)
python train_ts.py --config configs/mocap_ncdssmnl.yaml
# Train NCDSSM-LL on CMU MoCap (setup 2)
python train_ts.py --config configs/mocap2_ncdssmll.yaml
# Train NCDSSM-NL on USHCN Climate (first fold)
# --sporadic means that data is missing both in time and feature dimensions
python train_ts.py --sporadic --config configs/climate_ncdssmnl.yaml --data_fold 0
# Train NCDSSM-LL on Box
python train_pymunk.py --config configs/box_ncdssmll.yamlOnce trained, models can be evaluated on the test set using the eval_pymunk.py and eval_ts.py. Alternatively, you may download our pretrained checkpoints from this repository's Releases or from 🤗 hugging face and place them in ./checkpoints/.
# Download pretrained checkpoint from Releases
wget https://github.com/clear-nus/NCDSSM/releases/download/v0.0.1/checkpoints.tar.gz \
&& tar -xzf checkpoints.tar.gz \
&& rm checkpoints.tar.gz# Evaluate NCDSSM-NL on Damped Pendulum with 30% missing
python eval_ts.py --ckpt checkpoints/damped_pendulum_0.3_ncdssmnl.pt --smooth
# Evaluate NCDSSM-LL on Bouncing Ball with 80% missing
python eval_ts.py --ckpt checkpoints/bouncing_ball_0.8_ncdssmll.pt --smooth
# Evaluate NCDSSM-NL on CMU MoCap (Setup 1)
python eval_ts.py --ckpt checkpoints/mocap_ncdssmnl.pt --no_state_sampling --seed 0
# Evaluate NCDSSM-LL on CMU MoCap (Setup 2)
python eval_ts.py --ckpt checkpoints/mocap2_ncdssmll.pt --no_state_sampling --seed 0
# Evaluate NCDSSM-NL on USHCN Climate (first fold)
python eval_ts.py --sporadic --ckpt checkpoints/climate_ncdssmnl.pt
# Evaluate NCDSSM-LL on Box
python eval_pymunk.py --ckpt checkpoints/box_ncdssmll.pt --wass --smooth --no_state_sampling --seed 0For any questions regarding the code or the paper, please email Fatir.
If you find this repository or the ideas presented in our paper useful, please consider citing.
@inproceedings{
ansari2023neural,
title={Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series},
author={Ansari, Abdul Fatir and Heng, Alvin and Lim, Andre and Soh, Harold},
booktitle={International Conference on Machine Learning},
year={2023}
}
This repo contains parts of code based on the following repos.
| Repo | Copyright (c) | License |
|---|---|---|
| edebrouwer/gru_ode_bayes | Edward De Brouwer | MIT License |
| cagatayyildiz/ODE2VAE | Çağatay Yıldız | NA |
| mannyray/KalmanFilter | @mannyray | MIT License |
| simonkamronn/kvae | Simon Kamronn | MIT License |
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