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https://github.com/clear-nus/NCDSSM
PyTorch implementation of the NCDSSM models presented in the ICML '23 paper "Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series".
https://github.com/clear-nus/NCDSSM
continuous-time forecasting icml-2023 imputation kalman-filter state-space-model time-series
Last synced: 3 months ago
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PyTorch implementation of the NCDSSM models presented in the ICML '23 paper "Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series".
- Host: GitHub
- URL: https://github.com/clear-nus/NCDSSM
- Owner: clear-nus
- License: mit
- Created: 2023-05-19T13:19:03.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-07-09T07:03:36.000Z (over 1 year ago)
- Last Synced: 2024-08-01T04:02:19.802Z (6 months ago)
- Topics: continuous-time, forecasting, icml-2023, imputation, kalman-filter, state-space-model, time-series
- Language: Python
- Homepage:
- Size: 296 KB
- Stars: 22
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.bib
Awesome Lists containing this project
- Awesome-state-space-models - GitHub
README
# Neural Continuous-Discrete State Space Models (NCDSSM)
[](https://arxiv.org/abs/2301.11308)
[](https://opensource.org/licenses/MIT)
[](https://icml.cc/)
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*](https://arxiv.org/abs/2301.11308).
## Installation
NCDSSM requires Python 3.8 or higher.
* Create a conda environment (optional, but recommended).
```sh
conda create --name ncdssm python=3.9
conda activate ncdssm
```
* Clone this repo and install the package.
```sh
# Install package with all optional dependencies for running experiments
pip install --editable ".[exp]"
```
## Usage
### Generate or download datasets
* For bouncing ball, damped pendulum, climate and Pymunk datasets, generate/download them by running the scripts in `data/`.
```sh
# 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](https://github.com/cagatayyildiz/ODE2VAE) and place it in `data/mocap/mocap35.mat`.
### Training
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`.
```sh
# 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.yaml
```
### Evaluation
Once 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 [:hugs: hugging face](https://huggingface.co/abdulfatir/NCDSSM) and place them in `./checkpoints/`.
```sh
# 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
```
```sh
# 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 0
```
### Questions
For any questions regarding the code or the paper, please email [Fatir](mailto:[email protected]).
### BibTeX
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}
}
```
### Acknowledgement
This repo contains parts of code based on the following repos.
| Repo | Copyright (c) | License |
| ------------- | ---------- | ------------- |
| [edebrouwer/gru_ode_bayes](https://github.com/edebrouwer/gru_ode_bayes) | Edward De Brouwer | [MIT License](https://github.com/edebrouwer/gru_ode_bayes/blob/master/License.md) |
| [cagatayyildiz/ODE2VAE](https://github.com/cagatayyildiz/ODE2VAE) | Çağatay Yıldız | NA |
| [mannyray/KalmanFilter](https://github.com/mannyray/KalmanFilter) | @mannyray | [MIT License](https://github.com/mannyray/KalmanFilter/blob/master/LICENSE) |
| [simonkamronn/kvae](https://github.com/simonkamronn/kvae) | Simon Kamronn | [MIT License](https://github.com/simonkamronn/kvae/blob/master/LICENSE)