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Heteroscedastic Temporal Variational Autoencoder for Irregularly Sampled Time Series (HetVAE)

This repository is an official implementation of Heteroscedastic Temporal Variational Autoencoder for Irregularly Sampled Time Series. HeTVAE is a deep learning framework for probabilistic interpolation of irregularly sampled or sparse time series data.

Requirements

To run this project, you will need to install the requirements. The code requires Python 3.7 or later. The file requirements.txt contains the full list of required Python modules.

pip3 install -r requirements.txt

Training and Evaluation

To reproduce the results in the paper, run the following commands:

1. PhysioNet Dataset

python3 train.py --niters 2000 --lr 0.0001 --batch-size 128 --rec-hidden 128 --latent-dim 128 --width 128 --embed-time 128 --enc-num-heads 1 --num-ref-points 16 --n 8000 --dataset physionet --seed 1 --save --norm --intensity --net hetvae --bound-variance --shuffle  --sample-tp 0.5 --elbo-weight 1.0 --mse-weight 5.0 --mixing concat --k-iwae 1

2. MIMIC-III Dataset

python3 train.py --niters 2000 --lr 0.0001 --batch-size 128 --rec-hidden 128 --latent-dim 128 --width 512 --embed-time 128 --enc-num-heads 1 --num-ref-points 16 --dataset mimiciii --seed 1 --save --norm --intensity --net hetvae --bound-variance --shuffle  --sample-tp 0.5 --elbo-weight 1.0 --mse-weight 5.0 --mixing concat --k-iwae 1

3. Synthetic Dataset

python3 train.py --niters 2000 --lr 0.0001 --batch-size 128 --rec-hidden 16 --latent-dim 64 --width 512 --embed-time 128 --enc-num-heads 1 --num-ref-points 16 --n 2000 --dataset toy --seed 0 --save --norm --intensity --net hetvae --bound-variance --shuffle  --sample-tp 0.5 --elbo-weight 1.0 --mse-weight 1.0 --mixing concat --k-iwae 1

Demo

This notebook provides an example to reproduce the visualizations in the paper on synthetic dataset.

Ablations

Different components of the HeTVAE model are denoted as: HET: heteroscedastic output layer, ALO: augmented learning objective, INT: intensity encoding, DET: deterministic pathway. To reproduce the ablation results on PhysioNet, run the following commands:

1. HeTVAE - ALO

python3 train.py --niters 2000 --lr 0.0001 --batch-size 128 --rec-hidden 32 --latent-dim 32 --width 128 --embed-time 128 --enc-num-heads 1 --num-ref-points 8 --n 8000 --dataset physionet --seed 1 --save --norm --intensity --net hetvae --bound-variance --shuffle  --sample-tp 0.5 --elbo-weight 1.0 --mse-weight 0.0 --mixing concat --k-iwae 1

2. HeTVAE - DET

python3 train.py --niters 2000 --lr 0.0001 --batch-size 128 --rec-hidden 128 --latent-dim 64 --width 512 --embed-time 128 --enc-num-heads 1 --num-ref-points 8 --n 8000 --dataset physionet --seed 1 --save --norm --intensity --net hetvae_det --bound-variance --shuffle  --sample-tp 0.5 --elbo-weight 1.0 --mse-weight 10.0 --mixing concat --k-iwae 1

3. HeTVAE - INT

python3 train.py --niters 2000 --lr 0.0001 --batch-size 128 --rec-hidden 64 --latent-dim 128 --width 128 --embed-time 128 --enc-num-heads 1 --num-ref-points 8 --n 8000 --dataset physionet --seed 1 --save --norm --intensity --net hetvae --bound-variance --shuffle  --sample-tp 0.5 --elbo-weight 1.0 --mse-weight 5.0 --mixing interp_only --k-iwae 1

4. HeTVAE - HET - ALO

python3 train.py --niters 2000 --lr 0.0001 --batch-size 128 --rec-hidden 128 --latent-dim 128 --width 512 --embed-time 128 --enc-num-heads 1 --num-ref-points 8 --n 8000 --dataset physionet --seed 1 --save --norm --intensity --net hetvae --shuffle  --sample-tp 0.5 --elbo-weight 1.0 --mse-weight 0.0 --mixing concat --k-iwae 1 --const-var --std 0.8

5. HeTVAE - DET - ALO

python3 train.py --niters 2000 --lr 0.0001 --batch-size 128 --rec-hidden 128 --latent-dim 32 --width 256 --embed-time 128 --enc-num-heads 1 --num-ref-points 8 --n 8000 --dataset physionet --seed 1 --save --norm --intensity --net hetvae_det --bound-variance --shuffle  --sample-tp 0.5 --elbo-weight 1.0 --mse-weight 0.0 --mixing concat --k-iwae 1

6. HeTVAE - PROB - ALO

python3 train.py --niters 2000 --lr 0.0001 --batch-size 128 --rec-hidden 32 --latent-dim 128 --width 128 --embed-time 128 --enc-num-heads 1 --num-ref-points 8 --n 8000 --dataset physionet --seed 1 --save --norm --intensity --net hetvae_prob --bound-variance --shuffle  --sample-tp 0.5 --elbo-weight 1.0 --mse-weight 0.0 --mixing concat --k-iwae 1 --kl-zero

7. HeTVAE - INT - DET - ALO

python3 train.py --niters 2000 --lr 0.0001 --batch-size 128 --rec-hidden 128 --latent-dim 64 --width 512 --embed-time 128 --enc-num-heads 1 --num-ref-points 8 --n 8000 --dataset physionet --seed 1 --save --norm --intensity --net hetvae_det --bound-variance --shuffle  --sample-tp 0.5 --elbo-weight 1.0 --mse-weight 0.0 --mixing interp_only --k-iwae 1

8. HeTVAE - HET - INT - DET - ALO (HTVAE mTAN)

python3 train.py --niters 2000 --lr 0.0001 --batch-size 128 --rec-hidden 32 --latent-dim 64 --width 512 --embed-time 128 --enc-num-heads 1 --num-ref-points 8 --n 8000 --dataset physionet --seed 1 --save --norm --intensity --net hetvae_det --bound-variance --shuffle  --sample-tp 0.5 --elbo-weight 1.0 --mse-weight 0.0 --mixing interp_only --k-iwae 1 --const-var --std 0.8