Code for paper
Our approach utilizes Variational Autoencoders with Gaussian Process prior for time series imputation.
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The inference model takes time series with missingness and predicts variational parameters for multivariate Gaussian variational distribution.
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The Gaussian Process prior encourages latent representations to capture the temporal correlations in data.
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The generative model takes the sample from posterior approximation and reconstructs the original time series with imputed missing values.
- Python >= 3.6
- TensorFlow = 1.15
- Some more packages: see
requirements.txt
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Clone or download this repo.
cdyourself to it's root directory. -
Grab or build a working python enviromnent. Anaconda works fine.
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Install dependencies, using
pip install -r requirements.txt -
Download data:
bash data/load_{hmnist, sprites, physionet}.sh. -
Run command
CUDA_VISIBLE_DEVICES=* python train.py --model_type {vae, hi-vae, gp-vae} --data_type {hmnist, sprites, physionet} --exp_name <your_name> ...To see all available flags run:
python train.py --help
We provide a set of hyperparameters used in our final runs. Some flags have common values for all datasets by default. For reproducibility of reported results run:
- HMNIST:
python train.py --model_type gp-vae --data_type hmnist --exp_name reproduce_hmnist --seed $RANDOM --testing --banded_covar --latent_dim 256 --encoder_sizes=256,256 --decoder_sizes=256,256,256 --window_size 3 --sigma 1 --length_scale 2 --beta 0.8 --num_epochs 20 - SPRITES:
python train.py --model_type gp-vae --data_type sprites --exp_name reproduce_sprites --seed $RANDOM --testing --banded_covar --latent_dim 256 --encoder_sizes=32,256,256 --decoder_sizes=256,256,256 --window_size 3 --sigma 1 --length_scale 2 --beta 0.1 --num_epochs 20 - Physionet:
python train.py --model_type gp-vae --data_type physionet --exp_name reproduce_physionet --seed $RANDOM --testing --banded_covar --latent_dim 35 --encoder_sizes=128,128 --decoder_sizes=256,256 --window_size 24 --sigma 1.005 --length_scale 7 --beta 0.2 --num_epochs 40
